Platform technology for selective single cell RNA sequencing of transient events
department or school: Applied Science, Faculty of
department or school: Mechanical Engineering, Department of
data provider: DSpace
degree campus: UBCV
creator: Lee, JeongHyun
issued: 2023-04-30T07:00:00Z
issued: 2020
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Cells are encapsulated decision-making units that measure external stimuli, process incoming information, and develop appropriate responses. A key window into cellular decision-making are the transcriptional programs enacted after specific events. Recent advances in transcriptome analysis by single cell RNA sequencing have revealed the remarkable transcriptional heterogeneity between cells. However, current methods are unable to investigate the changes of the transcriptional profile adopted by specific cells after specific events. This dissertation presents See-N-Seq, a new method to selectively extract RNA from single cells identified by imaging in order to perform transcriptome profiling after specific events. Our process involves embedding the cell sample in a permeable hydrogel in a microwell, and then photo-patterning a second impermeable hydrogel inside the permeable hydrogel. Non-target cells are encapsulated in the impermeable hydrogel, while target cells are encapsulated in the permeable hydrogel. Target cells can then be lysed to release RNA, which is then captured, barcoded, reverse transcribed, amplified, and sequenced at high-depth. We initially tested this process by extracting single cell RNA from mixtures of transgenic cancer cells, which is then analyzed using qPCR. We demonstrated the utility of this technology by sequencing RNA from an in vitro model of immune synapses formed between Jurkat cells and Raji cells with Staphylococcal enterotoxin E (SEE) super-antigen. The RNA from synapse formed single cell pairs were selected and sequenced as well as non-synapse formed single cell pairs at 0, 4, 24 hour time points. As a control, RNA from the bulk and single cell samples of Jurkat cells and Raji cells were also sequenced. The transcriptome sequencing data showed an average of 0.39 +/- 0.11 million unique transcripts for each cell or cell pair, which represent most of the mRNA in single cells. Furthermore, we confirmed the potential for Jurkat cells …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/74228
note: PLATFORM TECHNOLOGY FOR SELECTIVE SINGLE CELL RNA SEQUENCING OF TRANSIENT EVENTS by JeongHyun Lee B.A.Sc., Myongji University, 2011 M. Sc., Myongji University, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2020 © JeongHyun Lee, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Platform technology for selective single cell RNA sequencing of transient events submitted by Jeonghyun Lee in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin Mechanical Engineering Examining Committee: Hongshen Ma, Mechanical EngineeringSupervisor Dana Grecov, Mechanical EngineeringSupervisory Committee Member Supervisory Committee Member Mu Chiao, Mechanical EngineeringUniversity Examiner Christian Kastrup, Biochemistry and Molecular BiologyUniversity Examiner Additional Supervisory Committee Members: Boris Stoeber, Mechanical EngineeringSupervisory Committee Member Karen Cheung, Electrical and Computer EngineeringSupervisory Committee Member iii Abstract Cells are encapsulated decision-making units that measure external stimuli, process incoming information, and develop appropriate responses. A key window into cellular decision-making are the transcriptional programs enacted after specific events. Recent advances in transcriptome analysis by single cell RNA sequencing have revealed the remarkable transcriptional heterogeneity between cells. However, current methods are unable to investigate the changes of the transcriptional profile adopted by specific cells after specific events. This dissertation presents See-N-Seq, a new method to selectively extract RNA from single cells identified by imaging in order to perform transcriptome profiling after specifi…
has type: Thesis/Dissertation
date issued: 2020-05
is shown at: 10.14288/1.0390037
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/74228