Science, Faculty of
DSpace
UBCV
MacDonald, Haley
Master of Science - MSc
University of British Columbia
Controlled progression through and exit from the cell cycle is essential for normal development and healthy tissue function, while cell cycle dysregulation and sustained proliferation is a key hallmark of cancer. With the advent of large-scale single cell transcriptomics, a variety of methods were developed to detect cells in distinct phases of the cell cycle, including G1, S and G2/M, but characterization of non-cycling cellular states and their role in tumour evolution remains limited. Current tools for inferring cell cycle phase from single cell RNA sequencing (scRNA-seq) datasets have not been comprehensively benchmarked, and most are not compatible with heterogeneous samples containing multiple cell types. Here we present Ouroboros, a comprehensive package to accurately identify cell cycle phase and non-cycling cellular states in scRNA-seq datasets. Ouroboros facilitates integrated cell cycle visualization through the generation of representative cell cycle UMAPs and provides a random forest model that outperforms existing cell cycle tools while classifying two additional non-cycling states. Applied to heterogeneous developmental and tumour datasets, Ouroboros reveals complex cell state dynamics over pseudotime and between patient treatment time points. By enabling comprehensive visualization and classification of cycling and non-cycling cells, we anticipate that Ouroboros will offer a means to explore the complex interplay between cellular proliferation and quiescence in health and disease.
RESOLVING CELL CYCLING AND NON-CYCLING DYNAMICS IN SCRNA-SEQ by Haley MacDonald B.Sc., The University of Guelph, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Bioinformatics) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2025 © Haley MacDonald, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Resolving cell cycling and non-cycling dynamics in scRNA-seq submitted by Haley MacDonald in partial fulfilment of the requirements for the degree of Master of Science in Bioinformatics Examining Committee: Dr. Adi Steif, Department of Medical Genetics, UBC Supervisor Dr. Marco Marra, Department of Medical Genetics, UBC Supervisory Committee Member Dr. Yongjin Park, Department of Pathology and Laboratory Medicine, UBC Supervisory Committee Member iii Abstract Controlled progression through and exit from the cell cycle is essential for normal development and healthy tissue function, while cell cycle dysregulation and sustained proliferation is a key hallmark of cancer. With the advent of large-scale single cell transcriptomics, a variety of methods were developed to detect cells in distinct phases of the cell cycle, including G1, S and G2/M, but characterization of non-cycling cellular states and their role in tumour evolution remains limited. Current tools for inferring cell cycle phase from single cell RNA sequencing (scRNA-seq) datasets have not been comprehensively benchmarked, and most are not compatible with heterogeneous samples containing multiple cell types. Here we present Ouroboros, a comprehensive package to accurately identify cell cycle phase and non-cycling cellular states in scRNA-seq datasets. Ouroboros facilitates integrated cell cycle visualization through the generation of representative cell cycle UMAPs and provides a random forest model that outperforms existing cell cycle tools while classifying two additional non-cycling states. Applied to heterogeneous developmental and tumour datasets, Ouroboros reveals complex cell state dynamics over pseudotime and between patient treatment time points. By enabling comprehensive visualization and classification of cycling and non-cycling cells, we anticipate that Ouroboros will offer a means to explore the complex interplay between cellular proliferation and quiescence in health and disease. iv Lay Summary The cell cycle is the molecular process in which one cell divides into two. This process is divided into phases, and it is also possible for a cell to be in a non-cycling state. Controlling when, how, where and why a cell enters or exits the cell cycle is important for healthy tissue function. ScRNA-seq allows us to capture the cell cycle state of each individual cell in a dataset, but there are currently no computational tools to reliably detect non-cycling cells and very few that can handle datasets with multiple cell types. Here we present Ouroboros, a computational tool that detect cell cycle phases and non-cycling cells in datasets with multiple cell types. This tool can help us better understand the biological circumstances in which cells either do or do not divide and can lead to insights about diseases that occur when cells are cycling abnormally, such as cancer. v Preface This thesis is original, unpublished, independent work by the author, H. MacDonald. Core concepts, guidance and suggestions for this project were provided by my supervisor A. Steif. The work reported in Chapters 2-3 was covered by the UBC general lab ethics certificate for analysis of human data H23-00379. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xiii Acknowledgements .................................................................................................................... xiv Chapter 1: Introduction ............................................................................................................... 1 1.1 Molecular regulation of the cell cycle .................................................................... 1 1.1.1 Cell cycle phases and the cyclin-CDK complexes that drive them ............ 1 1.1.2 Cell cycle checkpoints and negative regulation .......................................... 2 1.1.3 Non-cycling states: quiescence, senescence and apoptosis ........................ 3 1.2 Cell cycle involvement in other cellular functions ................................................. 4 1.2.1 Cell cycle involvement in embryogenesis .................................................. 5 1.2.2 Cell cycle involvement in differentiation ................................................... 5 1.2.3 The cell cycle in adult stem and differentiated cells ................................... 6 1.3 Cell cycle involvement in cancer ............................................................................ 7 1.3.1 Cell cycle control is involved in all hallmarks of cancer ............................ 7 vii 1.3.2 Cell cycle checkpoints are frequently dysregulated in cancer .................... 8 1.3.3 Conventional chemotherapies target cycling cells ...................................... 9 1.3.4 Quiescent cancers cells can contribute to recurrence and metastasis ......... 9 1.3.5 Cell cycling landscapes in high grade serous ovarian cancer ................... 10 1.4 Molecular approaches to distinguishing cell cycle phase ..................................... 11 1.4.1 DNA-content FACS sorting as a proxy of cell cycle phase ...................... 12 1.4.2 FUCCI system fluorescence indicates cell cycle phase ............................ 12 1.4.3 Molecular markers of G0 .......................................................................... 15 1.5 G0 as a catch-all term for non-cycling states ........................................................ 16 1.6 Single cell RNA sequencing allows profiling of single cell state ......................... 17 1.6.1 ScRNA-seq technical basics ..................................................................... 17 1.6.2 ScRNA-seq data analysis .......................................................................... 20 1.7 Exploring the cell cycle in scRNA-seq ................................................................. 25 1.7.1 Visualizing cell cycle phases and dynamics in scRNA-seq datasets ........ 26 1.7.2 Limitations of existing scRNA-seq cell cycle calling tools ...................... 26 1.7.3 There are two approaches to cell cycle calling in scRNA-seq .................. 27 1.7.4 Limitations of benchmarking analyses for existing cell cycle tools ......... 30 1.8 Random forests ..................................................................................................... 31 1.8.1 Training a random forest ........................................................................... 31 1.8.2 Advantages of random forests .................................................................. 32 1.8.3 SHAP values can be used to improve random forest feature selection .... 33 1.8.4 Gradient boosting ...................................................................................... 34 1.9 Project motivation, hypothesis and aims .............................................................. 34 viii 1.10 Ouroboros improves cell cycle visualization and classification in scRNA-seq ... 35 Chapter 2: Results ....................................................................................................................... 37 2.1 CC UMAPs improve visualization of cell cycle and G0 dynamics ...................... 37 2.2 Phase labelling paradigm allows non-FACS datasets to be classified .................. 41 2.3 Benchmarking reveals low accuracy of existing cell cycle calling tools .............. 44 2.3.1 Benchmarking in homogenous datasets with no G0 cells ........................ 44 2.3.2 Benchmarking in complex G0-containing dataset .................................... 46 2.4 Ouroboros classifies cell cycle phase and G0 with high accuracy ....................... 47 2.4.1 Shapley values allow assessment of phase-specific feature importance .. 48 2.4.2 Some feature genes are not annotated cell cycle genes ............................ 49 2.4.3 Cross-validation to assess RF performance on training dataset ................ 50 2.4.4 RF performance on mESC benchmarking dataset .................................... 52 2.5 Ouroboros recapitulates non-cycling and cycling cell dynamics in complex datasets .............................................................................................................................. 54 2.5.1 Expression of S and G2M markers in respective Ouroboros phases ........ 55 2.5.2 Proportions of cycling cells decrease over developmental pseudotime .... 57 2.6 Ouroboros resolves cycling landscapes in HGSOC .............................................. 59 2.6.1 CC UMAPs and RF reveal discrete phases in heterogenous HGSOC dataset ................................................................................................................... 60 2.6.2 Other tools fail to recapitulate distinct cell cycling and G0 states in HGSOC ................................................................................................................. 65 2.6.3 Ouroboros resolves post-platinum cycling population shifts in HGSOC . 69 2.6.4 Percent of non-G0 cells in naïve tumours is associated with low PFI ...... 71 ix Chapter 3: Methods .................................................................................................................... 73 3.1 Raw data processing ............................................................................................. 73 3.2 Generation of a master cell cycle gene list ........................................................... 74 3.3 Conversion of mouse genes to human orthologs .................................................. 75 3.4 Generation of CC UMAPs .................................................................................... 75 3.5 Labelling of non-FACS datasets with phase-assignment paradigm ..................... 76 3.6 Benchmarking of Tricycle, DeepCycle and Scanpy ............................................. 77 3.7 Ouroboros random forest creation, training and evaluation ................................. 78 3.7.1 Building a random forest training dataset ................................................. 78 3.7.2 Feature selection ....................................................................................... 79 3.7.3 Hyperparameter tuning ............................................................................. 79 3.7.4 Cross-validation for performance evaluation ............................................ 80 3.7.5 Application to non-training datasets ......................................................... 80 3.7.6 Gradient-boosting overfit to training dataset ............................................ 81 3.8 Assessing Ouroboros performance on the neurogenesis dataset .......................... 81 3.9 Determining Ouroboros accuracy in HGSOC ...................................................... 82 3.9.1 Expression of S and G2M markers in Ouroboros phases ......................... 82 3.9.2 Proportions of cycling and G1-G0 transition cells pre- and post-NACT . 82 3.9.3 Correlation between non-G0 cells and PFI ............................................... 83 3.10 Data and code availability ..................................................................................... 83 Chapter 4: Conclusion ................................................................................................................ 85 4.1 Ouroboros: a comprehensive framework to investigate cell cycle and G0 dynamics in scRNA-seq .................................................................................................... 85 x 4.2 Cell cycling landscapes are related to HGSOC patient outcomes ........................ 86 4.3 Future directions and limitations .......................................................................... 87 References .................................................................................................................................... 89 xi List of Tables Table 2.1 Cross-validation metrics of RF performance on training dataset ................................................................ 50 Table 3.1 GEO and SRA IDS for all datasets mentioned in this thesis ....................................................................... 81 xii List of Figures Figure 2.1 CC UMAPS enable visual integration of cycling cells across cell types in a complex differentiation dataset ........................................................................................................................................................................... 40 Figure 2.2 Phase labelling paradigm . ......................................................................................................................... 43 Figure 2.3 CC umaps showing tool classification output on the benchmarking datasets . ......................................... 45 Figure 2.4 Confusion matrix showing accuracy of benchmarked tools ....................................................................... 46 Figure 2.5 Benchmarking tools applied to pancreas dataset . ...................................................................................... 47 Figure 2.6 Absolute shap values for the top 15 RF features for each phase ................................................................ 49 Figure 2.7 Confusion matrix displaying cross-validation results of RF. ..................................................................... 51 Figure 2.8 Phase calling accuracies of cell cycle calling tools on mescs .................................................................... 53 Figure 2.9 Confusion matrix displaying rf accuracy on mesc dataset ......................................................................... 54 Figure 2.10 CC UMAP of neurogenesis ...................................................................................................................... 56 Figure 2.11 Expression of S and G2M marker genes in Ouroboros phases - neurogenesis ........................................ 57 Figure 2.12 Cell type and phase proportions over neurogenesis pseudotime .............................................................. 58 Figure 2.13 HGSOC HVG UMAP and CC UMAP. .................................................................................................... 61 Figure 2.14 Marker gene expression in HGSOC CC UMAP. ..................................................................................... 62 Figure 2.15 Expression of marker genes in Ouroboros phases - HGSOC. .................................................................. 63 Figure 2.16 CC UMAPS of pre- and post-treatment malignant HGSOC cells. ........................................................... 64 Figure 2.17 Scanpy phase assignments on HGSOC CC UMAP. ................................................................................ 66 Figure 2.18 Expression of S and G2M marker genes in Scanpy phases. ..................................................................... 67 Figure 2.19 Tricycle phase assignments on HGSOC CC UMAP. .............................................................................. 68 Figure 2.20 Expression of S and G2M marker genes in Tricycle phases .................................................................... 69 Figure 2.21 Proportions of non-G0 cells pre- and post-treatment .............................................................................. 71 Figure 2.22 Relationship between proprotion of malignent non-G0 cells and PFI. .................................................... 72 Figure 2.23 Relationship between proportion of stomal and immune non-G0 cells and PFI. ..................................... 72 Figure 3.1 CC UMAPs of FACS sorted datasets ......................................................................................................... 77 xiii List of Abbreviations CDK – Cyclin dependent kinase CDT1 – DNA replication factor 1 ESC – Embryonic stem cell EOC – Epithelial ovarian cancer FACS – Fluorescence activated cell sorting G1 – Gap 1 G2 – Gap 2 HGSOC – High grade serous ovarian cancer HVG – Highly variable gene M – Mitosis ML – Machine learning mESC – Mouse embryonic stem cell RF – Random Forest S – Synthesis scRNA-seq – Single cell RNA sequencing xiv Acknowledgements I would like to express my deepest appreciation to my supervisor, Dr. Adi Steif for allowing me to be her first student. Her support, encouragement and passion inspired me and kept me motivated, and her kindness kept me grounded. Thank you for the best Master’s experience one could ever hope for. Many thanks also to my committee members Dr. Marco Marra and Dr. Yongjin Park, who’s continued support, suggestions and insightful questions helped me see the broader scope of this work. Thank you to all members of the Steif lab, Marra and Gorski labs for your friendship and helpful feedback throughout this process, without which my academic life would not have been so fulfilling. Thank you to my partner Daniel for being my constant support through this process and for always making me laugh when I needed it the most. Finally, thank you to my parents for their unwavering support and encouragement that have been the foundation of my life. 1 Chapter 1: Introduction Cellular division is a fundamental process that underpins the continuation of all life. While much is now known about these active phases of the cell cycle,1 less in known about non-cycling states and regulation of the flow into and out of the cell cycle. This thesis outlines a comprehensive framework for the resolution of cell cycle dynamics in single cell RNA sequencing (scRNA-seq) datasets. By improving the resolution of proliferative and non-proliferative states, we offer a means for future research on the role of cell cycle regulation and dysregulation in health and disease. 1.1 Molecular regulation of the cell cycle We begin by exploring the basic molecular events and regulating factors that drive the cell cycle, as knowledge of these events is foundational to understanding cell cycling processes. 1.1.1 Cell cycle phases and the cyclin-CDK complexes that drive them The canonical model of the active cell cycle within multicellular organisms is subdivided into four coordinated phases: Gap 1 (G1), Synthesis (S), Gap 2 (G2) and Mitosis (M). In G1, a cell grows and prepares to divide. In S, a cell replicates its DNA. In G2, a cell continues to grow, and it synthesizes proteins necessary for division. Finally, the cell cycle ends with M and cytokinesis where a cell divides into two daughter cells.2 This process is coordinated by cyclin proteins which bind to and activate their specific cyclin dependent kinase (CDK) catalytic partners.3,4 CDKs are key regulators of the cell cycle which when activated phosphorylate downstream targets to drive cell cycle progression.4 Specific cyclins and CDKs accumulate 2 during different stages of the cell cycle in a cyclical pattern, and their activity drives cell cycle transcriptional and proteomic events.4 In G1, D type cyclins bind CDK4 and CDK6 and phosphorylate the retinoblastoma protein (Rb) family, inactivating them.5 When active, Rb proteins repress transcription of E2F transcription factors, which are necessary for activating the transition from G1 phase to S phase.4 Inactivation of Rb by CyclinD-CDK4/6 complexes releases E2F transcription factors from the control of Rb proteins, and allows them to drive the G1-S transition.4 Cyclin E-CDK2 complexes then also inhibit Rb and further contribute to the G1-S progression.6 At the end of S phase, Cyclin E is rapidly degraded, and Cyclin A takes its place and binds to CDK2.4 This Cyclin A-CDK2 complex functions to activate a number of S-phase related factors, and allows the cell to progress through S phase and into G2.4 Cyclin A then binds CDK1 and allows cells to transition from G2 into M.4 Finally, in M, Cyclin B binds CDK1 and facilitates breakdown of the nuclear envelope and assembly of the mitotic spindle, allowing M to proceed.7–9 1.1.2 Cell cycle checkpoints and negative regulation Within the cell cycle phases several checkpoints exist to ensure that appropriate resources are available and that proper coordination of the division machinery is achieved.10 The first checkpoint exists at the G1-S boundary wherein a cell will choose whether or not to divide.11 This decision is largely based on cell size, nutrient availability, the presence of growth factors and DNA integrity.11 If this checkpoint is activated, a cell may either pause and attempt to rectify the issue or it may exit the cell cycle and enter a non-cycling state.11,12 This checkpoint is often referred to as the ‘restriction point’ and once past it a cell becomes irreversibly committed to 3 completing the cell cycle, regardless of the presence or absence of extracellular signals.13 The second checkpoint exists at the G2-M boundary, and largely involves ensuring that all DNA was copied correctly in S phase and that there is no DNA damage that may affect progression through M phase or that will be passed down to daughter cells.14 When a cell cycle checkpoint activates, cyclin-CDK complex activity is negatively regulated by CDK inhibitors.4,15 The INK family proteins represent one class of CDK inhibitors that function by preventing CDK4 from binding D-type cyclins,4 and the CIP/KIP proteins represent another family that function by inhibiting other members of the CDK family.4 Some E3 ubiquitin ligases are also involved in regulating the expression of many mitotic proteins.4 If DNA damage is the cause of checkpoint activation, the DNA damage response is triggered in which the p53 protein is rapidly activated and stabilized through post-translational modifications.16 p53 is a transcription factor that when activated initiates expression of many genes involved in cell cycle arrest, including the CDK inhibitor p21. p21 inhibits a number of CDKs including CDK1, CDK2, CDK4 and CDK6, and stops them from phosphorylating and inactivating Rb. When Rb becomes active it represses expression of the E2F genes, which ultimately results in the repression of numerous cell cycle genes.17 Via these mechanisms, p53 activation through the DNA damage response pathway can result in either transient or permanent cell cycle arrest, and can even result in cell death via apoptosis.2,18 1.1.3 Non-cycling states: quiescence, senescence and apoptosis There are several non-cycling states a cell may enter, including quiescence and senescence. In quiescence, a cell exits the cell cycle in a temporary, reversible manner typically 4 in response to environmental cues, such as nutrient deprivation or growth factor absence.19,20 In senescence a cell exits the cell cycle permanently.21 Senescence is considered a protective mechanism often triggered in response to cellular stress or by DNA damage or telomere shortening.22 Though largely referred to as two distinct processes in the literature, recent evidence indicates that the quiescence and senescence pathways share key regulators, including Rb, p53 and p21, as well as other CDK inhibitors such as p27.23 A new pathway to senescence via a deepening quiescent phenotype has recently been proposed, and is being called the quiescence-origin model of senescence.24,25 This quiescence-origin model of senescence is presented in addition to the classic model in which senescence is initiated directly within the cell cycle by the DNA damage response, and in which a cell will bypass quiescence completely.26 The stimuli that induce the DNA damage response have also been shown to induce apoptosis, or programmed cell death. Apoptosis, like quiescence or senescence, necessitates exit from the cell cycle and its early stages involve activation of many of the same regulatory genes including p53 and p21.27,28 Later apoptotic regulatory pathways however are distinct from senescence pathways due to the involvement of Caspase proteins which coordinate breakdown of cellular structures leading to cell death.29 1.2 Cell cycle involvement in other cellular functions Control of cell cycle processes is deeply involved in the regulation of other cellular processes, including differentiation,30 embryogenesis,31 wound repair32 and tissue homeostasis.20 Our understanding of the role the cell cycle plays in these processes is still developing, and the 5 study of the regulatory factors that orchestrate these interrelated cellular processes represents an exciting and relatively unexplored area of research. 1.2.1 Cell cycle involvement in embryogenesis The cell cycle is known to play a major role in the timing of key developmental events within metazoan embryos.33 In early embryos, cells cycle through rapid mitotic divisions with no gap phases, oscillating between S and M phases. Embryonic stem cells (ESCs) extracted from the inner cell mass of an early blastocyst are known to display this fast-cycling phenotype. The molecular regulation of the cell cycle within ESCs has also been shown to differ from canonical somatic cell cycle regulation described above. ESCs express much higher levels of cyclins E, A and B and CDKs 1 and 2 than those seen in somatic cells. The levels of E and A cyclins remain consistently high throughout the embryonic cell cycle and do not oscillate like they do in other somatic cell types.9,34 As the embryo approaches gastrulation, the cell cycle slows and gap phases appear and with them cell cycle regulation shifts to the more canonical oscillatory pattern described above.33 This process is thought to dramatically alter the gene expression landscapes and cellular identities in the embryo,35 but more experimental work is needed to elucidate the role of the cell cycle and its regulatory networks in these processes.36 1.2.2 Cell cycle involvement in differentiation The timing of the cell cycle is known to influence the efficiency and timing of differentiation.33 The process of differentiation involves modification to a cell’s transcriptome, 6 chromosome architecture and epigenetic landscape in a highly coordinated manner that produces cell-type specific changes.33 Studies have shown that the cell cycle machinery impacts all of these cellular identity programs, and the expression of specific cell fate determining genes have been shown to be coupled to specific cell cycle regulatory mechanisms.33 Additionally, as cells differentiate and commit to a specialized function, they are known to gradually exit the cell cycle and enter a terminally differentiated post-mitotic state.37 The precise mechanisms linking terminal differentiation to cell cycle exit have yet to be unequivocally established, though the activation of p53 in human ESCs has been shown to promote differentiation.38 When activated in the differentiation context, p53 appears to regulate the cell cycle without inducting senescence or apoptosis.38 1.2.3 The cell cycle in adult stem and differentiated cells Once terminally differentiated, the majority of mature cells in a multicellular organism are held in a non-cycling state, though their ability to re-enter the cell cycle when presented with the appropriate molecular signals appears to be cell-type specific.19 Neurons and cardiomyocytes for example remain permanently arrested upon reaching terminal differentiation, but other cell types such as mature hepatocytes have been shown to re-enter the cell cycle in response to tissue damage.19 Despite their non-cycling phenotypes, terminally differentiated cells are only rarely referred to as quiescent or senescent in the literature. There also exist adult stem cells which do exist in a quiescent state and are referred to as such.39 Adult stem cells reside in small populations throughout the body and give rise to a 7 limited number of cell types that build the tissue within which they reside.39 These cells too are capable of re-entering the cell cycle upon receiving the appropriate extracellular signals,39 and can even stimulate other terminally differentiated cells such as keratinocytes, fibroblasts and endothelial cells to begin cycling for wound repair functions.40–43 The flexibility of adult stem cells and some terminally differentiated cell types in their ability to exit and re-enter the cell cycle highlights the potential diversity of cell cycling states that can be found within mature tissues, and identifying these states may be crucial to understanding the structure and function of these tissues. 1.3 Cell cycle involvement in cancer One of the most fundamental hallmarks of cancer cells is their ability to chronically proliferate. Within normal tissues, growth-promoting signals that instruct entry into and progression through the cell cycle are very tightly regulated to ensure homeostasis of cell number and structure such that normal tissue architecture and function is maintained. Cancer cells however deregulate these signals, and in doing so cause a disease state characterized by tumour growth and expansion.44 1.3.1 Cell cycle control is involved in all hallmarks of cancer The fundamental nature of cancer as a cell cycling disease becomes evident when analyzing Hanahan’s classic hallmarks of cancer through the lens of cell cycling dynamics.44 The first hallmark of cancer is the sustaining of proliferative signaling, which acts to induce entry 8 into and progression through the cell cycle in circumstances where a healthy cell would not cycle. The second is the ability to evade growth suppression mechanisms, which allows a cancer cell to continue cycling when they would not have otherwise. The third is the enabling of replicative immortality, such that a cancer cell may cycle endlessly regardless of biological age or stress factors. The fourth is the ability to resist cell death, which reflects a cancer cell’s ability to bypass cell cycle checkpoints. The fifth is the promotion of angiogenesis, which demonstrates cancer’s ability to induce proliferation in the tumour microenvironment.45 The final hallmark is the activation of invasion and metastasis programs, which demonstrate the control over cell cycle exit and re-entry as cancer cells enter a quiescent state during dissemination before re-entering the cell cycle in the metastatic site.46 1.3.2 Cell cycle checkpoints are frequently dysregulated in cancer Contrary to the profile of complete cell cycle dysregulation that has been attributed to cancers historically, it has recently been found that only specific aspects of cell cycle control need to be disrupted for cancer cells to achieve this consistently proliferating phenotype.3 Interestingly, the proper functioning of most cell cycling processes within cancer cells has been found to be essential for their viability.3 This is likely because if too many cell cycle functions become dysregulated, catastrophic events such as chromosome mis-segregation can occur, which can ultimately lead to cell death.47 As such, it has been found that cancer cells are most often compromised in their ability to exit the cell cycle through a DNA damage checkpoint.3 For example, TP53 (which codes for the p53 protein and plays a key role in cell cycle arrest in response to DNA damage) is one of the most commonly mutated genes across human cancers.48 9 1.3.3 Conventional chemotherapies target cycling cells Given that increased cell cycling is a distinguishing feature in cancer development, many current standard-of-care chemotherapies function to disrupt cell cycle progression.49 Some chemotherapies such as alkylating agents and platinum analogues function to damage DNA and drive the activation of cell cycle checkpoints to ultimately send the cell into senescence or apoptosis.50,51 Other chemotherapies such as antimetabolites or topoisomerase inhibitors block DNA replication and synthesis.52,53 With these mechanisms of action, most chemotherapies are designed to target proliferating cells in S, G2 or M, and the phase a cell is in has been shown to determine the efficacy of anticancer agents.49,54 1.3.4 Quiescent cancers cells can contribute to recurrence and metastasis Interestingly, despite the role of excessive proliferation in cancer, populations of quiescent cancer cells have been identified in several cancer types, including lung,55 breast,56 colorectal,57 glioblastoma58 and ovarian.54,59 These cells have been shown to be highly resistant to conventional chemotherapies that target actively proliferating cells and have been found to re-enter a proliferative state when conditions are more favourable.54,60,61 Given these abilities, quiescent cancer cells are thought to be a mechanism of both metastasis and drug tolerance that can contribute to patient relapse.62 Resolving these quiescent cancer cell populations in tumour datasets is key to understanding their role in treatment resistance and recurrence, and may be an essential step in developing novel, more effective treatments for chemotherapy-resistant cancers. 10 1.3.5 Cell cycling landscapes in high grade serous ovarian cancer Part of our analysis involved investigating cell cycling landscapes in a high grade serous ovarian cancer (HGSOC) dataset.63 Here we outline what is known about the cell cycle in this cancer type. 1.3.5.1 HGSOC is characterized by aberrant cell cycling Ovarian cancers display a high prevalence of TP53 mutations and elevated cyclin D-CDK4/6 activity, both of which can result in the loss of the G1 cell cycle checkpoint.64 This loss can result in DNA-damaged cells progressing through the cell cycle when they otherwise would have been forced to exit.65–68 Consequently, these cells resultantly can accumulate high levels of DNA damage and chromosomal abnormalities that may promote high clonal diversity, a feature that has been associated with poor patient outcomes.69–71 In addition to the dysfunction of cell cycle checkpoints, exit from the cell cycle is also thought to be important for HGSOC progression.72 Some studies have demonstrated the role of quiescence in HGSOC dissemination and metastasis, in which cancer cells are shed from the fallopian tubes and ovaries into the peritoneal space they enter a quiescent state.72 This quiescent state is thought to allow these cells to survive the nutrient poor and hypoxic conditions in the peritoneal cavity, until they can adhere to and establish on new surfaces with more favourable conditions and re-enter the cell cycle, causing metastatic lesions to form.72 These quiescent cells 11 are thought to also be a mechanism of therapeutic resistance,72 but the prevalence and function of quiescent cells in HGSOC is still not fully understood. 1.3.5.2 Platinum chemotherapies affect HGSOC cell cycling The current standard of care for HGSOC patients is platinum-based adjuvant or neo-adjuvant chemotherapy (NACT).73,74 Platinum-based chemotherapies create DNA damage by forming platinum-DNA adducts which result in cell cycle checkpoint activation.75 This treatment has been shown to lead to the accumulation and/or arrest of tumour cells in G2M, and typically results in either DNA repair or exit from the cell cycle into senescence or apoptosis.76 In support of this mechanism, naive tumours have been shown to be enriched in cell cycling pathway gene expression when compared to post-NACT tumours, which tend to downregulate proliferation markers.77 1.4 Molecular approaches to distinguishing cell cycle phase As outlined above, the control of cell cycle exit, entry and progression is intimately related to many other cellular processes and loss of this control can contribute to disease states. In order to study how the cell cycle affects other cellular processes, we first need to be able to identify it in our datasets with the use of molecular techniques. Here we outline two FACS-based methods of separating cells based on their expression of molecular indicators of cell cycle phase. 12 1.4.1 DNA-content FACS sorting as a proxy of cell cycle phase One common approach to identify cell cycle phase in live cells is to use fluorescence activated cell sorting (FACS) to separate cells based on the amount of DNA each cell contains.78,79 Hoechst 33342 is a commonly used DNA stain that does not require cell fixation, and therefore is suitable for use in assays that require live cells.80 Hoechst staining allows quantification of the DNA within each cell, and therefore allows separation into cell cycle phases with the underlying assumption that G2M cells will have double the amount of DNA that G1 cells will have, and S cells will have an amount in between G1 and G2M cells.81 One limitation of this approach however is that G0 cells and G1 cells have the same DNA content, and the two phases cannot be separated from each other using only a DNA stain.81 To separate live G0 cells from G1 cells, a RNA stain such as pyronin Y can be used in addition to the DNA stain.82,83 Some limitations of these assays include difficulties with photobleaching and phototoxicity which may cause cellular stress, as well as non-specific or weak binding that may cause accuracy issues with the fluorescent sensors.84 1.4.2 FUCCI system fluorescence indicates cell cycle phase Another molecular approach to identifying cell cycle phase in live cells is the Fluorescence Ubiquitin Cell Cycle Indicator (FUCCI) system85. The FUCCI system uses fluorescent markers tied to phase-specific proteins that are integrated into biological systems to monitor cell cycle progression. 13 1.4.2.1 Molecular basis of the FUCCI system The FUCCI system was developed in 2008 by Miyawaki et al. and is comprised of fusion proteins between red- and green-emitting fluorescent proteins and the degrons of the cell cycle proteins DNA replication factor 1 (Cdt1) and Geminin.85 Native Cdt1 and Geminin show reciprocal accumulation throughout the cell cycle and are degraded by ubiquitin-mediated proteolysis at different cell cycle phases. Cdt1 accumulates in G1 before being abruptly degraded in S, whereas Geminin accumulates in S and G2M before being degraded in late M and G1. Lentiviral vectors were used to transfect these degron-fluorescent constructs into HeLa cells where they showed phase-specific fluorescence consistent with accumulation of their native cell cycle proteins. The FUCCI constructs are degraded during the same cell cycle stages as their native proteins, resulting in red fluorescence during G1, yellow fluorescence in S and green fluorescence in G2/M.86 This reciprocal expression of the fluorescence markers can be used to FACS discrete cell cycle phases in living cells.87 Several extensions of the FUCCI system have since been developed, including the inclusion of another fluorescent marker comprised of a fusion protein between mVenus (a yellow fluorescent protein) and a defective mutant of p27 (a CDK inhibitor).86 This additional marker allows the visualization of quiescent cells at the G1-G0 transition. Bajar et al. also created FUCCI4, which includes two novel fluorescent markers including mTurquoise linked with an S-phase specific protein and mMaroon linked to histone H-1.0 which allows the visualization of chromosome condensation in M phase.88 FastFUCCI has also been developed which includes the original FUCCI genes under control of a single promoter.89 FastFUCCI results in equal expression of all reporter genes within a cell, which can help reduce noise in the resulting data.90 14 1.4.2.2 Limitations of the FUCCI system FUCCI sensors have been introduced into several model systems including cell lines, Drosophila, zebrafish and transgenic mice,87 and though these established model systems have contributed much to our understanding of cell cycling dynamics, it may still be desirable to integrate FUCCI sensors into novel cellular contexts. Unfortunately, first generation FUCCI models have been shown to display variable expression of the fluorescent markers, likely due to random integration of the expression constructs which results in multiple lines with variable characteristics.87 The variable expression of fluorescence models in first generation FUCCI models introduces a lot of noise to the system, and can make determining the true cell cycle phase of each cell very challenging. These challenges with integrating FUCCI in new contexts highlight the need for other methods to study cell cycle dynamics in non-FUCCI systems. Using FUCCI to study the cell cycle within cancer models may also present additional challenges due to the mutational landscapes of some cancers. Changes in the expression of the E3 ubiquitin ligases APC/C and SCF that are typically responsible for Cdt1 and Geminin degradation are common in many human cancers,91,92 which may impact the level of FUCCI fluorescence in these models. Many tumours also display copy number mutations or ploidy changes,93,94 which may also cause issues with FUCCI fluorescence. If the lentiviral integration sites containing the FUCCI constructs are multiplied or lost this may cause changes in the cell-specific level of expression of the fluorescent constructs and may result in cell cycle misclassification. 15 1.4.3 Molecular markers of G0 As discussed above, the use of pyronin Y to detect low RNA content or the use of an mVenus-p27 probe have been applied to detect G0 cells. Label retention assays using 5′-bromo-2′-deoxyuridine, tritiated thymidine or histone-fluorescent protein fusion probes can also be used to track cellular turnover and therefore identify G0 cells.19 G0 cells are also often characterized by their lack of cell proliferation markers, in particular Ki67.19,95 Finally, application of lineage-tracing techniques can also be used to identify non-cycling or low-cycling cellular sub-populations.96 Differentiating the varying types of G0 cells with molecular markers is complex due to the shared regulatory factors between these pathways as described above. Some marker gene sets do exist however, including a quiescent stem cell marker gene set proposed by Cheung and Rando which was created by comparing the gene expression profiles of different types of quiescent adult stem cells.19 They note that this signature is largely comprised of DNA replication and cell cycle progression genes that are downregulated in stem cell quiescence, rather than being comprised of genes commonly upregulated in quiescence.19 In 2007 Graham et al. also proposed a quiescence-downregulated gene signature composed primarily of cell cycling and epithelial to mesenchymal transition genes they obtained from comparing pyronin Y stained FACS sorted CD34+ cells.97 In 2022 Riba et al. proposed a set of G0-upregulated and G0-downregulated marker genes, though these genes were derived from a single population of human fibroblasts in cell culture.98 There has yet to be a comprehensive analysis investigating the generalizability of these quiescence gene marker sets (particularly quiescence-upregulated genes) across varying cellular contexts. 16 Detectable levels of β-galactosidase is the most extensively used marker for senescent cells,99 and is an indicator of their high lysosomal activity.100 A recent study from Mera-Rodriguez however questioned the specificity of this marker, and demonstrated that increased β-galactosidase can be found in terminally differentiated cells as well as in apoptotic cells in some embryonic contexts.99 Apoptosis involves several key processes that are readily identifiable with the use of molecular markers.101 Mitochondrial dysfunction is one of the early indicators of apoptosis and is readily identifiable with the use of a tetramethylrhodamine methyl ester probe which fluoresces when there is higher mitochondrial membrane potential. Fluorescent probes that identify caspase activity can also be used to identify cells undergoing apoptosis. In the later stages of apoptosis, DNA is fragmented.101 This fragmentation results in a lower ability to bind dye, which can allow the detection of apoptotic cells when using DNA content-based FACS.101 1.5 G0 as a catch-all term for non-cycling states The diversity of cycling and non-cycling states and their interconnected gene signalling pathways makes identifying non-cycling states challenging.102 These are progressive phenotypes with no widely accepted, clear-cut method of classification, and markers for each state are sparse in the literature. As discussed above, few molecular assays or marker gene sets exist that unequivocally distinguish the varying classes of non-cycling cells. Additionally, many cell cycle regulatory events occur at the proteomic level, and not all cell cycle proteins have been found to have oscillatory RNA expression over the course of the cell cycle.103 This proteomic regulation of the cell cycle may not be reflected in the transcriptome, which further complicates the 17 resolution of these states and highlights the need for the discovery of clear transcriptomic markers for cell cycle state.103 More research is needed to identify distinct signals that can be used to classify these cycling and non-cycling states effectively. ‘G0’ is term that has largely been used to describe quiescence, but that has also been applied to senescent or terminally differentiated cells across various papers.102,104 Due to the difficulty in distinguishing non-cycling states from one another, we have focused this work on simply resolving non-cycling cells from those that are actively cycling and we hereby use the term ‘G0’ as a catch-all to describe any non-cycling cellular state. We hope that by providing a more robust framework to investigate cell cycle landscapes we will enable future work to unravel the differences between these non-cycling states. 1.6 Single cell RNA sequencing allows profiling of single cell state Single cell RNA sequencing (scRNA-seq) is a revolutionary technology capable of dissecting transcriptional landscapes at the level of individual cells.105 It has become an increasingly popular method for profiling transcriptional variation between cells in heterogeneous datasets and can be used to resolve simultaneously both cell type identity and the position of a cell within a dynamic process.106,107 1.6.1 ScRNA-seq technical basics All scRNA-seq experiments can be broken down into the following steps: sample acquisition, cell isolation, cell lysis, library preparation and sequencing.108 Several commercially 18 available platforms are available for the cell isolation, lysis and library preparation steps, including droplet-based technologies such as 10x Genomics Chromium and well-based library preparation kits such as SMART-seq. All commercial platforms have the common steps of cell lysis, reverse transcription of RNA into cDNA and cDNA amplification.109 mRNA is most commonly captured though 3’ poly-A tail capture.109 Cell-specific barcodes and unique molecular identifiers (UMIs) are often added to each transcript during this process, which later allows deconvolution of the resulting sequencing data at the single cell level and removal of PCR duplicates.110 After library preparation, cDNA libraries are often sequenced with a next-generation sequencing platform such as Illumina or Nanopore.109 1.6.1.1 Widely adopted commercial droplet-based technologies The 10x Genomics Chromium platform is currently the most widely used technology for single cell gene expression quantification. On this microfluidic device, cell suspension fluid is partitioned into aqueous droplets within oil, alongside beads covered in RNA-capturing oligonucleotides and the reagents necessary for reverse transcription.108 The cell lysis and reverse transcription reactions are completed within each droplet in the emulsion.108 After this, each captured mRNA molecule is labelled with an identifying cell barcode and a UMI, which allows all droplets to be pooled and the final sequencing library to be built.108 The advantage of these droplet-based techniques is that they are easily scaled, and thousands of cells can be captured in a single run which can allow the analysis of rare cell types that may not otherwise be captured in large enough numbers.109 They also pose lower risk of contamination than other methods.109 One disadvantage of the 10x Chromium library preparation 19 approach is that it currently only captures the 3’ or 5’ ends of transcripts, enabling gene expression quantification but limiting analyses that rely on coverage across the gene body (e.g. alternative splicing, variant calling).111 This is due to the fragmentation step in the library preparation protocol where all but the first few hundred base pairs of the transcript that are bound to the oligo-covered bead are lost.111 Another disadvantage is that these microfluidics systems require large amounts of input cells, as capture efficiency is currently approximately 37% for droplet-based technologies.112 1.6.1.2 Alternative well-based technologies Well-based scRNA-seq relies on cell dispensing instruments (FACS or piezoelectric spotting instruments) to isolate cells within defined wells on a micro- or nanowell plate. This approach can offer advantages in that a researcher is not constrained in the reagents that can be used in the system like they would be if using a commercial microfluidics platform.111 This means that novel or more complex protocols can be performed. Additionally, well-based assays have been shown in benchmarking analysis to be more sensitive than the 10x Chromium chemistry, wherein they detect a greater number of genes per cell when sequenced to the same read depth.113,114 Unfortunately the number of wells available is a constraining factor to the number of cells that can be used to build a library, and so well-based libraries often contain far fewer cells than droplet-based scRNA-seq libraries.115 SMART-seq chemistry is a common library preparation method for well-based scRNA-seq assays.115 SMART-seq has an advantage over droplet-based library preparation protocols in that it employs a process known as tagmentation, wherein transposase enzymes are used to 20 incorporate PCR primers randomly across a transcript.116 This random incorporation results in read fragments with PCR primers both at the captured end of the transcript and internal reads that span across the length of a transcript. Reads from the captured end will include cell barcodes, and in newer versions of SMARTseq chemistry (versions 3 and up) unique molecular identifiers (UMIs), whereas internal reads spanning the interior of the transcript will not contain cell barcodes or UMIs. These internal reads can still be leveraged however, as the coverage depth across various introns and exons can be used to infer splicing dynamics and mutation status at the population level.116, 117 1.6.2 ScRNA-seq data analysis Computational processing of scRNA-seq data involves several steps to remove systematic and random noise such that the underlying biological signal is revealed. The basic steps of a scRNA-seq data analysis involve quality control, normalization, feature selection, dimensionality reduction and visualization.110 1.6.2.1 Quality control in scRNA-seq data Quality control of scRNA-seq data begins by filtering out low quality cells/droplets.110 Droplets which do not contain cells or in which barcoding or amplification processes were not successful will contain lower numbers of genes and UMIs and will be filtered out.110 Low quality dying cells will also be filtered out and are characterized by low count depth with a high proportion of mitochondrial reads.110 Packages such as SoupX can then be used to estimate and 21 remove ambient RNA concentration.118 Packages such as ScDblFinder can also be used to identify droplets that contain more than one cell (sometime called ‘doublets’) and remove them.119 1.6.2.2 Normalizing scRNA-seq data Differences in cell size or randomness in library construction can result in cells with different numbers of gene counts unrelated to other biological differences between them.110 The typical procedure to remove these signals includes scaling the gene counts and stabilizing the variance to ensure outlier profiles do not have a large effect on the overall structure of the data.110 Several accepted transformation methods to achieve this exist, including a shifted logarithm transformation, Scran and Pearson residual.110 The shifted logarithm transformation works well when performing subsequent dimensionality reduction.110 Scran starts by randomly pooling small subsets of cells together and summing the counts for each gene within a pool. Scran then uses the pooled counts to estimate a size factor for the entire group, and then uses a linear regression model to deconvolve this pool-level size factor back to individual cell size factor. Each gene’s expression is then divided by each cell’s size factor to normalize the counts. Scran has been found to perform well for batch correction tasks.110 Finally, the analytical approximation of Pearson residuals has been found to perform well when selecting biologically variable genes and identifying rare cell identities.110 22 1.6.2.3 ScRNA-seq feature selection and data visualization The normalized count matrices for these datasets consist often of thousands of cells, each with expression measures for thousands of genes.110 The expression of each gene within a cell represents a dimension that contributes to the identity of that cell. As such, there can be thousands of dimensions within these datasets that all contribute incrementally to explaining the variance within the data. As our human brains cannot conceptualize this space, the selection of relevant features (genes) and subsequent dimensionality reduction must be performed before attempting to visualize the data. These initially very high-dimensional datasets are very noisy, and these feature selection and dimensionality reduction techniques can transform the data into a lower-dimensional space, making 2D or 3D visualization and pattern recognition possible and the interpretation of biologically meaningful patterns in the data feasible.110 The first step in visualizing scRNA-seq data is often to subset the gene set to include only genes with ‘biological meaning’, which in practice involves prioritizing those genes that have the highest variance between clusters.110 These genes are deemed the ‘highly variable genes’ (HVGs) and they are often the only genes used to produce visualizations of scRNA-seq datasets.110 This highly variable gene (HVG) approach has been found to reduce noise and improve overall conservation of biological variance in heterogenous datasets.120 Following feature selection, a linear dimensionality reduction method such as PCA can be used for summarization of the data.110,121 PCA functions by first finding a vector that corresponds to the largest variance in the data. It then finds the next vector corresponding to the next largest variance that is uncorrelated to the first component. This process repeats until a new component explains functionally no variance in the data.122 Two or three of these components 23 can then be displayed as plot axes, with the corresponding points scattered within principal component space. PCA excels in capturing the global structure of the data, but as it is a linear projection method it fails to capture non-linear relationships amongst cells and can struggle to capture local structure within clusters in the datasets.123 Unlike PCA, two non-linear methods t-SNE and UMAP can preserve local structure and detect non-linear relationships within the data.123 Both methods model similarities between data points with Gaussian distributions, which capture how close each data point is to others in the high-dimensional space. When mapping the data to lower dimensions both algorithms allow points that are close in high dimensions to remain close in low dimensions and avoid forcing distant points to be too close.123,124 Despite their similarity, a comparison between the methods revealed UMAP preserves more large-scale structure than t-SNE, has faster run times, is more reproducible and has the most meaningful organization of cell clusters.125 As such we proceeded in this work using UMAP as our main dimensionality reduction and visualization technique. 1.6.2.4 Clustering and assigning cell identity Once dimensionality reduction has been performed, a clustering algorithm can be used to group cells with similar expression profiles to create communities.110 These groupings can then be utilized to assign cell identity. Benchmarking analyses have shown that of existing clustering methods, the Leiden clustering algorithm yields the best connections between cell clusters and is more computationally efficient than other algorithms.126 Once grouped, cell clusters can be annotated either manually or automatically.110 Automated cell-type annotation relies on existing classifiers or on a reference mapping, both of 24 which are sensitive to their training or reference datasets. Manual annotation involves leveraging known gene signatures (marker genes) and their expression within clusters to assign cell identities. 1.6.2.5 Visualizing dynamic processes with scRNA-seq In many biological processes, such as differentiation or the cell cycle, cells traverse a continuous range of cellular states. In a scRNA-seq dataset that represents a continuous biological process, each cell represents a snapshop in the pseudotime along that trajectory, and computational methods such as Monocle127 have been developed to order cells along that trajectory based on the similarities of their expression profiles.110 RNA velocity inference represents another means of investigating dynamic cellular processes in scRNA-seq.110 RNA velocity infers the rate and direction of transcriptomic change within a cell by comparing the ratios of unspliced and spliced mRNA.128 When doing so, a vector is created that represents each cell’s future transcriptional state.128 This vector can be projected onto low-dimensional embeddings like a UMAP to show cellular dynamics throughout a continuous process.128 In droplet-based scRNA-seq libraries the 3’ end of the transcript is captured and subsequently fragmented. Any resulting fragment that is not bound to the oligo-bead is then lost in further library preparation steps. This means that most of the sequence, including intronic regions, is lost after fragmentation.109 Coverage of non-3’ regions (including intronic sequences) is limited to regions which had complementarity to the probes (most often those with multiple A bases) and therefore were captured alongside the typical mRNA capture.128 As such, inferring RNA velocity dynamics in these datasets is possible but challenging, as not all transcripts 25 contain poly-As that are not their poly-A tails.116 Given the popularity of droplet-based techniques, existing RNA velocity inference tools are designed to function with droplet-based data, but the performance of these tools is much improved in libraries prepared with technologies that allow capture of the entire transcript, such as SMARTseq.111,116 This is because the internal reads produced by SMARTseq often span both intronic and exonic regions that are not captured with droplet-based library preparation methods. The capture of these regions allows the inference of splicing dynamics across all genes in these datasets, rather than just the genes that have poly-A sequences in their introns and exons, which can lead to more accurate RNA velocity estimates. 1.7 Exploring the cell cycle in scRNA-seq The ability of scRNA-seq to profile both discrete cellular subpopulations and dynamic cellular states109 presents a golden opportunity to study cell cycling in complex, dynamic contexts and can help us understand why, when and how cells flow into and out of the cell cycle and how this relates to other cellular processes.129 Despite this, the cell cycle has largely been considered a confounding variable to be regressed from scRNA-seq data to enable better cell type clustering or data integration.110,130 The lack of cell-cycle specific studies in the scRNA-seq research space presents many opportunities to unravel the mechanisms of how cell cycle regulation impacts the many other cellular processes described above. 26 1.7.1 Visualizing cell cycle phases and dynamics in scRNA-seq datasets ScRNA-seq best practice recommendations include subsetting to HVGs prior to dimensionality reduction,110 but a recent benchmarking analysis found this approach to be less effective at recapitulating cell cycle dynamics.120 This benchmarking analysis from Luecken et al.120 suggested that using the full gene set, rather than the HVG set was better for visualizing the cell cycle. Given that using a full gene set to produce a visualization introduces a lot of noise, and signal from other processes may influence the representation of the data, we hypothesized that using a full gene set may also not be the most effective approach for cell cycle visualization. There are currently no guidelines in the literature for how to best visualize cell cycling dynamics in a dataset, which is a deficiency we aim to address. 1.7.2 Limitations of existing scRNA-seq cell cycle calling tools While a variety of tools have been developed to infer cell cycle phase or trajectories from scRNA-seq datasets, these have several limitations. The majority of scRNA-seq datasets currently being generated are composed of heterogenous cell types, and are likely to contain a mixture of cycling and non-cycling cells.131 Despite this, no existing tools distinguish G0 cells from cycling cells, and instead either assume that all cells in a dataset are actively cycling (e.g. DeepCycle98, Tricycle132), or group G0 and G1 cells under a single category (e.g. Scanpy133).134 Additionally, most of these tools are not designed to work effectively in datasets with multiple cell types (e.g. DeepCycle98, Tricycle132). Of the tools that exist, most focus on discretely classifying cells by binning them into the classic cell cycle phases of G1, S and G2/M.135 Some recent tools classify cells along a 27 continuous pseudotime representative of cell cycle progression, though often these tools will also offer a discrete phase assignment function as well.98,132 1.7.3 There are two approaches to cell cycle calling in scRNA-seq There are two main computational approaches taken by existing scRNA-seq cell cycle calling tools.135 The first is a marker-gene based approach, in which S and G2M gene scores are calculated from the expression of a known set of marker genes and are used to assign cell cycle phase to individual cells.134,135 This is the approach taken by the popular Seurat (R)/Scanpy (Python) cell cycle calling functions,133 which in all official documentation use the S and G2/M marker gene sets compiled by Tirosh et al. in 2016.136 The second approach to cell cycle calling is the application of machine learning (ML) algorithms to fit a circular trajectory to a dataset.134 For example, two recently published tools Tricycle132 and DeepCycle98 employ ML algorithms to assign cell cycle pseudotime and discrete phase to cells. 1.7.3.1 Tricycle The authors of Tricycle used a transfer learning approach to call cycle phase. Transfer learning is the process of leveraging the patterns and relationships learned from one dataset to analyze new datasets without needing to repeat the learning process from scratch.137 This approach can reduce computational resources and time.137 28 Tricycle functions by projecting new data into a learned reference embedding built from a mouse cortical neurophere dataset.132 It then project new data into the reference embedding and uses the position of a cell within the embedding to assign cell cycle pseudotime.132 After pseudotime prediction, the authors have provided an improved re-implementation of another tool, Revelio,138 to call cell cycle phase.132,138 Revelio is a simple linear approach that rotates the principle components of a dataset until a circular pattern is found.138 After this, z-scores of the expression of highly expressed phase specific cell cycle marker genes are calculated, and phases are assigned to cells with the greatest z-scores for a particular phase.138 The Revelio marker gene set is composed of cell cycle genes compiled by Whitfield in 2002139 and Macosko in 2015.140 When re-implementing Revelio, the authors of Tricycle modified the package such that fewer NA values were produced.132 This ability to provide cell cycle classifications instead of NA values is especially important in smaller datasets with fewer cells, because if too many cells are assigned NA, cell cycling dynamics may be challenging to resolve and therefore understand in these datasets in a biologically meaningful way. One major disadvantage of transfer learning approaches is the reliance on the previously learned information.137 If data are missing or mismatched between the new dataset and the reference, or if the biological contexts differs too greatly, the accuracy of the model predictions can suffer.137 Tricycle’s reliance on its cortical neurosphere embedding has been criticized in other benchmarking analyses, which state that it does not function well on other non-neuronal cell types.135 29 1.7.3.2 DeepCycle DeepCycle uses an autoencoder, which is another ML approach, to call cell cycle pseudotime in scRNA-seq datasets. An autoencoder is a type of neural network that is often used to learn efficient representations of the input data.141 They consist of two main components: an encoder which reduces the dimensionality of the data into a latent space, and a decoder which attempts to reconstruct the input from the latent space representation.141 The training process for an autoencoder involves passing the input data through the encoder and decoder, and calculating the difference between the output and the input data, otherwise known as the reconstruction loss.141 An optimization algorithm then functions to adjust the weights and biases within the autoencoder to minimize the loss upon the next pass of the training data.141 This process continues until a until a specified stopping point is reached.141 Autoencoders excel at compressing and extracting high-level features without any labeled training data but are susceptible to overfitting to the training data.141 Training autoencoders requires careful selection of architecture layout and hyperparameter fine tuning, which can be challenging and computationally expensive.141 DeepCycle implements an autoencoder that infers cell cycle gene activation and degradation from spliced and unspliced gene counts and then uses these patterns to learn a latent variable representing cell cycle pseudotime.98 Interestingly, unlike most autoencoders which are typically trained on a training dataset and subsequently applied to new datasets for prediction tasks,142 DeepCycle re-trains the autoencoder every time it is run using a known cell cycle gene in the test dataset supplied by the user of the tool. This process ensures that the resulting pseudotime values are not generalizable across datasets. 30 It subsequently uses a heuristic approach based on the number of RNA counts and the expression of the S and G2/M marker genes compiled by Tirosh et al.136 to predict pseudotime phase boundaries between G1/S, S/G2M and M/G1.98 These phase boundaries can then be used to assign cell cycle phase. 1.7.4 Limitations of benchmarking analyses for existing cell cycle tools There is a distinct lack of ground truth single cell benchmarking analyses of these existing tools in the literature, making assessing their accuracy challenging.134 This lack of benchmarking analyses likely stems from the difficulty in obtaining orthogonal ground-truth measurements of cell cycle phase, though this can be achieved through use of the FUCCI system143 or by DNA-content FACS as described above.144 These methods however are not only time-intensive but were likely cost-prohibitive until very recently. Commercial scRNA-seq platforms only recently introduced a multiplexing feature, where multiple samples could be run at the same time without incurring the cost of additional runs.145 Given that FACS separates cells by phase, which on older scRNA-seq platforms would necessitate multiple expensive separate runs (thereby also introducing batch effects), it is no surprise that only a handful of scRNA-seq datasets with orthogonal measurements of cell cycle phase exist.103,146–150 This limits both the diversity and throughput of potential benchmarking datasets. One recently published paper from Guo and Chen reviewed the algorithmic approaches of fifteen existing tools and evaluated the performance of Tricycle, Seurat, CS1CC and Revelio on four small (<300 cells) homogenous datasets of which three are ESCs.135,146–149 Three of these datasets have either FUCCI or DNA-content FACS cell cycle phase labels, which allows 31 comparison with tool-generated phases as a measure of accuracy.135 These datasets are each comprised of a single cell type, which did not allow the authors to benchmark these algorithms in a heterogenous dataset composed of multiple cell types. Their results suggested that no one algorithm is better than another, and that algorithm accuracy is highly dependent on the similarity between the testing dataset and the dataset used to inform model training.135 Chervov et al. also suggested that existing tools such as Seurat and Tricycle do not function as well on ESC datasets due to their differences in cell cycle regulatory patterns.134 The lack of comprehensive benchmarking analyses with larger datasets (with at least several thousand cells) and in a wider variety of cellular contexts, including the differentiation and cancer contexts, makes evaluating the accuracy of these tools challenging. 1.8 Random forests Random forest classifiers are ensemble machine learning methods that function by constructing a multitude of decision trees during training and then using those decision trees to output classifications on new data.151,152 1.8.1 Training a random forest Training a random forest involves creating a serious of decision trees that are all slightly different from one another using a dataset with known classes.151 This is done by building the nodes of each tree one by one so that they result in the most efficient classifications of the labelled training dataset.152 When each tree is built, the dataset is ‘bootstrapped’, which is a process in which different subsets of the original dataset are created by sampling with 32 replacement from that original dataset.151 The features are randomly sub-sampled at each node, ensuring that slightly different datasets and features sets are used to build every tree which results in no trees within the forest being exactly the same.151 To decide how to make the split at each node, the algorithm evaluates all possible splits using a metric known as Gini impurity.153 It iterates through each possible feature, and in the case of numerical data such as gene expression values, each possible threshold for each feature, and divides the data down into two child nodes based on the theoretical split. It calculates the Gini impurity, which is a measure of how often a randomly chosen element would be incorrectly classified if it was randomly labeled according to the distribution in the node. A Gini impurity of 0 implies that a node is ‘pure’ wherein it only contains one class. At each node the algorithm selects the feature and split value that results in the greatest reduction in Gini impurity.153 The algorithm moves down the tree in a recursive manner creating nodes that are progressively more pure until a specified stopping point is reached.153 Ideally the algorithm will eventually create nodes containing samples from a single class, or with very low impurity which will allow the tree to make future accurate predictions when new data is run through the now-existent trees.153 1.8.2 Advantages of random forests Random forests are simple but effective classifiers that require less tuning of model parameters and are less prone to overfitting than many other ML algorithms.151,152 Random forests can capture complex, non-linear relationships in data that linear regression models cannot,152 and are less sensitive to outliers and noise than other ML methods because each tree only considers a subset of data and features.151 This makes them especially effective in noisy 33 high dimensional data like scRNA-seq. Finally, Random Forests are more interpretable than other ML methods in that feature importance can be obtained, which can not only inform biological interpretation but also allow users to exclude features that are not providing much insight into the model.151,152 Random forests balance well between performance and computational intensity, but despite their clear advantages no existing cell cycle calling tool has utilized them for classification. 1.8.3 SHAP values can be used to improve random forest feature selection It is important to note that the feature importance scores output by a random forest are susceptible to being inflated if there are correlated features, which can lead to misinterpretation of the model.154 The importance scores of the model are also aggregated across all classes, so in a multi-class predictors it is impossible to determine the extent to which each feature is impacting the prediction of individual classes from the feature importance output by the random forest.154 To address these limitations, SHapley Additive exPlanations (SHAP) values can be used to estimate the importance of each feature in the prediction of a particular class.155 SHAP values are based on cooperative game theory, where each feature in a model is considered a “player” contributing to the “payout” (prediction).156 For each data point, SHAP values calculate the contribution of a feature by considering how much the prediction changes if the feature is included versus if it is excluded.156 SHAP values are model-agnostic. In a tree-based model 34 SHAP efficiently computes these expectations by weighting the contribution of each feature for each class along the path of each tree.156 The contributions across all trees for the feature are then averaged and incorporated into the feature’s importance score, which allows SHAP to take into account how interactions between features impact the model output.156 To understand which features are generally important for each class, SHAP values can be aggregated across all instances that belong to a particular class. By examining SHAP values per class, features that are predictive for specific classes can be identified, which allows a more nuanced understanding of feature relevance and can allow class specific feature selection when creating a model. 1.8.4 Gradient boosting Gradient boosting, like a random forest, is a tree-based classification method. Unlike a random forest however, this algorithm adds trees sequentially to correct the errors of previous trees.157 This technique often achieves a high predictive accuracy but can struggle with overfitting and may not generalize well across a range of datasets.157 This contrasts with random forests which have been shown to generally provide more stable performance across a range of datasets.151 1.9 Project motivation, hypothesis and aims To summarize, control of the cell cycle is a fundamentally important cellular process that is intertwined with the regulation of many other cellular processes and disease states. Single cell RNA-seq provides an opportunity to study how the cell cycle relates to these other cellular 35 processes with single-cell resolution, but there are no existing cell cycle analysis tools that allow for these investigations to be made. No current guidelines exist for the visualization of cell cycling dynamics. Additionally, existing tools do not identify cells in G0, and most are not designed for use in heterogenous datasets. We hypothesized that by leveraging known cell cycle and G0 marker gene sets and FACS labelled datasets a better framework for the investigation of cell cycle dynamics in scRNA-seq could be created. In this project we aimed to develop integrated visualizations of cell cycle and G0 dynamics, benchmark popular and/or recently published existing cell cycle calling tools, train a supervised model for improved cell cycle and G0 classification in heterogenous datasets and apply that model to complex developmental and tumour datasets to characterize their cell cycle dynamics. 1.10 Ouroboros improves cell cycle visualization and classification in scRNA-seq To address the limitations of existing scRNA-seq cell cycle calling tools, we created Ouroboros: a comprehensive framework for accurately investigating and identifying cell cycle phase and G0 in complex, heterogenous scRNA-seq datasets. Ouroboros facilitates cell cycle visualization by producing representative cell cycle UMAPs and implements a random-forest classifier that not only outperforms existing cell cycle calling tools when identifying G1, S and G2M phases but also identifies two novel non-cycling states: a transition state between G1 and G0, and true G0. We demonstrate Ouroboros’s utility in exploring cell cycling landscapes within the cancer context by applying it to a high grade serous ovarian cancer (HGSOC) dataset to investigate relationships between cycling cells and patient outcomes. We also contribute an additional benchmarking analysis by testing three cell cycle calling tools including Scanpy, 36 Tricycle and DeepCycle on two ground-truth datasets, including a large (939 cells) U2OS dataset103, and a mESC dataset150 that has not appeared in any existing benchmarking analysis and has not been used to inform the creation of any existing model. 37 Chapter 2: Results Here we summarize our main results which demonstrate the cell cycle visualization technique and functionality of the random forest (RF) classifier provided by the comprehensive framework of Ouroboros. We conclude by demonstrating the utility of these techniques in discovering relationships between cell cycling landscapes and patient outcomes in HGSOC. 2.1 CC UMAPs improve visualization of cell cycle and G0 dynamics A recent benchmarking analysis suggested that using the full gene set of an scRNA-seq experiment rather than just the HVGs resulted in better UMAP visualizations of cell cycling dynamics,120 but we found that using a targeted approach by subsetting to annotated cell cycle and G0 genes prior to dimensionality reduction produced visualizations that better recapitulated cell cycle dynamics. Distinct phase clusters are more visible in our cell cycle UMAP visualizations (CC UMAPs) than they are when using HVG-subset or full geneset visualization methods, and these clusters are often arranged in a loop and stem structure consistent with the cell cycle and G0. Using RNA velocity inference with our CC UMAPs produces vectors consistent with cell cycle progression and exit, where these dynamics were not present in the HVG or full geneset approaches. Our CC UMAP approach removes sources of variation that are not related to cell cycling genes, which allows cells within the same cell cycle phase to cluster together regardless of cell type or other biological variability. This approach allows for better visualization of the complete cell cycle and G0. We demonstrate the utility of the CC UMAP using a well-characterized 38 pancreatic endocrinogenesis dataset158 that has an HVG generated UMAP used in several publications (Figure. 2.1A).159–161 The count matrix used to generate this classic UMAP is provided with the ScVelo package, and it is missing 100 of our annotated cell cycling and G0 genes which were likely filtered out in the initial preprocessing steps of the original publication. 158 The classic UMAP has a cluster of cells with upregulation of cell cycle genes that appears along the differentiation trajectory away from the other clusters with cell cycle gene upregulation, and it appears to emphasize cell type and differentiation dynamics rather than cell cycling dynamics (Figure 2.1B, C). We created our own count matrix using the published raw data and found that the inclusion of the missing 100 genes with the full gene set produced visualizations that displayed localization of most of the cycling cells at the beginning of the trajectory, including many of the cells placed further along the trajectory in the original UMAP (though they did not integrate with the other cycling cells in this representation) (Figure 2.1E-G). Sub-setting to our full list of cell cycle and G0 genes (including the originally missing 100 genes) to create the CC UMAP resulted in a much better integrated visualization of cell cycle phases and dynamics with almost all actively cycling cells localized together around a clear loop structure and all non-cycling cells clustered along a stem structure (Figure 2.1H). Visualization of the well-characterized Tirosh S and G2M marker gene sets136 confirmed that cells are clustered by cell cycle phase within the loop (Fig 2.2 Mouse pancreatic endocrinogenesis panels coloured by Tirosh S and G2M genes). The stem and loop structure that is representative of the cell cycle as well as the expression of phase-specific marker genes is much clearer in our CC UMAP created from our count matrix that includes the missing 100 cell cycling genes than it is in any other visual representation of this dataset, including a CC UMAP made with the original filtered dataset (Figure 2.1F). The 39 clarity in our CC UMAPs highlights both the utility of the CC UMAP and the need to be conservative when performing initial gene filtering in scRNA-seq datasets, especially when studying cellular processes or states that may not represent the largest source of variation in the dataset. 40 Figure 2.1 CC UMAPs enable visual integration of cycling cells across cell types in a complex differentiation dataset The top row of panels display the classic HVG UMAP from filtered dataset (missing 100 cell cycle genes) provided by the scVelo Python package showing original cluster labels (A), the cumulative expression of S and G2M marker genes136 (B), cycling clusters by cell type (C) and the CC UMAP produced using this count matrix (D). The bottom row of panels display the full geneset UMAP produced using our custom generated count matrix containing all annotated cell cycle and G0 genes coloured by original cluster labels (E), the cumulative expression of S and G2M marker genes136 (F), cycling clusters by cell type (G) and the CC UMAP produced using this count matrix (H). 41 2.2 Phase labelling paradigm allows non-FACS datasets to be classified Given that no current tools are designed to identify G0 cells, we used the increased cell cycle resolution in CC UMAPs and the expression of several published gene marker sets to design a phase-labelling paradigm to classify phase in non-FACS datasets. We based the paradigm phase boundaries on the consistent patterns of marker gene expression within CC UMAPs, including those seen in a U2OS dataset that had been FACS sorted for the expression of FUCCI markers as orthogonal validation of cell cycle phase.103 S and G2M phases were classified by identifying Leiden clusters with high expression of Tirosh S and G2M marker genes, respectively, which had RNA velocity vector direction consistent with cell cycle progression and that were positioned around the characteristic cell cycle loop structure seen in CC UMAPs. G1 phase was classified by identifying Leiden clusters that were positioned around the cell cycle loop with low but not absent expression of Tirosh S and G2M genes, and with RNA velocity vector direction consistent with progression from the putative phase in one cluster into the putative phase of the next cluster (i.e. the cluster with the putative phase of G1 has RNA velocity direction pointing towards the cluster with the putative phase of S.) Cut points between phases were identified by selecting Leiden clusters and accepting boundaries between clusters as boundaries between phases. G0 cells were classified by identifying Leiden clusters with completely absent expression of Tirosh S and G2M genes, as well as the complete absence of expression of known G0-downregulated genes characterized by Graham et al.97 G0 clusters were also identified by selecting Leiden clusters with either no clear direction in RNA velocity, or RNA velocity direction attributable to other progressive processes such as differentiation. We 42 also noted that in our datasets with a clear G0 population, there also appeared to be another population of cells that were in a transition state between G1 and G0. We classified these cells as G1-G0 transition and identified them by finding Leiden clusters with low expression of Tirosh136 and Graham97 genesets that were positioned between the loop and stem structures on the CC UMAP with RNA velocity direction consistent with exit from the cell cycle (Figure 2.2). We used this paradigm to classify Leiden clusters as either G1, S, G2M, G1-G0 transition or true G0 in unlabeled datasets. The U2OS dataset contained a population of cells with a non-cycling phenotype that was excluded from the analysis of the initial publication. We found these cells had downregulation of Tirosh cell cycling genes and RNA velocity direction away from the active cell cycle, so we classified them as G1-G0 transition cells by our phase labelling paradigm and included them in the training of our RF model (Figure 2.2 Human U2OS osteosarcoma cell line panels showing G1-G0 transition cells in red, the RNA velocity direction of these cells pointing away from the cell cycle, downregulation of cell cycle genes and upregulation of p53). This paradigm allowed us to estimate cell cycle phase in non-FACS sorted datasets, and we applied it to both the well-studied pancreas dataset and a human fibroblast dataset published by Riba et al.98 to assign classic cell cycle phases (G1, S, G2M), true G0 and a transitional state between G1 and G0 (G1-G0 transition) (Figure 2.2). Application of the phase labelling paradigm to these larger, G0-containing datasets provided the opportunity to benchmark existing cell cycle calling tools in heterogenous datasets and allowed us to build a large comprehensive labelled dataset composed of multiple cell cycling contexts. 43 Figure 2.2 Phase labelling paradigm A full characterization of datasets labelled with our phase assignment paradigm and used for random forest training, showing the criteria for phase categorization, including CC UMAPs, RNA velocity directionality, cell specific sum of expression of S, G2M136 and G097 marker sets and GSEA enrichment score for the p53 pathway. Differences in the number of genes used to create UMAPs between the datasets result from differences in the mouse and human genomes and their respective annotations. 44 2.3 Benchmarking reveals low accuracy of existing cell cycle calling tools We next performed a benchmarking analysis of existing scRNA-seq cell cycle phase assignment tools to address the dearth of these analyses in the literature and to gain insight into their respective accuracies across a variety of contexts. We chose to test Scanpy’s built-in cell cycle calling function133 as it is the most widely used cell cycle calling tool currently. We also chose to test Tricycle132 as it is a more novel method with a fair degree of popularity. Finally, we chose to include DeepCycle98 for its novelty and unique approach, and because it does not appear in other cell cycle calling benchmarking analysis. 2.3.1 Benchmarking in homogenous datasets with no G0 cells Tricycle and DeepCycle are not designed to work in datasets with multiple cell types so to fairly benchmark their accuracy we first tested them using two datasets composed of single cell types without any G0-like cells. These datasets include a mESC dataset FACS sorted for DNA content as a proxy for cell cycle phase150 and the above human U2OS dataset FACS sorted for FUCCI expression levels, with the G1-G0 transition cells excluded.103 To assess the discrete phase calling accuracy of these tools, we compared FACS phases in these datasets to the phases assigned by each tool. Scanpy was the most accurate of the tools tested when comparing tool-output phase to FACS phase, with overall accuracies of 70% on the mESC dataset and 65% on the U2OS dataset, though in both datasets it achieved a G1 classification accuracy of only 27% (Figure 2.4A). 45 DeepCycle was relatively inaccurate in its assignment of discrete phase and had an overall accuracy of only 40% on mESCs and 36% on U2OS cells (Figure 2.4B). Tricycle divides the cell cycle into more stages than the other tools tested, and their phase labels include the intermediary G1/S and M/G1 labels in addition to S and G2M phases. We accepted these intermediary labels as ‘correct’ if either of their phases was the same as the FACS label for the cell. Tricycle was reasonably accurate when assigning cell cycle phase (89% in mESC and 81% in U2OS), but it returned NA values for 52% of the mESC dataset, and 46% of the U2OS dataset, making the overall accuracies only 42% and 44% respectively (Figure 2.4). Figure 2.3 CC UMAPs showing tool classification output on the benchmarking datasets CC UMAPs of mESC (top) and U2OS (with no G1-G0 transition cells) (bottom) datasets showing FACS assigned phase labels, and phase-assignment tool outputs. 46 Figure 2.4 Confusion matrix showing accuracy of benchmarked tools Quantitative results of benchmarking analysis showing the percentages of phase classifications for each algorithm on cells from both benchmarking datasets. Tricycle multi-phase predictions were collapsed and accepted as correct if either phase matched the FACS phase for a particular cell. 2.3.2 Benchmarking in complex G0-containing dataset To demonstrate the deficiencies of these tools in assigning cell cycle phase to datasets with multiple cell types and G0 cells, we next applied them to the well-characterized pancreas dataset and compared their output to the phase assignment paradigm labels. Scanpy classified S and G2M cells in the expected regions of the CC UMAP, but the phase boundaries extended much further into G1 than expected by the phase-labelling paradigm. Very few paradigm-labelled G1 cells were correctly classified, which is a finding corroborated by Scanpy’s poor G1 performance in FACS-labelled datasets. Instead, Scanpy classified most of the cells in the paradigm-G0 tail as G1. Tricycle classified many of the paradigm G1, S and G2M cells correctly but also assigned many of the cells in the paradigm-G0 tail as actively cycling and assigned just as many ‘NA’ labels as it did phase labels. DeepCycle completely failed to recapitulate cycling dynamics in this dataset (Fig 2.5). 47 Figure 2.5 Benchmarking tools applied to pancreas dataset CC UMAP demonstrating paradigm labelled phases and cell cycle calling algorithm phase labels in the pancreas dataset. 2.4 Ouroboros classifies cell cycle phase and G0 with high accuracy To address the limitations of existing cell cycle calling tools, we trained a random forest-based (RF) classifier capable of predicting both active cell cycle phases and G0 across a variety of contexts and have provided it as a part of the Ouroboros package. We used our large comprehensive dataset composed of the human U2OS, mouse pancreatic and human fibroblast cells with a combination of FUCCI FACS ground truth (U2OS) and phase labelling paradigm phases to train this model. Our goal was to present the model with a training dataset composed of cell cycling and G0 dynamics across a variety of contexts to avoid the model overfitting to a particular training context as seen with other existing cell cycle calling tools.135 Our training 48 dataset has cycling and G0 cells in the normal, cancer and differentiation contexts and includes both mouse and human samples. Here we show it performs well across cell cycling contexts, including in ESCs which other tools have been shown to perform poorly on.134 2.4.1 Shapley values allow assessment of phase-specific feature importance Our feature selection process involved using a Wilcoxin rank-sum test within each training dataset to identify genes with high expression in one phase compared to other phases. We tested all genes in our reference genome, not only annotated cell cycle and G0 genes. To ensure we were selecting genes that represented the cell cycle across biological contexts, we only kept genes that were upregulated in at least one phase in all training datasets. We then used SHAP values to identify the importance of each gene for the prediction of each phase and used this information to retain the topmost important genes for each phase. The presence or absence of a gene’s expression within a phase can be a determining feature in our model, so it is unsurprising that well-annotated cell cycle genes with clear phase specific expression such as TOP2A appear as important features for all of our model’s output phases (Figure 2.6). It is also unsurprising that many of the genes that are important for the classification of G0 are known to be upregulated within the cell cycle, and this reinforces the theme in the literature that G0 cells are more easily identified by their lack of a cell cycling signature than they are by any other unifying feature. 49 Figure 2.6: Absolute SHAP values for the top 15 RF features for each phase Barplots displaying the top 15 features and their mean absolute SHAP values for each cell cycle phase Ouroboros predicts. The absolute SHAP values are a measure of how much each feature contributed to the prediction given to a cell, so here we display the average absolute SHAP value for all cells within each particular class for every gene. 2.4.2 Some feature genes are not annotated cell cycle genes Interestingly 51 of our ultimately 176 feature genes were not present on our master list of annotated cell cycle and G0 genes. Many of these genes appear to have cell-cycle adjacent function in the literature, such as CLDN7 which is essential for controlling epithelial stem cell 50 survival and self-renewal, and the deletion of which results in downregulation of cell cycle genes.162,163 Given that the cell cycle is largely driven by proteomic events, the identification of transcriptomically regulated markers is necessary for the development of tools capable of resolving cell cycling phenotypes in transcriptomic space. These non-cell cycle annotated genes represent novel transcriptomic cell cycle markers that the Ouroboros classifier leverages to more effectively identify cell cycle phases and G0 in scRNA-seq datasets. See appendix A for full feature set. 2.4.3 Cross-validation to assess RF performance on training dataset A 5-fold cross validation strategy was used to evaluate our model’s performance on the combined training dataset, with an overall accuracy of 88% and a weighted average F1-score of 0.88 (Table 2.1) Almost all misclassifications occurred between neighboring phases (for example confusing S for G2M) which given the continuous nature of the cell cycle is not an unexpected outcome (Figure 2.7A). The same cross validation strategy was used to analyze the model’s performance on the benchmarking U2OS dataset, in which the Ouroboros classifier outperformed all other algorithms tested with an accuracy of 86% on G1, S and G2M phases. It’s accuracy in detecting paradigm-labelled G1-G0 transition cells in this dataset was 71% (Figure 2.7B). 51 Figure 2.7 Confusion matrix displaying cross-validation results of RF Phase specific accuracies of the Ouroboros random forest classifier when evaluated with cross-evaluation on the entire dataset (A) and the benchmarking U2OS dataset (B). 52 Class Precision Recall F1-Score # of cells G0 0.94 0.96 0.95 728 G1 0.82 0.90 0.86 902 G1-G0 transition 0.86 0.57 0.69 314 G2M 0.93 0.92 0.92 971 S 0.87 0.87 0.87 969 Accuracy 0.88 3884 Macro Avg. 0.88 0.85 0.86 3884 Weighted Avg. 0.89 0.88 0.88 3884 Table 2.1 Cross-validation metrics of RF performance on training dataset Cross-validation performance metrics for the random forest model. Precision reflects the model’s accuracy in positive predictions, recall measures its ability to capture all instances of each phase, and the F1-score balances both metrics. Support indicates the number of true instances per class, while accuracy denotes the overall correct prediction rate across all classes. Macro and weighted averages summarize performance, with the latter accounting for class imbalance. 2.4.4 RF performance on mESC benchmarking dataset I then applied the Ouroboros classifier to the mESC benchmarking dataset to assess its performance on non-training data. Our model achieved an overall accuracy of 85% and was more accurate than all other tools tested when classifying all classic cell cycle phases (Figure 2.8). Our model also did not misclassify any cells as either G1-G0 transition or G0, demonstrating that it functions well in datasets comprised entirely of cycling cells (Figure 2.9). 53 All cell cycle calling tools including Ouroboros struggled to classify G1 cells in this dataset. This is likely because mESCs differ in their expression of many cell cycle genes, including cyclins and CDKs as discussed above. mESCs are known to have higher levels of expression of cell cycling genes throughout the cycle, and constitutive rather than oscillatory expression of cell cycle genes. This results in higher levels of cyclin and CDK expression in G1 phase than there is in other cell types, and is likely the source of the G1 misclassification rate across these algorithms. Figure 2.8 Phase calling accuracies of cell cycle calling tools on mESCs Barplots displaying the phase-specific accuracies of each benchmarking tool and the Ouroboros classifier when tool-output phase is compared to FACS phase in the mESC data 54 Figure 2.9 Confusion matrix displaying RF accuracy on mESC dataset Ouroboros random forest classifier phase specific accuracy when applied to the benchmarking mESC dataset. 2.5 Ouroboros recapitulates non-cycling and cycling cell dynamics in complex datasets To assess the performance of the Ouroboros classifier in a heterogenous dataset containing G0 cells and with a differentiation trajectory composed of multiple cell types, we applied it to a human neurogenesis dataset produced by Paun et al.164 This dataset consists of 35,755 human induced pluripotent stem cells that were differentiated into cortical neurons using a dual-SMAD inhibition protocol. These cells were also treated with the γ-secretase inhibitor DAPT to accelerate and synchronize cell cycle exit and differentiation.164 As human cortical neural precursors differentiate they are known to gradually acquire a post-mitotic state,165 so we postulated that this dataset would allow reasonable assessment of the Ouroboros classifier’s 55 accuracy, given that the more stem-like cells should be actively cycling and the differentiated neuronal cells should not be. 2.5.1 Expression of S and G2M markers in respective Ouroboros phases The Ouroboros classifier performed well when compared to the expected phases given by the phase labelling paradigm. Ouroboros S and G2M phases are well defined and occur in regions with high expression of S and G2M marker genes respectively. G1 appears in the expected region of the CC UMAP loop with lower expression of cell cycle genes (Figure 2.10). Expression of the S and G2M phase marker gene sets were significantly higher in Ouroboros S and G2M phase cells respectively, when compared to all other cell cycle phases (one-sided Mann-Whitney U test, S phase: U = 4.10 ´ 107, p = 0.0; G2M phase: U = 3.47 ´ 107, p = 0.0) (Figure 2.11). 56 Figure 2.10 CC UMAP of neurogenesis CC UMAP of the human neurogenesis dataset164 depicting the original publication’s cell type classifications, Ouroboros phase calls and the cell-specific sum of expression of the Tirosh S and G2M marker gene sets. 57 Figure 2.11 Expression of S and G2M marker genes in Ouroboros phases - Neurogenesis Cell-specific sum of log-normalized expression of S and G2M marker gene sets136 expression in the human neurogenesis dataset split by Ouroboros-assigned phase. The sum of expression of Tirosh136 S marker genes is significantly higher in Ouroboros S phase than other Ouroboros phases, and the sum of Tirosh136 G2M marker genes is significantly higher in Ouroboros G2M phase than other Ouroboros phases, demonstrating that the Ouroboros RF classifier is functioning well in this dataset. 2.5.2 Proportions of cycling cells decrease over developmental pseudotime Ouroboros classified cortical neurons at the end of the differentiation pathway as G0 as expected, and the G1-G0 transition phase occurs at the boundary between the active cell cycle and the cortical neurons (Figure 2.10). To confirm that Ouroboros calls more G0 cells as differentiation progresses, we used Monocle127 to assign a differentiation pseudotime in this dataset. We applied a logistic generalized additive model (GAM), which fits a logistic curve to 58 our data, to test the relationships between pseudotime and the proportion of cells in G0, G1-G0 transition and that were actively cycling (in G1, G2M or S) and found statistically significant relationships between these variables (p < 0.001 for all). The proportion of G1, S and G2M cells decreased over developmental pseudotime as the cycling progenitor cell type decreased. The proportion of G0 cells increased over developmental pseudotime as the proportions of transitional progenitors and finally neurons increased. G1-G0 transition cells had low proportions overall, but their prevalence also decreased over developmental pseudotime, dropping to 0 by late development (Figure 2.12). The Ouroboros classifier phase assignments are consistent with expectations, which highlights its ability to distinguish cell cycle phase including G0 in a complex dataset with multiple cell types and a differentiation trajectory. Figure 2.12 Cell type and phase proportions over neurogenesis pseudotime 59 Proportion of each Ouroboros phase (with G1, S and G2M cells summed) and cell type across neurogenesis pseudotime bins. 2.6 Ouroboros resolves cycling landscapes in HGSOC We next applied Ouroboros to a cancer dataset to explore how cell cycling landscapes in patient tumour samples relate to treatment response and patient outcomes. We used a dataset from a patient cohort consisting of 11 high grade serous ovarian cancer (HGSOC) patients originally published by Zhang et al.63 All patients underwent a diagnostic laparoscopy followed by 3 cycles of platinum-taxane and interval debulking (IDB) surgery (NACT therapy). Single cell RNA-seq data was collected on both the diagnostic laparoscopy samples and the IDB samples, allowing analysis of cell cycling landscapes both pre- and post- platinum treatment. Progression free interval (PFI) values were available for each patient in the cohort and represent the time from treatment end to relapse. PFI values have been found to have prognostic significance for long term survival in HGSOC and are a common measure of treatment efficacy in this disease.166,167 Platinum chemotherapies are known to target actively cycling cells168 so we hypothesized that post-NACT tumours would have lower amounts of cycling cells than their naive counterparts. We also hypothesized that patients with more actively cycling cells in their naive tumours would be more susceptible to platinum chemotherapy and would have longer PFIs than those with fewer cycling cells. 60 2.6.1 CC UMAPs and RF reveal discrete phases in heterogenous HGSOC dataset This dataset is heterogenous and is composed of cells from 11 patients with 3 broad annotated cell types (epithelial ovarian cancer (EOC), immune and stromal cells) and 2 sampling timepoints (pre- and post-NACT). We generated both a classic HVG-based UMAP and a CC UMAP for this dataset. In the HVG-based UMAP, Ouroboros-classified cycling cells are seen within four different clusters which segregate by cell type rather than cell cycling dynamics. Distinct cell cycle phase regions are not present in this representation of the data and in some clusters G1-G0 transition cells are scattered throughout G0 cells. Progression through the cell cycle and transition in/out of it is not clear using this representation of the data. Conversely, in our CC UMAP one integrated cluster of cycling cells from all three cell types is visible (Figure 2.13). Regions of S and G2M marker gene expression are visible in our CC UMAP and coincide with grouping of Ouroboros S and G2M phase cells (Figure 2.14), and the expression of these gene sets was found to be significantly higher in their respective phases when compared to all other phases (one-sided Mann-Whitney U test, S phase: U = 8.60 ´ 106, p = 3.26 ´ 10-110; G2M phase: U = 5.42 ´ 106, p = 2.02 ´ 10-70) (Figure 2.15). G1 and G1-G0 transition cells are also clearly visible but do form distinct cell-type specific regions in the CC UMAP. Ultimately the CC UMAPs and Ouroboros classified phases reveal clearer cell cycling and G0 sub-populations when compared to the conventional HVG UMAP approach to visualizing scRNA-seq data. When the full dataset is subset to include only malignant cells, CC UMAPs reveal very clear cell cycling and G0 dynamics in both the naive and treated tumours (Figure 2.16). 61 Figure 2.13 HGSOC HVG UMAP and CC UMAP Full dataset HVG UMAP (top) and CC UMAP (bottom) displaying cell type and Ouroboros classified phases for all 11 patients and both treatment timepoints. 62 Figure 2.14 Marker gene expression in HGSOC CC UMAP Cell-specific log-normalized expression of well characterized Tirosh S and G2M marker gene sets136 in the complete HGSOC dataset CC UMAP. 63 Figure 2.15 Expression of marker genes in Ouroboros S and G2M phases - HGSOC Cell-specific log-normalized expression sums of S and G2M marker gene set136 in the full HGSOC dataset split by Ouroboros-assigned phase. The sum of expression of Tirosh136 S marker genes is significantly higher in Ouroboros S phase than other Ouroboros phases, and the sum of Tirosh136 G2M marker genes is significantly higher in Ouroboros G2M phase than other Ouroboros phases, demonstrating that the Ouroboros RF classifier is functioning well in this dataset. 64 Figure 2.16 CC UMAPs of pre- and post-treatment malignant HGSOC cells CC UMAPs of malignant HGSOC cells in both pre- (upper) and post-NACT (lower) samples showing Ouroboros classifications, the cell-specific log-normalized sums of S and G2M marker gene expression136 and patient IDs 65 2.6.2 Other tools fail to recapitulate distinct cell cycling and G0 states in HGSOC To further highlight the increased resolution Ouroboros provides in complex datasets when compared to other cell cycle calling tools, we also applied Scanpy and Tricycle to this HGSOC dataset. As DeepCycle requires spliced and unspliced count values which were not published with this dataset, we were unable to apply it in this thesis, but we note that it is also not designed to work in heterogenous datasets so is unlikely to have performed well. 2.6.2.1 Scanpy cell cycle calling in HGSOC Scanpy called many of the immune cells with little or no expression of cell cycle genes as either S or G2M. It also labelled many of our Ouroboros G1-G0 transition cells as being either S or G2M, and many of our Ouroboros G0 cells as G1 (Figure 2.17). Though the Scanpy S and G2M called phase cells did have significantly higher expression of the Tirosh S and G2M marker sets (one-sided Mann-Whitney U test, S phase: U = 4.96 ´ 108, p = 0.0; G2M phase: U = 4.32 ´ 108, p = 0.0) (Figure 2.18), the difference in the medians of S marker gene expression between the Scanpy S and G2M groups was only 7.8 whereas it was 65.17 in the Ouroboros S and G2M groups. Similarly, the difference in the medians of G2M marker gene expression in the Scanpy S and G2M groups was 8.78, whereas it was 124.12 in the Ouroboros S and G2M groups. The larger difference in medians between these groups highlights Ouroboros’s ability to better separate these phases when classifying cells. 66 Figure 2.17 Scanpy phase assignments on HGSOC CC UMAP CC UMAP of our HGSOC dataset coloured by the phases output by the Scanpy cell cycle calling tool when run with default parameters. 67 Figure 2.18 Expression of S and G2M marker genes in Scanpy phases Cell-specific log-normalized expression sums of S and G2M marker gene expression136 in the full HGSOC dataset split by Scanpy-assigned phase 2.6.2.2 Tricycle cell cycle calling in HGSOC Clear cell cycle phase sub-populations are not evident when using Tricycle phase labels (Figure 2.19), and only G1.S phase output by Tricycle had significantly higher expression of the S marker gene set when compared to other phases (one-sided Mann-Whitney U test: U = 1.43 x 108, p = 4.0 ´ 10-4), which makes it challenging to resolve S cells from G1 cells. No Tricycle output phase had significantly higher expression of the G2M marker gene set when compared to other phases (Figure 2.20). 68 Figure 2.19 Tricycle phase assignments on HGSOC CC UMAP CC UMAP of our HGSOC dataset coloured by the phases output by Tricycle when run with default parameters. 69 Figure 2.20 Expression of S and G2M marker genes in Tricycle phases Cell-specific log-normalized expression sums of S and G2M marker gene set136 expressions in the full HGSOC dataset split by Tricycle-assigned phase. 2.6.3 Ouroboros resolves post-platinum cycling population shifts in HGSOC We found the proportions of both actively cycling and G1-G0 transition malignant cells were significantly decreased between pre- and post-NACT samples when not accounting for specific patients. The proportion of actively cycling malignant cells (Ouroboros phases G1, S and G2M) dropped significantly from an average of 6.4% of all malignant cells in naive tumours to 2.6% in treated tumours (Wilcoxon rank-sum = 101, p = 0.008). The proportion of malignant G1-G0 transition cells also dropped significantly, decreasing from an average of 7.0% of all malignant cells in naive tumours to 2.1% post-treatment (Wilcoxon rank-sum = 103, p = 0.006). 70 The proportions of actively cycling immune cells also dropped significantly from an average of 2.5% of all immune cells in naive samples to 0.79% post-NACT (Wilcoxon rank-sum = 100, p = 0.010). This significant drop in actively cycling cells was also seen in stromal cells, which dropped from 1.18% actively cycling out of all naive stromal cells to 0.24% post-NACT (Wilcoxon rank-sum = 97, p = 0.013) (Figure 2.21). Interestingly, at the patient-specific level we found that 2 patients (EOC733 and EOC87) did not follow the majority trend of reduction in cycling cells post-treatment and instead had an increased proportion of non-G0 (G1, S, G2M and G1-G0 transition) EOC cells post-NACT (Figure 2.21). One of these patients was identified in the original publication using the expression of Ki-67 as a marker for proliferation (EOC733), but the increased resolution provided by Ouroboros allowed us to identify a second patient with these dynamics (EOC87). Both patients had low PFIs (83 and 30 days respectively), but several other patients without an increase in cycling cells post-treatment also had PFIs in this range suggesting that this unlikely to be the sole predictor of low PFI in this cohort. 71 Figure 2.21 Proportions of non-G0 cells pre- and post-treatment Proportions of non-G0 cells (G1, S, G2M and G1-G0 transition) in naive and treated tumours for all cell types. Legends provide the number of non-G0 cells in pre- and post- treatment samples for each patient. 2.6.4 Percent of non-G0 cells in naïve tumours is associated with low PFI Interestingly, we found that the proportion of malignant non-G0 cells (combined cycling and G1-G0 transition) in naive tumours was significantly negatively associated with PFI (Pearson correlation: r = −0.65, p = 0.030) (Figure 2.22). Post-treatment this metric had no significant relationship with PFI. This relationship was not seen in either the immune or stromal cells, or when using all cell types together as would be typical in a standard bulk RNA-seq experiment (Figure 2.23). 72 Figure 2.22 Relationship between proportion of malignant non-G0 cells and PFI Scatterplots with an OLS regression line representing the relationship between the % of non-G0 malignant cells in a patient’s naive (left) and treated (right) tumours and their PFI. Figure 2.23 Relationship between proportion of stromal and immune non-G0 cells and PFI Scatterplots with an OLS regression line representing the non-significant relationships between the % of non-G0 immune and stromal cells in a patient’s naive (left) and treated (right) tumours and their PFI. 73 Chapter 3: Methods In this chapter we outline all methods needed to produce the above results, including data processing, benchmarking tool application, visualization techniques, the creation, training and testing of our random forest model and the statistical tests applied to find significant relationships between cell cycling landscapes and patient outcomes. 3.1 Raw data processing Raw FASTQ files for all datasets (excluding the HGSOC) were downloaded from SRA archives. The raw count matrix for the HGSOC dataset was downloaded from GEO.63 10x genomics datasets were aligned to the appropriate reference genome (hg38 or m39) and count matrices were produced using STARSolo (2.7.11a). At the time of publication, STARSolo was unable to produce spliced and unspliced count matrices on non-10x genomics datasets, so a custom workflow was used to process the U2OS (SMARTSeq2) and mESC (RAMDA-seq) libraries. We transformed the BAM files for these libraries into pseudo-10x BAM files that STARSolo would recognize and process using a custom workflow. In this workflow raw FASTQ files for each cell were trimmed using fastp (0.23.4) with default parameters and subsequently aligned to the appropriate reference genome (hg38 or m39) with STAR (2.7.11a). Duplicates were removed with GATK (v. 4.3.0.0) MarkDuplicates and secondary alignments were removed with Samtools (1.18). Read groups were added with GATK AddOrReplaceReadGroups and a custom Python function was used to add 10x-style barcode and quality tags to each read in the BAM file. BAM files containing reads for each 74 individual cell were then merged using Samtools to resemble a 10x droplet-based library and STARSolo was used with the –soloType Droplet parameter to generate a count matrix. This workflow can be found on GitHub (see section 3.10: Data and code availability). Scanpy (1.8.2) was used to filter poor quality cells according to each dataset’s original publication. ScDblFinder (v.1.16.0) was used to identify doublets and SoupX (v.1.6.2) was used to remove ambient RNA contamination from droplet-based libraries. We normalized the expression values by first scaling the total counts across genes for each cell to equal the median of total counts for all cells before normalization, followed by log transformation and scaling to zero mean and unit variance. Principle component analysis (PCA) was performed, the nearest neighbors distance matrix and a neighborhood graph of observations (kNN) were computed, and Uniform Manifold Approximation and Projection (UMAP) representations of each dataset were generated. Leiden clusters were calculated with Scanpy default parameters for all datasets. scVelo (0.2.3) was used to generate and plot RNA velocity estimates. 3.2 Generation of a master cell cycle gene list A master list of cell cycle and quiescence genes was compiled by combining all genes from AmiGO2 with the accession id ‘GO:0007049’ (‘cell_cycle’) with 367 G0-related genes compiled from several RNA-seq and scRNA-seq analyses that compared cycling and non-cycling cell populations.19,97,98,100,169 Our final list consisted of 2120 cell cycle and G0 related genes. 75 3.3 Conversion of mouse genes to human orthologs To create a master mouse-human ortholog gene list, mouse orthologs for all human genes were identified using Ensembl and GeneCards. To then convert an expression matrix of mouse genes to their human orthologs, we accepted mouse and human genes with a 1:1 mapping directly. Human genes with no mouse orthologs were then removed. In cases where multiple human genes mapped to one mouse gene all human genes were removed. In cases where multiple mouse genes mapped to one human gene, the human gene was kept and expression values for all mouse genes was summed. To create a mouse cell cycle master gene list equivalent to our human list, we subset our master mouse-human ortholog gene list to include only orthologs for the genes in our human master cell cycle and quiescence gene list 3.4 Generation of CC UMAPs Count matrices were subset to include only cell cycle/quiescence genes included in the cell cycle gene master list (be it mouse or human). Scanpy was used to perform PCA and an appropriate number of PCs (U2OS: 20, Human fibroblast: 20, pancreatic endocrinogenesis: 15, mESC: 20, Neurogenesis: 20, Ovarian cancer: 30) was selected by generating an elbow plot prior to kNN graph generation and UMAP embedding. 76 3.5 Labelling of non-FACS datasets with phase-assignment paradigm The expression of Tirosh S and G2M marker genes136, CC UMAP shape, RNA velocity direction and Leiden clustering was used to assign phases in datasets without FACS measurements. Leiden clusters within the CC UMAP loop that displayed clear expression of S and G2M marker genes were assigned as S or G2M phases respectively. We based the boundary placement of the phase calls on the expression patterns of these markers as seen in the FACS labelled datasets (Figure 3.1). Cell clusters with lower expression of cell cycle genes but still within the CC UMAP loop, that also had RNA velocity direction consistent with cell cycle progression were labelled as G1. Clusters at the base of the tail in the CC UMAP that displayed low but not absent expression of S and G2M marker genes as well as low expression of a quiescence-downregulated gene set from Graham et al.97 and that also had RNA velocity direction consistent with exit from the active cell cycle loop were labelled as G1-G0 transition. Cell clusters further along the CC UMAP tail with completely absent expression of cell cycle and Graham quiescent downregulated genes and either no clear direction in RNA velocity, or RNA velocity direction consistent with other progressive processes such as differentiation were labelled as G0 (Figure 2.2). 77 Figure 3.1 CC UMAPs of FACS sorted datasets CC UMAPs of the FACS labelled U2OS (without presumptive non-cycling population) (upper) and mESC (lower) datasets and the expression of Tirosh S and G2M gene sets69 within them. 3.6 Benchmarking of Tricycle, DeepCycle and Scanpy Cells in the U2OS dataset that were identified by the original publication as sequestered from the cell cycle103 were removed from the dataset for the purposes of benchmarking. Scanpy’s (1.10.2) score_genes_cell_cycle() function was applied to all datasets using default parameters. For the benchmarking analysis the phases output were compared to FACS phases as a measure of accuracy. Prior to running Tricycle, Python anndata (0.7.5) objects were converted to R SingleCellExperiment objects with zellkonverter (1.4.0). Tricycle (1.10.0) was run with default parameters on all datasets. Six cell cycle phases were output (G1.S, S, G2, G2.M, M.G1 and 78 NA), and intermediary phases were accepted as a ‘correct’ labelling if either phase matched the FACS phase of the cell. DeepCycle was run using default parameters with the hotelling filter on both benchmarking datasets and the pancreatic dataset. MELK, Nusap1 and Ube2b were used as training genes for the U2OS, mESC and pancreatic datasets respectively. Phases were assigned to cells between the output phase boundaries and were considered a ‘correct’ labelling if they were the same as the FACS phase. DeepCycle output two M/G1 boundaries for the mESC dataset, and the boundary with the higher percentage of correctly labelled cells was used to evaluate performance. 3.7 Ouroboros random forest creation, training and evaluation There were several steps required to build the Ouroboros random forest phase classifier, including the building of a training dataset, feature selection, hyperparameter tuning and cross-validation for performance evaluation and application to new datasets. 3.7.1 Building a random forest training dataset To assemble a training dataset for our new classifier, we first converted the mouse genes in the pancreatic dataset to their human orthologs. The raw counts of the gene-converted pancreatic dataset were then merged with the raw counts of the full U2OS and fibroblast datasets to create a training dataset for Ouroboros. Cells removed from the U2OS dataset for the benchmarking analysis were included in the training dataset and classified as G1-G0 transition. U2OS FUCCI FACS phases as well as paradigm-labelled phases in the other datasets were used 79 as target labels in the training process. After merging, the number of cells within the G0, G1, S and G2M classes were made equal by randomly subsampling to the number of the smallest class (G1-G0 transition class was considered to be part of G0), leaving 3884 cells overall. We then normalized the expression values in the merged dataset by scaling the total counts across genes for each cell to 10 000 followed by a log transformation. 3.7.2 Feature selection A feature set was created by applying a Wilcoxin rank-sum test with Scanpy’s sc.tl.rank_genes_groups() function to all cell cycle and quiescence genes in the cell cycle gene master list, followed by the Benjamini-Hochberg (BH) procedure to correct for multiple testing. The top 8% of positive log fold change values were selected for genes in each phase in each dataset, and genes that were common between all three training datasets were retained. Shapley values were then calculated using SHAP (0.42.0), and the top absolute shapley values (top 5% of S and G2M related genes, top 12% of G1, G1-G0 transition and G0 genes) were retained to create the final feature set of 176 genes. To identify features that were not annotated cell cycling genes, we compared our feature set to to our master list of cell cycle and G0 genes created for generating CC UMAPs. 3.7.3 Hyperparameter tuning Scikit-learn’s (1.4.2) RandomForestClassifier was used to initialize a model, and we performed a grid search over key hyperparameters using 5-fold stratified cross-validation to tune 80 its performance with Scitkit learn functions GridSearchCV() and StratifiedKFold(). The optimal hyperparameters were {‘max_depth’: 20, ‘min_samples_leaf’: 1, ‘min_samples_split’: 1, ‘n_estimators’: 300}. These hyperparameters were then used to re-initialize the model for all subsequent training and testing. 3.7.4 Cross-validation for performance evaluation We first evaluated the performance of the model on the training dataset by implementing a 5-fold cross validation strategy with Scikit learn’s StratifiedKFold() and cross_val_predict() functions. Predictions for each test fold were retained and compared to the initial target labels as a measure of accuracy. We then re-trained the classifier using the full training dataset. 3.7.5 Application to non-training datasets To apply the classifier to all other non-training datasets, they were first normalized to the same standards as the training dataset (scaled total counts to 10 000 followed by log transformation). All datasets except HGSOC dataset contained counts for all training genes, and the fully trained model was then applied to estimate cell cycle phase in these datasets. The HGSOC dataset was missing eight training genes, which were likely filtered out by the original authors.63 As such, the RF model was re-trained as above but without these eight feature genes. It was subsequently applied to estimate phase in this dataset. 81 3.7.6 Gradient-boosting overfit to training dataset In an attempt to improve performance, we also tested a gradient boosting model with XGBoost (2.1.1), trained using the same training dataset and feature set. Though this model was slightly more accurate than the random forest when cross-validation was performed on the training dataset, we found that it did not generalize as well to other datasets, with lower accuracy than the random forest model on the mESC benchmarking dataset, and more admixing of G1-G0 transition and G0 phases in the neurogenesis and HGSOC datasets. For this reason, we chose to implement the random forest model as the final version of the Ouroboros classifier. 3.8 Assessing Ouroboros performance on the neurogenesis dataset Cell type identities were labelled in the neurogenesis dataset using leiden clustering and expression of the cell type marker genes listed in the original publication.164 We calculated the sum of the expression of the Tirosh S marker genes136in each cell in this dataset and then split the cells into two groups: those classified as S by Ouroboros and those not. We then performed a one-sided Mann-Whitney U test using Scipy’s (1.13.1) mannwhitneyu() function to test if the median expression of these genes in the S cells was higher than in the non-S cells. We repeated this process with the Tirosh G2M marker gene set. We applied Monocle (2.34.0) to the neurogenesis dataset to estimate developmental pseudotime. We used default parameters and supplied the list of cell identity marker genes used in the original publication as ordering genes. We split developmental pseudotime into ten bins 82 and calculated the proportion of each Ouroboros phase in each bin to investigate the change in population proportions over pseudotime. We repeated this process with each cell type. We also binarized each Ouroboros phase (actively cycling, G1-G0 transition and G0) and the remaining cells, and used Pygam’s (0.9.1) LogisticGAM() to fit a logistic general additive model to each of the phases to test if there was a statistically significant relationship with pseudotime. 3.9 Determining Ouroboros accuracy in HGSOC The Ouroboros classifier was applied to the entire HGSOC dataset including all cell types, patients and treatment timepoints. The phases output were then used in all subsequent cell-cycle related analysis for this dataset. 3.9.1 Expression of S and G2M markers in Ouroboros phases We tested if the expressions of S and G2M marker genes were significantly higher in Ouroboros S and G2M phases respectively with a Mann-Whitney U test as described in the neurogenesis section. This analysis was repeated for both Scanpy and Tricycle output phases. 3.9.2 Proportions of cycling and G1-G0 transition cells pre- and post-NACT We calculated the proportion of both cycling cells (G1, G2M and S) and G1-G0 transition cells for each cell type (malignant, immune and stromal) using the Ouroboros predicted phases in both naive and post-NACT cells and applied the mannwhitneyu() function from the statsmodels (0.14.0) Python library to assess if the changes in proportions between pre- 83 and post-treatment tumours were significant. We also calculated these proportions in each patient to identify patients that did not display a decrease in cycling proportions post-treatment. 3.9.3 Correlation between non-G0 cells and PFI We grouped the Ouroboros G1, S, G2M and G1-G0 cells together to create the non-G0 group and calculated the proportions of this group in each patient’s naïve EOC cells. We used the pearsonr() function from Scipy’s stats module to determine if this variable was statistically significantly correlated with PFI. We repeated this analysis with post-treatment malignant cells, as well as immune and stromal cell types. 3.10 Data and code availability All code needed to reproduce the results from this thesis is available on GitHub [https://github.com/haleymac/Ouroboros_paper.git], and the Ouroboros PyPI tool is available here [https://github.com/haleymac/Ouroboros.git]. The workflow to generate STARSolo compatible BAM files for non-10x libraries workflow is available here [https://github.com/haleymac/STARSolo_for_non10x]. All datasets used in this analysis are publicly available and can be downloaded through SRA or GEO (Table 3.1). 84 Dataset GEO ID SRA ID mESC GSE98664 SRP106644 U2OS GSE146773 SRP252355 Human Fibroblast GSM5111565 SRX10176017 Pancreatic endocrinogenesis GSE132188 SRP200419 Neurogenesis GSE214700 SRP400828 HGSOC GSE165897 NA Table 3.1: GEO and SRA IDs for all datasets mentioned in this thesis 85 Chapter 4: Conclusion Accurate computational tools for the identification of cell cycle phases and G0 have been missing from the scRNA-seq toolkit until now. Distinguishing clear cell cycle phases in visualizations of these datasets amongst other cellular signals has been challenging using classic embedding approaches, and our benchmarking analysis revealed the inaccuracies and limitations of existing cell cycle calling tools. Here we address the limitations of these existing methods by presenting Ouroboros: a comprehensive framework for investigating the cell cycle and G0 in scRNA-seq data. 4.1 Ouroboros: a comprehensive framework to investigate cell cycle and G0 dynamics in scRNA-seq With Ouroboros, we provide a feature selection strategy that we have shown produces better cell type integration and enables clear visualization of cell cycling and G0 sub-populations. We also provide a random forest classifier that is more accurate when classifying G1, S and G2M phases and can reliably identify not only G0 cells but also a state of G1-G0 transition. We have demonstrated that our classifier generalizes well across a variety of cellular contexts and have shown its utility in exploring cell cycling landscapes in highly complex and heterogenous datasets. Our classifier is also a computationally inexpensive, fast and user-friendly tool that integrates easily into standard scRNA-seq analysis workflows. 86 4.2 Cell cycling landscapes are related to HGSOC patient outcomes By applying Ouroboros to a HGSOC patient cohort,63 we revealed a significant negative association between the proportion of non-G0 cells in naive tumours and PFI. This was unexpected given that platinum is more cytotoxic to actively cycling cells170,171 and most of the patients in this cohort had very few or no non-G0 cells in their post-treatment tumours, which would suggest they responded positively to treatment. Some HGSOC tumours are inherently more aggressive than others however,172 and though our cohort is small and additional validation using a larger cohort is necessary, this relationship may be an indicator that the proportion of non-G0 cells in naive tumours could be an early prognostic indicator of PFI and may be an opportunity to stratify HGSOC patients by likely response to platinum chemotherapy. Ouroboros also allowed the identification of two outlier patients that did not have decreased proportions of cycling cells post-treatment but instead had higher cycling and G1-G0 transition proportions post-treatment. The naive tumours of these patients did not have abnormally high proportions of cycling cells, suggesting that they may represent a subset of patients with an abnormal response to platinum chemotherapy. These patients also had low PFIs, and it is possible that the failure of platinum chemotherapy to reduce the number of cycling cells in these patients may represent a distinct resistance mechanism worthy of more study, and that Ouroboros may allow the identification of patients with this phenotype. Though these were not the only patients with a low PFI, these results indicate that the failure of platinum to reduce malignant cycling cells may have prognostic value, and may be a clinical predictor of PFI in HGSOC patients. To confirm this hypothesis, analysis of a larger cohort is needed to elucidate the molecular basis of this resistance and its implication for patient outcomes. 87 These finding highlight the potential of Ouroboros as an accurate scRNA-seq cell cycle and G0 calling tool. Without its use, we would not have been able to identify the relationship between the proportion of non-G0 malignant cells and PFI or the patient-specific resistance to platinum, as existing cell cycle calling tools are not able to identify G0 or G1-G0 transition cells, and these relationships are not apparent when using all cells as would be common in traditional bulk RNA-seq experiment. 4.3 Future directions and limitations Ouroboros’ accuracy and functionality could be further confirmed by testing it in a wider range of biological contexts, for example other cancer or normal tissue samples, ideally with orthogonal ground truth validation of cell cycle phases. Our training and testing datasets were limited in that none had an orthogonal molecular marker of a G0 state beyond cell type, so future directions could include benchmarking Ouroboros performance on a dataset with FACS for pyronin Y staining or FUCCI with a mVenus-p27 reporter to identify G0 cells as a measure of ground truth. Given our validation of a G0-state through the use of marker gene expression and RNA velocity we believe our classifier is still capable of discerning non-cycling cells without this benchmarking analysis however. Some aspects of cell cycling and G0 dynamics remain to be resolved, namely differentiating between various G0 phenotypes including quiescence and senescence. The molecular regulation of these states is still under active investigation, and more research is needed to understand the features that may be helpful to resolve these states before a classifier capable of differentiating them can be built. Assigning cells along a continuous cell cycle 88 pseudotime is another aspect of cell cycle resolution that remains a challenge, as no current existing tool is capable of this in heterogenous datasets and/or in datasets with G0 cells. It may also be possible to further refine our CC UMAP approach to achieve better resolution of the dynamics of the restriction point, as well as separate cell cycle exit and re-entry streams. Molecular data indicates these dynamics exist, but our approach fails to capture such events with high enough resolution to characterize them in these datasets. Surprisingly, there were 51 non-cell cycle annotated genes that significantly contributed to our ability to predict cell cycle phases. The identification of these genes within our feature set suggests their involvement with cell cycle processes, but more molecular studies will be needed to elucidate the mechanisms by which they are involved with the cell cycle and G0. 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Thesis/Dissertation
eng
Bioinformatics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Steif, Adi
Resolving cell cycling and non-cycling dynamics in scRNA-seq
Text