Medicine, Faculty of
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UBCV
Bomkamp, Claire
Doctor of Philosophy - PhD
University of British Columbia
Brain function is dependent on both the properties of the individual neurons of which it is made up, as well as the precise pattern of connections between them. Both aspects of brain circuitry are determined in large part by the expression of specific genes. The products of these genes, including adhesion molecules, ion channels, and transcription factors, go on to shape neuronsâ properties and as well as their connectivity. Neurons direct their connectivity in part by expressing trans-synaptic cell adhesion molecules, which act as âmolecular velcroâ in order to form physical connections between axons and dendrites. These adhesion molecules are able to recruit the necessary components for synaptic transmission, through mechanisms which are incompletely understood. Chapter 2 focuses on one presynaptic adhesion molecule, PTPÏ, and its interaction with intracellular scaffolding proteins. PTPÏ is a phosphatase, but I found that its phosphatase activity is not needed for its ability to induce new synapses. However, interaction with the presynaptic scaffolding protein liprin-α is required. Because liprin-α binds to multiple presynaptic components, this finding suggests a mechanism through which PTPÏ recruits liprin-α to nascent synapses, and liprin-α in turn recruits other components, eventually leading to recruitment of vesicles, calcium channels, and everything else necessary for functional presynaptic release. Phenotypes of individual neurons, including electrophysiological activity and morphology, are known to be under genetic control. However, given the number of genes expressed by one organism and the difficulty of experimentally exploring the consequences of loss of function of any one gene, our understanding of the molecular underpinnings of any particular phenotype remains incomplete. In Chapter 3, I took the approach of searching for correlations between gene expression and neuronal phenotypes in a publicly available dataset. I found that controlling for broad cell class (that is, whether cells are inhibitory or excitatory) made a substantial difference to the results, and found much better correspondence with other datasets and with previously published literature when I took this step. In this work, I generated many testable hypotheses regarding the relationship between specific genes and neuronal phenotypes, which I hope will help to guide future studies.
MECHANISMS UNDERLYING PTP SIGMA-MEDIATED SYNAPTIC DIFFERENTIATION AND NEURONAL DIVERSITYbyClaire BomkampB.A., Western Washington University, 2011A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Neuroscience)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2019© Claire Bomkamp, 2019The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:Mechanisms underlying PTPÏ-mediated synaptic differentiation and neuronal diversitysubmitted by Claire Bomkamp in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin NeuroscienceExamining Committee:Dr. Ann Marie CraigSupervisor Dr. Shernaz BamjiSupervisory Committee Member Dr. Catharine RankinUniversity ExaminerDr. Joerg GsponerUniversity ExaminerAdditional Supervisory Committee Members:Dr. Timothy OâConnorSupervisory Committee MemberDr. Douglas AllanSupervisory Committee MemberiiAbstractBrain function is dependent on both the properties of the individual neurons of which it is made up, as well as the precise pattern of connections between them. Both aspects of brain circuitry are determined in large part by the expression of specific genes. The products of these genes, including adhesion molecules, ion channels, and transcription factors, go on to shape neuronsâ properties and as well as their connectivity.Neurons direct their connectivity in part by expressing trans-synaptic cell adhesion molecules, which act as âmolecular velcroâ in order to form physical connections between axons and dendrites. These adhesion molecules are able to recruit the necessary components for synaptic transmission, through mechanisms which are incompletely understood. Chapter 2 focuses on one presynaptic adhesion molecule, PTPÏ, and its interaction with intracellular scaffolding proteins. PTPÏ is a phosphatase, but I found that its phosphatase activity is not needed for its ability to induce new synapses. However, interaction with the presynaptic scaffolding protein liprin-α is required. Because liprin-α binds to multiple presynaptic components, this finding suggests a mechanism through which PTPÏ recruits liprin-α to nascent synapses, and liprin-α in turn recruits other components, eventually leading to recruitment of vesicles, calcium channels, and everything else necessary for functional presynaptic release.Phenotypes of individual neurons, including electrophysiological activity and morphology, are known to be under genetic control. However, given the number of genes expressed by one organism and the difficulty of experimentally exploring the consequences of loss of function of any one gene, our understanding of the molecular underpinnings of any particular phenotype remains incomplete. In Chapter 3, I took the approach of searching for correlations between gene expression and neuronal phenotypes in a publicly available dataset. I found that controlling for broad cell class (that is, whether cells are inhibitory or excitatory) made a substantial difference to the results, and found much better correspondence with other datasets and with previously published literature when I took this step. In this work, I generated many testable hypotheses regarding the relationship between specific genes and neuronal phenotypes, which I hope will help to guide future studies.iiiLay SummaryNeurons, the cells that make up the brain, are distinguished by their specific properties as well as their connections with other neurons. PTPÏ is a protein that helps to form connections between neurons by helping them physically âstick together.â At the same time, it pulls together other proteins required for one neuron to signal to its partner. In Chapter 2, I show evidence that PTPÏ needs to be able to stick to another protein called liprin-α in order to perform this second function. Liprin-α may act as a sort of âhub,â based on the fact that it interacts with a large number of other proteins which are important for neurons to signal one another. In Chapter 3, I explored how neuronsâ properties are determined, and made predictions of specific genes which might be involved in determining the shapes of neurons and how they communicate.ivPrefacePortions of Chapter 2 resulted from collaborations with Dr. Christopher Loewen and Dr. Jesse Chao; and with Dr. Tabrez Siddiqui, Dr. Nirmala Padmanabhan, and Benyamin Karimi. A manuscript similar to that presented in this thesis has been submitted for publication. Siddiqui, T., Padmanabhan, N., and Karimi, B. performed the confirmation of LAR-RPTP triple knockdown. I was responsible for all other data collection as well as writing the first draft of the manuscript. I received assistance regarding experimental design of the DHFR assay from Loewen, C., and was instructed regarding yeast husbandry by Chao, J.Chapter 3 resulted from a collaboration with Dr. Paul Pavlidis, Dr. Shreejoy J. Tripathy, Carolina Bengtsson Gonzales, and Dr. Jens Hjerling-Leffler. This work has been submitted for publication, and a preprint is available at https://www.biorxiv.org/content/early/2019/01/18/524561. I was responsible for analysis of the AIBS dataset, while Tripathy, S.J. performed the comparison with the Patch-seq datasets and Bengtsson Gonzales, C. collected Patch-seq data. I was responsible for writing the majority of the manuscript, with substantial help from Tripathy, S.J. and input from all authors.I assisted in the publication of the following manuscript: Tripathy SJ, Toker L, Bomkamp C, Mancarci BO, Belmadani M, Pavlidis P (2018) Assessing Transcriptome Quality in Patch-Seq Datasets. Front Mol Neurosci 11:363 Available at: https://doi.org/10.3389/fnmol.2018.00363. The bulk of the analysis was performed by Tripathy, S.J. I contributed to refinement of the analysis methods, editing of the manuscript, and design of online resources.I assisted in producing the following manuscript: Roppongi RT, Dhume S, Padmanabhan N, Silwal P, Zahra N, Bomkamp C, Patil C, Karimi B, Champagne-Jorgensen K, Zhang P, Craig AM, Jackson MF, Siddiqui TJ. LRRTMs organize synapses through differential engagement of neurexin and PTPÏ. Manuscript Submitted for Publication. I generated several of the plasmids used in this work as well as the PTP triple knockdown AAV, and contributed to editing the manuscript.vTable of ContentsAbstract................................................................................................................................................... iiiLay Summary.......................................................................................................................................... ivPreface.......................................................................................................................................................vTable of Contents.................................................................................................................................... viList of Tables.............................................................................................................................................xList of Figures..........................................................................................................................................xiList of Abbreviations............................................................................................................................ xiiiAcknowledgements.............................................................................................................................. xviiChapter 1: Introduction.......................................................................................................................... 11.1 Presynaptic structure & function................................................................................................. 11.1.1 Vesicles..................................................................................................................................11.1.2 Calcium channels.................................................................................................................. 21.1.3 SNAREs & release machinery..............................................................................................31.1.4 Scaffolding proteins.............................................................................................................. 41.2 Role of adhesion molecules in synapse formation.......................................................................51.2.1 Neurexins.............................................................................................................................. 61.2.2 LAR-RPTPs.......................................................................................................................... 71.2.3 Other synaptogenic adhesion molecules...............................................................................81.3 The LAR-RPTP family.............................................................................................................. 111.3.1 Structure and binding partners of the LAR-RPTP intracellular region...............................111.3.1.1 Liprin-α........................................................................................................................ 121.3.1.2 Caskin.......................................................................................................................... 151.3.1.3 Trio...............................................................................................................................151.3.2 Regulation of neurite outgrowth by LAR-RPTPs...............................................................181.3.3 Links to disease...................................................................................................................191.4 Intracellular mechanisms of presynaptic differentiation............................................................191.5 Rationale and Hypotheses..........................................................................................................201.6 Electrophysiological and morphological correlates of functional properties in circuits and organisms......................................................................................................................................... 21vi1.6.1 Historical perspective..........................................................................................................211.6.2 Classification of cell types based on electrophysiology and/or morphology.....................221.6.3 Effects of electrophysiological and morphological diversity on circuit information content......................................................................................................................................................231.6.4 Disruption of neuronal electrophysiology and morphology in disease..............................241.7 Genetic and molecular underpinnings of electrophysiological and morphological properties..241.7.1 Regulation of electrophysiological properties by expression of ion channels....................251.7.2 Processes regulating neuronal morphology and the cytoskeleton......................................261.7.3 Challenges in systematic discovery of candidate genes related to electrophysiology or morphology.................................................................................................................................. 271.8 Rationale and hypotheses...........................................................................................................28Chapter 2: Mechanisms of PTPÏ-mediated presynaptic differentiation.......................................... 312.1 Introduction................................................................................................................................312.2 Results........................................................................................................................................342.2.1 LAR-RPTPs regulate neuronal morphology and presynaptic induction............................342.2.2 Liprin-α2 and caskin1 are recruited to synapses induced through PTPÏ............................452.2.3 PTPÏ requires both intracellular domains, but not phosphatase activity or HSPG binding, to mediate synaptogenesis............................................................................................................472.2.4 Identification of PTPÏ point mutants to disrupt specific interactions................................552.2.5 Measurement of binding between PTPÏ mutants and liprin-α2.........................................632.2.6 Disrupting binding of PTPÏ to liprin-α, but not to caskin or to itself, abolishes synaptogenic activity................................................................................................................... 662.2.7 Disrupting binding between PTPÏ and liprin-α abolishes recruitment of liprin-α2 to induced synapses..........................................................................................................................692.2.8 Disrupting binding between PTPÏ and liprin-α reduces recruitment of caskin1 to induced synapses....................................................................................................................................... 732.3 Discussion.................................................................................................................................. 772.3.1 The LAR-RPTP family is required for normal neuronal morphology................................772.3.2 Requirement of LAR-RPTPs for synaptogenesis triggered by TrkC & NGL-3.................782.3.3 Relative importance of liprin, caskin, homodimerization, phosphatase activity, and HSPGs for synaptogenesis........................................................................................................................792.3.4 Domain requirements for liprin-α binding to PTPÏ............................................................82vii2.3.5 Mechanism of caskin recruitment to presynaptic sites....................................................... 832.4 Materials and Methods...............................................................................................................862.4.1 DNA Constructs.................................................................................................................. 862.4.2 Neuron Culture, Transfection, and AAV.............................................................................882.4.3 Coculture Assay.................................................................................................................. 882.4.4 HEK Cell Cultures & Clustering Assay..............................................................................892.4.5 Immunocytochemistry and imaging................................................................................... 892.4.6 Image Analysis and Quantification.....................................................................................912.4.7 Yeast cultures and DHFR Assay......................................................................................... 92Chapter 3: Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types........................................................................................................................ 953.1 Introduction................................................................................................................................953.2 Results........................................................................................................................................983.2.1 Primary Dataset...................................................................................................................983.2.2 Analysis Approach............................................................................................................ 1023.2.3 Accounting for cell class results in the identification of a distinct but overlapping set of genes.......................................................................................................................................... 1063.2.4 Divergent gene-property relationships in inhibitory versus excitatory cell classes..........1133.2.5 Results from the class-conditional model are more likely to validate using independent methods...................................................................................................................................... 1193.2.6 Assessing within-cell type correlations using Patch-seq datasets.....................................1223.2.7 The expected relationship between voltage-gated potassium channels and AHP amplitude is apparent only after accounting for cell class.......................................................................... 1273.2.8 Evidence of causal support for specific gene-property relationships...............................1293.2.9 Novel gene-property relationships....................................................................................1303.3 Discussion................................................................................................................................ 1363.3.1 Novel putative gene/electrophysiology relationships....................................................... 1383.3.2 Limitations and Caveats....................................................................................................1403.3.3 Future Directions.............................................................................................................. 1423.4 Materials and Methods.............................................................................................................1433.4.1 AIBS Dataset.....................................................................................................................143viii3.4.2 Filtering and matching datasets........................................................................................ 1433.4.3 Electrophysiological and morphological measures..........................................................1443.4.4 Defining cell types............................................................................................................ 1443.4.5 Modeling the relationship between gene expression and electrophysiology/morphology....................................................................................................................................................1463.4.6 Non-projecting class-specific correlations in the NeuroElectro/NeuroExpresso dataset. 1473.4.7 Data Analysis and Visualization....................................................................................... 1483.4.8 Data Availability............................................................................................................... 1483.4.9 Analysis of Patch-seq datasets.......................................................................................... 149Chapter 4: Conclusion......................................................................................................................... 1524.1 Summary of findings................................................................................................................1524.2 Future directions...................................................................................................................... 1534.2.1 PTPÏ-mediated synaptogenesis........................................................................................ 1534.2.2 Relating neuronal gene expression and phenotypes......................................................... 155References............................................................................................................................................. 158ixList of TablesTable 2.1 Summary of PTPÏ mutants...................................................................................................... 54Table 3.1 Criteria used for defining cell types from the AIBS dataset according to the cre line and layer they were isolated from as well as excitatory/inhibitory identity.......................................................... 102Table 3.2 Overlap between class-independent and class-conditional models........................................112Table 3.3 Overlap between class-conditional and interaction models................................................... 118Table 3.4 Description of Patch-seq datasets re-analyzed in this study...................................................126Table 3.5 Top correlated genes for each electrophysiological property.................................................135xList of FiguresFigure 1.1 Presynaptic structure and function........................................................................................... 5Figure 1.2 Overview of synaptic organizing proteins..............................................................................10Figure 1.3 Structures of LAR-RPTP, liprin-α, caskin, and direct binding partners thereof.....................17Figure 2.1 Knockdown confirmation and morphological characterization of neurons lacking LAR-RPTPs...................................................................................................................................................... 35Figure 2.2 Diagram of neuron/COS coculture assay and quantification pipeline...................................39Figure 2.3 Coculture activity of TrkC and NGL-3, but not Nlg2, is abolished by PTP triple knockdown and rescued by PTPÏ................................................................................................................................41Figure 2.4 Example images of NGL-3 and Nlg2 cocultures with neurons lacking LAR-RPTPs...........43Figure 2.5 Additional quantification of TrkC, NGL-3, and Nlg2 coculture activity after loss of LAR-RPTPs...................................................................................................................................................... 44Figure 2.6 Liprin-α2 and caskin1 are recruited to synapses induced through PTPÏ...............................47Figure 2.7 PTPÏ requires its D1 and D2 domains, but not its phosphatase activity or HS-binding, to mediate TrkC coculture activity............................................................................................................... 50Figure 2.8 Additional quantification of coculture after treatment with PTPÏ mutants............................52Figure 2.9 Alignment of LAR-RPTP C-terminal sequences from human, mouse, worm, and fly..........60Figure 2.10 Identification of PTPÏ mutations to disrupt specific interactions........................................ 62Figure 2.11 Additional visualization of PTPÏ interaction scores............................................................ 63Figure 2.12 Certain intracellular mutations in PTPÏ prevent co-clustering with liprin-α2 in HEK cells..................................................................................................................................................................65Figure 2.13 Disrupting the liprin-α2 interaction, but not the caskin1 interaction or homodimerization ability, of PTPÏ abolishes TrkC coculture activity.................................................................................. 67Figure 2.14 PTPÏ surface staining in PTPÏ point mutant coculture experiment.....................................69Figure 2.15 Recruitment of liprin-α2 by TrkC coculture in the presence of PTPÏ mutants....................71Figure 2.16 PTPÏ surface staining in liprin-α recruitment experiment with PTPÏ mutants...................73Figure 2.17 Recruitment of caskin1 by TrkC coculture in the presence of PTPÏ mutants......................74Figure 2.18 PTPÏ surface staining in caskin1 recruitment experiment with PTPÏ mutants...................76Figure 2.19 A possible model for presynaptic recruitment of caskin......................................................85Figure 3.1 Justification for cell type definitions in the AIBS dataset.................................................... 100xiFigure 3.2 Methods for modeling relationships between gene expression and electrophysiological or morphological properties with respect to cell class............................................................................... 105Figure 3.3 Different sets of genes are associated with electrophysiological and morphological properties after correcting for cell class................................................................................................. 110Figure 3.4 Identification of divergent gene-property relationships in excitatory versus inhibitory cell classes.....................................................................................................................................................115Figure 3.5 Interactions do not result primarily from low gene expression in one cell class..................117Figure 3.6 Modeling gene/electrophysiology relationships using the class-conditional model is more predictive than the class-independent model of correlations in an independent dataset containing non-projecting cell types only....................................................................................................................... 121Figure 3.7 Assessing gene-property relationships within cell subclasses using Patch-seq....................125Figure 3.8 Accounting for cell class changes the interpretation of the relationship between potassium channel expression and after-hyperpolarization amplitude................................................................... 128Figure 3.9 Examples of experimentally supported or otherwise potentially interesting genes.............132xiiList of AbbreviationsAAV Adeno-associated virusAHP After-hyperpolarizationAIBS Allen Institute for Brain ScienceAMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acidAnk Ankyrin repeatsANOVA Analysis of varianceAP Action potentialAPV DL-2-amino-5-phosphonovaleric acidASD Autism spectrum disorderBGS Bovine growth serumC1QL Complement component 1, Q subcomponent-like 3CaMK Calcium and calmodulin-dependent protein kinaseCASK Calcium/calmodulin-dependent serine protein kinaseCaskin CASK-interacting proteinCC Coiled coilCD4 Cluster of Differentiation 4CFP Cyan fluorescent proteinCID CASK interaction domainCOS CV-1 in Origin with SV-40 genesCSPG Chondroitin sulfate proteoglycanDCC Deleted in colorectal cancerDCV Dense core vesicleDHFR dihydrofolate reductaseDIV Days in vitroDMEM Dulbeccoâs modified Eagleâs mediumDYRK1A Dual specificity tyrosine-phosphorylation-regulated kinase 1AEDTA Ethylenediaminetetraacetic acidELKS Rich in the amino acids E, L, K, and SxiiiFDR False discovery rateFL Full lengthFN-III Fibronectin type IIIGABA γ-aminobutyric acidGAP GTPase activating proteinGEF Guanine nucleotide exchange factorGIT G-protein-coupled receptor-kinase-interacting proteinGluK Kainate receptorGluRδ Glutamate receptor deltaGPCR G protein-coupled receptorGPI GlycosylphosphatidylinositolGRIP Glutamate receptor interacting proteinGTP Guanosine triphosphateHEK Human Embryonic Kidney cellsHSPG Heparan sulfate proteoglycanICR Intracellular regionID Intellectual disabilityIDD Intrinsically disordered domainIg ImmunoglobulinIL-1RAcP Interleukin-1-receptor accessory proteinIL-1RAPL1 Interleukin-1-receptor accessory protein-like 1ISI CoV Interstimulus interval coefficient of variationKIF1A Kinesin family 1ALAR Leukocyte common antigen relatedLiprin LAR-interacting protein-related proteinLrrk2 Leucine-rich repeat kinase 2LRR Leucine-rich repeatLRRTM Leucine rich repeat transmembrane neuronal proteinsMALS/Veli Mammalian LIN-7/ Vertebrate homologue of LIN-7xivMAP2 Microtubule-associated protein 2Mint Munc-18-interacting proteinmRNA Messenger RNAMSG Monosodium glutamatemSyd1A Mammalian synapse defective-1, isoform AMTX MethotrexateMunc Mammalian uncoordinatedN/C-terminus Amino/carboxy-terminusNA Numerical aperatureNav Voltage-gated sodium channelNE NeuroElectro/NeuroExpressoNgCAM Neuron-glia Cell Adhesion MoleculeNGL Netrin-G ligandNlg NeuroliginNMDA N-methyl-D-aspartateNrx NeurexinOD Optical densityPBH Piccolo/Bassoon Homology domainPBS Phosphate buffered salinePCR Polymerase chain reactionPDL Poly-D-lysinePH Pleckstrin homology domainPLL Poly-L-lysinePRR Proline-rich regionPSD-95 Postsynaptic density protein of 95 kilodaltonsPSK Protein serine kinaseRIM-BP RIM-binding proteinRIM Rab3-interacting moleculeRNA-seq RNA sequencingxvRNA Ribonucleic acidROI Region of interestRPTP/PTP (Receptor) protein tyrosine phosphataseRRP Readily releasable poolRT-PCR Reverse transcription polymerase chain reactionRT-qPCR Reverse transcription quantitative polymerase chain reactionS14 Sec14 lipid binding domainSALM Synaptic adhesion-like moleculeSAM Sterile alpha motifscRNA-seq Single-cell RNA sequencingSEM Standard error of the meanSh3 SRC homology 3 domainSlitrk Slit and NTRK-like family proteinsSMART Switching Mechanism At the end of the 5â-end of the RNA TranscriptSNAP-25 Synaptosomal nerve-associated protein 25SNARE Soluble NSF Attachment Protein ReceptorSR Spectrin repeatsSTRT Single-cell-tagged reverse transcription (STRT)SV Synaptic vesicleSYD Synapse-defectiveSynCAM Synaptic cell adhesion moleculeTM TransmembraneTrkC Neurotrophin receptor kinase CVAMP Vesicle-associated membrane proteinvg Viral genomesYFP Yellow fluorescent proteinxviAcknowledgementsThanks to Dr. Ann Marie Craig for her patience and support. Her high standards pushed me to always do better. Thanks also to Dr. Jackie Rose, who was an early source of inspiration and guidance. I also offer thanks to my supervisory committee, Drs. Shernaz Bamji, Tim OâConnor, and Doug Allan, for their helpful suggestions and feedback over the years. I was fortunate to work with several helpful and brilliant collaborators at UBC. I thank Drs. Chris Loewen, Paul Pavlidis, and Shreejoy Tripathy for the time they spent helping me to develop new skills.Thanks to UBC for generously providing me with a four-year fellowship, and to CIHR for funding.My fellow students were an endless source of support during my time at UBC. I am especially grateful to Zeina Waheed, Eli York, Matt Sacheli, Dr. Aarthi Gobinath, Thariq Badiudeen, Sara Knauft, and Golnoush Alamian. You dug me out of many holes and helped me deal with the consequences.Thanks to Sophie Scofield-Selby, Holly Kvalheim, Robby Kvalheim, Laura Nichols, Diane Simunek, Ben Miller, Kelly Waks, and Matthew Underbrink for still being my friends.Andrew Kvalheim, thanks for moving to Canada with me, putting up with my endless complaining, and not giving up on me. Sorry this took seven years.Special thanks to my family for their love and support.xviiChapter 1: Introduction1.1 Presynaptic structure & functionThe role of a synapse is to transmit a signal from a presynaptic neuron to a postsynaptic neuron. Accordingly, the presynaptic compartment must be capable of sensing an incoming action potential, and transforming that electrical signal into a chemical signal. This is achieved through the interplay of a complex web of presynaptic proteins and other molecules. The components mediating presynaptic release are synaptic vesicles, which contain neurotransmitter; calcium channels, which allow calcium ions into the presynaptic compartment in response to an incoming action potential; and SNARE (Soluble NSF Attachment Protein Receptor) proteins, which mediate the fusion of the vesicle with the presynaptic membrane in response to the calcium signal. A fourth and equally important set of components of this molecular machinery are the scaffolding proteins, which tether the rest of the parts together. This section will describe some of the components of the presynaptic release machinery (shown schematically in Figure 1.2) and how they work together to achieve rapid and reliable release.1.1.1 VesiclesThe presynaptic compartment contains a number of small, membrane-bound synaptic vesicles (SVs) containing neurotransmitter, which may be released in response to an incoming action potential. Vesicles are filled through the action of an ATPase which acidifies the lumen of the vesicle, combined with a specific neurotransmitter transporter which uses the resulting pH and/or voltage gradient as an energy source to fill the vesicle with the appropriate neurotransmitter (Nakanishi-Matsui et al., 2010; Farsi et al., 2016). SVs are often categorized as belonging to the âreadily releasable poolâ (RRP), recycling pool, or reserve pool, depending on whether they are released immediately upon stimulation, 1upon prolonged stimulation, or those which are not easily released by stimulation but can become part of the RRP as it becomes depleted (Südhof, 2004). These functional categories appear to correspond with morphological observations of SVs as observed by electron microscopy, with the RRP consisting of SVs which are physically docked to the presynaptic membrane, and the recycling pool being undocked but tending to be located physically close to the active zone (Schikorski and Stevens, 2001). SVs in the RRP are docked to the presynaptic membrane via the interaction between the GTPase Rab3, which is directly associated with the vesicle, and the active zone protein Rab3 Interacting Molecule (RIM) (Gracheva et al., 2008). Consistent with this, RIM is essential for maintaining normal release probability (Schoch et al., 2002). Vesicles in the reserve pool are regulated at least in part by synapsin, which at rest functions to tether SVs to one another and to the actin cytoskeleton, but upon stimulation becomes dephosphorylated and allows SVs to dissociate and potentially join the RRP (Evergren et al., 2007). Another key component of SVs which regulates their activity prior to the arrival of an action potential is the transmembrane calcium sensor synaptotagmin. In a low-calcium environment, synaptotagmin is thought to act as a âbrakeâ to prevent docked SVs from being released (Araç et al., 2006).1.1.2 Calcium channelsFor SVs to be released in response to an incoming action potential, calcium needs to be able to enter the presynaptic terminal. Furthermore, it needs to do so in such a manner that it quickly reaches the docked SVs in order to achieve rapid synaptic transmission. Accordingly, voltage-gated calcium channels are positioned in close proximity to docked vesicles. This tight localization of calcium channels is achieved at least in part by the direct interaction between RIM and both N-type and P/Q-type calcium channels (Kaeser et al., 2011). Additionally, the family of RIM-binding proteins (RIM-2BPs) bind to both RIM as well as directly to calcium channels (Wang et al., 2000; Hibino et al., 2002). The dual role of RIM in both tethering SVs and localizing calcium channels (both directly as well as via RIM-BP) thus leads to very precise apposition of vesicles bearing calcium sensors with calcium channels ready to respond to an incoming action potential. Consistent with this, loss of RIM leads to decreased coupling between calcium channels and vesicles as measured by slower kinetics of neurotransmitter release (Han et al., 2011).1.1.3 SNAREs & release machineryUpon the opening of calcium channels and subsequent entry of calcium into the presynaptic compartment, vesicles fuse with the presynaptic membrane at the active zone (Rizo and Xu, 2015). This fusion is thought to be achieved by the binding of of three SNARE proteins: VAMP (also called Synaptobrevin), present on synaptic vesicles, and syntaxin and SNAP-25 present on the presynaptic membrane (Söllner et al., 1993). This model posits that the initial assembly of the SNARE complex causes SVs to be âdockedâ to the presynaptic membrane and ready for release, and that, upon calcium entry, tight binding between the three SNARE proteins brings the vesicle membrane into close enough proximity with the presynaptic membrane that the two membranes are induced to bend and eventually fuse, releasing neurotransmitter. The calcium sensitivity of this fusion process is achieved by the presence of synaptotagmin, which binds to the pre-assembled SNARE complex and acts as a âclampâ until the influx of calcium causes it to dissociate (Söllner et al., 1993). Although the exact mechanisms remain somewhat elusive, Munc18-1 (Munc stands for âmammalian Unc,â referring to the uncoordinated or âuncâ phenotype observed in C. elegans bearing a mutation in this gene) is required for synaptic vesicle docking and priming, likely primarily through its interaction with syntaxin (Rizo and Xu, 2015). Consistent with this notion, knockout of Munc-18 completely abolishes 3neurotransmitter release (Verhage et al., 2000). Munc-13 (named as above; no relationship to Munc-18) is also required for neurotransmitter release, in this case likely via its ability to bind syntaxin and release it from an autoinhibited conformation (Ma et al., 2011). Similarly to Munc-18, knockout of Munc13-1 and 13-2 abolishes synaptic transmission (Varoqueaux et al., 2002).1.1.4 Scaffolding proteinsIn addition to vesicles, calcium channels, and release machinery, the active zone contains a number of scaffolding proteins, some of which have already been discussed in the context of their regulation of other presynaptic components. The primary function of these scaffolding proteins seems to be the tethering of other presynaptic proteins to one another (Südhof, 2012), as exemplified by the interaction of RIM with both voltage-gated calcium channels and synaptic vesicles (Kaeser et al., 2011). RIM, RIM-BP, and Munc-13, discussed above, as well as ELKS and liprin-α, are considered the âcoreâ scaffolding proteins of the active zone (Südhof, 2012). The function of ELKS is not entirely clear, but it may be involved in limiting the size of the RRP at inhibitory synapses (Kaeser et al., 2009). Liprin-α proteins play a role in both synapse formation and function (Zhen and Jin, 1999; Spangler et al., 2013), and will be discussed in more detail in a later section. In addition, the presynaptic compartment contains a number of other, more peripheral scaffolding proteins, including bassoon and piccolo, which exhibit multiple binding interactions with presynaptic proteins and play a variety of roles including regulation of voltage gated calcium channels, synaptic vesicles, and the actin cytoskeleton (Fujimoto et al., 2002; Wang et al., 2009; Davydova et al., 2014; Gundelfinger et al., 2016). The primary function of piccolo and bassoon seems to be the recruitment of synaptic vesicles to synaptic sites (Hallermann et al., 2010; Mukherjee et al., 2010), but like most scaffolding molecules which contain multiple domains and engage in multiple binding interactions it is likely that they also are involved in multiple processes. 4Figure 1.1 Presynaptic structure and functionPresynaptic bouton is shown in grey. Proteins which are directly touching indicate binding interactions. LAR-RPTP is shown twice in order to show all relevant interactions. Interactions shown are based on (Söllner et al., 1993; Serra-Pagès et al., 1995, 1998; Wang et al., 1997; Betz et al., 2001; Schoch et al., 2002; Ko et al., 2003b; Olsen et al., 2005; Araç et al., 2006; Hofmeyer and Treisman, 2009; Takahashi et al., 2011).1.2 Role of adhesion molecules in synapse formationA central process in initiating the formation of a new synapse, which is still incompletely understood, involves binding between adhesion proteins expressed on the axon and dendrite of two different neurons. Several families of molecules which act in this way have been described, in general with more 5molecular diversity present in the components expressed on the postsynaptic membrane compared to the presynaptic (Takahashi and Craig, 2013; Südhof, 2017). This section will provide an overview of the known synaptic organizing proteins (Figure 1.2). For the purposes of this discussion, âsynaptic organizing proteinsâ refers to those which can independently induce formation of new synapses, as assessed experimentally by presenting single adhesion molecules to neurons using either transfected non-neuronal cells or beads (Siddiqui and Craig, 2011). Other classes of adhesion proteins also present at synapses, such as the cadherins, serve as important regulators of synapse formation and final density, without having the ability to independently specify new synaptic sites (Aiga et al., 2011).1.2.1 NeurexinsTranssynaptic adhesion leading to the induction of a new synapse was first demonstrated for the postsynaptic adhesion protein neuroligin and the presynaptic adhesion protein neurexin by showing that co-culturing neurons with non-neuronal cells expressing neuroligin was sufficient to induce recruitment of synaptic vesicles and active vesicle turnover (Scheiffele et al., 2000). It was later demonstrated that the opposite experimental setup, in which the non-neuronal cells express neurexin instead, triggers the formation of postsynaptic specializations that include PSD-95 as well as both N-methyl-D-aspartate (NMDA) and γ-aminobutyric acid (GABA) receptors (Graf et al., 2004), and that the neurexin/neuroligin interaction depends on the splice variant of both proteins (Boucard et al., 2005; Chih et al., 2006; Comoletti et al., 2006). The postsynaptic adhesion protein family the leucine rich repeat transmembrane neuronal proteins (LRRTMs) are able to induce presynaptic induction similarly to the neuroligins (Linhoff et al., 2009), and also act via binding to neurexins (Siddiqui et al., 2010). Similarly, neurexins binding to postsynaptic ligand calsyntenin3 can induce presynaptic differention (Pettem et al., 2013). In a variation on this theme, the soluble proteins cerebellin 1 and 2 can bind 6simultaneously to presynaptic neurexins and postsynaptic GluRδ1 or GluRδ2, inducing presynaptic differentiation (Uemura et al., 2010; Yasumura et al., 2012). Soluble C1ql2 and 3 also binds to presynaptic neurexin and postsynaptic GluK2 and 4 and thus recruits GluK2 and 4 to sites of contact, although a role for this complex in presynaptic differentiation has not been conclusively established (Matsuda et al., 2016). Neurexins also have a number of other binding partners, including the latrophilins, neurexophilins, and dystroglycans, although the roles of these complexes in direct induction of synaptic differentiation is less clear (Südhof, 2017). The in vivo role of neurexins seems to be somewhat context-dependent; conditional knockout of all mouse neurexins resulted in phenotypes including reduced synapse density, altered synaptic transmission, and altered axon guidance, in a manner dependent on the specific brain region and synapse population analyzed (Chen et al., 2017).1.2.2 LAR-RPTPsA second family of presynaptic organizers which functions analogously to the neurexins are the leukocyte common antigen related (LAR)-type receptor protein tyrosine phosphatases (LAR-RPTPs). This family consists of three members, LAR, PTPÏ, and PTPδ, the extracellular regions of which contain three immunoglobulin (Ig)-like domains near the N-terminus, followed by either four or eight fibronectin type III (FN-III) domains, and a cleavage site near the transmembrane domain which results in the non-covalent attachment of the cleaved extracellular region to the protein stub (Streuli et al., 1988; Pulido et al., 1995a, 1995b).All three LAR-RPTPs bind via their first two FNIII domains to the leucine rich repeat (LRR) domains of netrin-G ligand 3 (NGL-3), and this interaction is sufficient to induce clustering of pre- or postsynaptic structures in the coculture assay (Woo et al., 2009; Kwon et al., 2010). Neurotrophin receptor kinase C (TrkC) binds via its LRR and first immunoglobulin-like (Ig) domain to PTPÏ 7specifically, primarily through PTPÏâs Ig domains (Takahashi et al., 2011). The interaction between the LAR-type RPTPs and the Slit and NTRK-like family proteins (Slitrk1-6), which occurs via the first three LAR-RPTP Ig domains and the first Slitrk LRR domain, specifies excitatory and inhibitory synapse formation in an isoform-specific manner, with PTPÏ specifying excitatory synapses via interaction with all Slitrks except Slitrk3, and PTPδ specifying inhibitory synapses via interaction with Slitrk3 (Takahashi et al., 2012; Yim et al., 2013; Um et al., 2014). Interleukin-1-receptor accessory protein-like 1 (IL-1RAPL1) interacts specifically with PTPδ in a manner dependent on PTPδ splicing, and bidirectionally triggers excitatory synapse formation (Valnegri et al., 2011; Yoshida et al., 2011), as does the related interleukin-1-receptor accessory protein (IL-1RAcP) (Yoshida et al., 2012). Synaptic adhesion-like molecule (SALM) 3 and 5, but not other members of the SALM family, interact with all three LAR-RPTPs and bidirectionally induce synaptogenesis (Mah et al., 2010; Li et al., 2015). The extracellular regions of the LAR-RPTPs are further able to interact with heparan sulfate proteoglycans (HSPGs) and chondroitin sulfate proteoglycans (CSPGs) (Aricescu et al., 2002; Shen et al., 2009). While these two classes of interactions appear to play opposing roles in promoting or inhibiting neurite outgrowth in development or after injury (Coles et al., 2014), the functional significance of LAR-RPTP binding to HSPGs or CSPGs in the context of synapse formation remains unclear.1.2.3 Other synaptogenic adhesion moleculesWhile the neurexins and LAR-RPTPs are the two most well-recognized categories of presynaptic organizers, each with a diverse array of postsynaptic ligands, several other transsynaptic pairs of adhesion molecule not involving either of these partners also play a role in synaptogenesis. Synaptic cell adhesion molecule, or SynCAM, is a homophilic cell adhesion molecule expressed on both 8presynaptic and postsynaptic membranes, and its transsynaptic binding is able to induce clustering of presynaptic but not postsynaptic components (Biederer et al., 2002; Fogel et al., 2007). SynCAM additionally engages in cis interactions within a single membrane, which appears to also be important for its synaptogenic function (Fogel et al., 2011). A second pair of synaptogenic molecules are the Ephs and ephrins, both of which may be expressed on either the presynaptic or the postsynaptic membrane depending on the brain region (Hruska and Dalva, 2012). EphB2 is capable of inducing presynaptic differentiation, albeit somewhat weakly, in the coculture assay (Kayser et al., 2006). In separate experiments, interfering with signaling downstream of EphB2 reduced synapse density (Segura et al., 2007). Cadherins are homophilic adhesion molecules, like SynCAMs, and although they do not directly induce synaptic differentiation in the coculture assay (Graf et al., 2004), together with their intracellular interacting protein β-catenin they have been shown to play a variety of roles in regulating synapse number, function, and plasticity (Bamji et al., 2003; Jungling et al., 2006; Bozdagi et al., 2010). Interestingly, some functional links have been shown between cadherins and both neuroligins and LAR-RPTPs. Both cadherin and β-catenin are substrates of PTPÏ phosphatase activity (Siu et al., 2007). N-cadherin (the neuronal form of cadherin) and neuroligin1 colocalize at many synapses, and each is able to at least partially rescue the phenotype induced by knockdown of the other (Aiga et al., 2011). NGL-1 and NGL-2, which unlike the closely related NGL-3 discussed previously bind to netrin-G1 and netrin-G2, respectively, are also capable of inducing synaptogenesis in the coculture assay, although to a much weaker extent than NGL-3. A role for the related secreted protein netrin-1 and its receptor deleted in colorectal cancer (DCC) in both pre- and postsynaptic differentiation has been demonstrated using bead-clustering assays (similar to the coculture assay, except that the synaptogenic molecule is 9presented on a small bead instead of expressed on a cell), although this effect is again somewhat weak (Goldman et al., 2013).Figure 1.2 Overview of synaptic organizing proteins10Presynaptic membrane is shown on top and postsynaptic on bottom. For simplicity, intracellular domains are schematized for presynaptic proteins only. Arrows indicate binding interactions thought to be functionally important for inducing synaptogenesis. For LAR-RPTPs and ligands, blue and red arrows indicate interactions which primarily induce excitatory and inhibitory synaptogenesis, respectively.1.3 The LAR-RPTP familyThe LAR-RPTP family, despite being one of the two (at least as far as current research has revealed) main families of synaptogenic adhesion molecule expressed on the presynaptic membrane, has received considerably less attention than the neurexins. Mice lacking PTPÏ show stunted growth and reduced survival, smaller olfactory bulbs, slower conduction velocities in peripheral nerves and disrupted myelination, as well as a variety of non-neuronal phenotypes (Elchebly et al., 1999; Wallace et al., 1999). Understanding the mechanisms by which LAR-RPTPs signal the recruitment of presynaptic components such as SVs is important both from a basic science perspective as well as in the search for treatments for neuropsychiatric disorders, since these genes and those encoding some of their interacting partners have been linked to a number of disorders. Additionally, LAR-RPTPs play dual roles in regulating neurite outgrowth as well as synaptogenesis, and the link between these two processes is incompletely understood. This section will expand on what is known about the LAR-RPTPs, focusing on intracellular protein-protein interactions, roles in neurite outgrowth, and known links to neuropsychiatric disorders.1.3.1 Structure and binding partners of the LAR-RPTP intracellular regionThe intracellular portion of the LAR-RPTPs contains two homologous phosphatase domains, termed D1 and D2 (Pulido et al., 1995b). Despite the sequence conservation between the D1 and D2 domains, only the membrane-proximal D1 domain is catalytically active (Johnson and Van Vactor, 2003). The 11LAR-RPTPs form homodimers between the D1 domain of one LAR-RPTP molecule and a small âwedgeâ domain between the transmembrane domain and the D1 domain of a second molecule (Hofmeyer and Treisman, 2009). The D2 domain is known to bind to three intracellular proteins which might be of interest in the context of the synaptogenic function of the LAR-RPTPs, based on their interactions with other important presynaptic components (Figure 1.3).1.3.1.1 Liprin-αThe four members of the liprin-α family bind to all three of the LAR-RPTPs, as first identified for LAR and liprin-α1 (Serra-Pagès et al., 1995), and later confirmed for the other members of both families (Pulido et al., 1995b; Serra-Pagès et al., 1998; Chagnon et al., 2010). The domain structure of liprin-α consists of an N-terminal coiled-coil region as well as a region near the C-terminus which is highly conserved between isoforms of liprin-α and the related liprin-β, as well as between human and worm liprins (Serra-Pagès et al., 1998), and which is made up of three âsterile alpha motifâ (SAM) domains (Stapleton et al., 1999). The binding interface between liprin-α and LAR-RPTPs involves the membrane-distal LAR-RPTP D2 domain and the liprin-α SAM domains (Serra-Pagès et al., 1995). Loss-of-function approaches in both worms and flies have revealed a requirement for homologues of liprin-α for the formation of structurally and functionally normal synapses (Zhen and Jin, 1999; Kaufmann et al., 2002). Acute knockdown of liprin-α2 in cultured rat neurons causes a decrease in RRP size and defects in presynaptic release, along with reduced synaptic localization of many core synaptic components (Spangler et al., 2013).There are two liprin-β isoforms in mammals and one in invertebrates, which share a domain structure and overall homology with liprin-α, but have a substantially shorter coiled coil domain (Serra-Pagès et al., 1998). Liprin-α and liprin-β are able to heterodimerize with one another via the SAM domains, 12whereas both types of liprin are able to form homodimers through the N-terminal coiled coil region (Serra-Pagès et al., 1998). In Drosophila, a third family member termed liprin-γ (most closely related to the vertebrate protein Kazrin E) binds to both liprin-α (via the coiled coil regions of both proteins) and LAR (via the LAR D2 and liprin-γ SAM domains) and seems to negatively regulate the function of both liprin-α and liprin-β, although these interactions are complex and context-dependent, and their presence or significance in vertebrate systems is not clear (Astigarraga et al., 2010).Liprin-α binds to a number of presynaptic scaffolding proteins through both its coiled coil region and its SAM domains. Through its coiled coil region, liprin-α binds to the C-terminal C2B domain of the scaffolding protein RIM1α, and RIM1α loss of function experiments reveal defects in presynaptic neurotransmitter release (Schoch et al., 2002). RIM proteins also bind to synaptotagmin1 via the same C2B domain (Schoch et al., 2002), to RIM-binding proteins (RIM-BPs) via a central proline-rich region (Wang et al., 2000), to the synaptic vesicle-associated GTPase Rab3 via the RIM zinc-finger domain in a GTP-dependent manner (Wang et al., 1997), and the scaffolding protein Munc13 (Betz et al., 2001) via the N-terminal zinc-finger domain. The coiled-coil region of liprin-α also binds to N-terminal regions of the scaffolding protein ELKS (also called ERC or CAST), and this interaction promotes the synaptic localization of liprin-α (Ko et al., 2003b). In addition, liprin-α binds to the scaffolding protein CASK, and in brain lysates is part of a complex including CASK and MALS/Veli (Olsen et al., 2005). Although it was originally reported that this binding occurs between first SAM domain of liprin-α and the CAMK and first L27 domain of CASK, later crystallography experiments showed that the binding interface involves both the first two SAM domains of liprin-α as well as an alternatively-spliced and vertebrate-specific loop (the âPQ loopâ) between these two domains, and the CAMK but not L27 domain of CASK (Wei et al., 2011). While liprin-α has not been shown to directly bind to the active 13zone scaffolds Bassoon or Piccolo, it shows a number of indirect links with both, as Bassoon binds to Piccolo, Munc13, RIM, and RIM-BP (Wang et al., 2009; Davydova et al., 2014), and Piccolo further binds to ELKS, Munc13, and RIM (Fujimoto et al., 2002; Wang et al., 2009).In addition to scaffolding proteins, liprin-α binding partners include synaptic proteins with enzymatic activity as well as motor proteins. In C. elegans, SYD-2 (the homologue of liprin-α; âSYDâ stands for âsynapse defectiveâ) acts as a master regulator of synapse formation together with the RhoGAP SYD-1 (Hallam et al., 2002), with loss-of-function of either of these proteins causing mislocalization of multiple presynaptic components (Patel et al., 2006). A mammalian homologue of this protein, mSyd1A, has also been identified, and has been shown to bind via its intrinsically disordered domain both to Munc18-1 and to the same PQ loop between SAM1 and SAM2 of liprin-α that binds to CASK (Wentzel et al., 2013). Liprin-α binds via the C-terminal half of its coiled coil region to the stalk region of the kinesin motor KIF1A (Shin et al., 2003). In line with this, liprin-α has been shown in invertebrate systems to regulate axonal transport of both SVs (Miller et al., 2005; Wagner et al., 2009) and DCVs (Goodwin and Juo, 2013).Although its localization is primarily presynaptic (Wyszynski et al., 2002; Yeh et al., 2005), liprin-α is also present at postsynaptic sites has been shown in interact directly with both glutamate receptor interacting protein (GRIP) and GIT and to target AMPA receptors at the postsynaptic membrane via these interactions (Wyszynski et al., 2002; Ko et al., 2003a). It has also been shown to play a role, via its interaction with the kinesin motor KIF1A, in targeting dense core vesicles to postsynaptic sites (Stucchi et al., 2018).141.3.1.2 CaskinA second binding partner of the LAR D2 domain is Caskin, which like liprin-α accomplishes this binding via SAM domains (Weng et al., 2011). Furthermore, there is some evidence that the binding sites for caskin and liprin-α overlap, and thus that binding is competitive (Weng et al., 2011). Vertebrate caskin has two isoforms, and Caskin1 but not Caskin2 competes with Mint1 for binding to CASK (Tabuchi et al., 2002). Caskin also has a number of other interaction partners, including neurexin 2, synaptotagmin1, and Nck/Dock, although the functional consequences of these interactions has not been determined (Balázs et al., 2009). Via its interaction with Nck/Dock, caskin associates with and is phosphorylated by EphB1, leading to a conformational change within caskinâs sh3 domain (Pesti et al., 2012). Additionally, the SAM domains of caskin can bind to one another, leading to the formation of polymers capable of simultaneous binding to CASK (Stafford et al., 2011). Loss-of-function studies in Drosophila revealed a role for caskin in axon pathfinding which resembled the phenotype of LAR mutants (Weng et al., 2011). However, functional roles for caskin in synaptogenesis have not been explored.1.3.1.3 TrioA third interaction partner of the D2 domain is Trio, a large protein containing a Rac GEF, a Rho GEF, and a kinase domain; the binding interface involves the kinase and adjacent immunoglobulin-like domain to trio (Debant et al., 1996). Based on genetic interactions in Drosophila, trio appears to act downstream of both liprin-α and liprin-β in the context of axon targeting (Astigarraga et al., 2010). Loss of trio in mice causes perinatal lethality, as well as defects in organization of the hippocampus and olfactory bulb (OâBrien et al., 2000). Neural-specific trio knockout results in smaller body size as well as reduced brain/body weight ratio, with both differences developing postnatally, as well as reduced 15survival, severe defects in cerebellar organization, and impaired granule cell migration (Peng et al., 2010). Trio has a well-characterized role in regulating axon outgrowth and guidance in both vertebrate and invertebrate systems (Awasaki et al., 2000; Bateman et al., 2000; Newsome et al., 2000; Chen et al., 2011; DeGeer et al., 2013). Interestingly, trio interacts genetically with Dock, although unlike Caskin it has not been demonstrated to bind to it, and is instead proposed to signal via its GEF-D1 domain to Rac and Pak (Newsome et al., 2000).16Figure 1.3 Structures of LAR-RPTP, liprin-α, caskin, and direct binding partners thereof.Lines indicate binding, and brackets indicate how precisely and to which part of the protein the interaction has been mapped. Proteins contained within the grey box have been demonstrated to bind the central region of caskin1, but the binding sites on these interacting proteins have not been mapped (Balázs et al., 2009). PTPÏ, liprin-α, liprin-β, 17and caskin are shown as homodimers. All proteins are shown with the N-terminus on the left, with the exception of the upper copy in the liprin-α and liprin-β homodimers. For simplicity, LAR-RPTP and neurexin are shown as the 4-FNIII splice variant and β-neurexin isoform, respectively. All domains are labeled for LAR-RPTP, liprin-α, and caskin; for all others only domains involved in binding are labeled. Abbreviations: Ig, immunoglobulin-like domain; FNIII, Fibronectin type III domain; TM, transmembrane region; D1 & D2, LAR-RPTP phosphatase domains; Ank, Ankyrin repeats; Sh3, SRC homology 3 domain; CID, CASK interaction domain; SAM, Sterile alpha motif; PRR, Proline-rich region; CTD, caskin C-terminal domain; CC, coiled coil region; PQ, liprin-a "PQ loop" (Wentzel et al., 2013); IDD, intrinsically disordered domain; CaMK, calcium/calmodulin-dependent protein kinase domain; C2B, second C2 domain of RIM; PBH, Piccolo/Bassoon Homology domain; S14, Sec14 lipid binding domain; SR, spectrin repeats; GEF, guanine nucleotide exchange factor; PH, pleckstrin homology domain; PSK, protein serine kinase domain. Interactions shown in this figure are based on previously reported data (Serra-Pagès et al., 1995, 1998; Debant et al., 1996; Hata et al., 1996; Schoch et al., 2002; Tabuchi et al., 2002; Ko et al., 2003b; Balázs et al., 2009; Wang et al., 2009; Hofmeyer and Treisman, 2009; Wei et al., 2011; Weng et al., 2011; Wentzel et al., 2013; Terry-Lorenzo et al., 2016).1.3.2 Regulation of neurite outgrowth by LAR-RPTPsPTPÏ has been thoroughly described in mice as an inhibitor of axon outgrowth following injury of both peripheral nerves (McLean et al., 2002; Thompson et al., 2003) and the optic nerve (Sapieha et al., 2005). A similar effect has been observed in cultured cortical and dorsal root ganglion neurons (Thompson et al., 2003; Siu et al., 2007). This growth-inhibitory effect has been attributed, at least for LAR, to binding with chondroitin sulfate proteoglycans (CSPGs) (Fisher et al., 2011). In contrast, blocking the activity of the chicken homologue of PTPÏ (cPTPÏ) by treating cultures with either antibodies or a truncated version of cPTPÏ reduced axon outgrowth from retinal explants only in the presence of the basement membrane, implying that cPTPÏ promotes growth via interaction with some ligand found there (Ledig et al., 1999). It was later demonstrated that the growth-promoting ligands 18found in the basement membrane were heparan sulfate proteoglycans (HSPGs) (Aricescu et al., 2002). This apparent contradiction was resolved by the finding that PTPÏ can indeed bidirectionally regulate axon growth, depending on the presence of either HSPGs or CSPGs, with HPSGs promoting PTPÏ multimerization and axon growth and CSPGs inhibiting the same processes (Coles et al., 2011).1.3.3 Links to diseaseSeveral of the proteins discussed above have known links with neuropsychiatric disease. PTPÏ, PTPδ, and a number of their postsynaptic ligands have been documented as being mutated in patients with disorders including autism spectrum disorder (ASD), schizophrenia, attention deficit hyperactive disorder, intellectual disability (ID), bipolar disorder, epilepsy, and major depression (Takahashi and Craig, 2013). Additionally, the LAR-RPTP intracellular ligand Trio has been implicated in ASD and ID (De Rubeis et al., 2014). Liprin-α1, but none of the other three liprin-α genes, has also been found disrupted in patients with ASD and ID (Schluth-Bolard et al., 2013).1.4 Intracellular mechanisms of presynaptic differentiationThe mechanisms downstream of the LAR-RPTPs and the neurexins which are responsible for recruiting synaptic vesicles, calcium channels, and other presynaptic components are poorly understood. The LAR-RPTP-interacting protein liprin-α binds to mSYD1A (mouse synapse-defective 1A), which in turn interacts with munc18-1 and is required for normal levels of synaptic vesicle docking and synaptic transmission, but not for synapse numbers (Wentzel et al., 2013). Liprin-α appears to be one of the earliest components to appear at nascent presynaptic sites; Drosophila neuromuscular junctions show early accumulations of liprin-α, SYD-1, and Unc13B, followed by a complex of Bruchpilot (the Drosophila homologue of ELKS), RBP, and Unc13A (Böhme et al., 2016). 19Furthermore, both SYD-1 and SYD-2, the C.elegans homologues of mSyd1A and liprin-α, are required for normal synaptic accumulation of several presynaptic markers. In contrast to the LAR-RPTPs, the intracellular region of neurexin does not appear to be necessary for synaptogenesis, since a GPI-anchored form of neurexin was shown to be fully capable of rescuing neurexin-mediated synaptogenesis (Gokce and Südhof, 2013). 1.5 Rationale and HypothesesI hoped to elucidate some of the mechanisms by which binding between LAR-RPTPs and their extracellular ligands triggered the recruitment of presynaptic components. Specifically, I wondered which intracellular domains of PTPÏ, and which of its intracellular binding partners, were necessary for its role in synaptogenesis. Based on previous literature, several hypotheses seemed likely:â¢ Because no binding partners of the D1 domain have been reported besides the LAR-RPTPs themselves, I hypothesized that either the D1 domain would be dispensable for synaptogenic activity, or that either phosphatase activity or homodimerization would be required. Another model which I also considered and tested was one in which the D1 domain was required simply as a spacer, in order to separate the D2 some distance from the membrane.â¢ Because liprin-α is a core scaffolding protein at presynaptic sites (Südhof, 2012), I further hypothesized that its binding to PTPÏ would be required to induce presynaptic differentiation. A related prediction was that the D2 domain would be required. A requirement for either caskin or trio binding to PTPÏ also seemed plausible, given what is known about their functions and binding interactions in the literature.201.6 Electrophysiological and morphological correlates of functional properties in circuits and organismsA second project which I was involved in focused on the attempt to systematically identify novel genes with evidence for a role in regulating electrophysiological and morphological properties of neurons. Although the idea that changing electrophysiological or morphological properties is likely to have downstream consequences for a circuit or an organism perhaps seems like common sense, understanding in more detail the links between these cellular-level changes and their more macroscopic consequences is worthwhile. Thus, this section will explore what is known about links between electrophysiological and morphological phenotypes and downstream results on the computational properties of circuits or behavioral characteristics of organisms.1.6.1 Historical perspectiveThe generation of electricity by living animals has been known since John Walsh observed it in electric eels in the 1700âs (Piccolino and Bresadola, 2002), and was conclusively demonstrated in the nervous system by Leopoldo Nobili, and later Carlo Matteucci, in the 1800âs (Verkhratsky and Parpura, 2014). Once precise recordings from the intact mammalian brain became possible, it became clear that neurons engage in a diverse array of sometimes complex electrophysiological patterns, which differ between not only brain areas but also between states of consciousness (Renshaw et al., 1940). More recent experiments have attempted to understand how electrophysiological diversity relates to cell type identity, how electrophysiological properties of single neurons or circuits contribute to coding capacity, as well as the relationship between electrophysiological changes at the cellular level and functional outcomes in the living organism, including in disease states. With regard to morphology, the complex and diverse shapes of different types of neurons have been well-appreciated since the work of Santiago 21Ramón y Cajal in the 1800âs and 1900âs (Jones, 2007), who was able to guess at the function of certain cells and circuits, sometimes with impressive accuracy, simply based on their morphology. Since then, neuronal morphology has continued to be a focus for many researchers, but its effects on function, and the mechanisms that allow each type of neuron to develop its unique shape, still remain only partially understood.1.6.2 Classification of cell types based on electrophysiology and/or morphologyThe systematic classification of neurons into distinct types is a daunting task for which neuroscience has not yet settled on a single satisfactory solution. It is perhaps not surprising that the two features which most prominently set neurons apart from the other cells of the body, that is, their complicated patterns of electrical activity and their distinctive and often beautiful shapes, are the features which have often been proposed as organizing schemes to distinguish types of neurons from one another. A skeptical observer might wonder whether neuroscientists, being human, were being led astray by their ability to observe these features in detail, but not others. Are electrophysiology and morphology in fact a good way to classify neurons, or would their âtrueâ categories be better defined based on a different set of characteristics, perhaps ones which we lack proper tools to measure? Classification schemes based primarily on morphological characteristics have been proposed (Ascoli and Wheeler, 2016), and find some experimental support based on the existence of systematic differences between morphologically-defined types in both electrophysiological characteristics and connectivity (Jiang et al., 2015). Electrophysiological measurements have also been used to produce a robust classification of neuronal cell types, and types defined in this way correspond well with those defined based on either morphological methods or transgenic reporter expression (Gouwens et al., 2018). The correspondence between neuronal cell types defined according to schemes based on electrophysiology and morphology, 22as well as their correspondence with connectivity and molecular features, although imperfect, suggests that both the three-dimensional shape characteristics and the electrophysiological output of a given neuron form a core part of its identity.1.6.3 Effects of electrophysiological and morphological diversity on circuit information contentResearchers are still attempting to understand, largely through computational modeling studies, the ways in which the diverse patterns of electrophysiological activity displayed by different cell types, or even individual neurons within a type (Gouwens et al., 2018), determine the informational content of a given circuit. Simulations which varied the level of between-cell electrophysiological diversity in a population of neurons have shown that increasing diversity can increase the circuitâs ability to encode information, even when the modeled population of cells is generally understood to be quite homogenous (Padmanabhan and Urban, 2010; Tripathy et al., 2013). To make matters more complicated, changing the morphology of simulated neurons without altering channel density was sufficient to produce a wide variety of electrophysiological phenotypes (Mainen and Sejnowski, 1996). The Blue Brain Project has managed to digitally reconstruct a small volume of cortical tissue with electrophysiological and morphological properties, cell numbers and distributions, and connectivity patterns matching those observed in vivo (Markram et al., 2015). Despite being designed simply based on observations of real brain tissue without the goal of reproducing specific experimental results, this reconstructed microcircuit was able to reproduce some of the emergent properties seen in real circuits, and results of real experiments, without the need for parameter tuning to match the intended result. This simulation is by no means simple, but is based on simple principles of reproducing individual neurons that are realistic in their morphology and electrophysiological response characteristics as well as realistic connectivity between these neurons. The fact that this approach is able to reproduce previous 23biological findings implies that understanding the shapes and behavior of individual cells and the principles by which they are organized is sufficient to reproduce realistic computational properties.1.6.4 Disruption of neuronal electrophysiology and morphology in diseaseThe physiological importance of the precise tuning of neuronal electrophysiology is further underscored by the observation that ion channel genes are frequently linked to neuropsychiatric disorders, including various forms of ataxia, epilepsy, and dyskinesias (Bernard and Shevell, 2008). In the case of epilepsy, the relationship between channel expression and disease state is complex and exhibits strikingly incomplete penetrance even in the presence of multiple possibly-deleterious mutations, but the full complement of ion channels expressed in a given person seems to be at least somewhat predictive of whether they exhibit the disease (Klassen et al., 2011). Morphological changes in neurons have also been linked to disease; Parkinsonâs disease-linked mutations in the gene Lrrk2 have been shown to disrupt neurite branching (MacLeod et al., 2006; Dächsel et al., 2010; Borgs et al., 2016).1.7 Genetic and molecular underpinnings of electrophysiological and morphological propertiesThere already exist many examples where changes in expression of a single gene (usually, due to technical factors, this is assayed using either knockout, knockdown, or overexpression techniques) has been directly linked to a neuronal phenotype. Although situations involving the combinatorial action of many genes may be common and are likely to be more difficult to detect, clear relationships involving single genes are well documented. This section will explore some examples of situations where a mechanistic link between expression of a specific gene and a downstream electrophysiological or morphological phenotype is clear.241.7.1 Regulation of electrophysiological properties by expression of ion channelsIt was already understood at least to some extent by Hodgkin & Huxley that the electrophysiological behavior of a given system depended on its permeability to sodium and potassium ions (Hodgkin and Huxley, 1952a, 1952b). It is now known that this permeability, in turn, depends on the expression and surface localization of selectively permeable channel proteins (Stuhmer et al., 1988; Coetzee et al., 1999; Catterall et al., 2005).The primary alpha subunits of voltage-gated sodium channels are composed of four domains, each of which contributes six transmembrane alpha helices to the pore structure, and expression of different alpha subunits can alter electrophysiological properties of the cell based on that subunitâs particular voltage dependence and kinetics of activation and inactivation (Catterall et al., 2005). Although the main functional role of these channels is in the initiation and propagation of an action potential (Catterall et al., 2005), some types of alpha subunit can contribute to other aspects of neuronal function, for example the NaV1.9 channel, which is activated at lower membrane potentials than typical voltage-gated sodium channels and can contribute to setting the resting potential (Cummins et al., 1999; Herzog et al., 2001). Expression of auxiliary beta subunits can further modulate the activity of these channels (Catterall et al., 2005), potentially leading to very fine-grained control of voltage-dependent membrane properties.Potassium channels exhibit a great deal more diversity than do sodium channels, both structurally and functionally. These channels, in contrast to sodium channels, are tetramers, with subunits composed of two, four, or six transmembrane helices, as well as auxiliary subunits, and may be gated by voltage, calcium, or sodium, or may fall into the categories of inwardly-rectifying or leak channels (Coetzee et al., 1999). Potassium channels also contribute to electrophysiological phenotypes; for example, the 25GABAB receptor-coupled inwardly rectifying potassium channel GIRK2 appears to be involved in the etiology of epilepsy within infants with Down syndrome based on work in mouse models of the disorder (Joshi et al., 2016).Expression of a particular complement of ion channel subunits is an important mechanism through which a particular cell type can achieve the particular electrophysiological properties needed for its function. Indeed, even between cells within an individual defined type, there can be diversity in the levels of various channels which are expressed (Schulz et al., 2006), opening the door to even finer-grained control of neuronal properties. In addition to ion channel expression playing a role in setting stable, long-term electrophysiological properties of a cell, channel expression and associated properties can in some cases undergo changes in response to experience. For example, GABAA receptor function is regulated dynamically through mechanisms including palmitoylation, sorting, trafficking, endocytosis, phosphorylation, as well as regulation of its localization within the plasma membrane to either synaptic or extrasynaptic sites (Jacob et al., 2008).1.7.2 Processes regulating neuronal morphology and the cytoskeletonNeuronal morphology is also regulated, sometimes dynamically, and in some instances this regulation has been shown to depend on specific genes or proteins. This can occur via modification of cytoskeletal proteins. For example, the kinase DYRK1a phosphorylates β-tubulin and inhibits its polymerization, and is required for the normal dendrite morphogenesis of certain classes of neurons in Drosophila (Ori-McKenney et al., 2016). Morphogenesis also depends on lipid homeostasis, for example through synthesis of phosphatidylethanolamine via the kinase easily shocked (Meltzer et al., 2017). G protein-coupled receptors (GPCRs) can dynamically regulate neuronal morphology by acting on the actin cytoskeleton; experimental activation, blockade, or knockdown of a variety of GPCRs, including 26serotonin, dopamine, endocannabinoid, and metabotropic glutamate receptors, has been shown to alter spine density and morphology (Leung and Wong, 2017). CaMKIIα has been shown to stabilize dendritic arbors and to limit their elongation and branching through an autophosphorylation-dependent mechanism (Wu and Cline, 1998; Pattinson et al., 2006). Additionally, various members of the protocadherin and atypical cadherin subfamilies have been shown to regulate dendrite morphogenesis, in most but not all cases as positive regulators of dendritic branching (Keeler et al., 2015).1.7.3 Challenges in systematic discovery of candidate genes related to electrophysiology or morphologyAlthough many genes, including those discussed above, have been linked to electrophysiological or morphological phenotypes based on experimental evidence, this has mainly been achieved in a piecemeal fashion, with single genes being identified based on approaches such as random mutagenesis in model organisms or identification of naturally-occurring mutations in human patients. It would also be desirable to link genes and phenotypes in a systematic and unbiased manner based on correlations between gene expression and phenotype in wild-type cells, in order to predict which genes would be worth following up experimentally. This has been done on a small scale using multiplex RT-PCR, but this approach was limited to a small selection of genes, and additionally allowed for only binary assessment of gene expression rather than precise quantification (Toledo-Rodriguez et al., 2004). Newer approaches based on RNA-seq provide opportunities to study these relationships on a genome-wide scale. The newly-developed Patch-seq methodology, in which cytoplasm is harvested and subjected to RNA-seq following electrophysiological recordings of single cells (Cadwell et al., 2016), is a promising approach which in theory could be used to precisely measure correlations between gene expression and electrophysiological properties among individual cells. However, at this time the 27method suffers from technical challenges related to mRNA quality and contamination from surrounding neuropil (Tripathy et al., 2018). A previous study used the approach of identifying correlations between expression of specific genes and the values of electrophysiological properties across defined neuronal cell types, rather than individual cells, in order to define candidate regulators of these properties (Tripathy et al., 2017). This work identified a number of novel candidate genes, as well as some genes whose relationships to particular electrophysiological properties were supported by previous literature. However, one major limitation resulted from the fact that overall differences between excitatory and inhibitory cell types could, and in some cases did, either obscure true correlations or introduce correlations which were primarily driven by cell class rather than by graded differences within classes.1.8 Rationale and hypothesesIn order to examine relationships between electrophysiological and morphological phenotypes of neurons and specific genes in an unbiased manner, I made use of two datasets produced by the Allen Institute for Brain Science as part of their Cell Types project (http://celltypes.brain-map.org/). One dataset contained RNA-seq data and the other measured electrophysiological and morphological properties in a separate population of cells. Briefly, I assigned individual cells from both datasets to âcell typesâ based on the Cre-line and layer they were isolated from and their identity as inhibitory or excitatory cells. I was then able to look for combinations of genes and neuronal properties with statistical relationships between them. This is similar to a previously published approach (Tripathy et al., 2017), but in this work I attempted to circumvent issues related to correlations either driven by or obscured by overall differences between excitatory and inhibitory cell types by introducing the broad cell class identity of each cell type into my model. I attempted to validate the set of gene-property correlations produced, both with regard to the overall validity of the results, and with regard to whether 28it was an improvement over a simpler model which did not consider cell class, using three main approaches. Briefly, I looked for cross-dataset correspondence with another dataset containing different cell types but processed in a similar manner, I looked for correspondence of my results with a simple and well-understood example of ion channels controlling a specific electrophysiological property, and I asked whether my results were consistent with results derived from comparing single cells within a type using the recently-developed Patch-seq methodology. I had three key hypotheses related to this project:â¢ First, I suspected that a substantial portion of the gene-property relationships which would be found if cell class was not considered were in fact driven by class, and would no longer show up as significant using the improved model. Further, I suspected that these relationships were unlikely to be biologically relevant. In other words, I thought that the model which considered cell class would be more specific to relationships with a high probability of being meaningful.â¢ Second, I suspected that there were also many gene-property relationships that would be missed using a simple model that did not consider cell class. This hypothesis, if true, would mean that the model considering cell class would be more sensitive, and would pick up true relationships which would be missed by the simpler model.â¢ A third hypothesis concerned the relationship between gene-property correlations existing across a range of cell types and those existing within a single cell type. One plausible model is one in which relationships between genes and neuronal phenotypes are highly context-dependent, and different sets of genes are responsible for establishing the broad properties of a given cell type and fine-tuning the properties of a specific cell within that type. A second model 29could be that fine-tuning within cell types is simply achieved by small changes in expression of the same genes which set the properties of that cell type to begin with. Although I suspected that both models were true to some extent, for the purposes of this work I hypothesized that I would see at least some evidence of the second model when comparing the gene-property correlations derived when comparing across cell types with those calculated within single, homogenous types. That is, I expected to see some correspondence between correlations existing across cell types and those existing within cell types.30Chapter 2: Mechanisms of PTPÏ-mediated presynaptic differentiation2.1 Introduction A key early step in the formation of a new synapse involves physical contact and binding between specific adhesion molecules, termed synaptic organizing proteins, expressed on the axon of one neuron and the dendrite of another. At least in vitro, the presentation of a single synaptic organizing protein expressed on the surface of a non-neuronal cell can be sufficient to induce clustering of pre- or postsynaptic machinery, exemplified by the first discovered and best known pair of synaptic organizing proteins, the postsynaptic neuroligins (Scheiffele et al., 2000) and the presynaptic neurexins (Graf et al., 2004). In addition to the neuroligins and neurexins, a variety of other organizing proteins with similar synaptogenic activities have been described, including the presynaptically-expressed LAR, PTPÏ, and PTPδ, which together make up the LAR-RPTP family and interact with postsynaptic NGL-3 (Woo et al., 2009), TrkC (Takahashi et al., 2011), Slitrk1-6 (Takahashi et al., 2012), Il1RAPL1 (Yoshida et al., 2011), IL1RAcP (Yoshida et al., 2012), and SALM3 (Li et al., 2015).The mechanism by which synaptic organizing proteins signal the formation of a nascent synapse must involve more than simply adhesion, since presentation of a single pre- or postsynaptic ligand is sufficient to cause clustering of postsynaptic scaffolds and neurotransmitter receptors, or of synaptic vesicles and functional release sites (Scheiffele et al., 2000; Graf et al., 2004). In the case of neurexin, recruitment of intracellular proteins can occur at least under some circumstances without the presence of the neurexin intracellular region (Gokce and Südhof, 2013), presumably through some as yet unidentified co-receptor. However, the same does not appear to be true for the LAR-RPTPs, since a version of PTPÏ lacking the intracellular region acted as a dominant-negative suppressor of 31synaptogenesis (Takahashi et al., 2011). However, the mechanism by which these proteins exert their effects, including which intracellular interacting proteins and/or co-receptors are involved, is poorly understood.Modification of synaptic organizing proteins or their interaction partners by heparan sulfate (HS) sugar is known to play a role in synaptogenesis. While neither the LAR-RPTPs nor the PTPÏ postsynaptic partner TrkC are known to be HS-modified themselves, PTPÏ binds to heparan sulfate proteoglycans (HSPGs) via a binding interface which overlaps with the binding interface for TrkC (Coles et al., 2014), suggesting that TrkC and HSPGs might compete for PTPÏ binding. Conversely, HS binding activity of LAR-RPTPs could be necessary for binding of unknown extracellular co-receptors which might either be necessary for, or be positive modulators of, synaptogenic function.The structure of the LAR-RPTPs, including PTPÏ, features extracellular Ig and FNIII domains which mediate ligand binding (Takahashi and Craig, 2013). Following the single transmembrane domain, the LAR-RPTPs have two phosphatase domains, termed D1 and D2, of which only D1 is catalytically active (Streuli et al., 1990; Takahashi and Craig, 2013). The D2 domain binds to the scaffolding proteins of the liprin-α family (Serra-Pagès et al., 1995), the GEF/kinase Trio (Debant et al., 1996), and to the CASK-interacting proteins Caskin1 and Caskin2 (Weng et al., 2011). In C. elegans, the homologues of the LAR-RPTPs and liprin-α interact genetically in the context of synapse formation (Ackley et al., 2005). Although caskin has been studied thus far in the context of axon guidance but not synapse formation (Weng et al., 2011), it is known to interact with several important presynaptic proteins through which it might be able to transduce signals from PTPÏ, including CASK, neurexin, and synaptotagmin (Tabuchi et al., 2002; Balázs et al., 2009). Trio interacts with the presynaptic scaffolding proteins Bassoon and Piccolo (Terry-Lorenzo et al., 2016), and additionally shows genetic 32interactions in Drosophila with liprin-α in the context of neuromuscular synapse formation (Astigarraga et al., 2010). There are also several known enzymatic substrates of the LAR-RPTPs which could potentially mediate their synaptogenic effects. PTPÏ dephosphorylates p250GAP, leading to decreased neurite outgrowth in PC12 cells via Rac (Chagnon et al., 2010). It has also been shown that LAR is able to dephosphorylate β-catenin (Müller et al., 1999; Dunah et al., 2005), and there is evidence that postsynaptic LAR may play a role in increasing numbers of excitatory synapses and dendritic spines via a dephosphorylation-dependent mechanism (Dunah et al., 2005). Additionally, PTPÏ inhibits axon outgrowth by dephosphorylating N-cadherin (Siu et al., 2007). It has been suggested but not conclusively shown that LAR might dephosphorylate its binding partner liprin-α, which is tyrosine phosphorylated when expressed in S2R+ cells (Hofmeyer et al., 2006). Here, we used a molecular replacement strategy in which one or more intermolecular interactions were disrupted by domain deletion or point mutagenesis in order to provide insight into the mechanism by which PTPÏ signals the formation of a nascent synapse. We find that, at least in our culture system, the ability of PTPÏ to mediate the induction of new synapses does not depend on its ability to dephosphorylate targets or to bind HSPGs, but it does require that PTPÏ be able to bind to the scaffolding protein liprin-α. Our results also suggest that, contrary to previous reports, binding between PTPÏ and liprin-α involves both the D1 and D2 domains of PTPÏ.332.2 Results2.2.1 LAR-RPTPs regulate neuronal morphology and presynaptic inductionFirst, we sought to assess the role of the LAR-RPTP family in regulating neuronal morphology. To this end, we infected primary neuronal cultures with AAV carrying shRNAs targeting PTPÏ, PTPδ, and LAR (shPTP) or carrying four copies of an shRNA targeting GFP (shGFP) as a control. Efficacy of shRNAs was validated using RT-qPCR (Figure 2.1A) for all LAR-RPTPs as well as by Western blot against PTPÏ (Figure 2.1B). In order to test the ability of PTPÏ to rescue any defects, we transfected the same neurons with either shRNA-resistant V5-PTPÏ or V5-CD4 as a control. Neurons were fixed at DIV 14 and stained with antibodies against Tau and MAP2 to mark axons and dendrites, respectively (example images shown in Figure 2.1C). There was a significant difference in the area occupied by MAP2+ dendrites between the shCtrl + CD4 and shPTP + PTPÏ conditions, but this was not consistent between cultures (Figure 2.1D). We observed a consistent reduction in the area occupied by Tau+ axons, either alone or as a ratio with MAP2 area, in response to shPTP, which was not rescued by expression of PTPÏ (Figure 2.1E, F). Unfortunately, these differences in morphology precluded us from analyzing electrophysiological changes in response to shPTP, since assays which would be informative about effects on synapse density, such as recordings of miniature postsynaptic currents, would be difficult to interpret in the context of altered axon density.34Figure 2.1 Knockdown confirmation and morphological characterization of neurons lacking LAR-RPTPs (A) Quantification of mRNA levels for the three LAR-RPTPs, as well as Gapdh and β-actin as control housekeeping genes, measured by RT-qPCR in neurons treated with shCtrl or shPTP. *p < 0.0001 versus Gapdh, ANOVA with post hoc Dunnettâs multiple comparison test, n = 3 technical replicates each from 3 independent cultures.35(B) Western blot against PTPÏ in neurons treated with either shCtrl or shPTP.(C) Example images of hippocampal neurons electroporated with either V5-CD4 or V5-PTPÏ (columns) and treated at DIV 6 with AAVs carrying either shGFP or shPTP sequences (rows). Neurons were fixed at DIV 14 and stained for Tau (axons) MAP2 (dendrites), and DAPI (nuclei), and imaged at 10x. Scale bar represents 200 μM.(D) Quantification of total MAP2-positive area per field, normalized to the number of DAPI-stained nuclei in the same field. *** p < 0.001, Dunnâs post-hoc test (applies also to panels B and C). Overall p-value for the experiment was 0.00157, Kruskal-Wallis, n = 160-161 fields per condition from four independent cultures.(E) Quantification of total Tau-positive area per field, normalized to the number of DAPI-stained nuclei in the same field. Overall p-value for the experiment was 2.021 * 10-36, Kruskal-Wallis.(F) Quantification of the ratio between Tau and MAP2 total area per field. Overall p-value for the experiment was 1.578 * 10-36, Kruskal-Wallis.Next, we sought to test the hypothesis that the LAR-RPTPs would be required for TrkC, as well as another LAR-RPTP binding partner, NGL-3, to induce the formation of new synapses. To this end, we used a neuron-fibroblast coculture assay in which COS-7 cells were transfected with the appropriate postsynaptic organizing protein and then added to primary neuronal cultures as diagrammed in Figure 2.2 (Scheiffele et al., 2000). We cocultured neurons treated as above with COS cells expressing HA-tagged CD4, TrkC, NGL-3, or neuroligin2 (Nlg2) from DIV 13-14 (example images shown in Figure 2.3A for TrkC and Figure 2.4 for other ligands). As expected, the non-synaptogenic molecule CD4 did not induce clustering of synapsin. Under control conditions (V5-CD4 and shGFP), TrkC induced robust recruitment of synapsin at sites of contact between the axons and COS cells, and this was abolished by treatment with shPTP. Cultures transfected with V5-PTPÏ showed synapsin recruitment when treated with either shCtrl or shPTP, indicating that PTPÏ was able to rescue the synaptogenic activity of TrkC (Figure 2.3B). Synaptogenic activity of NGL-3 was similarly abolished by shPTP and rescued by V5-36PTPÏ (Figure 2.3C). Nlg2, a ligand of the neurexin family, showed similar synaptogenic activity with or without shPTP and with or without overexpression of PTPÏ (Figure 2.3D). Measuring the extent to which axons were recruited to transfected COS cells revealed the same broad trend in that axon recruitment to TrkC- and NGL-3-expressing cells was reduced by shPTP and at least partially rescued by expression of PTPÏ, but interestingly there was also a slight reduction in axon recruitment by Nlg2 in shPTP-treated neurons, which was significant compared to controls in the shPTP + PTPÏ condition but not shPTP + CD4 (Figure 2.5A-C), possibly consistent with the reduction in axon outgrowth as a result of shPTP. Differences in recruitment of synapsin and tau could not be explained by differences in surface levels of the postsynaptic inducer (Figure 2.5D-F).3738Figure 2.2 Diagram of neuron/COS coculture assay and quantification pipeline(A) Schematic drawing of the neuron/COS coculture assay for postsynaptic molecules.(B) Generation of ROIs for image measurement. (1) Fluorescence images are acquired. (2) The Mexican Hat Filter is applied to the Tau channel using a threshold of 5. This allows for reliable automatic thresholding in the following step, and was not done for all experiments presented here. (3) Images are thresholded either manually or automatically. (4) The MAP2 channel is first dilated by 5 pixels to capture dendritic spines, then inverted so that the final ROI will include only the MAP2-negative region. Note that the MAP2 antibody used here produces some background staining on the nuclei of COS cells. We consider this to be an advantage, since the nucleus often produces out-of-focus signal and/or background staining in other channels which is excluded along with the dendrites using our analysis method. (5) ROIs from steps 3 and 4 are combined. The COS cell is shown in magenta, Tau in yellow, and MAP2 in black. Overlapping areas of Tau and MAP2 are shown in green, or in cyan if they overlap the COS cell. MAP2 overlapping the COS cell without Tau is shown in blue. Red indicates the absence of any of the three markers. White areas indicate overlap of Tau with the COS cell in the absence of MAP2, which we define as the "contact area". (6) Final ROIs. The ROI on the left (âCOSâ), corresponding to the magenta and white areas in step 5, is the COS cell after excluding the areas with MAP2 signal (blue/cyan). The ROI on the right (âContactâ), corresponding to the white areas only, shows the overlap of the Tau signal with the COS cell in areas without MAP2. (7) The intensity of a fourth channel is measured within the ROI regions defined above as a measure of recruitment by transfected COS cells. Myc-liprin-α2 was measured in the example shown here, but the same steps would apply for synapsin or any other protein of interest.(C) Quantification. Area of the âContactâ ROI divided by the area of the âCOSâ ROI gives a measure of "Tau recruitment per COS cell area." Integrated density of the myc channel within the âContactâ ROI was normalized to the area of the âContactâ ROI to give "myc recruitment per contact area." In experiments where Tau was not stained, recruitment was measured by taking the integrated density of the myc channel within the âCOSâ ROI divided by the area of the âCOSâ ROI. The resulting measure, "myc recruitment per COS cell area" is used in place of "myc recruitment per contact area."3940Figure 2.3 Coculture activity of TrkC and NGL-3, but not Nlg2, is abolished by PTP triple knockdown and rescued by PTPÏ.(A) Representative images of HA-TrkC cocultures in which neurons were treated with either control (shCtrl) or PTP triple knockdown (shPTP)-expressing AAVs, and rescued using V5-CD4 as a control or RNAi-resistant V5-PTPÏ. Synapsin (Syn) was recruited in TauI-positive axons at sites of contact with TrkC-expressing COS cells. Microtubule-associated protein 2 (MAP2) labeling of dendrites was used to exclude native synapses from analysis. Scale bar represents 10 μm.(B-D) Quantification of synapsin recruitment by the indicated postsynaptic inducer, or by CD4 as a control (all HA-tagged). Control cocultures with CD4-expressing COS cells were performed using neurons treated with shCtrl + CD4 (shared between panels). âContact areaâ is defined as the region where axons contact the inducer-expressing COS cell, excluding the area overlapped by MAP2-positive dendrites. Values are normalized to the mean value of recruitment in the shCtrl + CD4/TrkC coculture condition from the same culture. Data are expressed as mean ± SEM. Overall p-values for each experiment were 5.05 * 10-20 (TrkC), 6.69 * 10-13 (NGL3), and 4.70 * 10-15 (Nlg2), Kruskal-Wallis, n = 26-32 cells per condition from 3 independent cultures. *** p < 0.001 compared to shCtrl + CD4 with CD4 coculture, ### p < 0.001 compared to shCtrl + CD4 with TrkC, NGL-3, or Nlg2 coculture, Dunnâs post-hoc test.4142Figure 2.4 Example images of NGL-3 and Nlg2 cocultures with neurons lacking LAR-RPTPs. Representative images of cocultures with HA-tagged CD4, NGL-3, and Nlg2 (âInducer)â treated with either control (shCtrl) or PTP triple knockdown (shPTP)-expressing AAVs, and rescued using V5-tagged CD4 as a control or RNAi-resistant V5-PTPÏ. Synapsin (Syn) was recruited in Tau-positive axons at sites of contact with HA-TrkC expressing COS cells. Microtubule-associated protein 2 (MAP2) labeling of dendrites was used to exclude native synapses from analysis. Scale bar represents 10 μm.43Figure 2.5 Additional quantification of TrkC, NGL-3, and Nlg2 coculture activity after loss of LAR-RPTPs. (A-C) Quantification of axon recruitment by the indicated postsynaptic ligands. âContact areaâ is defined as the region where axons contact the inducer-expressing COS cell, excluding the area overlapped by MAP2-positive dendrites. âCOS areaâ also excludes the region occupied by MAP2 signal. Overall p-values for each experiment were 444.02 * 10-19 (A), 4.07 * 10-13 (B), and 2.44 * 10-13 (C), Kruskal-Wallis. Data are expressed as mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.001 compared to CD4 coculture, Dunnâs post-hoc test. # indicates significance at the same levels compared to shCtrl + CD4 (also applies to D-F).(D-F) Surface-expressed HA-inducer intensity on COS cells. Overall p-values were 0.66 (D), 0.034 (E, although none of the individual comparisons were significant based on Dunnâs post-hoc test), and 0.40 (F), Kruskal-Wallis.2.2.2 Liprin-α2 and caskin1 are recruited to synapses induced through PTPÏTo assess the potential for various known intracellular ligands of PTPÏ to be involved in its synaptogenic function, we tested whether we could visualize recruitment of these ligands using TrkC cocultures. We expected that if either or both of these proteins were required for PTPÏ-mediated synaptogenesis, they would also be present at presynaptic sites induced by PTPÏâs interactions with its postsynaptic binding partners. We transfected neurons with myc-tagged liprin-α2 or caskin1, along with V5-PTPÏ, at the time of plating. Preliminary experiments (not shown) indicated that co-transfection with PTPÏ increased the amount of recruitment that could be seen. When we performed cocultures with TrkC on neurons treated in this manner, we saw significant recruitment of both liprin-α2 and caskin1 to transfected COS cells (Figure 2.6).The recruitment of liprin-α2 is the most striking out of the two proteins tested, both visually as well as based on the relative amounts recruited to COS cells expressing TrkC versus CD4 (Figure 2.6A). Recruitment of Caskin1 can be clearly seen on some cells, but at least with the conditions used here appears somewhat weaker (Figure 2.6B). Some of these differences may be due to different levels of expression of these three constructs, but in any case both proteins are appropriately localized to mediate the synaptogenic effects of PTPÏ (Figure 2.6C, D).4546Figure 2.6 Liprin-α2 and caskin1 are recruited to synapses induced through PTPÏ(A-B) Representative images of cocultures with either CFP-CD4 or TrkC-CFP in which neurons were transfected with and stained for myc-tagged Liprin-α2 or Caskin1. The transfected protein was recruited by COS cells expressing TrkC but not by those expressing CD4. Microtubule-associated protein 2 (MAP2) labeling of dendrites was used to exclude native synapses from analysis. Scale bar represents 10 μm.(C-D) Quantification of recruitment shown in A-B. Measurement was performed on the contact area only, defined as the region where axons contact the inducer-expressing COS cell, excluding the area overlapped by MAP2-positive dendrites. Values are normalized to the mean value of recruitment by TrkC from the same culture. Data are mean ± SEM. *** p <0.001, Mann-Whitney, n = 96 cells per condition from 3 independent cultures.2.2.3 PTPÏ requires both intracellular domains, but not phosphatase activity or HSPG binding, to mediate synaptogenesisWe next sought to understand which domains and functions of PTPÏ were required for its synaptogenic activity. To do this, we used a molecular replacement strategy in which neurons were treated with shPTP-containing AAV to knock down native PTPs and rescued with shRNA-resistant wild-type or mutant PTPÏ, or CD4 as a negative control. Neurons thus treated were cocultured with COS cells expressing the postsynaptic ligand TrkC and assayed for their ability to recruit the presynaptic marker synapsin. The mutants tested were a deletion of the entire intracellular region (ÎICR), a deletion of the D1 or D2 domain (ÎD1 and ÎD2), a mutant in which the D1 domain was replaced with a second copy of D2 (D2D2), a phosphatase-dead point mutant (C1142S) (Streuli et al., 1990), and a mutation of the extracellular region known to disrupt binding to HSPGs (4K4A) (Aricescu et al., 2002) (schematic view of mutants shown in Figure 2.7A, and details relating to these and other mutants used in later experiments in Table 2.1).47We hypothesized that, because liprin-α, caskin, and trio all bind to the D2 domain, while there are no known binding partners of the D1 domain, loss of the D2 domain would prevent PTPÏ from mediating presynaptic differentiation in the coculture assay. We also thought it possible that phosphatase activity against p250GAP, β-catenin, N-cadherin, or an unknown target could be necessary for PTPÏâs synaptogenic activity, meaning that the D1 domain would also be required. Alternatively or in addition, the D1 domain could act as a spacer, causing the D2 domain to sit some distance from the cell membrane, which might be important for assembly of multi-protein presynaptic structures. Replacing the D1 domain with a second copy of D2 (D2D2) would restore such a steric role for the D1 domain but not a specific catalytic or interaction role. We further hypothesized that disruption of HSPG binding by the 4K4A mutation might lead to higher activity in coculture with TrkC, since it has been shown that the HSPG-binding interface on PTPÏ overlaps with the TrkC-binding interface and that HSPGs can compete with TrkC for binding to PTPÏ (Coles et al., 2014), or alternatively that binding to some unknown extracellular HSPG might be required for PTPÏâs function.Consistent with our hypothesis, we found that deletion of the D2 domain completely abolished activity in the coculture assay with TrkC (Figure 2.7B, C). Surprisingly, however, we also found that loss of D1 abolished activity, but loss of phosphatase activity via the C1142S mutation had no effect. The D2D2 mutation also abolished activity, ruling out the possibility that the effects of the ÎD1 mutation were due to steric effects related to the position of the D2 domain relative to the cell membrane. Finally, we found no effect of the 4K4A mutation, indicating that the synaptogenic activity of PTPÏ does not depend on extracellular HSPG binding. These effects were not due to differences in surface expression of the PTPÏ mutants, since we stained for the extracellular V5 tag and chose cells for analysis based on the levels of V5 found on the COS cells (Figure 2.8A). The lack of activity of the ÎD1 and D2D2 48mutants is surprising given the high degree of 3D structural similarity between the wedge + D1 domains and D2 domain; aside from a small linker region between the wedge and D1 domain, the wedge + D1 structure overlaps almost perfectly with the D2 domain (Figure 2.8B-D). Altogether, these data suggest that specific interactions with both the D1 and D2 domains of PTPÏ are required for its function in presynaptic differentiation.49Figure 2.7 PTPÏ requires its D1 and D2 domains, but not its phosphatase activity or HS-binding, to mediate TrkC coculture activity.50(A) Schematic view of mutants used in this experiment. Each schematic corresponds to the label and column of images directly below it in panel B.(B) Representative images of HA-TrkC cocultures where neurons were treated with shPTP-expressing AAVs, and rescued using V5-CD4 as a control or RNAi-resistant V5-PTPÏ carrying the indicated mutations. Microtubule-associated protein 2 (MAP2) labeling of dendrites was used to exclude native synapses from analysis. Scale bar represents 10 μm.(C) Quantification of synapsin recruitment shown in panel B. âCOS areaâ indicates only the portion of the TrkC-expressing COS cell not overlapping with MAP2 signal. All values are normalized to the mean of the wild-type (WT) condition from the same culture. Data are expressed as mean ± SEM. Overall p-value for this experiment was 1.32 * 10-44, Kruskal-Wallis, n = 33-100 cells per condition from 2 cultures, or 3 cultures for WT. *** p < 0.001 compared to CD4, ### p < 0.001 compared to WT, Dunnâs post-hoc test.51Figure 2.8 Additional quantification of coculture after treatment with PTPÏ mutants. (A) Quantification of surface V5-PTPÏ WT or mutant recruited to TrkC-expressing COS cells. Values are normalized to the mean of the wild-type (WT) condition from the same culture. Overall p-value (excluding CD4) was 0.44, Kruskal-Wallis. 52(B-D) Alignment of the wedge + D1 domains with the D2 domain of PTPÏ (3SR9; Hou et al. 2011) showing their 3-dimensional similarity. Isolated wedge + D1 and D2 domains are shown in D and E, respectively, in the same orientation as in the aligned image in F. Note the gap in panel C (right side of molecule), indicating that the D1 wedge domain is positioned differently from its counterpart in the D2 domain.53Disruption of binding interactionsRecruitment by TrkC cocultureMutation Source/StrategyPurpose DHFR assay liprin-α2 in HEKliprin-α2 caskin1 synapsinWT Positive control +++ +++ +++ +++ +++ÎICR Negative control NT - NT NT -ÎD1 Is D1 domain required, as a spacer, for binding, or for phosphatase activity?NT + + - -ÎD2 Is D2 required for binding?NT - - - -D2D2 Is D1 required as something other than a spacer?NT - - - -C1142S (Streuli et al., 1990)Is D1 phosphatase activity required?NT NT NT NT +++4K4A (Aricescu et al., 2002)Is HSPG binding required for or modulatory towards synaptogenic activity?NT NT NT NT +++PPLL (Hofmeyer and Treisman, 2009)Is PTPÏ homodimerization required?Liprin-α2 +++Caskin1 ++PTPÏ + +++ +++ +++ +++QFG based on homology and surface accessibilityIs binding to liprin-α required?Liprin-α2 +Caskin1 +++Trio +++PTPÏ +++- - + -EGFID Is binding to caskin required?Liprin-α2 ++Caskin1 -Trio +++PTPÏ ++++++ ++ +++ +++Table 2.1 Summary of PTPÏ mutants Mutations in PTPÏ tested in this study for their ability to disrupt binding to various ligands in the DHFR assay, binding to liprin-α2 in HEK cells, recruitment of liprin-α2 and caskin1 in neurons, and synaptogenic activity of TrkC 54in the coculture assay. +++ indicates wild type-like activity, ++ indicates a substantial but weak disruption, + indicates a strong disruption, and â indicates a complete or nearly complete disruption of activity. NT = not tested.2.2.4 Identification of PTPÏ point mutants to disrupt specific interactionsIn order to further elucidate the mechanisms by which the PTPÏ intracellular domain signals the formation of a new presynaptic compartment, we sought to identify mutations that would specifically disrupt the binding of PTPÏ to one or another of its intracellular ligands. We chose to use this approach rather than one based on knockdown of each individual interacting protein because it more directly addresses whether binding between PTPÏ and the protein of interest is required, which is a different question from whether the protein itself is required. While this work was in progress, it was shown that knockdown of liprin-α2 and α3 reduced the ability of both TrkC and Nlg2 to induce synapses in the coculture assay (Han et al., 2018). While this result is consistent with the hypothesis that binding of the associated presynaptic organizing proteins, PTPÏ and neurexin, to liprin-α (indirectly in the case of neurexin) might be a necessary step in the synaptogenic process, it is also consistent with alternative models in which liprin-α is a general regulator of synaptogenesis which is either recruited to the synapse via other proteins with which it interacts, or is required within the neuron but does not need to be recruited to nascent synapses.We identified 22 candidate mutations consisting of between 2 and 7 amino acids each which were well-conserved at least within mammalian LAR-RPTP isoforms and in many cases in C.elegans and Drosophila homologues as well, surface-accessible, and located close to one another on the protein surface (Figure 2.9). We also assayed the PPLL mutation which was previously shown to disrupt PTPÏ homodimerization (Hofmeyer and Treisman, 2009). Next, we screened each of these mutants for its ability to bind to liprin-α2, caskin1, trio, or to a second molecule of PTPÏ. To do this, we used the 55dihydrofolate reductase (DHFR) protein complementation assay, in which the ability of yeast to grow in the presence of the drug methotrexate (MTX) is dependent on the amount of binding between two putative interacting proteins, each of which is fused to one of two fragments (termed F1-2 and F3) of the DHFR enzyme (Rochette et al., 2015). We assessed the association of PTPÏ or its intracellular domain fused to the DHFR N-terminal fragment with the interacting regions of liprin-α2, caskin1, and trio, as well as with PTPÏ itself, fused to the DHFR C-terminal fragment. Negative control strains were constructed in which one of the two interacting proteins was replaced with either YFP or NgCAM. Growth of the resulting strains was measured in media containing either MTX (200μg/mL, 1% DMSO) or DMSO alone. For each strain, an interaction score was calculated (see Methods) such that a score of 0 indicates growth equivalent to that of the negative control strain, and 1 indicates wild-type growth (Figure 2.10A).Several mutations (MELEFK, WENNS, KLREMG, and HQYWP) moderately to severely disrupted all interactions tested, and thus were considered non-specific (interaction score < 0.3 for at least 3 out of 4 ligands, shown in grey in Figure 2.10B and Figure 2.11). These mutations may disrupt activity in the DHFR assay by affecting protein folding, trafficking, or stability. Once these non-specific mutations are ignored, mutants affecting binding to liprin-α2 and caskin1 each form a cluster on the surface of the protein (Figure 2.10B). In the case of liprin-α2, binding is partially disrupted (with interaction scores between 0.21 and 0.37) by the nearby AEY, DWPE, QVHK, QFG, and MRYE mutations (blue). The caskin1 interaction is abolished completely by the MRYE and EGFID mutations (purple). Mutations on the opposite side of the protein, aside from those which were non-specific, did not have major effects on binding of either protein. Thus, we believe that we have very roughly mapped the binding sites on the PTPÏ D2 domain for liprin-α2 and caskin1. The apparent liprin-α2 binding site is larger than that 56for caskin1, with many individual amino acids presumably making weak contributions to binding strength. It is noteworthy that these putative binding sites overlap one another, since this could mean that simultaneous binding of PTPÏ to both liprin-α2 and caskin1 is not possible, in line with the finding that LAR is unable to act as a bridge between caskin and liprin-α in yeast two-hybrid assays (Weng et al., 2011). Clear binding sites were not seen in the D2 domain for PTPÏ homodimerization or for trio. In the case of homodimerization, this is not surprising given that binding is thought to be mediated by the wedge domain and the D1 domain (Hofmeyer and Treisman, 2009), whereas most of our mutations were in the D2 domain. Those mutations that did affect PTPÏ homodimerization were generally not strong and were not specific to this interaction (eg AEY or MRYE), even for those which did not meet our criteria for being labeled as fully non-specific, so these disruptions may be a result of weak effects on folding, trafficking, or stability rather than on binding.There were several mutations that primarily disrupted binding to liprin-α2, and of these QFG appeared to be the most specific, with an interaction score of 0.37, compared to between 0.77 and 0.88 for its interaction with caskin1, trio, and PTPÏ WT (Figure 2.10C). The EGFID mutation is highly effective as well as largely specific in its disruption of caskin1 binding, with an interaction score of -0.11 for caskin1, and between 0.73 and 1.02 for the other interactions. While the EGFID mutation did have a significant effect on binding to liprin-α2, this effect was mild (interaction score of 0.73), suggesting that it likely slightly reduces liprin-α binding but does not abolish it. These two mutations were chosen for further study, since we wondered if liprin-α2 and caskin1 might have redundant effects on synaptogenesis, given their structural similarity (Serra-Pagès et al., 1995; Tabuchi et al., 2002) and the fact that they both bind to CASK (Tabuchi et al., 2002; Olsen et al., 2005). The PPLL mutation, which was previously reported to disrupt PTPÏ homodimerization only when present on both copies of the 57protein (Hofmeyer and Treisman, 2009), did indeed disrupt activity in the DHFR assay, albeit not completely. In contrast to the previous report, we find little difference between one copy of the mutation (interaction score of 0.46) versus two (interaction score of 0.35). This difference likely results from the fact that we used a different method to assess binding. We also find that this mutation partially disrupts binding to caskin1 (interaction score of 0.51) but not liprin-α2. This may complicate interpretation of this mutantâs effects on synaptogenesis, but fortunately its effects on caskin1 binding are substantially weaker than that of EGFID, so we reasoned that the PPLL mutant might still be somewhat informative for assessing the necessity of PTPÏ homodimerization for its synaptogenic effects.Unfortunately, we were not able to assay the domain deletion or swap mutants in the DHFR assay, since we observed a strong effect of protein size on the growth rate. This observation is consistent with a previous study which found faster growth of controls expressing the DHFR fragment fused to a linker only, compared to background growth in a large-scale screen (Rochette et al., 2015). Based on our experiences with this assay, we would strongly urge other researchers to use caution when comparing proteins of differing sizes using this approach. We were able to assay the strength of binding between liprin-α2 and the PTPÏ domain mutants using an independent methodology (see next section), but were unable to systematically assay this for caskin1, trio, or for PTPÏ homodimerization.5859Figure 2.9 Alignment of LAR-RPTP C-terminal sequences from human, mouse, worm, and fly. Alignment includes part of the D1 domain, the entire D2 domain, and the C-terminus. Mutations used in the DHFR assay (except for PPLL, which is N-terminal to the sequence shown here) are indicated below the alignment. Dashes indicate a single mutation (eg MELEFK) and spaces indicate two separate mutations (eg PARS and YTYIQ). Mutations shown in bold are those used in the coculture assay. Alignment was created using BOXSHADE.6061Figure 2.10 Identification of PTPÏ mutations to disrupt specific interactions(A) Heatmap showing interaction scores for 22 different multi-point mutations in the DHFR assay. NC1 indicates the negative control where the DHFR C-terminal fragment was fused to YFP or NgCAM instead of the indicated ligand; NC2 indicates the negative control where the DHFR N-terminal fragment was fused to NgCAM (for liprin-α2, caskin1, and PTPÏ experiments) or YFP (for trio experiments). Interaction scores represent the growth rate of the indicated strain in MTX-containing media relative to MTX-free media and relative to controls, with 0 indicating the same relative growth rate as NC1 and 1 indicating the same relative growth rate as wild-type (WT). Values higher than 1 or lower than 0 are clipped to 1 and 0. White indicates no data (residues affected by the PPLL mutation are not present in the fragment of PTPÏ used to test interaction with Trio). Data are based on two experiments per condition and 3 replicates per experiment.(B) Interaction scores for each ligand mapped onto the PTPÏ crystal structure (Hou et al., 2011). D1 domain is shown on the right and D2 on the left. Interaction scores are divided into 5 bins and colored accordingly: <0.2 purple; 0.2-0.4 blue; 0.4-0.6 teal; 0.6-0.8 green; >0.8 yellow. Mutants which showed an interaction score <0.3 for at least 3 ligands are shown in grey.(C) Quantification of mutants that were used in subsequent experiments. Note that x axis is different for PPLL since this mutation could not be tested for interaction with Trio, and since in the case of PTPÏ homodimerization we also tested the condition where both copies of PTPÏ carried the mutation (PTPÏ (x2)). Data are mean ± SEM. Small models above each plot are the same crystal structure above with the indicated mutation shown in red. The model for PPLL is rotated forward slightly relative to the others in order to make these residues visible. There was a significant effect of transfection condition (i.e. the combination of F1-2 and F3 plasmids) on interaction score based on a one-way ANOVA, F(103, 465) = 162, p = 1.13 * 10-309. * p < 0.05, ** p < 0.01, *** p < 0.001 for the indicated pairwise comparisons as assessed using the Tukey HSD test.62Figure 2.11 Additional visualization of PTPÏ interaction scores.Interaction scores for each ligand mapped onto the PTPÏ crystal structure (Hou et al 2011), flipped 180° horizontally compared to those shown in Figure 2.10B, to show mutations residing on the opposite side of PTPÏ. In this view, the D1 domain is shown on the left and D2 on the right. Interaction scores are divided into 5 bins and colored accordingly as in Figure 2.10B: <0.2 purple; 0.2-0.4 blue; 0.4-0.6 teal; 0.6-0.8 green; >0.8 yellow. Mutants which showed an interaction score <0.3 for at least 3 ligands are shown in grey.2.2.5 Measurement of binding between PTPÏ mutants and liprin-α2Liprin-α has been previously shown to form small clusters when transfected in cell lines, and to recruit LAR-RPTPs to these same clusters (Serra-Pagès et al., 1998). As a second measure of the interaction 63strength between the PTPÏ mutants and liprin-α2, we co-transfected myc-liprin-α2 with V5-PTPÏ WT or mutant as well as CFP as a cell fill into HEK 293 cells. Consistent with previous reports, we saw that in controls which did not receive PTPÏ, liprin-α2 formed small puncta in some cells (Figure 2.12A). PTPÏ in the absence of liprin-α2 was mostly diffuse. When the two WT proteins were co-transfected, the distribution of liprin-α2 became more distinctly clustered, the shape of the clusters became more irregular in some cells, and PTPÏ was recruited to sites of liprin-α2 clustering. In order to generate a quantitative measure of the interaction between PTPÏ and liprin-α2 in this context, we calculated a ratio between the intensity of PTPÏ fluorescence within the liprin-α2 patches and the intensity in the remainder of the cell. Unfortunately, we were not able to perform similar experiments for caskin1, trio, or PTPÏ homodimerization, since we did not observe the same co-clustering phenomenon.When we transfected HEK cells with liprin-α2 together with mutant versions of PTPÏ, we found that the ÎICR, ÎD2, and QFG mutants disrupted the co-clustering of the two proteins, such that the distribution of both resembled that seen in singly-transfected cells. The PPLL and EGFID mutants were essentially unaffected, and the ÎD1 and D2D2 mutants showed a partial disruption of the co-clustering phenomenon. Visually, the liprin-α2 clusters in cells co-transfected with ÎD1 or D2D2 appear intermediate between the WT and ÎICR forms, and the mutant PTPÏ is recruited to these patches, though to a lesser extent than with the WT protein. Quantification also reveals an intermediate phenotype. When recruitment is measured based on total PTPÏ staining, both are statistically different from WT but not ÎICR, whereas when total PTPÏ is measured, the ÎD1 mutant is significantly different from ÎICR but not WT (Figure 2.12B, C). These results are consistent with a model in which binding between liprin-α and PTPÏ is mainly mediated by the PTPÏ D2 domain as previously reported (Serra-Pagès et al., 1995), but also involves some contribution from the D1 domain.64Figure 2.12 Certain intracellular mutations in PTPÏ prevent co-clustering with liprin-α2 in HEK cells(A) Representative images of HEK cells transfected with myc-liprin-α2, V5-PTPÏ WT or mutant, and CFP. Scale bar represents 5 μm.(B) Quantification of the extent to which PTPÏ is recruited to sites of liprin-α clustering. âTotal PTPÏ Recruitmentâ refers to the ratio between the intensity of PTPÏ colocalized with liprin-α2 puncta versus the remaining area of the cell, normalized to the mean value for cells in the WT condition from the same culture. * p < 0.05, ** p < 0.01, *** p < 0.001 compared to WT. # indicates significance at the same levels compared to to ÎICR (also applies to C). Overall p-value was 7.5 * 10-23, Kruskal-Wallis.65(C) Same as B, but using surface stained V5-PTPÏ rather than total. Overall p-value was 2.3 * 10-31, Kruskal-Wallis.2.2.6 Disrupting binding of PTPÏ to liprin-α, but not to caskin or to itself, abolishes synaptogenic activityIn order to determine the relative contributions to PTPÏâs synaptogenic activity of the binding interactions between PTPÏ and liprin-α, caskin, and itself, we used the QFG, EGFID, and PPLL mutants to rescue activity in the coculture assay. Transfection of shRNA-resistant V5-PTPÏ, infection by AAV shPTP, and coculture with HA-TrkC were performed as in the earlier experiment with the domain mutants. Fields were selected in order to ensure equal levels of axonal surface V5-PTPÏ in contact with the TrkC-expressing COS cells, and recruitment of synapsin was measured. We found that the QFG mutation reduced synapsin clustering to levels similar to the ÎICR mutant (Figure 2.13). In contrast, synapsin clustering by the PPLL and EGFID mutants was not significantly different from WT. Thus we conclude that binding of PTPÏ to liprin-α is essential for synaptogenesis induced by the TrkC/PTPÏ trans-synaptic complex. PTPÏ homodimerization and binding to caskin, in contrast, are either dispensable or play only a minor role.66Figure 2.13 Disrupting the liprin-α2 interaction, but not the caskin1 interaction or homodimerization ability, of PTPÏ abolishes TrkC coculture activity.(A) Representative images of HA-TrkC cocultures where neurons were treated with shPTP-expressing AAVs, and rescued using RNAi-resistant V5-PTPÏ carrying the indicated mutations. Synapsin (Syn) and V5 were recruited by TrkC-expressing COS cells. Microtubule-associated protein 2 (MAP2) labeling of dendrites was used to exclude 67native synapses from analysis. The PPLL, QFG, and EGFID mutations were shown to primarily disrupt binding of PTPÏ to itself, liprin-α2, and caskin1, respectively, in the DHFR assay (see Figure 2.10). Scale bar represents 10 μm.(B) Quantification of synapsin recruitment in (A). Measurements were limited to the area of the COS cell which lacked MAP2 signal. All values are normalized to the mean of the wild-type (WT) condition from the same culture. Data are expressed as mean ± SEM. Overall p-value for this experiment was 9.86 * 10-31, Kruskal-Wallis, n = 34-71 cells per condition from 3 cultures. *** p < 0.001 compared to WT, ### p < 0.001 compared to ÎICR, Dunnâs post-hoc test.68Figure 2.14 PTPÏ surface staining in PTPÏ point mutant coculture experiment.Quantification of surface V5-PTPÏ wild-type (WT) or mutant recruited to COS cells expressing TrkC. Values are normalized to the mean of the WT condition from the same culture. Overall p-value was 0.17, Kruskal-Wallis.2.2.7 Disrupting binding between PTPÏ and liprin-α abolishes recruitment of liprin-α2 to induced synapsesIn order to further corroborate these results, we tested whether recruitment of liprin-α2 to axon contacts with TrkC-expressing COS cells would be disrupted by the same PTPÏ mutations which we observed to be deficient in liprin-α2 binding in either HEK cells or yeast, and which were unable to rescue activity in the coculture assay. It is possible that liprin-α could be recruited to potential synaptic sites even in the presence of binding-deficient forms of PTPÏ if other scaffolding proteins such as caskin or trio are able to recruit other components which in turn bind liprin and bring it in proximity to a nascent synapse. However, if liprin-α is indeed the main downstream effector of PTPÏ in the context of synapse induction and is necessary and sufficient for recruitment of other presynaptic components, we would 69expect it not to be recruited either directly or indirectly by mutants which are incapable of inducing synaptogenesis.To this end, we performed cocultures as before with TrkC-CFP, using neurons which had been treated with AAV shPTP as before and also transfected with a mixture of shRNA-resistant V5-PTPÏ and myc-liprin-α2. In addition to WT PTPÏ, we tested the ÎD1, ÎD2, D2D2, PPLL, QFG, and EGFID mutants. We again selected cells such that the surface levels of V5-PTPÏ were equal across conditions. The results of this experiment revealed a parallel in the extent of recruitment of myc-liprin-α2 (Figure 2.15) and the induction of synapsin clustering (Figure 2.7and Figure 2.13) with the same mutants. We found that ÎD1, ÎD2, D2D2, and QFG all reduced the extent of liprin-α2 recruitment substantially, although ÎD1 exhibited a very weak amount of recruitment which was significantly higher than that of ÎD2. The level of recruitment by PPLL was similar to WT. Recruitment by EGFID was significantly different from both WT and the ÎD2 control, although it was much closer to WT. These data are consistent with a model in which direct binding between PTPÏ and liprin-α is required for liprin-α to be recruited to a nascent synapse (i.e. it is not recruited indirectly by mutual binding partners), and in which the presence of liprin-α is required for other presynaptic components, including synapsin, to be recruited.70Figure 2.15 Recruitment of liprin-α2 by TrkC coculture in the presence of PTPÏ mutants.(A) Representative images of neurons co-transfected with myc-liprin-α2 and V5-PTPÏ wild-type or mutant and cocultured with COS cells expressing TrkC. Scale bar represents 10 μm.71(B) Quantification of the extent of liprin-α2 recruitment relative to PTPÏ recruitment as shown in (A). Measurements were limited to the area of the COS cell which lacked MAP2 signal. All values are normalized to the mean of the wild-type (WT) condition from the same culture. Data are expressed as mean ± SEM. Overall p-value for this experiment was 3.43 * 10-39, Kruskal-Wallis, n = 20-50 cells per condition from 2 cultures. *** p < 0.001 compared to WT; ### p < 0.001, ## p < 0.01 compared to ÎD2, Dunnâs post-hoc test.72Figure 2.16 PTPÏ surface staining in liprin-α recruitment experiment with PTPÏ mutants. Quantification of surface V5-PTPÏ wild-type (WT) or mutant recruited to COS cells expressing TrkC in the liprin-α2 recruitment experiment. Values are normalized to the mean of the WT condition from the same culture. Overall p-value was 0.56, Kruskal-Wallis.2.2.8 Disrupting binding between PTPÏ and liprin-α reduces recruitment of caskin1 to induced synapsesWe also tested which of the same mutants would disrupt recruitment of caskin1 in the coculture assay in the same manner as for liprin-α2. Surprisingly, the same mutants which disrupted recruitment of both liprin-α2 and synapsin also disrupted caskin1 recruitment, although to differing extents (Figure 2.17). PPLL and EGFID were both capable of recruiting caskin1, and not significantly different from WT. ÎD1, ÎD2, and D2D2 abolished or nearly abolished caskin1 recruitment as they did for liprin-α2. Recruitment by QFG was intermediate, and differed significantly from both WT and ÎD2. This result is consistent with a model in which caskin is recruited to synaptic sites not only via direct binding to PTPÏ but also in large part via some indirect mechanism involving binding between PTPÏ and liprin-α.73Figure 2.17 Recruitment of caskin1 by TrkC coculture in the presence of PTPÏ mutants.(A) Representative images of neurons co-transfected with myc-caskin1 and V5-PTPÏ wild-type or mutant and cocultured with COS cells expressing TrkC. Scale bar represents 10 μm.74(B) Quantification of the extent of caskin1 recruitment relative to PTPÏ recruitment as shown in (A). Measurements were limited to the area of the COS cell which lacked MAP2 signal. All values are normalized to the mean of the wild-type (WT) condition from the same culture. Data are expressed as mean ± SEM. Overall p-value for this experiment was 1.13 * 10-24, Kruskal-Wallis, n = 25-38 cells per condition from 2 cultures. *** p < 0.001, p < 0.05 compared to WT; ### p < 0.001, # p < 0.05 compared to ÎD2, Dunnâs post-hoc test.75Figure 2.18 PTPÏ surface staining in caskin1 recruitment experiment with PTPÏ mutants. Quantification of surface V5-PTPÏ WT or mutant recruited to COS cells expressing TrkC in the caskin1 recruitment experiment. Values are normalized to the mean of the wild-type (WT) condition from the same culture. Overall p-value was 0.44, Kruskal-Wallis.762.3 Discussion2.3.1 The LAR-RPTP family is required for normal neuronal morphologyWe observed a reduction in axon outgrowth by neurons treated with shPTP compared to controls, with no consistent differences in dendritic morphology. The inability of PTPÏ to rescue this phenotype could result from the fact that only a single LAR-RPTP family member was used instead of all three, the fact that rescue was performed using electroporation, which only targets a portion of the cells, or some combination of these two effects. Previous experiments using PTPÏ knockout mice have shown accelerated axon outgrowth following injury to the sciatic, facial, or optic nerves, as well as faster axon extension in very early cultures of cortical or dorsal root ganglion neurons (McLean et al., 2002; Thompson et al., 2003; Sapieha et al., 2005; Siu et al., 2007). In contrast, disrupting the function of CRYPα, the chicken homologue of PTPÏ, inhibited retinal axon outgrowth (Ledig et al., 1999). The extracellular region of PTPÏ can bind to both HSPGs and chondroitin sulfate proteoglycans (CSPGs), and its status as an activator or inhibitor of axon outgrowth appears to depend on the balance of HSPGs and CSPGs present in the neuronâs environment (Coles et al., 2011). Interestingly, in the case of CRYPα, the extracellular ligand needed for CRYPα-dependent promotion of axon outgrowth, although it was not identified specifically, was determined to come from glial endfeet (Ledig et al., 1999). It is possible that the culture system used in our experiments, in which neurons are in close contact with substrates coated with glial-derived factors, results in an environment in which the ligands PTPÏ (and possibly PTPδ and LAR) are exposed to are, on the whole, growth-promoting rather than growth-inhibiting, and that we might observe the opposite result under different culture conditions. Whatever the reason for the reduction in axon outgrowth, this change in neuronal morphology would complicate the interpretation of many assays for native 77synapses, since any potential reductions in synapse density could result from either a reduction in axon/dendrite contacts as a result of reduced axon length overall, or from specific effects of the LAR-RPTPs on the ability of neurons to form functional contacts. Thus, reductions in synapse density and in frequency of miniature synaptic currents reported with knock-down of LAR-RPTPs in hippocampal cultures (Han et al., 2018) could reflect deficits in synapse development and/or in axon outgrowth. 2.3.2 Requirement of LAR-RPTPs for synaptogenesis triggered by TrkC & NGL-3We found that shPTP abolished the coculture activity of both TrkC and NGL-3 almost entirely, whereas there was no effect on activity of the neurexin ligand Nlg2. Our findings contrast with those of another recent study, which found no effect on coculture with NGL-3 in response to knockdown of any or all of the LAR-RPTPs (Han et al., 2018). It could be that NGL-3âs activity requires a fairly low threshold amount of LAR-RPTPs for activity and that the extent of knockdown differed between the two studies. The finding that loss of the LAR-RPTPs is sufficient to abolish NGL-3 activity is consistent with the observation that NGL-3 binds to all three family members (Kwon et al., 2010) and has no other known extracellular binding partners.Mice in which PTPÏ is knocked out entirely show a phenotype which includes increased axon growth and branching, increased synapse density, and increased susceptibility to seizures (Horn et al., 2012). Our finding that the LAR-RPTPs are required for synapse formation triggered by TrkC could be seen as somewhat in contrast to this, but there are several possible explanations for this discrepancy. First, as PTPÏ in the intact brain seems to act as a repressor of axonal outgrowth, the reduction in synapse density might result from differences in outgrowth, despite the LAR-RPTPs acting as overall positive regulators of synaptogenesis itself. Second, the effects of LAR-RPTPs on synapse formation, axon outgrowth, or both might be dependent on context, including brain region or neuronal cell type, age, 78and presence of HSPGs or CSPGs in the local microenvironment. Thus, PTPÏ and the other LAR-RPTPs can both act as mediators of synapse formation as well as limit synapse density in certain contexts.2.3.3 Relative importance of liprin, caskin, homodimerization, phosphatase activity, and HSPGs for synaptogenesisOne major aim of this study was to examine the downstream pathways and mechanisms which might mediate the synaptogenic effects of PTPÏâs interactions with its postsynaptic ligands. The D2 interacting proteins liprin-α and caskin were considered interesting candidates. Additionally, we suspected that homodimerization between PTPÏ molecules might be required for synaptogenic activity (Hofmeyer and Treisman, 2009), that binding of PTPÏ to one or more HSPGs might either be required or play a modulatory role in triggering synaptogenesis, or that PTPÏ acted by dephosphorylating downstream targets. Staining of cocultures transfected with tagged forms of liprin-α and caskin revealed that both were recruited to sites of axon contact. In this experiment, recruitment of liprin-α2 was the most strong and consistent; recruitment of caskin1 was weaker but clearly present. However, it is possible that this is simply an effect of the transfection or culture conditions used here.In order to test the functional relevance of PTPÏâs ability to bind liprin-α, caskin, and itself, we employed a series of point mutants identified using a protein complementation assay. We chose to employ this strategy because it specifically tests the hypothesis that binding between two proteins, as distinct from the mere presence of both, is required. Unfortunately, we were not able to test the importance of a third D2-interacting protein, trio, using this strategy since we did not find any appropriate point mutants. Like the recruitment experiment described above, this experiment pointed to liprin-α as the most important downstream interacting partner of PTPÏ. The QFG mutant, which was 79able to bind caskin and to homodimerize with PTPÏ WT, but not bind liprin-α, almost completely abolished coculture activity. The EGFID and PPLL mutants, which disrupted caskin binding and homodimerization respectively, had mild if any effects.Our results agree with those of Han et al. in identifying liprin-α as a likely mediator of PTPÏâs synaptogenic effects. If binding between PTPÏ and liprin-α is required, one would expect that both proteins themselves would be necessary for PTPÏ-mediated synaptogenesis to take place. Han et al. found that knockdown of either PTPÏ or of liprin-α2 and -α3 impaired the ability of PTPÏ ligands to induce synaptogenesis in the coculture assay (Han et al., 2018). Our work corroborates and extends upon this finding by demonstrating that not only are LAR-type RPTPs and liprin-α required for synaptogenesis, but that these two proteins must be able to bind one another.We did not find any effect of the extracellular 4K4A mutation on coculture activity, indicating that PTPÏâs ability to bind HSPGs is not required for its synaptogenic effects. Furthermore, although it was previously shown that HSPGs can compete with TrkC for binding to PTPÏ (Coles et al., 2014), under the culture conditions used here it does not appear that disrupting PTPÏ/HSPG binding changed the strength of TrkC-induced synaptogenesis. We also found no effect of the phosphatase-dead C1142S mutant, indicating that dephosphorylation of targets is not necessary. Homodimerization via the intracellular region of PTPÏ also appeared to be dispensable, based on the ability of the non-homodimerizing PPLL mutant to induce clustering of both liprin-α2 and synapsin. However, this does not preclude the possibility that indirect dimerization or multimerization of PTPÏ might be necessary. The PTPδ postsynaptic ligand SALM5 has been shown to induce formation of tetramers containing two molecules each of PTPδ and SALM5, independently of any direct interactions between PTPδ molecules (Lin et al., 2018).80In contrast to our finding of no effect of the C1142S mutant, Han et al. reported that the same mutation (C1157S in their PTPÏ construct) impairs or abolishes coculture activity of Slitrk1 and TrkC. Two potentially important methodological differences between our study and Han et al.âs are the staining and exclusion of MAP2+ dendrites and the measurement of surface levels of PTPÏ in our study. While under most circumstances the effects of including presynaptic marker signal associated with dendrites should be expected to be similar across conditions, manipulations which change synapse density on those dendrites might cause apparent changes in coculture activity which are in fact caused by changes in the density of dendrite-associated synapses. Indeed, overexpression of a dominant negative phosphatase-dead mutant reduced spine and synapse density in hippocampal cultures through a postsynaptic mechanism (Dunah et al., 2005). Additionally, PTPÏ and its homologues have been shown to either increase or decrease (presumably depending on the culture system and conditions) axon outgrowth in culture (Ledig et al., 1999; Thompson et al., 2003; Siu et al., 2007), which we also observed here. It is not clear whether phosphatase activity plays a role in this phenomenon. Even if the wild-type and mutant proteins are equally expressed, changes in axon outgrowth could feasibly alter the amount of PTPÏ contacting the transfected cell and thus the apparent level of synaptogenic activity. Changes in the extent or pattern of axon outgrowth, together with changes in synapse density, might confound interpretation of cocultures performed without excluding MAP2-associated presynaptic signal and without ensuring equal levels of surface-expressed wild-type or mutant PTPÏ. Thus, the findings of Han et al. with regard to the phosphatase-dead form of PTPÏ may represent a true effect, but not one which is specific to the process of synaptogenesis.In summary, our results indicate that liprin-α binding is required for PTPÏâs synaptogenic effects, and that caskin binding, homodimerization, phosphatase activity, and HSPG binding are all dispensable. It 81is possible that caskin itself is required, but can be recruited independently of direct binding to PTPÏ. The possibility that trio might also be required still remains an open question.2.3.4 Domain requirements for liprin-α binding to PTPÏSince no binding partners of the D1 domain besides PTPÏ itself are known, our initial hypothesis was that the D1 domain would either be dispensable for synaptogenic activity or would be required for its phosphatase activity or for homodimerization. The finding that the ÎD1 mutation abolished coculture activity, but the C1142S phosphatase-dead and PPLL non-homodimerizing mutations had no effect, was surprising. Another hypothesis which we considered was that the D1 domain was not required for any catalytic or binding function, but instead acted as a spacer which positioned the D2 domain some distance from the plasma membrane, allowing for the formation of multi-protein complexes required for synapse formation. This hypothesis also turned out to be incorrect, as the D2D2 mutation in which the D1 domain was replaced by a second copy of the similarly-sized D2 domain, also abolished activity. Thus it appeared that the D1 domain must be performing some function which was previously unknown. One clue to this puzzle came from the fact that the ÎD1 mutant surprisingly showed a phenotype intermediate between WT and ÎICR when we tested its ability to co-cluster with liprin-α2 in HEK cells. Similarly, replacement of the D1 domain by a second D2 domain (D2D2) disrupted the ability of PTPÏ to co-cluster with liprin-α2 in HEK cells. Our data are consistent with a model in which binding to liprin-α2 is primarily mediated by the D2 domain of PTPÏ, but the D1 domain is required for full-strength binding. Binding between LAR and liprin-α was previously reported to be mediated by the D2 domain, based on the observation that the isolated D2 domain but not the isolated D1 domain showed interaction with liprin-α in a yeast two-hybrid assay (Serra-Pagès et al., 1995). However, this does not rule out the possibility that D1 is necessary for full-strength binding.822.3.5 Mechanism of caskin recruitment to presynaptic sitesOur initially hypothesized model of how caskin might be recruited to presynaptic sites was that it was directly recruited by binding to PTPÏ. We were somewhat surprised to find that the ÎD1 and D2D2 mutants disrupted recruitment of caskin1 in the coculture assay, as did QFG to an intermediate extent, but EGFID did not. It appears based on the behavior of the EGFID and QFG mutants that binding between caskin and PTPÏ is neither necessary nor sufficient to induce wild-type levels of caskin1 recruitment to presynaptic sites, contradicting the direct binding model. A possible explanation for this finding is that the primary pathway by which caskin is recruited is indirect, likely through mutual binding partner(s) of liprin-α and caskin (Figure 2.19). The ability of caskin to form polymers via interaction between the first and second SAM domain (Stafford et al., 2011) may also serve to amplify the amount of caskin which can be recruited, relative to that which would be recruited based on direct interactions with LAR-RPTPs and/or other binding partners.Consistent with this, the ÎD1 and D2D2 mutants also fail to cluster caskin1 as well as liprin-α2. Previous work has shown that caskin1 binds primarily to the D2 domain of LAR, although the D1 domain might also mediate very weak binding (Weng et al., 2011). Given that both liprin-α and caskin bind to LAR-RPTPs using SAM domains, and that this study (Figure 2.10) as well as a previous report (Weng et al., 2011) suggest that the binding sites on LAR-RPTPs for liprin-α and caskin overlap with one another, it seems likely that weak binding between the D1 domain and both liprin-α and caskin might be involved in mediating the full-strength interactions. Unfortunately, we were not able to directly test the role of the D1 domain in binding between PTPÏ and caskin1, as the DHFR assay is sensitive to protein size (Rochette et al., 2015), and we did not observe a co-clustering phenotype in HEK cells as we did for liprin-α. Although we are not able to make any conclusions with confidence 83about any possible contribution of the LAR-RPTP D1 domain to binding of caskin, such a phenomenon would be consistent with our results. Thus, the lack of caskin1 recruitment by both PTPÏ ÎD1 and D2D2 might reflect some combination of a loss of recruitment by some binding partner of liprin-α (similar to the effects of the QFG mutation) as well as a possible loss of direct binding between caskin1 and the D1 domain.84Figure 2.19 A possible model for presynaptic recruitment of caskinA possible model of how caskin might be recruited to presynaptic sites via direct interaction with LAR-RPTPs as well as indirectly by some mutual binding partner(s) of liprin-α and caskin (left, WT). In the absence of binding between LAR-RPTPs and liprin-α, caskin recruitment is reduced but not completely abolished, and recruitment of SVs is abolished (center, QFG). In the absence of binding between LAR-RPTPs and caskin, liprin-α binding shows a partial reduction, but both caskin and SVs are recruited at relatively normal levels (right, EGFID). This model is consistent with the data presented here, but is admittedly speculative.852.4 Materials and Methods2.4.1 DNA ConstructsThe following constructs were described previously: AAV6-GFP-4xshRNA, HA-CD4, pLL-CFP (shGFP-resistant) (Zhang et al., 2018), HA-TrkC and TrkC-CFP (both the non-catalytic form) (Takahashi et al., 2011). pLL3.7-hSyn-V5-PTPÏ WT, ÎICR, C1142S, and 4K4A were generated based on C1-YFP-mouse PTPÏ with 4 FNIII domains and lacking both the meA and meB splice inserts (Takahashi et al., 2011). The pFB-shPTP vector used to package AAV6-shPTP was generated based on L315-shGFPx4 (Gokce and Südhof, 2013), pFB-AAV-shGFPx4 (Zhang et al., 2018), and shRNA sequences against PTPÏ (5â-GGCATCATGGGTAGTGATT-3â), PTPδ (5â-GTGCCGGCTAGAAACTTGT-3â (Dunah et al., 2005)), and LAR (5â-GGCCTACATAGCTACACAG-3â (Mander et al., 2005)). This plasmid was packaged into AAV6 by Virovek. V5-PTPÏ ÎD1, ÎD2, D2D2, PPLL, QFG, and EGFID were created from V5-PTPÏ WT. HA-NGL-3 was created based on NGL-3-CFP (Siddiqui et al., 2013). V5-CD4 was created from YFP-CD4 (Takahashi et al., 2011). C1-myc-Trio (Terry-Lorenzo et al., 2016) was a kind gift from Dr. Craig Garner (German Center for Neurodegenerative Diseases, Bonn, Germany) and contains the human trio gene sequence. pBA-myc-Trio was created by transferring the open reading frame from the C1 vector to the pBA vector which contains the CAG chicken β-actin promoter, which was a kind gift from Dr. Gary Banker (Oregon Health & Sciences University, Portland, OR).86Mouse Caskin1 cDNA (accession number BC060720) was obtained from Open Biosystems. A small portion of the 5â end of the gene was missing, and was replaced by PCR. A myc tag was added and the gene transferred into the pLL3.7-hSyn vector to create pLL-myc-caskin1.CMV-HA-liprin-α2 was a kind gift from Dr. Susanne Schoch (Institute of Neuropathology, Bonn, Germany) containing the mouse liprin-α2 gene. We mutated the sequence GGGGCTGATCCACCGGAGTTT to GGaGCcGATCCtCCaGAaTTT in order to make the sequence resistant to an shRNA (not used in this work) without changing the amino acid sequence. From the resulting plasmid, we transferred the open reading frame to the pBA vector and added a myc tag to generate pBA-myc-liprin-α2.Dihydrofolate reductase (DHFR)-F3 fusions were made based on p41-HPH-TEF-SspBYGMF-linker-DHFR-F3 (Tarassov et al., 2008). PTPÏ, liprin-α2, caskin1, and trio sequences were subcloned either in full or in truncated form from the plasmids described above so that they were fused at their C-termini via a short linker to the F3 fragment. DHFR-F1-2 fusions were cloned into the p413 vector (Mumberg et al., 1995). A cassette containing the DHFR F1-2 fragment and the Adh1 terminator from pAG25-F1-2 (Tarassov et al., 2008) was fused to the C-terminus of PTPÏ full-length (FL) or intracellular region (ICR) under the control of the TEF promoter. PTPÏ-FL-DHFR both F1-2 and F3 contained an N-terminal V5 tag in addition to the C-terminal DHFR fragment. NgCAM, which was a kind gift from Dr. Peter Sonderegger (University of Zurich, Zurich, Switzerland), and YFP were each separately fused to F1-2 and to F3 as controls. Point mutations were introduced into PTPÏ FL or ICR-DHFR F1-2, and also into PTPÏ FL-DHFR F3 in the case of the PPLL mutant (Hofmeyer and Treisman, 2009).872.4.2 Neuron Culture, Transfection, and AAVPrimary embryonic day 18 (E18) rat hippocampal neurons were cultured essentially according to the method described in (Kaech and Banker, 2006). For expression of DNA constructs, the AMAXA nucleofector system (Lonza) was used to deliver an appropriate amount of plasmid to 1-2 million freshly dissociated cells. For some experiments in which surface levels of V5-PTPÏ would be used to select fields for imaging, the amount of plasmid DNA was increased for certain mutants which preliminary experiments showed tended to express at somewhat lower levels. Cells were plated on poly-L-lysine-coated coverslips at an initial density of approximately 700,000-900,000 per 6 centimeter dish. Neurons were maintained with a glial feeder layer, and cytosine arabinoside (5 μM) was added at DIV 2 to prevent glial overgrowth. DL-2-amino-5-phosphonovaleric acid (APV, 100 μM) was added starting from DIV 5 in order to limit excitotoxicity and improve neuronal survival.AAVs described above were produced by Virovek. Delivery of AAVs was accomplished by incubating DIV 6 neurons face-up in 12-well plates, each well containing 700 uL of glial-conditioned media with 5 * 109 viral genomes (vg) of the appropriate viral construct. Conditioned media was harvested from either the neuronsâ own dishes or from similar dishes, and was centrifuged for 5 minutes at 1500 g before use, and APV was added at a concentration of 100 μM. Neurons were incubated in the virus-containing solution for 4 hours and then transferred back to their home dishes.2.4.3 Coculture AssayCOS cells were maintained in Dulbeccoâs modified Eagleâs medium (DMEM) with 10% bovine growth serum (BGS). When neurons reached the age of DIV 12, near-confluent COS cells were harvested using 0.25% trypsin-EDTA and plated in 12-well plates, then transfected the following day at ~85% 88confluence using polyethylenimine (Boussif et al., 1995) with 1 ug of plasmid DNA encoding a tagged form of either a postsynaptic organizing protein or CD4 as a non-synaptogenic control. The day after transfection, when neurons were at DIV 13, COS cells were harvested again as before, washed twice with DMEM with 10% BGS, resuspended in glial conditioned media (treated as above for viral infection), and plated at a density of ~20,000 cells per coverslip on top of the neurons. Cocultures were allowed to incubate for 18-24 hours prior to fixation and staining. 2.4.4 HEK Cell Cultures & Clustering AssayMaintenance, harvesting, and transfection of HEK cells were performed in the same manner as for COS cells, except that the concentration of trypsin-EDTA used was 0.05%. Prior to transfection, cells were plated in 12-well plates on coverslips coated in poly-D-lysine (PDL). The clustering assay was performed by transfecting HEK cells with a mixture of 0.2 ug of pBA-myc-liprin-α2, 0.6 ug of pLL-V5-PTPÏ WT or mutant, and 0.2 ug of pLL CFP. Control coverslips replaced either the liprin-α2 or the PTPÏ plasmid with an equal amount of additional pLL CFP plasmid, such that the total DNA amount was always 1 ug.2.4.5 Immunocytochemistry and imagingLive surface staining for both HEK cells and neurons was performed by incubating coverslips face-up in antibody solution for 30 minutes on an ice block in a 37°C, 5% CO2, humidified incubator. Antibody solution was made with fresh Neurobasal media. APV was added at a concentration of 100 μM for experiments involving neurons. Coverslips were washed with Neurobasal media 3 times prior to fixation.89Neurons were fixed in warm phosphate-buffered saline (PBS) with 4% paraformaldehyde and 4% sucrose for 12 minutes. Surface staining of V5-PTPÏ in neurons was performed after fixation but before permeabilization in order to improve the signal quality. For experiments involving surface staining of both V5-PTPÏ and HA, staining was performed after fixation for both antibodies. Non-permeabilized neurons were blocked for 30 minutes in blocking buffer (3% BSA, 5% normal goat serum in PBS) at 37°C, then incubated in primary antibody solution in blocking buffer at 4°C for two nights prior to permeabilization. Cultures were permeabilized using 0.2% Triton-X100 in PBS for 5 minutes. Cultures were blocked as above and remaining primary antibodies were applied in blocking buffer overnight at 4°C. Secondary antibodies were applied in blocking buffer for 45 minutes at 37°C. Primary antibodies used were mouse IgG1 anti-SynapsinI (Synaptic Systems, cat no. 106 011, 1:40,000, used in Figure 2.3 and Figure 2.4), rabbit anti-SynapsinI (Millipore, cat no. ab5905, 1:10,000, used for all other synapsin staining), mouse IgG2a anti-TauI (Millipore, cat no. PC1C6, 1:2000), chicken anti-MAP2 (Abcam, cat no. ab5392, 1:2000), mouse IgG2b anti-HA (Abcam, cat no. 12CA5, 1:500), mouse IgG1 anti-myc (Santa Cruz, cat no. sc-40, 1:500, used in Figure 2.12) rabbit anti-myc (Sigma, cat no. C3956, 1:2000, used for all other myc staining), rabbit anti-V5 (Cell Signalling Technology, cat no. 13202, 1:5000, used in Figure 2.12 for total V5 staining), and mouse IgG2a anti-V5 (Thermo Fisher, cat no. R960, 1:5000, used for all V5 surface staining). Secondary antibody against chicken was AMCA AffiniPure Goat Anti-Chicken IgY (IgG) (H+L) (Jackson, cat no. 103155155, 1:200). All other secondary antibodies were from Thermo Fisher, conjugated to Alexa Fluor 488, 568, or 647. Alexa Fluor-conjugated secondary antibodies were used at a concentration of 1:1000.90Imaging of HEK cells for the co-clustering assay was performed on a Zeiss LSM700 with a 40x oil immersion objective at 2x zoom. Cells were selected based on the channels containing CFP and liprin-α2 only. Cells without expression of both CFP and liprin-α, as well as cells showing diffuse liprin-α signal, were avoided.For all other experiments, imaging was performed on either the same microscope in epifluorescence mode or on an Axioplan 2, with either a 10x/0.45 NA air (morphology experiments) or a 40x/1.4 NA oil immersion (all others) objective and a Hamamatsu Orca-Flash4.0 CMOS camera. For most experiments, images were acquired as a z-stack containing 3 slices at 1.46 μm spacing (10x), or 11 slices at 0.23 μm spacing (40x), then combined into a single plane using the Zeiss Extended Depth of Focus module.Imaging and measurement were performed blind to experimental condition. For experiments in which cells were chosen in order to ensure even surface expression of V5 across conditions, an initial measurement was performed (see next section) that included only V5 levels and MAP2-negative COS cell area. Based on these results, an additional round of imaging was performed if needed in order to acquire more bright or dim cells for coverslips which overall showed too low or too high surface V5 levels, still blind to experimental condition. Cells showing high or low V5 levels were excluded such that all conditions contained cells with equal surface V5 levels before analyzing downstream measures such as synapsin intensity.2.4.6 Image Analysis and QuantificationImages were analyzed in FIJI (Schindelin et al., 2012; Rueden et al., 2017) using custom-written Python scripts. Regions of interest (ROIs) were generated by manually choosing a lower brightness 91threshold for each image and then converting the thresholded image to an ROI using the command âCreate Selection.â For experiments where measurements were taken from an ROI based on multiple channels, single-channel ROIs were combined using the ROI Manager tool. For coculture experiments, the ROI based on the MAP2 channel was first dilated by 5 pixels using the command âEnlarge,â then inverted using âMake Inverse.â The purpose of these two steps was to limit the final ROI to areas of the image which did not contain dendrites, and to also exclude the area immediately around the dendrite which could contain spine-associated synapses not directly overlapping the MAP2 signal. Measurements of intensity and/or area were taken by applying the appropriate ROI to a given image and using the command âMeasure.â The order of images was randomized in order to minimize the effects of experimenter bias during thresholding.Measurement results were exported as a CSV file and further analyzed using Jupyter Notebook (Kluyver et al., 2016).2.4.7 Yeast cultures and DHFR AssayYeast (strain BY4741) were transformed with two plasmids, the first containing DHFR-F1-2 fused to the C-terminus of either PTPÏ full-length (FL) or intracellular region (ICR) or a control, and the second containing DHFR-F3 fused to the C-terminus of the indicated interacting protein or a control. DHFR-F3 constructs contained the SAM domains of liprin-α2 and caskin1, the immunoglobulin-like and protein serine kinase domains (IgPSK) of Trio, or the FL version of PTPÏ. PTPÏ ICR-DHFR-F1-2 was used to test for interactions with Trio IgPSK since preliminary experiments (not shown) revealed that the growth rate of yeast transformed with Trio IgPSK and PTPÏ FL was very low. All other DHFR-F3 constructs were transformed along with PTPÏ FL-DHFR-F1-2. Negative controls replaced either the F1-2 or the F3 fusion with a control protein of a similar size. PTPÏ FL was replaced by NgCAM. PTPÏ 92ICR, liprin-α2 SAM, caskin1 SAM, and trio IgPSK were all replaced by YFP. Transformed strains were grown on monosodium glutamate (MSG) minimal media without histidine and containing hygromycin (100μg/mL, his- hygro+) to select for doubly transformed cells.Transformed strains were grown in his- hygro+ MSG media without adenine (Michnick et al., 2016) overnight at 30°C with shaking, then diluted in a 96-well plate with 200μL/well to an OD of of 0.05 in the same media containing 1% DMSO with or without 200 μg/mL methotrexate (MTX). Each plate contained 6 blank wells with no yeast added, as well as a positive control strain with two wild-type interacting proteins and two YFP or NgCAM negative controls. Each yeast strain had 3 replicate wells each with DMSO alone or DMSO+MTX. The plate lid was coated with tritonX-100 (0.05% in 20% EtOH) to minimize condensation, and the cultures were incubated in a BioTek Epoch 2 plate reader at 29-31°C (2° gradient intended to prevent condensation on the plate lid) with OD600 readings taken every 10 minutes at least until the blanked log2(OD600) reached a value of -2, which generally took between 20 and 30 hours.Data were inspected visually for any wells showing abrupt drops in OD600 readings or other abnormalities, and such wells were excluded from the dataset. Data were log2 transformed. For each well, the slope of the growth curve in the linear range between log2(OD600) values of -3 and -2 was calculated. In one case where the experiment was stopped slightly too early, the slope was taken between -3 and -2.2 instead. For each MTX-containing well, the ratio between that wellâs slope value and the mean slope value for DMSO-containing wells from the same experiment containing the same strain was calculated. Interaction scores were calculated according to the formula (ratio x â ratioNC) / (ratioPC â ratioNC), where ratiox, ratioNC, and ratioPC are the MTX/DMSO ratios of the strain of interest, 93the negative control containing either NgCAM-DHFR-F3 or YFP-DHFR-F3, and the positive control, respectively.94Chapter 3: Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types3.1 IntroductionTwo prominent features that distinguish neurons from other cells are their electrical activity and their characteristic morphology. The specific pattern of electrophysiological activity displayed by a given neuron is a core property of its identity as one type of neuron or another. Similarly, different cell types often show striking differences in their size, branching complexity, and other morphological features. Neuronal cell types defined according to their electrophysiological or morphological characteristics show substantial correspondence with one another as well as with those defined using classification schemes based on transcriptomic criteria (Gouwens et al., 2018). Electrophysiological characteristics of neurons, as well as their connectivity patterns, give rise to the computational properties of a given circuit (Padmanabhan and Urban, 2010; Markram et al., 2015). Additionally, modeling studies show that morphological changes in simulated neurons can critically change their signaling capabilities (Connors and Regehr, 1996; Chklovskii, 2004; Stiefel and Sejnowski, 2007). Thus, understanding the origins of neuronal electrophysiology and morphology is an important step in understanding the mechanisms of brain function, both in the context of basic research and in the search for treatments for neuropsychiatric disorders.A comprehensive understanding of the mechanisms that give rise to electrophysiological or morphological diversity must necessarily include a catalogue of the genes whose products contribute to these properties. Many genes have been shown experimentally to influence neuronal electrophysiology through a variety of mechanisms, including but not limited to ion channel activity, protein trafficking, 95and transcription factor activity (Kopp-Scheinpflug et al., 2003; Qin et al., 2015; Marcotti et al., 2016). Processes such as axon guidance and the development of dendrite morphology are also known to be under genetic control (Santiago and Bashaw, 2014). Despite this, our understanding of the relationship between gene expression and electrophysiological or morphological properties is quite limited.In previous work (Tripathy et al., 2017), we combined publicly accessible electrophysiological and gene expression datasets in order to examine the relationship between gene expression and electrophysiological properties. By matching groups of cells inferred to be similar based on multiple information sources, such as the transgenic reporter line and the brain region cells were isolated from, we were able to combine separate datasets containing gene expression and electrophysiological data to generate lists of genes which were correlated with one of several electrophysiological properties (as outlined in Figure 3.2A). The goal of this approach was to identify candidate genes that could be further studied using knockout or knockdown approaches in order to determine whether a causal relationship was present.One caveat in our prior study is that the gene-electrophysiology correlations we identified may have been confounded by overall differences between broad cell classes. Across multiple datasets and cellular characterization methods, including gene expression (Zeisel et al., 2015, 2018, Tasic et al., 2016, 2018), and electrophysiology and morphology (Gouwens et al., 2018), clustering cellular phenotypes in an unbiased manner reveals the major taxonomic difference between neurons to be between projecting and non-projecting neurons (Zeisel et al., 2018), or in the case of those cell types present in the cortex or hippocampus, excitatory and inhibitory neurons (Zeisel et al., 2015; Tasic et al., 2016, 2018). Thus, the commonly held view that a neuronâs identity is first and foremost defined by its 96excitatory or inhibitory identity (Petilla Interneuron Nomenclature Group et al., 2008) is corroborated across multiple data sources and experimental modalities.Therefore, we reasoned that the dataset we used previously was potentially susceptible to this confounding effect of cell class, since it contained a mixture of cells from different broad cell classes. In this work, we will use the term âcell typeâ to refer to narrowly-defined cell types, and âcell classâ to refer to those which are broadly-defined (excitatory versus inhibitory or projecting versus non-projecting). We refer to correlations between gene expression and electrophysiological or morphological properties that are explained by differences between cell classes as âclass-driven,â (e.g. Figure 3.2B) and to those that exist based on graded differences within broad cell classes as ânon-class-drivenâ (e.g. Figure 3.2C). We reason that gene-property relationships that are non-class-driven would be more likely to be potential causal regulators of the associated property. Although some class-driven correlations likely do reflect true relationships between genes and properties which distinguish excitatory from inhibitory cells, separating these relationships from instances where one cell class has a higher value of a property and coincidentally higher or lower expression of a gene without additional sources of data is not possible. Effectively, such situations are analogous to attempting to draw conclusions about correlations with only two data points.Due to limitations in available data, we were unable to address the effect of cell class in our previous work (Tripathy et al., 2017). Since then, the RNA-seq and electrophysiology datasets from the Allen Institute for Brain Science (AIBS) (which we originally used as validation data) have expanded greatly, with more cells and more transgenic lines represented. This increase in size, together with the fact that the AIBS data were collected using standardized protocols, suggests that this dataset might prove valuable for discovering genes correlated with electrophysiological and morphological properties. In 97addition, the growing use of the Patch-seq methodology (Cadwell et al., 2016), allowing transcriptomic, electrophysiological, and morphological characterization of the same single cell, also affords an opportunity to test gene-property correlations.Leveraging the larger size of the new AIBS dataset, we were able to address limitations of our previous study related to excitatory versus inhibitory cell class by employing statistical methods to help mitigate the effects of cell class. These methods, together with the larger number of cell types represented in the new dataset, allowed us to identify novel electrophysiological and morphological property-related gene sets which are potentially more likely to represent meaningful biological relationships.3.2 Results3.2.1 Primary DatasetThe primary dataset we used combined groups of cells from mouse visual cortex characterized by the Allen Institute for Brain Science (AIBS; http://celltypes.brain-map.org/), where multiple Cre-driver lines were used to target cells for characterization. Standard electrophysiological protocols were used to characterize cells in vitro, with a subset of these cells further undergoing detailed morphological characterization (Gouwens et al., 2018). In addition, a separate group of cells were subjected to deep single-cell RNA-sequencing to characterize cellular transcriptomes (Tasic et al., 2018). Because the same Cre-lines were used to characterize cells along multiple modalities of neuronal function, we were able to summarize these data to the âcell typeâ level (reflecting Cre-line, cortical layer, and major neurotransmitter; shown in Table 3.1) by pooling and combining cellular characterization data across different animals and data modalities. The definition of multiple cell types within one Cre-line based on 98cortical layer and major neurotransmitter is supported by cross-layer differences in gene expression (Tasic et al., 2018) and in electrophysiological properties (Figure 3.1).The final combined dataset is composed of 34 inhibitory GABAergic and 14 excitatory glutamatergic types (48 total) with electrophysiological data, and 30 inhibitory and 13 excitatory types (43 total) with morphological data. The increased size of this dataset is a considerable advance over our prior analysis (Tripathy et al., 2017), which employed an older version of the same dataset (only 12 cell types) (Tasic et al., 2016). This was made possible in part because of more Cre-lines available for analysis and finer cortical layer dissections for the transcriptomic data. For each cell type thus defined, we computed the mean expression value for each gene represented in the RNA-seq dataset and the mean value of each of sixteen electrophysiological and six morphological properties (described in Table 3.2).99Figure 3.1 Justification for cell type definitions in the AIBS datasetCell types defined based on the same Cre line but different layers and/or excitatory/inhibitory identity show differences in electrophysiological features. Data are represented as mean ± SEM.100RNA-seq Electrophysiology MorphologyChat-IRES-Cre-neo L2/3 inh 36 45 15Chat-IRES-Cre-neo L4 inh 8 18 6Chrna2-Cre_OE25 L5 inh 57 29 9Chrna2-Cre_OE25 L6 inh 40 10Chrna2-Cre_OE25|Pvalb-T2A-Dre L5-L6 inh 59 8Ctgf-T2A-dgCre L6 exc 150 43 16Esr2-IRES2-Cre L5-L6 exc 37 20 3Gad2-IRES-Cre L1 inh 298 7 3Gad2-IRES-Cre L5 inh 327 7 3Htr3a-Cre_NO152 L2/3 inh 32 70 16Htr3a-Cre_NO152 L4 inh 31 21 4Htr3a-Cre_NO152 L5 inh 30 39 12Htr3a-Cre_NO152 L5-L6 exc 9 7Htr3a-Cre_NO152 L6 inh 23 8 3Htr3a-Cre_NO152|Pvalb-T2A-Dre L5-L6 inh 7 10 3Ndnf-IRES2-dgCre L1 inh 52 39 9Nkx2-1-CreERT2 L1-L4 inh 32 22 6Nkx2-1-CreERT2 L5-L6 inh 13 26Nos1-CreERT2 L1-L4 inh 92 8 4Nos1-CreERT2 L5-L6 inh 34 10 3Nos1-CreERT2|Sst-IRES-FlpO L1-L4 inh 3 3Nos1-CreERT2|Sst-IRES-FlpO L5-L6 inh 20 35 13Nr5a1-Cre L4 exc 243 55 15Ntsr1-Cre_GN220 L6 exc 97 51 16Oxtr-T2A-Cre L1-L4 exc 22 4Oxtr-T2A-Cre L1-L4 inh 117 21 8Oxtr-T2A-Cre L5-L6 exc 71 14 4Oxtr-T2A-Cre L5-L6 inh 62 6Penk-IRES2-Cre-neo L5-L6 exc 44 8 3Pvalb-IRES-Cre L2/3 inh 213 42 13Pvalb-IRES-Cre L4 inh 53 69 9Pvalb-IRES-Cre L5 inh 218 83 28Pvalb-IRES-Cre L6 inh 47 22 10Rbp4-Cre_KL100 L4 exc 16 21 4Rbp4-Cre_KL100 L5 exc 695 55 20101Scnn1a-Tg2-Cre L4 exc 22 32 8Scnn1a-Tg3-Cre L2/3-L4 exc 107 58 13Sim1-Cre_KJ18 L4-L6 exc 74 32 6Slc32a1-T2A-FlpO|Vipr2-IRES2-Cre L1-L4 inh 17 25 10Sst-IRES-Cre L1-L2/3 inh 80 15 8Sst-IRES-Cre L4 inh 11 23 3Sst-IRES-Cre L5 inh 140 66 21Sst-IRES-Cre L6 inh 125 17 6Tlx3-Cre_PL56 L4-L6 exc 115 41 11Vip-IRES-Cre L1-L2/3 inh 149 32 6Vip-IRES-Cre L4 inh 67 16 4Vip-IRES-Cre L5 inh 91 17Vip-IRES-Cre L6 inh 38 29 3Vipr2-IRES2-Cre L1-L6 exc 43 16Vipr2-IRES2-Cre L1-L6 inh 36 11 5Number of Types 50 48 43Table 3.1 Criteria used for defining cell types from the AIBS dataset according to the cre line and layer they were isolated from as well as excitatory/inhibitory identity.For each cell type, the number of cells meeting the criteria which were profiled for each of the three data modalities are indicated. For electrophysiology and morphology, blank cells indicate that not enough cells meeting the criteria were present in that dataset, so that cell type was not included in the analysis.3.2.2 Analysis ApproachOur goal was to identify, for each electrophysiological or morphological property, genes that were correlated with the property (Figure 3.2A). However, overall differences between excitatory and inhibitory cell classes can make the interpretation of such relationships more complicated in several ways. For example, Figure 3.2B shows an example of a gene-property correlation that appears almost entirely class-driven, meaning that although no relationship appears within either cell class, the apparent relationship is entirely driven by differences between cell classes. In this case, inhibitory cell 102types show higher expression of the gene and a greater value of the property compared to excitatory cell types. In contrast, Figure 3.2C shows a non-class-driven relationship, meaning one that manifests in both cell classes, but which may be obscured by baseline differences when the cell classes are grouped. In this example, a correlation that appears within both classes independently is obscured by a higher value of the property in inhibitory compared to excitatory cell types. Although this obscuring effect is present in this particular example, it is not required for a relationship to be considered non-class-driven; we expected to see some relationships that were consistent both within each class as well as among all cell types.In order to computationally account for these possibilities, we evaluated each combination of gene and property using a statistical model that assesses the predictive value of the gene on the property while controlling for the effects of cell class. We termed this model the class-conditional model. This model would be expected to identify a significant result when a non-class-driven relationship is present (Figure 3.2C), but would not identify relationships that are class-driven (Figure 3.2B). For comparison, we modeled the same gene-property pairs using a class-independent model, which assesses the predictive value of the gene on the property irrespective of cell class. This model is similar in principle to the correlational method used in our previous work (Tripathy et al., 2017) and would be expected to produce a significant result in cases showing class-driven relationships (such as Figure 3.2B) but might miss some instances of non-class-driven relationships (such as Figure 3.2C).Another possible gene-property relationship is one where there is an interaction between gene and class, meaning that the gene-property relationship is different in excitatory and inhibitory cell types. An interaction could indicate either that excitatory and inhibitory cell types both show a correlation between the gene and property, but the slopes are in opposite directions (as in the example in Figure 1033.2D), or that the gene is correlated with the property only in one cell class. To detect such situations, we introduced a third model, the interaction model, which tested whether the relationship between gene expression and the property in question was significantly different between excitatory and inhibitory cell types. In summary, the three models are designed to answer three different questions: Class-independent model: Is expression of the gene a significant predictor of the property if we assume that cell class is not a factor?Class-conditional model: After accounting for cell class, is the geneâs expression a significant predictor of the property?Interaction model: Is the relationship between the geneâs expression and the property statistically different in inhibitory and excitatory cells?104Figure 3.2 Methods for modeling relationships between gene expression and electrophysiological or morphological properties with respect to cell classA. Schematic for defining cell types from single-cell transcriptomic or electrophysiological and morphological data. We divided cells into types based on Cre-driver expression as well as cortical layer and excitatory/inhibitory identity (left). Right panel shows summarization of cellular features by cell type for a hypothetical gene and property, where each point in the scatter plot represents each cell typeâs mean gene expression (x-axis) and the mean value of an electrophysiological or morphological property (y-axis).B. A hypothetical class-driven relationship between a gene and an electrophysiological or morphological property, in which neither cell class (excitatory or inhibitory) shows a relationship between gene expression and the property (solid lines), but an overall relationship appears because of systematic cross-class differences in both data modalities 105(dashed line). For B-D, small points represent individual cells and larger circles or diamonds represent cell type averages.C. A hypothetical example of a non-class-driven relationship, where the gene-property relationship appears within each major cell class (solid lines), but would be obscured if modeled in a class-independent manner (dashed line).D. A hypothetical example of a gene-property relationship exhibiting an interaction with cell class. Here, expression of the gene is positively correlated with the property in excitatory cell types but negatively correlated in inhibitory types (solid lines).3.2.3 Accounting for cell class results in the identification of a distinct but overlapping set of genesWe first set out to understand how accounting for cell class identity (excitatory or inhibitory) affects the interpretation of gene-property relationships. We modeled each relationship with or without including an indicator variable for cell class, using the class-conditional or class-independent models described above. For most properties, we found that the degree of overlap between the sets of genes identified in the two models (at a false discovery rate (FDR) < 0.1) was substantial but far from a complete intersection (Figure 3.3A, Venn diagrams, and Table 3.2). For example, for after-hyperpolarization (AHP) amplitude, we found ~6000 significantly-associated genes in the class-independent model and ~6500 in the class-conditional model; out of these, ~3700 genes were shared between models. Thus, accounting for cell class results in the identification of a substantially different set of candidate genes, which suggests that many of the genes identified in our previous work (Tripathy et al., 2017) might reflect class-driven gene-property relationships.We next asked how overall differences in morphological and electrophysiological properties between excitatory and inhibitory cells affect gene-property relationships. To this end, we used a linear model to 106estimate the effect of cell class on each property. For most properties, there was a significant (p < 0.05) effect of cell class. The features of action potential (AP) threshold, input resistance, sag, rheobase, branchiness, soma surface, and bifurcation angle are exceptions to this. The existence of a significant difference in most properties between excitatory and inhibitory cell types highlights the importance of taking cell class into account when attempting to relate these properties to gene expression. The properties without a significant difference are likely to be less susceptible to class-driven effects, but the class-independent model still might miss potentially interesting relationships due to differences in gene expression between classes, resulting in genes which are identified by the class-conditional model only.We compared the strength and direction of the relationship in both the class-independent and class-conditional models by directly comparing the slopes derived from each model for each gene-property relationship (where slope indicates the change in the property per 2-fold change in gene expression; shown for AHP amplitude in Figure 3.3B). While there is broad agreement between the class-independent and class-conditional models (rSpearman = 0.52), a substantial number of gene-property relationships are significant in one model but not the other (FDR < 0.1). In other words, these relationships are either class-driven (significant in the class-independent model only) or non-class-driven and obscured by class (significant in the class-conditional model only). For example, the relationship between the gene Gprasp1 and AHP amplitude illustrates an example of a class-driven relationship where the apparent relationship is entirely due to broad differences in excitatory and inhibitory classes (Figure 3.3C). The gene Camk2g shows a non-class-driven relationship with the same property that is obscured in the class-independent model by higher AHP amplitude values in 107inhibitory cell types (Figure 3.3D). However, many genes, such as Xxylt1, are identified using either model (Figure 3.3E).108109Figure 3.3 Different sets of genes are associated with electrophysiological and morphological properties after correcting for cell class.A. Number of genes significantly associated with each property in the class-conditional model at various levels of significance (only properties with significant genes in this model are shown). Darkness of the bar represents the significance level of each group of genes. Venn diagrams to the left indicate the extent of overlap (pink; middle) between the gene sets identified by the class-independent (gold; left) and class-conditional (teal; right) models, where the area of each segment is proportional to the significant gene count at a threshold of FDR < 0.1. Venn diagrams for different properties are not to scale with one another. See Table 3.2 for descriptions of electrophysiological and morphological properties analyzed here, as well as gene counts for all properties.B. Comparison of model-based slopes from the class-independent and class-conditional models. Each point represents a single geneâs relationship with the electrophysiological property AHP amplitude and is colored according to whether the relationship is significant in one or both models (FDR < 0.1). Example genes in C-E are indicated. For clarity of visualization, only a random subset of genes (2% total) are shown to mitigate over-plotting.. Dashed line indicates identity.C-E. Examples of genes showing significant associations with AHP amplitude that are class-driven (C; significant in class-independent model only), non-class-driven (D; significant in class-conditional model only), or non-class-driven but significant by either model (E). Solid lines indicate linear fits within excitatory or inhibitory cell classes only and dashed line indicates a linear fit including all cell types. Gene expression is quantified as counts per million (CPM).110Class-independent modelClass-conditional modelSignificant in both modelsDefinition Units TransformSoma Surface 0 0 0 Surface area of the cell body μm2 linearTotal Volume 1019 0 0 Volume of the cell, including cell body as well as processes μm3 log10Total Length 3398 0 0 Total length of all processes μm log10Max Branch Order 4308 4 3Maximum number of times that a process bifurcates between the soma and branch tiplog10Branchiness 35 132 35 Number of bifurcations encountered per process length log10Bifurcation Angle 0 0 0 Mean angle across all bifurcation points degrees linearAdaptation Ratio 4164 3220 2220Ratio of durations between early and late AP inter-spike intervals in an AP trainratio log10Input-Output Curve Slope 6424 7022 4583Slope of the relationship between current injection and resulting firing frequency, based on multiple long current stepsHz/pA log10Max Firing Frequency 6113 6320 3977Maximum observed AP discharge rate Hz log10Latency 566 0 0Latency to fire the first action potential during a long current steps log10Interspike Interval Coefficient of Variation (ISI CoV)30 0 0Variability between interspike intervals within one sweep, measured as standard deviation/meanratio log10Average Interspike Interval5405 4447 2699 Average time elapsed between spikes during a sweep ms log10Sag 0 2 0Measure of the extent to which the membrane potential recovers toward resting potential when the neuron is strongly hyperpolarized (between -90 and -110 mV)ratio log10Resting 1546 443 280 Membrane potential at the onset mV linear111Membrane Potential of whole-cell recordingInput Resistance 2615 3373 2404Input resistance measured at steady-state voltage response to current injectionMΩ log10Time Constant Tau 3204 1441 1078Time constant for the membrane to repolarize after a small current injection of fixed amplitude and durationms log10Capacitance 5508 1736 1144Neuron capacitance, typically measured by dividing membrane time constant by membrane resistancepF log10After-hyperpolarization (AHP) Amplitude6056 6568 3681Calculated as the voltage difference between AP threshold and AP trough. Commonly defined using first AP in train at rheobase current.mV log10Action Potential (AP) Amplitude 4969 3438 1997Voltage indicating height of action potential. Usually calculated as the difference between AP peak and AP threshold voltages. Commonly measured using first AP in train at rheobase current.mV linearAP Half-width 5384 4522 2489Calculated as the AP duration at the membrane voltage halfway between AP threshold and AP peak. Most commonly calculated using first AP in train at rheobase current.ms linearAP Threshold 31 24 11Voltage at which AP is initiated (as assessed by measuring rising slope of membrane voltage)mV linearRheobase 3238 4108 3237 Minimum current injected somatically required to fire AP pA log10Table 3.2 Overlap between class-independent and class-conditional modelsComparison of the number of genes showing a significant result (FDR < 0.1) for each electrophysiological or morphological property in the class-independent or class-conditional model, and extent of overlap between these two sets of genes. Definitions of electrophysiological properties are reproduced from (Tripathy et al., 2017), except for 112input-output curve slope, latency, ISI CoV, average ISI, and sag, which are described based on the Allen Cell Types database (http://celltypes.brain-map.org/). Morphological features are described based on (Gouwens et al., 2018).3.2.4 Divergent gene-property relationships in inhibitory versus excitatory cell classesWe next wondered whether some gene-property relationships might be potentially different within, or specific to, excitatory or inhibitory cell types. To test this, we incorporated an interaction term between gene expression and excitatory versus inhibitory cell class to assess whether the gene-property relationships (i.e. slopes) were different within each cell class. For nearly all properties, there were fewer significant genes in the interaction model compared to the class-conditional model (Figure 3.4A, Venn diagrams, and Table 3.3). For example, out of the ~6500 genes significantly associated with AHP amplitude in the class-conditional model, ~2000 also show interactions, and there are an additional ~700 which show an interaction but are not significant in the class-conditional model. This could indicate that âtrueâ interactions are comparatively rare, but this finding is also likely partly explained by differences in statistical power. In addition, these interactions do not appear to be merely the result of low or no gene expression within one cell class but not the other; we did not observe strong correlations for any property between the interaction model slope and the average difference in expression levels between inhibitory and excitatory cell types (Figure 3.5).For all properties, we found that the slopes of the gene-property relationships within excitatory cell types were poorly correlated with those within inhibitory cell types (example features maximum branch order and AHP amplitude shown in Figure 3.4B, C). By definition, the genes with significant interaction terms were those where the slopes calculated within excitatory and inhibitory classes were very different from each other (pink and purple points in Figure 3.4B, C). If the majority of gene-property relationships are shared between excitatory and inhibitory cell types, as suggested by the 113greater number of significant genes in the class-conditional model than in the interaction model for most properties, one might expect a positive correlation between slopes calculated in inhibitory and excitatory cell types. However, such a correlation may be lacking in this analysis because we would expect most genes to have no relationship to a given property and thus most slopes to be near zero.The properties maximum branch order and sag are unusual in that they show few significant genes using the class-conditional model, but many (1914 and 1174, respectively) in the interaction model (Figure 3.4A, Venn diagrams, and Table 3.3; slopes for maximum branch order plotted in Figure 3.4B). We hypothesize that this might be because these properties are under stronger (or otherwise more readily identified) genetic control in excitatory compared to inhibitory cell types (see Discussion). Figure 3.4D, E show examples of genes with significant interaction terms for AHP amplitude. The class-conditional model also shows a significant relationship in the case of Man1c1 (Figure 3.4E) but not Nrxn3 (Figure 3.4D). In other words, the interaction model identified a potentially interesting relationship in the case of Nrxn3 which was missed by the class-conditional model. For Man1c1, the interaction model does not reveal a new relationship, but instead highlights the fact that this gene-property relationship, if real, is potentially more complicated than would be assumed based on the class-conditional model alone. Man1c1 is an enzyme involved in the maturation of N-linked oligosaccharides (Herscovics, 2001), and is thus a plausible regulator of AHP amplitude, since N-linked glycosylation of voltage-gated potassium channels or their auxiliary subunits is known to regulate both surface trafficking and channel function (Huang et al., 2017; Vicente et al., 2018). The apparent class-specificity of this relationship could result from class-specific co-expression of certain potassium channels or other enzymes involved in glycan synthesis or maturation.114Figure 3.4 Identification of divergent gene-property relationships in excitatory versus inhibitory cell classes115A. Number of genes showing a significant interaction effect between gene and class for each property. Darkness of the bar represents the significance level of each group of genes. Venn diagrams to the left indicate the extent of overlap (pink; middle) between the class-conditional (teal; left) and interaction (purple; right) models, where the area of each segment is proportional to the significant gene count at a threshold of FDR < 0.1. Venn diagrams for different properties are not to scale with one another.B-C. Slope values within excitatory cell types (x axis) plotted against the slope values for the same set of genes in inhibitory cell types (y axis). Each point represents a single geneâs relationship to the morphological property maximum branch order (B) or electrophysiological property AHP amplitude (C), and is colored according to its significance in one or both models (see inset legend). Example gene-property relationships highlighted in D-E are marked in panel C. For clarity of visualization, only a random 2% subset of the total number of genes are plotted. Dashed lines indicate positive and negative unity lines.D. Example of a gene with a significant interaction term which is not significant in the class-conditional model. For D and E, solid lines indicate linear fits including only excitatory or only inhibitory cell types, and dashed line indicates a linear fit including all cell types.E. Example of a gene which is significant in both the class-conditional and interaction models.116Figure 3.5 Interactions do not result primarily from low gene expression in one cell classBetween-class differences in gene expression plotted against differences in gene-property slope in the interaction model for the property AHP amplitude. Each point represents a single gene; grey points do not have a significant interaction and others are colored according to their significance level in the interaction model. For clarity of visualization only a random subset of the data (10% of the total number of genes) are plotted.117Class-conditional modelInteraction modelSignificant in both modelsSoma Surface 0 0 0Total Volume 0 0 0Total Length 0 0 0Max Branch Order 4 1914 0Branchiness 132 5 0Bifurcation Angle 0 0 0Adaptation Ratio 3220 325 253Input-Output Curve Slope 7022 408 388Max Firing Frequency 6320 335 312Latency 0 0 0ISI CoV 0 0 0Average Interspike Interval 4447 54 47Sag 2 1174 0Resting Membrane Potential 443 10 1Input Resistance 3373 99 89Time Constant Tau 1441 123 103Capacitance 1736 156 101AHP Amplitude 6568 2962 2222AP Amplitude 3438 658 457AP Half-width 4522 96 73AP Threshold 24 4 0Rheobase 4108 91 77Table 3.3 Overlap between class-conditional and interaction modelsComparison of the number of genes showing a significant result (FDR < 0.1) for each electrophysiological or morphological property in the class-conditional or interaction model, and extent of overlap between these two sets of genes.1183.2.5 Results from the class-conditional model are more likely to validate using independent methodsWe next asked how the gene-property relationships from the class-independent and class-conditional models, based on our analysis of the AIBS cortical cell types dataset, might generalize to other datasets. We first compared the results reported here to those from our earlier NeuroElectro/NeuroExpresso (NE) literature-based dataset (Tripathy et al., 2017), after subsetting these data to include only non-projecting cell types (reflecting 19 cell types in total sampled throughout the brain, described in detail in the Methods). We chose to use non-projecting cell types in the NE dataset, as these were recently described by a mouse brain-wide transcriptomic survey as corresponding to a single broad cell class (Zeisel et al., 2018). To this end, we calculated Spearman correlations between genes and electrophysiological properties in the NE dataset. Next, for gene-property relationships from both the class-independent and class-conditional models, we assessed their aggregate consistency with those from the NE dataset. Here, we defined âconsistencyâ for a given model (i.e. class-independent or class-conditional) and property as the correlation between gene-property slopes calculated from the AIBS dataset with the Spearman correlations for the same set of gene-property relationships in the NE dataset (illustrated in Figure 3.6B).In Figure 3.6A we show a comparison of the gene/electrophysiology correlations from the AIBS dataset with the model slopes (beta) from the NE dataset (Tripathy et al., 2017). We found that for seven out of the eleven electrophysiological properties shared between the datasets, both AIBS dataset-based statistical models were consistent with analogous gene-property relationships based on the NE dataset (rSpearman as high as 0.305 and 0.35 for class-independent and class-conditional, respectively). For six out of the eleven features, we found that the class-conditional model was considerably more consistent than 119the class-independent model with relationships in the NE dataset. For only two features, capacitance and membrane time constant (tau), was the class-independent model more consistent than the class-conditional with the NE dataset. Figure 3.6B shows an example of how consistency was measured for AP half-width. The relationship between Atp2a2 expression and AP half-width is shown in Figure 3.6C, D as an example of a gene-property relationship which is consistent between the NE (r = -0.742) and AIBS datasets for the class-conditional (beta = -0.099 ± 0.024; FDR = 0.002) but not the class-independent (beta = -0.024 ± 0.034; FDR = 0.62) model.120Figure 3.6 Modeling gene/electrophysiology relationships using the class-conditional model is more predictive than the class-independent model of correlations in an independent dataset containing non-projecting cell types onlyA. Aggregate gene-property relationship consistency between AIBS and NeuroExpresso/NeuroElectro (NE) datasets. Error bars indicate a 95% confidence interval, and asterisk indicates a significant (p < 0.05) difference in the consistency metric between the class-independent and class-conditional models, calculated using 100 bootstrap resamples of the original values (not indicated for properties where both values are negative). 121B. Direct comparison of gene-property relationships between the AIBS and NE datasets. Each point represents the relationship between a single gene and the property AP half-width. The model slope from the AIBS dataset is plotted on the x axis (with the class-independent model (ind) slopes in gold, and the class-conditional model (cond) slopes in teal), and the Spearman correlation for the same set of genes in the NE dataset on the y axis. For clarity of visualization only 10% of the total number of genes are plotted. Lines indicate a linear fit for each set of points. The correlation within each set of points is used as a measure of cross-dataset consistency (plotted for all properties in panel A).C-D. Example of a gene showing consistent results between the NE dataset and the AIBS dataset using the class-conditional model, but not the class-independent model. C shows the relationship within the AIBS dataset, and D shows the same gene and property in the NE dataset. Solid lines indicate a linear fit including only types belonging to one cell class, and dashed line indicates a linear fit including all cell types.3.2.6 Assessing within-cell type correlations using Patch-seq datasetsWe next wondered whether these between-cell type gene-property relationships might be predictive of cell-to-cell heterogeneity within a given cell type. We reasoned that the recently developed Patch-seq methodology, allowing morphological, electrophysiological, and transcriptomic characterization from the same single cell, presents a unique opportunity to test this possibility (Cadwell et al., 2016). While these data at present are limited by relatively modest sample sizes and technical factors such as inefficient mRNA capture and potential off-target cellular mRNA contamination (Tripathy et al., 2018), we nonetheless sought to use these data to assess the nature of within-cell type gene-property relationships.To this end, we performed an integrated analysis of 5 Patch-seq datasets, with each dataset characterizing transcriptomic and electrophysiological diversity of mouse forebrain inhibitory cells from the neocortex, hippocampus, and striatum (Table 3.4). Our analysis includes one novel dataset of 12219 Pvalb-Cre positive interneurons recorded in region CA1 of the mouse hippocampus, reported here for the first time. Cells in this dataset (referred to as the Bengtsson Gonzales dataset), were characterized as described in (Muñoz-Manchado et al., 2018).To jointly analyze these Patch-seq datasets, we first mapped Patch-seq sampled cells to the cell type level, using a transcriptome-based classifier that assigns cells to cell types as defined by cellular dissociation-based single-cell RNAseq reference atlases from the cortex and striatum (Muñoz-Manchado et al., 2018; Tasic et al., 2018). Specifically, we resolved individual cells to the level of major cell types; for example, Pvalb, Sst, Vip, Lamp5, etc. (referred to in Tasic et al., 2018 as âsubclassesâ). Next, for each cell type, we identified genes that are highly variable in their expression levels within cells of the same type. We reasoned that these highly-variable genes might be those most likely to drive or appear correlated with electrophysiological heterogeneity within each cell type. Lastly, we performed a joint analysis across Patch-seq datasets to assess the strength of gene-property relationships within cell types where the gene was highly variable. Here, we used a mixed-effects regression model, with gene expression as a fixed effect and dataset and cell type as random effects and with cells weighted by their estimated transcriptome quality (see Methods).Despite the limitations of the Patch-seq data, we found a small number of genes whose expression levels were significantly associated with cell-to-cell electrophysiological heterogeneity within cell types (FDR < 0.1; Figure 3.7A). For example, we found that expression of Kcna1, which encodes the potassium channel Kv1.1, was inversely correlated with AP half-width (Figure 3.7B; BetaPatch-seq = -0.0484 ± 0.0106, FDRPatch-seq = 0.0683) within hippocampal Pvalb and striatum Pthlh cells (the only cell types in which the variability in Kcna1 expression met our threshold for analysis). Importantly, there was also a significant relationship with the same directionality for Kcna1 and AP half-width in the 123AIBS dataset (Betaclass-conditional = -0.048 ± 0.011, FDRclass-conditional = 0.001). Moreover, the relationship between Kcna1/Kv1.1 expression and action potential width has been experimentally reported previously (Brew et al., 2003). As another example, we saw an inverse correlation between Fxyd6 expression and AHP amplitude, based on cortical Lamp5- and striatum Th- cells (Figure 3.7C, BetaPatch-seq = -0.695 ± 0.118, FDRPatch-seq = 0.00841). We also saw a similar relationship in the AIBS dataset (Betaclass-conditional = -0.021 ± 0.003, FDRclass-conditional = 0.00001). Intriguingly, Fxyd6 encodes phosphohippolin, a regulator of Na+/K+ ATPase activity (Delprat et al., 2007) and is thus plausibly involved in the AHP and action potential repolarization. Intriguingly, in a separate single-cell RNA-seq study of CA1 interneurons, Fxyd6 was found to be more highly expressed cells known to spike more slowly (Harris et al., 2018).In general, we found that when a gene-property relationship was statistically significant in both the Patch-seq and AIBS class-conditional analyses (FDR < 0.1), this relationship was usually in the same direction in both analyses (Figure 3.7A; 10 out of 13 gene-property relationships total). Results were similar in the class-independent model, except with a smaller set of gene/ephys relationships matching between both (7 out of 9 relationships were in a consistent direction). All of the genes which were consistent between the class-independent and Patch-seq analyses were also consistent in the class-conditional model. While our analyses of these Patch-seq datasets should be considered preliminary (pending the availability of larger and higher-quality datasets), we find the correspondence with our earlier analysis encouraging. Namely, this analysis suggests that some of the same genes that appear to drive large differences across cortical cell types might also be defining more subtle within-cell type heterogeneity.124Figure 3.7 Assessing gene-property relationships within cell subclasses using Patch-seqA. Number of genes associated with each electrophysiological property based on a joint cross-laboratory analysis of 5 Patch-seq datasets. Genes shown are significant at FDR < 0.1, based on a mixed-effects regression model, treating gene expression as a fixed effect and dataset identity and cell type as random effects. Bar color denotes overlap of Patch-seq based gene-property relationships with analogous relationships from the AIBS class-conditional model 125analysis. Note that analysis of gene-property relationships in the Patch-seq datasets are independent from those in the AIBS cell types analysis.B, C. Examples of genes showing significant associations with electrophysiological features in the class-conditional analysis of the AIBS dataset (left-most panel) and the mixed-effects analysis of the Patch-seq datasets (other panels). Dataset name and cell type is shown in the subpanel title and solid lines indicate linear fits within cell classes (AIBS) or fits within each Patch-seq dataset and cell type, after weighting cells by transcriptome-quality (see Methods). Based on differences in mRNA quantification, x-axis units for AIBS, Cadwell, and Földy datasets are log2 (CPM+1), and for Bengtsson Gonzales, Muñoz, and Fuzik datasets are log2 normalized molecule counts (normalized to 2000 unique molecules per cell). Dataset Description RNA amplification Number of cells AccessionCadwell(Cadwell et al., 2016)Cortical layer 1 interneurons Smart-seq2 57 E-MTAB-4092Fuzik(Fuzik et al., 2016)Cortical layer 1/2 interneurons and pyramidal cellsSTRT-C1 (with unique molecule identifiers) 80 GSE70844Földy(Földy et al., 2016)Hippocampal CA1 and subiculum pyramidal cells and regular- and fast-spiking interneuronsSMARTer 93 GSE75386Muñoz-Manchado(Muñoz-Manchado et al., 2018) Striatum interneuronsSTRT-C1 (with unique molecule identifiers) 99 GSE119248Bengtsson Gonzales Hippocampal CA1 Pvalb-Cre interneuronsSTRT-C1 (with unique molecule identifiers) 19N/ATable 3.4 Description of Patch-seq datasets re-analyzed in this study.Depending on the dataset, RNA amplification was performed using variations on single-cell-tagged reverse transcription (STRT) (Islam et al., 2012) or Switching Mechanism At the end of the 5â-end of the RNA Transcript (SMART) (Zhu et al., 2001). The Bengtsson Gonzales dataset reflects a novel dataset reported here for the first time.1263.2.7 The expected relationship between voltage-gated potassium channels and AHP amplitude is apparent only after accounting for cell classWe next asked whether we see a relationship between an electrophysiological feature and a category of genes which are known regulators of that feature. Voltage-gated potassium channels are known to be involved in producing the after-hyperpolarization following an action potential (Hodgkin and Huxley, 1952a; Stuhmer et al., 1988) (AHP amplitude; illustrated by the dashed arrow in Figure 3.8A). We thus hypothesized that for many of these genes, higher expression levels would be associated with larger AHP amplitudes (although not all voltage-gated potassium channels necessarily contribute directly to AHP amplitude). We further hypothesized that this relationship would be more apparent after accounting for cell class, in part because AHP amplitudes differ considerably between excitatory and inhibitory cell classes (Figure 3.8B-D). Indeed, our previous work found a spurious negative correlation between expression of the Kcnb1 gene and AHP amplitude which resulted from higher expression of Kcnb1 in excitatory cell types compared to others (Tripathy et al., 2017).We evaluated model slopes between each of 29 voltage-gated potassium channel genes (Alexander et al., 2017) and AHP amplitude in the AIBS dataset for each of the class-independent and class-conditional statistical models (examples shown in Figure 3.8B-D and summary in Figure 3.8E). Examples of voltage-gated potassium channel genes associated with AHP amplitude include Kcnh3 (Figure 3.8B) in a class-driven and Kcnh7 and Kcnc2 in a non-class-driven manner (Figure 3.8C, D). In total, the class-independent model identified 17 significant genes (at a stringent threshold of FDR < 0.01), with 8 of these genes having positive slopes and 9 negative. In contrast, there were 12 genes that were significantly associated with AHP amplitude in the class-conditional model at the same statistical threshold, and 11 of these genes had slopes in the positive direction. Thus the results obtained using the 127class-conditional model are consistent with our a priori hypothesis that expression levels of voltage-gated potassium channel genes are more likely to show positive than negative relationships with AHP amplitude, whereas the results obtained using the class-independent approach do not appear to support this conclusion.Figure 3.8 Accounting for cell class changes the interpretation of the relationship between potassium channel expression and after-hyperpolarization amplitudeA. Schematic view of an action potential trace, with the dashed line representing the AHP amplitude value.B-D. Examples of voltage-gated potassium channel genes significantly associated with AHP amplitude in the class-independent model (B), the class-conditional model (C), or both (D) at a threshold of FDR < 0.01. Solid lines indicate a linear fit including only excitatory or only inhibitory cell types, and dashed line indicates a linear fit including all cell types.E. Comparison of class-independent and class-conditional approaches for detecting associations between voltage-gated potassium channels and AHP amplitude. Each point indicates a single gene, and x and y axes are the slopes 128from the class-independent and class-conditional models, respectively. Labeled points are the example genes shown in B-D. Dashed line indicates identity.3.2.8 Evidence of causal support for specific gene-property relationshipsTo further validate the gene-property correlations found in the AIBS dataset, we asked whether any of the same relationships showed direct support in the literature. In some cases we found that previously published work showed that manipulation of the gene of interest caused electrophysiological effects in line with what would be predicted by our analysis.Kcna1, a voltage-gated potassium channel, is significantly related to a number of electrophysiological features in our analysis, including maximum firing frequency (FDR = 0.0002; Figure 3.9A). This finding of a relationship between Kcna1 expression and maximum firing frequency is consistent with a published study on the same gene. Kopp-Scheinpflug et al. (2003) examined mice with a knockout of the Kcna1 gene and found that firing rates in auditory neurons were reduced in the knockouts only at high intensities of an auditory stimulus, and that this difference was more robust in the inhibitory neurons of the medial nucleus of the trapezoid body (MNTB) compared to excitatory ventral cochlear nucleus (VCN) bushy cells (Kopp-Scheinpflug et al., 2003).Expression of Scn1b, a voltage-gated sodium channel subunit, shows a negative relationship with action potential half-width in the class-conditional model (FDR = 0.0008; Figure 3.9B), as well as a number of other properties. This relationship is obscured in the class-independent model due to overall longer half-widths in excitatory cell types. Consistent with the idea that Scn1b might function to shorten AP half-widths, layer 5 cortical pyramidal neurons from mice lacking the Scn1b gene show longer half-widths than controls, due to changes in protein stability of voltage-gated potassium channels (Marionneau et al., 2012).129Interestingly, the Lrrk2 gene, mutations in which contribute to Parkinsonâs disease (Paisán-Ruiz, 2009), is positively correlated with neurite branchiness (number of branch points per μm) in the class-conditional model, but not the class-independent model (FDR = 0.046; Figure 3.9C). Lrrk2 has been shown by several studies to regulate neurite outgrowth and branching in cultures (MacLeod et al., 2006; Dächsel et al., 2010; Häbig et al., 2013; Borgs et al., 2016).Not only do the genes discussed here provide important validation for our method, but the existence of a smooth correlation between these genes and their associated properties is potentially interesting. The previous studies cited above provide causal evidence for gene-property relationships via gain- and loss-of-function approaches, which are likely more reminiscent of pathological states than of natural variability between cell types. Our results suggest that these genes could additionally play an instructive role in setting the precise levels of electrophysiological or morphological properties between cell types under normal physiological conditions. In addition, since morphological features are in part established due to developmental gene expression patterns (Barnat et al., 2017), such features may show poor correlations with mRNA sampled from adult cells.3.2.9 Novel gene-property relationshipsIn addition to those discussed above, we identified many genes whose function in regulating neuronal electrophysiology or morphology is less well characterized. These present testable hypotheses for future study. In Table 3.5, we list some of the top significant genes from the class-conditional model for each property, chosen based on significance levels and/or previous studies into their cellular function (also shown in Figure 3.9D).130One notable feature from this analysis is that many of these genes, like Kcna1 and Scn1b discussed above, are significantly associated with several or many different properties. For example, maximum firing frequency, input-output curve slope, and average interspike interval show a similar pattern in the strength of their association with this set of genes. These features all measure similar aspects of neuronal function (broadly speaking, whether a neuron tends to fire rapidly or not), so it would be surprising if they did not show correlations with the same genes. Two more properties that closely share associated genes are AP half-width and AHP amplitude, which measure distinct aspects of the action potential waveform, but might share genetic underpinnings related to rapid channel opening and closing (Bean, 2007). The genes most strongly associated with various electrophysiological properties tend not to show significant associations with the morphological properties of branchiness and max branch order. However, some of the genes associated with these morphological properties do show some (generally weak) associations with some electrophysiological properties (for example Mgat5 and Ifitm10).Several of the genes for which we were unable to find conclusive loss-of-function studies in the current literature (Figure 3.9E-H) seem particularly intriguing, given what is known about their cellular function. In the discussion, we briefly speculate about how these genes might function as regulators of the properties with which they are associated in our analysis. However, further study will be needed to determine what role, if any, these genes play in regulating electrophysiological or morphological properties.131Figure 3.9 Examples of experimentally supported or otherwise potentially interesting genes132A-C. Examples of genes showing statistically-significant gene-property relationships in the class-conditional model (FDR < 0.1) that also have experimental support for their causal regulation of the property in the literature. Solid lines indicate linear fits including only excitatory or only inhibitory cell types, and dashed line indicates a linear fit including all cell types (also applies to E-H).D. Heatmap showing a subset of the most significant genes for each property in the class-conditional model, sorted along both axes by similarity. Dendrogram represents cross-property similarity between the significance levels for the genes shown here; properties appearing closely linked in the dendrogram are those which are strongly associated with the same genes in our analysis. For each property, up to 3 top genes were chosen that were significant (FDR < 0.1) in the class-conditional model, and also non-significant (FDR > 0.2) in both the class-independent and interaction models for the same property. In addition, genes marked by asterisks are shown here based on their known function based on the literature in addition to at least one significant result in the class-conditional model, shown as scatterplots in A-C and E-H. Light grey indicates a non-significant result in the class-conditional model (FDR > 0.1).E-H. Examples of under-studied but plausibly causal genes showing significant results in the class-conditional model (see text).Property Gene Gene Name FDR DirectionRheobase Slc6a1solute carrier family 6 (neurotransmitter transporter, GABA), member 10.001 +Rheobase Rbms3 RNA binding motif, single stranded interacting protein 0.001 +Rheobase Dlx2 distal-less homeobox 2 0.002 -AP Threshold Arid5a AT rich interactive domain 5A (MRF1-like) 0.008 +AP Threshold Kcnf1 potassium voltage-gated channel, subfamily F, member 1 0.008 -AP Threshold Tuba8 tubulin, alpha 8 0.023 +AP Half-width Krt1 keratin 1 4.3E-07 +AP Half-width Necab2 N-terminal EF-hand calcium binding protein 2 1.8E-06 +133AP Half-width Lrrc20 leucine rich repeat containing 20 2.5E-06 -AP Amplitude Itpr1 inositol 1,4,5-trisphosphate receptor 1 4.0E-06 -AP Amplitude Rac3 RAS-related C3 botulinum substrate 3 5.0E-05 +AP Amplitude Acap2 ArfGAP with coiled-coil, ankyrin repeat and PH domains 2 6.0E-05 -AHP Amplitude Igf1 insulin-like growth factor 1 5.1E-09 -AHP Amplitude Sema3csema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3C1.0E-06 -AHP Amplitude Dusp14 dual specificity phosphatase 14 1.1E-06 +Capacitance Lrrc4c leucine rich repeat containing 4C 7.1E-07 -Capacitance AW551984 expressed sequence AW551984 2.3E-06 +Capacitance Oxtr oxytocin receptor 2.9E-06 +Time Constant Tau Celf6 CUGBP, Elav-like family member 6 1.6E-06 +Time Constant Tau Fam81a family with sequence similarity 81, member A 5.4E-06 -Time Constant Tau Arap2 ArfGAP with RhoGAP domain, ankyrin repeat and PH domain 2 6.6E-05 -Input Resistance Ctxn1 cortexin 1 8.0E-06 +Input Resistance Enc1 ectodermal-neural cortex 1 8.0E-05 +Input Resistance Slc6a1solute carrier family 6 (neurotransmitter transporter, GABA), member 13.2E-04 -Resting Membrane Potential Ehbp1l1 EH domain binding protein 1-like 1 0.012 +Resting Membrane Potential Egfl7 EGF-like domain 7 0.012 +Resting Membrane Potential Tagln3 transgelin 3 0.014 +Sag Kcnf1 potassium voltage-gated channel, subfamily F, member 1 0.064 +Sag Tuba8 tubulin, alpha 8 0.064 -Average Interspike Interval Igf1 insulin-like growth factor 1 4.2E-07 +Average Interspike Interval Arhgef2rho/rac guanine nucleotide exchange factor (GEF) 2 4.9E-06 -Average Interspike Interval Krt1 keratin 1 8.0E-06 +134Max Firing Frequency Igf1 insulin-like growth factor 1 5.9E-12 -Max Firing Frequency Itpr1 inositol 1,4,5-trisphosphate receptor 1 1.9E-09 +Max Firing Frequency Arhgef2 rho/rac guanine nucleotide exchange factor (GEF) 2 2.5E-08 +Input-Output Curve Slope Igf1 insulin-like growth factor 1 3.8E-13 -Input-Output Curve Slope Itpr1 inositol 1,4,5-trisphosphate receptor 1 6.6E-10 +Input-Output Curve Slope Sytl2 synaptotagmin-like 2 4.4E-08 +Adaptation Ratio Igf1 insulin-like growth factor 1 3.2E-04 -Adaptation Ratio Sox2ot SOX2 overlapping transcript (non-protein coding) 3.4E-04 -Adaptation Ratio Fstl5 follistatin-like 5 0.001 -Branchiness Efcab6 EF-hand calcium binding domain 6 0.007 +Branchiness Mthfd2lmethylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2-like0.016 -Branchiness Ifitm10 interferon induced transmembrane protein 10 0.019 -Max Branch Order Mgat5 mannoside acetylglucosaminyltransferase 5 0.017 -Max Firing Frequency Kcna1* potassium voltage-gated channel, shaker-related subfamily, member 1 1.9E-04 +AP Half-width Scn1b* sodium channel, voltage-gated, type I, beta 0.001 -Branchiness Lrrk2* leucine-rich repeat kinase 2 0.046 +AHP Amplitude Rab33a* RAB33A, member RAS oncogene family 0.004 +AHP Amplitude Med23* mediator complex subunit 23 0.057 +AP Half-width Nphp4* nephronophthisis 4 (juvenile) homolog (human) 0.037 -AP Half-width Daam1* dishevelled associated activator of morphogenesis 1 0.097 -Table 3.5 Top correlated genes for each electrophysiological property.Genes marked with asterisks are significantly associated (FDR < 0.1) with the indicated property in the class-conditional model, and selected based on their reported function in the literature. All other genes are significant (FDR < 0.1) in the class-conditional model and non-significant (FDR > 0.2) in both the class-independent and 135interaction models for the indicated property. âDirectionâ indicates the direction of the model slope; for example, high expression of Daam1 in a cell type predicts a low value of AP half-width and vice versa.3.3 DiscussionIn this work we presented a series of correlations between gene expression and electrophysiological or morphological properties, each representing a testable hypothesis for future studies. Our key insight here is to introduce cell class (i.e., excitatory and inhibitory cell type identity) as an indicator variable when modeling the relationship between genes and properties. This has the advantage of 1) avoiding the identification of class-driven correlations, 2) helping identify a subset of non-class-driven correlations that might have been obscured by overall differences between excitatory and inhibitory cell types, and 3) revealing instances where gene-property relationships might be different for excitatory versus inhibitory cell types.Although the idea that non-class-driven correlations would have a higher chance of being biologically relevant compared to class-driven ones seems straightforward, we evaluated this prediction through a number of specific empirical tests. First, we found better correspondence between gene-property relationships from the class-conditional model with those derived from the non-projecting cell type subset of our prior NeuroExpresso/NeuroElectro dataset. Second, we observed consistency between the class-conditional model and gene-property relationships derived from five independently-collected Patch-seq datasets, suggesting that the relationships described here might be predictive of gene-property relationships within narrowly-defined cell types. Third, our analysis of the relationship between action potential after-hyperpolarization (AHP) amplitude and voltage-gated potassium channel genes suggests that genes and electrophysiological features showing a significant result in the class-conditional model are more likely to reflect known functions of those genes.136The Patch-seq and voltage-gated potassium channel analyses highlighted distinct advantages of the class-conditional model. The class-conditional model revealed higher overlap between the Patch-seq and AIBS datasets, compared to the class-independent model, where most shared relationships (for both models) were in a consistent direction. This indicates that the class-conditional model might be more sensitive to certain relationships, which have some evidence for their biological relevance. In contrast, the main advantage of the class-conditional model in the voltage-gated potassium channel analysis was primarily to avoid class-driven correlations. In other words, the class-conditional model exhibits increased specificity, an important factor when considering that these results might be used to help prioritize genes for experimental study.In this work, we have operationalized the concepts of class-driven and non-class-driven correlations as those which produce a significant result in the class-independent model only or in the class-conditional model, respectively. This is a simplification, since both effects can exist simultaneously to differing degrees (for example, Daam1 and AP half-width, Figure 3.9H) and our ability to distinguish them with confidence is limited by the number and composition of cell types in the dataset. It should be emphasized that, since these categories are defined based on significance thresholds, the distinction between, for example, a non-class-driven relationship which is obscured by class and one which is significant in either model is not meaningful in a statistical sense and should not be interpreted as being directly informative about the underlying biology. Bearing this in mind, the distinction may be useful in practice for prioritizing genes for further examination. Thus, we have shown that thresholding the set of all genes based on one model or the other results in the identification of a distinct but overlapping set of genes, meaning that the choice of model is consequential.137A novel feature of our analysis is the investigation of gene-property relationships that are divergent within excitatory and inhibitory cell types. Using the interaction model, we found a small subset of genes showing significant associations in the class-conditional model that also have a significant interaction term, indicating that their relationship with the property in question is dependent on cell class. We also found another small set of gene-property relationships that have a significant term in the interaction but not the class-conditional model. In contrast to all other properties analyzed, for the properties sag and maximum branch order, the interaction model identified many more genes compared to the class-conditional model. One possible explanation is that for both of these features, the absolute slopes in excitatory cells tend to be higher than those in inhibitory cells (shown in Figure 3.4B for maximum branch order), suggesting either that these features might be under stronger genetic control in excitatory types compared to inhibitory, or that the genes associated with them in excitatory cell types are more readily identified by our analysis. Since this dataset contains more inhibitory than excitatory types, an inhibitory-specific relationship may be identified in the class-conditional model by virtue of the number of cell types, but an excitatory-specific relationship would likely be âdilutedâ by the larger number of inhibitory cell types not showing the relationship. It is also possible that, in the case of maximum branch order, this effect is partially explained by methodological differences in the dataset, since inhibitory but not excitatory morphological reconstructions contain axons in addition to dendrites (Gouwens et al., 2018).3.3.1 Novel putative gene/electrophysiology relationshipsOur primary motivation for comparing gene expression to neuronal properties is to identify candidate genes that might influence those properties. While directly testing the functional relevance of specific 138gene-property predictions is beyond the scope of this work, we have highlighted below some of our potentially novel findings that might be of greatest interest for further follow up.Rab33a expression is positively correlated in the AIBS dataset with AHP amplitude with a significant interaction (Figure 3.9E), and also shows significant positive correlations with input-output curve slope, maximum firing frequency, and rheobase, and significant negative correlations with AP half-width and average interstimulus interval (ISI). Rab33a is a small GTPase thought to be involved in regulation of vesicle trafficking, likely at stages prior to plasma membrane docking (Tsuboi and Fukuda, 2006; Nakazawa et al., 2012). One hypothesis for how Rab33a could regulate AHP amplitude and/or AP half-width is that Rab33a might facilitate the transport and/or insertion of vesicles containing voltage-gated potassium channels, or regulators thereof, into the axonal membrane, leading to narrower action potentials and larger AHPs. Our analysis of the AIBS data suggests that any effects of Rab33a expression on AHP amplitude would be present only in inhibitory cell types.Med23 (also known as Crsp3), a subunit of the mediator complex which acts as a transcriptional co-activator for RNA polymerase II (Kelleher et al., 1990; Ryu et al., 1999), shows a positive correlation with AHP amplitude (Figure 3.9F). Although the complete set of roles played by Med23 are incompletely understood, it has been shown to modulate signaling by the BMP, Ras/ELK1, and RhoA/MAL pathways (Yin et al., 2012; Zhu et al., 2015). Thus it has the potential to regulate a variety of genes, including potentially voltage-gated potassium channels or interacting proteins thereof. Given Med23âs role in regulating transcription through a variety of signaling pathways, it is notable that our analysis showed only one feature with which it was convincingly associated. It is also interesting to note that mutations in Med23 have previously been associated with intellectual disability, in some cases with a predisposition to seizures (Hashimoto et al., 2011; Trehan et al., 2015).139Expression of Nphp4 encoding the cytoskeletal-associated protein nephrocystin-4 was negatively correlated with AP half-width (Figure 3.9G) as well as with resting membrane potential and maximum firing frequency. Although Nphp4 is primarily understood for its function in the kidney, Nphp4 mutations often cause co-morbid deficits in the nervous system (Hoefele et al., 2005). Furthermore, Nphp4 has been shown to regulate actin networks via its interaction with the polarity protein Inturned and with the formin Daam1 (Yasunaga et al., 2015). Daam1 is also negatively correlated with AP half-width (Figure 3.9H), and not significantly correlated with any other features. The actin network in the axon forms a highly regular lattice structure which includes regularly interspersed voltage-gated sodium channels (Xu et al., 2013). A similar relationship between the actin network and other voltage-gated ion channels has not been tested, but seems plausible. A potential mechanism through which Nphp4 and Daam1 could regulate the shape of the action potential might involve the organization of the axonal actin network structure, which might change the local levels or relative positioning of voltage-gated ion channels, especially potassium channels, or their regulators.3.3.2 Limitations and CaveatsWe note that the gene-property relationships reported here are by definition correlational. Demonstrating that any specific gene is involved in regulation of any electrophysiological or morphological property is beyond the scope of this work. Our goal in this study was to generate testable hypotheses which, together with the current body of published literature, will help guide future experiments. We expect that this list of putative relationships contains some proportion of causal genes, and based on our analyses expect that this proportion may be higher than that in our previous work (Tripathy et al., 2017), However, causality can only be determined for a given gene and property using direct experimental methods.140Additionally, as in our prior work (Tripathy et al., 2017), we have limited our analyses to models in which expression levels of a single gene predict downstream properties in an approximately linear fashion, and in which that gene is regulated primarily at the transcriptional level. Some instances of mechanisms involving interactions between multiple genes, or those involving a non-linear relationship between log-gene expression and an electrophysiological or morphological property, are likely to have been missed here. In addition, for mechanisms through which electrophysiological or morphological properties are controlled at the translational or post-translational level, our analysis is unlikely to provide insight into the gene whose product directly controls the property. However, this analysis has the power to identify transcripts whose products are involved in the translation, modification, or trafficking of proteins which in turn regulate electrophysiology or morphology.Furthermore, the generalizability of the gene-property relationships reported here might be limited by the fact that the AIBS dataset only reflects cells sampled from the adult mouse primary visual cortex. Therefore, the relevance of our results to other brain regions depends on the assumption that many of the same genes regulate electrophysiological or morphological properties in different cell types. This assumption of generalizability across brain areas appears to be appropriate in the case of Kcna1 and maximum firing frequency (Figure 3.9A and (Kopp-Scheinpflug et al., 2003)). Additionally, this assumption is supported by our comparisons with the NeuroExpresso/NeuroElectro dataset and Patch-seq datasets, both of which contain cells sampled from other brain regions. However, some relationships may not generalize across brain regions due to differences in expression of other genes or the presence of post-translational modifications which modify the consequences of expressing a given gene. 141Another potential confounding factor in our reliance on the AIBS datasets is the uneven balance in the count of inhibitory versus excitatory cell types. The practical consequence of this is that the results from the class-conditional model are likely biased towards explaining gene-property relationships within inhibitory cell types, and might be missing relationships that are specific to excitatory cell types. Even in the absence of a significant interaction term, gene-property relationships may have stronger evidence in one cell class than the other. An example of this is Lrrk2 and branchiness (Figure 3.9C), where despite very similar slopes between classes and no statistical evidence of an interaction, the correlation among excitatory cells is much tighter than that among inhibitory cells. For this reason, when prioritizing genes for future study, we strongly recommend making a plot of gene, property, and cell class before concluding that the overall result is likely to apply to both classes.3.3.3 Future DirectionsThe primary goal of this project was to produce a list of genes which we can recommend for future study based on their correlations with electrophysiological and morphological properties in the AIBS dataset. We believe that some of the genes we identified are promising candidates for future study. In order to facilitate the use of our results by others in prioritizing genes for investigation, we are providing a Jupyter Notebook file to facilitate exploration of the data (available at https://github.com/PavlidisLab/transcriptomic_correlates). We have endeavored to make this easy to use for researchers with little or no coding experience. We encourage those who are interested in a particular electrophysiological or morphological property, gene, or set of genes, to explore the data and to make their own judgements as to which genes are worth following through on experimentally and which measures should be prioritized for recording. Our recommendation is to use the gene list in conjunction with other sources of information about gene function, such as Gene Ontology annotations 142(Ashburner et al., 2000; Carbon et al., 2017) and previously published literature, in prioritizing genes for future study.3.4 Materials and Methods3.4.1 AIBS DatasetThe RNA-seq dataset from (Tasic et al., 2018) was accessed via the Allen Institute for Brain Scienceâs Cell Types database (http://celltypes.brain-map.org/) on June 19, 2018, and contains 15,413 cells isolated by microdissection and fluorescence-activated cell sorting from primary visual cortex of mice expressing tdTomato under the control of various Cre driver lines. Electrophysiological and morphological data were also accessed via the Allen Institute for Brain Science Cell Types database on June 21, 2018. The dataset includes electrophysiological recordings from 1920 cells, of which 1815 are reporter-positive, from the visual cortex of mice also expressing tdTomato driven by Cre, many of which are from the same lines represented in the RNA-seq dataset. A subset of these cells (509, of which 471 are reporter-positive) have morphological reconstruction data available. Cells in both the electrophysiology/morphology and RNA-seq datasets are annotated according to the cortical layer they reside in (for electrophysiology/morphology this is always a single layer, and for RNA-seq may be a single layer, subset of layers, or all layers), their Cre-line, and whether they express the reporter.3.4.2 Filtering and matching datasetsSingle-cell RNA-sequencing data, summarized as counts per million reads sequenced (CPM), were log2-transformed prior to combining with electrophysiological and morphological data. Cells from the RNA-seq dataset were excluded if they were annotated as having failed quality control checks, if they were negative for expression of tdTomato, or if they were labeled as non-neuronal or unclassified. Cells 143in the electrophysiology/morphology dataset were excluded if they were negative for expression of tdTomato. 3.4.3 Electrophysiological and morphological measuresElectrophysiological data were downloaded from http://celltypes.brain-map.org/ and summarized as described previously (Tripathy et al., 2017) except for the features response frequency versus stimulus intensity (input-output) curve slope, average interstimulus interval (ISI), and sag, which we did not use previously as they were not represented in the NE dataset. All three of these new features were pre-computed in the downloaded dataset. In order to include only sag values which could be meaningfully compared, any cells having a value of âvm-for-sagâ (the membrane voltage at which sag values were measured) not between -90 and -110 mV, or having a resting membrane potential lower than -80 mV, were excluded from analyses of sag, but were used for analyses of other electrophysiological features. The morphological features âaverage_bifurcation_angle_localâ, âmax_branch_orderâ, âsoma_surfaceâ, âtotal_lengthâ, and âtotal_volumeâ were pre-computed in the dataset. We defined âbranchinessâ according to the pre-computed feature ânumber_branchesâ divided by âtotal_lengthâ as a measure of how often a given cell produces branches per unit of neurite length. For the features input resistance, tau, capacitance, rheobase, maximum firing frequency, AHP amplitude, adaptation ratio, input-output curve slope, latency, branchiness, max branch order, total length, and total volume, values were log10-transformed prior to use in order to mitigate underlying skew or non-normality in these data values.3.4.4 Defining cell typesCell types in the AIBS dataset were defined according to the Cre-line they were isolated from, whether they were excitatory or inhibitory, and in most cases either a single cortical layer or a range of layers. 144Where multiple layer dissections containing a sufficient number of cells were present for a Cre-line in the RNAseq data, we decided on whether and how to combine layers based on the following criteria: 1) producing the maximum number of cell types, 2) producing the most homogenous cell types possible, and 3) producing cell types containing sufficiently large numbers of cells in both the RNA-seq and electrophysiology or morphology datasets. The first two criteria favored splitting layers more finely, whereas the last favored combining layers. Only cell types where both datasets contained at least 6 cells (for the electrophysiology analysis) or at least 3 cells (for the morphology analysis) were included in the final analysis. Cell type definitions, along with the numbers of cells meeting the criteria for each type, are shown in Table 3.1.Splitting cells from certain Cre-lines into multiple types based on their layer location and their identity as excitatory or inhibitory allowed us to increase the number of types in our analysis. Splitting cell types in this way makes biological sense in that cells isolated from the same Cre-line but different layers often belong to different transcriptomically-defined cell types. For example, cells isolated from from the upper cortical layers of Sst-Cre mice primarily belong to the Sst Cbln4 type, whereas the majority of cells from lower layers belong to either the Sst Myh8 or Sst Th types (Tasic et al., 2016). We have further justified this decision based on the fact that there are frequently electrophysiological differences between cells from the same Cre-line but from different layers (examples of three electrophysiological properties are shown in Figure 3.1).After the two datasets were matched, the combined dataset contained 1359 cells belonging to 48 types with electrophysiological data, 369 cells belonging to 43 types with morphological data, and 4403 cells belonging to 50 types with RNA-seq data (Table 3.1). The remaining cells in the original datasets were those whose types could not be matched, either because the Cre-line or layer they were isolated from 145was not sampled in the other datasets, or because the number of cells belonging to that type was below our threshold for the number of cells per type required.3.4.5 Modeling the relationship between gene expression and electrophysiology/morphologyMean expression values for each gene and mean values for each electrophysiological or morphological property were calculated for each cell type as defined above. If more than two cell types showed zero expression of any given gene, those cell types were removed from analyses for that gene. We found this step to be necessary in initial analyses because differences in electrophysiology/morphology among these cell types could not be assessed in relation to differences in gene expression, potentially producing spurious correlations. Any genes for which this left fewer than eight samples were excluded. Out of all genes represented in the RNA-seq dataset, ~26% passed this thresholding step. For the remaining genes, and for each electrophysiological or morphological property, we fit one or more linear models relating the property (P) to expression of the gene (G) and/or cell class (C). Model 1 (P~G; âclass-independent modelâ) attempted to explain the property based on only expression of the gene. For genes which were expressed in both excitatory and inhibitory types, we fit three additional models. Model 2 (P~C) related property to cell class, model 3 related the electrophysiological parameter to the gene and cell class (P~G+C), and model 4 related the electrophysiological parameter to gene, cell class, and an interaction term between gene and cell class (P~G+C+G*C). Models 2 and 3, as well as models 3 and 4, were compared to one another using an ANOVA, resulting in the âclass-conditional modelâ (P~G|C) and âinteraction modelâ (P~G*C|G+C), respectively. Beta coefficients from models 1, 3, and 4 (separately for each cell type) were recorded, as well as p-values from model 1 and from both ANOVAs. Prior to filtering for significantly-correlated genes, false discovery rate (FDR) correction was performed using the Python package statsmodels.sandbox.stats.multicomp.fdrcorrection0 with an 146alpha level of 0.05. Model 2 was also used directly to test for significant differences between cell classes in the value of each property. 3.4.6 Non-projecting class-specific correlations in the NeuroElectro/NeuroExpresso datasetThe NeuroElectro and NeuroExpresso datasets were described previously (Tripathy et al., 2017). In order to limit the dataset to only non-projecting cell types (Zeisel et al., 2018), we chose cells whose major type was annotated as anything other than âPyramidal,â âGlutamatergic,â or âMSNâ. Cells of the types âCtx Htr3aâ and âCtx Oxtrâ were excluded due to their lower transcriptomic quality compared to others in the dataset (Mancarci et al., 2017). After subsetting, 19 cell types remained. Average values were calculated for gene expression and electrophysiological properties across cells within a type, and Spearman correlations were calculated for each combination of gene and electrophysiological property.In order to assess cross-dataset consistency, we calculated a Spearman correlation between the beta coefficients (slopes) resulting from the class-independent or class-conditional model in the AIBS dataset and the correlation values calculated in the NE dataset. If there was a significant positive correlation between the AIBS slope and the NE correlation value, we concluded that the results of the two analyses were consistent (although this does not imply that they were highly consistent). For those comparisons which were consistent, we considered one method to be âmore consistentâ than the other if the AIBS/NE correlation value was higher (with non-overlapping 95% confidence intervals) than that derived using the second method.1473.4.7 Data Analysis and VisualizationAll statistical analyses and data visualization were performed using Jupyter Notebook (Kluyver et al., 2016) and Python 2.7, and the following packages: scipy.stats, numpy, pandas, matplotlib, mpl_toolkits, matplotlib_venn, seaborn, statsmodels.sandbox.stats.multicomp.fdrcorrection0, mygene.Bootstrapped confidence intervals and significance between models for correlations between the NE and AIBS datasets were calculated as follows: Starting with the list of paired correlation values and beta coefficients for a given electrophysiological feature and model (class-independent or class-conditional), in which each pair represented a single gene and each value in that pair was calculated using one of the two datasets, a new list of paired correlation values of the same length was calculated by resampling with replacement. A new Spearman correlation was then calculated based on the resampled list. The resampling procedure was repeated 100 times, and the upper and lower ends of the confidence intervals were calculated by finding the values at the 2.5th and 97.5th percentiles. Significance was determined by finding the difference between each pair of resampled correlations from the two models, and then again finding the values at the 2.5th and 97.5th percentiles. If this interval did not contain zero, the two consistency metrics were said to be significant at p < 0.05.Hierarchical clustering in Figure 3.9D was performed using the seaborn.clustermap tool using the âaverageâ (UPGMA) method and the euclidean metric (Bar-Joseph et al., 2001; Müllner, 2011).3.4.8 Data AvailabilityAnalysis code and processed data will be available at https://github.com/PavlidisLab/transcriptomic_correlates. Included there is a Jupyter notebook file with some recommended steps for filtering and visualizing results, which can be run directly from the userâs 148web browser without any need for installation of software. We have made an effort to make this resource approachable for researchers with little or no coding experience. The Bengtsson Gonzales Patch-seq dataset will be made publicly available.3.4.9 Analysis of Patch-seq datasetsOverview of datasets used. Our analysis of the Patch-seq datasets builds on our analysis described previously (Tripathy et al., 2018). Here, we made use of four previously published Patch-seq datasets that have characterized interneurons of the mouse forebrain, described in detail in Table 3.4. (âCadwell,â âFöldy,â âFuzik,â âMuñozâ; (Cadwell et al., 2016; Földy et al., 2016; Fuzik et al., 2016; Muñoz-Manchado et al., 2018)). Our analysis also includes one novel dataset of 19 Pvalb-Cre positive interneurons recorded in region CA1 of the mouse hippocampus, reported here for the first time. Cells in this dataset (referred to as the Bengtsson Gonzales dataset), were treated, processed, and analyzed using the same methodology as described in (Muñoz-Manchado et al., 2018).Datasets were processed and normalized as described in (Tripathy et al., 2018) with a small number of exceptions. First, datasets employing unique molecule identifiers (UMIs), including the Fuzik, Muñoz and Bengtsson Gonzales datasets, were normalized to a total library size of two thousand UMIs per cell. Similarly, the Cadwell and Földy datasets were normalized to counts per million (CPM), to be more directly comparable with how we have normalized the AIBS datasets here. Second, because Patch-seq sampled cells varied considerably in amount of mitochondrial and other non-coding mRNAs, when normalizing cells to the total count of reads detected in each cell, we only quantified reads mapping to protein coding genes, as defined by biomaRt (Smedley et al., 2015). Furthermore, we used biomaRt to help reconcile gene names between Patch-seq datasets.149Assigning Patch-seq single cells to transcriptomically-defined cell types. We implemented a nearest-centroid classifier to map Patch-seq transcriptomes to transcriptomically defined clusters, as defined in the Tasic 2018 cortical and Muñoz-Manchado 2018 striatum reference atlases. Specifically, for each transcriptomically-defined cluster in these reference datasets, we first calculated the mean expression level across all cells assigned to the cluster. Next, using the two thousand most variable genes amongst inhibitory cell types in the Tasic dataset (described in the section below), we calculated the Spearman correlation of each Patch-seq cell to every cluster in the dissociated cell dataset and assigned cells to the cluster that they were most correlated with (we compared all Patch-seq datasets except the striatum Muñoz dataset to the Tasic cortical dataset). For cortical and hippocampal cell types, to increase the number of cells defined per transcriptomic type, we made use of the âsubclassâ mappings provided in the Tasic 2018 dataset, mapping neurons to the Pvalb, Sst, Vip, Lamp5, and Sncg major interneuron cell types. To estimate transcriptome quality we used the âquality scoreâ metric from our prior analysis, using the full set of âonâ and âoffâ marker genes.Identifying highly variable genes per cell type. We used the âdecomposeVarâ function from the âscranâ R package (Lun et al., 2016) to identify highly variable genes in each subclass in the Tasic 2018 dataset and each cell type in the Muñoz-Manchado reference datasets.Mixed effects statistical model to identify gene-property relationships in Patch-seq cell types. We used a mixed effects model of the following form with gene expression as a fixed effect and dataset and cell type as random effects: m1 = ephys_prop ~ Beta*log2(norm_gene_expr) + (1|dataset*cell_type)150where we used an anova to test for the significance of the beta associated with the gene expression term by comparison to an equivalent statistical model without the gene expression term. We used the quality score as a weight in the regression analysis, and normalized these across datasets. We used the âlmerâ function within the âlme4â R package for fitting mixed-effects models. We performed this analysis on the top 250-most variable genes per cell type and for genes that were highly variable in at least one cell type across at least 2 (of the 5 total) Patch-seq datasets used here. In addition, we did not use Patch-seq cell types where gene expression was detected in fewer than 33% of cells and with fewer than 5 cells expressing the gene.151Chapter 4: Conclusion4.1 Summary of findingsIn Chapter 2, I presented several key findings related to PTPÏ and its role in inducing presynaptic differentiation. First, I identified several functions of PTPÏ which are dispensable, at least in our culture system, for induction of synapses. PTPÏ does not need an active phosphatase domain to induce synapses, it does not need to bind directly to the scaffolding protein caskin, and it does not need to bind to extracellular HSPGs. Second, I showed that both of the intracellular domains of PTPÏ are necessary, and that PTPÏ needs to bind directly to liprin-α in order to induce new synapses. The requirement for binding between PTPÏ and liprin-α is consistent with the finding that knockdown of liprin-α2 and liprin-α3 disrupts activity in the coculture assay (Han et al., 2018). Third, I presented evidence that, contrary to prior reports that the D2 domain of the LAR-RPTPs is responsible for binding to liprin-α (Serra-Pagès et al., 1995), binding appears to be mediated by a combination of the D1 and D2 domains. Even though the D2 domain seems to be the primary mediator of binding to liprin-α, the D1 domain is likely also involved, based on the intermediate loss of PTPÏ/liprin-Î± co-clustering in heterologous cells as a result of deleting the D1 domain compared to the complete loss when D2 is deleted. Furthermore, loss of the D1 domain either by deletion or by replacement with a second D2 domain abolishes synaptogenic activity.In Chapter 3, I presented an approach for combining datasets measuring different data modalities from different animals in order to correlate gene expression with measures of electrophysiological or morphological measures, in a manner that avoided the confounding influence of cell class on the results. My main hypothesis was that controlling for cell class in this manner would lead to results 152which were more likely to represent true causal relationships, compared to a model in which cell class was not considered. While the ideal method for confirming this prediction would be to perform loss-of-function experiments on a large number of genes predicted by each model, this approach would have been quite time- and labor-intensive. As a substitute, I compared the two models in terms of their consistency with an independent dataset and found that there was higher consistency for most electrophysiological properties tested. As a second approach to making this comparison, I asked whether the results from each model would be consistent with the hypothesis that genes encoding voltage-gated potassium channels would be more likely to show positive rather than negative correlations with AHP amplitude. I found that the results of my analysis supported this conclusion only when I controlled for cell class. Additionally, my results were somewhat consistent with gene expression and electrophysiological measurements collected from single cells belonging to a single cell type using the newly developed Patch-seq methodology.4.2 Future directions4.2.1 PTPÏ-mediated synaptogenesisSeveral key questions are raised by the results presented in Chapter 2 with regard to PTPÏ and its downstream binding partners. Given my finding that PTPÏ must bind to liprin-α, together with a previously reported finding that liprin-α itself is required for PTPÏ-mediated synaptogenesis (Han et al., 2018), an obvious next step would be to understand the specific role of presynaptic liprin-α in the context of the earliest steps of synapse formation. Since liprin-α has a number of different binding partners, some of which are key components of the presynaptic apparatus, a single most likely mechanism is not obvious. Disrupting interactions between liprin-α and its various interaction partners, either by point mutagenesis or by the use of blocking peptides or antibodies, might clarify this issue. 153Such experiments might reveal that some liprin-α binding partners and not others need to bind. Alternatively, since many of these components are involved in multiple binding interactions, forming a âwebâ within the presynaptic compartment, it might be that none of these interactions are individually required, and that each component of the presynaptic compartment may be recruited via several different pathways.Second, the importance of caskin and trio is not entirely clear at this point. I was able to show that disrupting the binding interaction between PTPÏ and caskin, in contrast to liprin-α, did not disrupt synaptogenic activity. However, since caskin can still be recruited to presynaptic sites when it is unable to bind to PTPÏ, we cannot rule it out as a necessary component for a synapse to form. Additionally, I was unable to test the importance of trio since I did not find point mutants of PTPÏ which disrupted this interaction. Further study will be needed to understand the functional significance of both caskin and trio in the context of presynaptic induction.Third, the structural basis for binding of PTPÏ to both liprin-α and caskin is not clear. The idea that liprin-α and caskin might compete for binding to PTPÏ is consistent with my finding that the point mutations which disrupt both interactions lie very close together on the protein surface, and is also supported by a published finding that PTPÏ is unable to act as a âbridgeâ between liprin-α and caskin in a yeast three-hybrid assay (Weng et al., 2011). My results also suggest that the D1 domain of PTPÏ is likely involved in binding at least to liprin-α, in contrast to previous work (Serra-Pagès et al., 1995). Quantitative measurement of the strength of the interaction between PTPÏ either full-length or the D2 domain only and liprin-α or caskin would resolve this issue. Crystal structures of both of these complexes would also shed light on whether and how the D1 domain is involved, as well as addressing the potential existence of overlapping binding sites for liprin-α and caskin. If the D1 domain is indeed 154required for binding to liprin-α, this could explain why deleting this domain disrupted synaptogenic activity, despite neither phosphatase activity nor PTPÏ homodimerization being required. If not, this is another question that will require further investigation.4.2.2 Relating neuronal gene expression and phenotypesAs the goal of the study presented in Chapter 3 was essentially to generate testable hypotheses, the most immediate next step would be to test the functional relevance of some of the gene-property correlations reported here in setting electrophysiological or morphological properties of neurons. Prioritizing the large number of significantly-associated genes will likely depend on a combination of factors, including the strength of the association, previous literature related to the gene, presence or absence of an interaction effect, and individual researchersâ interests. Although there were thousands of significant genes for some properties when I used a significance threshold of FDR < 0.1, filtering results using a much more stringent threshold would be a good first step when dealing with a property with many genes associated, such as AHP amplitude. Many of the genes reported have little or nothing reported on them in the literature, while others already have an established role in regulating electrophysiological or morphological properties based on experimental results. The former category is exciting since these results might represent novel functions of unknown genes if supported experimentally, while the latter provides useful validation for the method, but does not contribute new information about gene function. Perhaps the most interesting set of genes are those which fall in between these two categories, having no known function that would explain the correlation with the property in question, but some previous literature related to their function. For some of these genes, it is possible to speculate about a mechanism that might link what is known about that geneâs function to the property with which it was correlated in my analysis. 155The presence or absence of interaction effects is an important consideration for two different reasons. One approach to prioritizing genes, if the goal is simply to easily identify genes related to neuronal function, would be to avoid gene-property relationships showing evidence of interaction effects. Such relationships might be expected to be more complex and context-dependent compared to those without interaction effects, and might be more likely to be brain region- or age-specific. The âlow-hanging fruitâ in this case might be the genes with a more universal function in regulating electrophysiological or morphological properties in all, or at least many, types of neurons. These genes would be expected to show a similar correlation in excitatory and inhibitory cell types, and thus not to show an interaction. There is also the issue of statistical power, since a relationship which appears in all cell types is supported by a larger number of data points than one which exists in only one class (especially if that class is excitatory cell types, of which there are fewer in the AIBS dataset). At the same time, relationships where an interaction exists might turn out to be the most interesting. If a gene-property pair showing an interaction does turn out to represent a true causal relationship, what is the mechanism behind the cell class specificity? Perhaps an ion channel regulatory subunit which increases channel conductance in the presence of a second regulatory subunit either decreases conductance, or has no effect, in the absence of the second subunit. Perhaps a transcription factor serves as either an activator or a repressor depending on the cell type and the other factors expressed in that cell. These mechanisms, while potentially more difficult to study, may be more interesting than those without interactions.One rather disappointing technical limitation in this study was the fact that, although the cells in the RNA-seq dataset were classified into specific, narrowly-defined cell types based on their expression of a host of marker genes, I was not able to use this data, and instead had to rely on the less sophisticated 156method of defining cell types according to Cre-line, layer, and whether they were excitatory or inhibitory. This limitation arose because there was no way of knowing the transcriptomic types of the cells in the electrophysiology/morphology dataset. The Patch-seq method, in which electrophysiological recordings, and potentially morphological tracing and reconstruction, are taken from a single cell, and at the end of recording (and dye injection, if performed) the cytosol is removed and the RNA contained within sequenced (Cadwell et al., 2016), seems at first glance to be a solution to this problem. Both, or all three, data modalities could be collected from the same cells and the correlation between the two determined. However, this method is still very much in its infancy, and the quality of the sequencing results is susceptible to contamination and other issues (Tripathy et al., 2018). A third, hybrid approach seems like it would be possible given current technology, and at this point in time would likely only be limited by the small amount of Patch-seq data which has been collected. In this hypothetical approach, cells processed using the Patch-seq method would be assigned to cell types based on their transcriptional profiles. Next, cells belonging to each type would be averaged with one another, as I did in this study. The same would be done for the AIBS RNA-seq data (or a similar dataset), again based on transcriptional profiles. Third, the datasets would be combined and gene-property relationships assessed. The advantage of this approach over the one I performed would be that cell types would be rigorously defined, rather than relying on expression of Cre reporters, meaning that cells within a type would likely be much more homogenous and the correspondence between the cells belonging to a specific type in each dataset would be much more similar than the cell types I used. 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Thesis/Dissertation
eng
Neuroscience
Vancouver : University of British Columbia Library
Attribution-NonCommercial-ShareAlike 4.0 International
Graduate
Mechanisms underlying PTP sigma-mediated synaptic differentiation and neuronal diversity
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