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Foundations and Applications of Single-Cell RNA Sequencing

Citation

Booeshaghi, Ali Sina (2022) Foundations and Applications of Single-Cell RNA Sequencing. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/ptbp-a779. https://resolver.caltech.edu/CaltechTHESIS:05292022-204424650

Abstract

Single-cell RNA-sequencing is an experimental technique for studying cellular gene expression, with a multitude of engineering challenges. These challenges transcend the boundaries of traditional academic disciplines and the field of mechanical engineering, that aims to address roadblocks in critical technologies towards engineering our environment, is central to this endeavor.

This thesis addresses three engineering challenges that must be met in order to realize the goal of bringing single-cell RNA sequencing to the clinic. The first is scalable cellular isolation and sampling. Chapter 2 describes the poseidon and colosseum instruments that enable massive scale single-cell isolation and collection. They each have novel design elements that reduce cost and enable modularity, at a similar accuracy to expensive commercial alternatives.

The second challenge is the rapid preprocessing of single-cell RNA-sequencing data. Chapter 3 describes the kallisto | bustools command-line tools that make scalable scRNAseq analysis fast and efficient. These tools implement novel algorithms for sequence read-alignment, barcode error correction, and molecular counting that helps resolve ambiguities in sequence mapping.

The third challenge is refining gene expression data to the isoform level. This refinement is crucial for understanding transcriptional regulation and the effects of alternative splicing in biological processes. Towards that end, I have extended the kallisto | bustools workflow to process full-length scRNAseq data taking advantage of expectation maximization algorithm to disambiguate sequence alignments. Chapter four describes how I used these tools to assemble the first ever spatially-resolved single-cell isoform atlas, and in particular one of great interest in the neuroscience community (the mouse primary motor cortex) with data generated with three RNA-sequencing assays.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:single cell rna sequencing
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Mechanical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Pachter, Lior S.
Thesis Committee:
  • Greer, Julia R. (chair)
  • Colonius, Timothy E.
  • Melsted, Páll
  • Pachter, Lior S.
Defense Date:27 May 2022
Record Number:CaltechTHESIS:05292022-204424650
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:05292022-204424650
DOI:10.7907/ptbp-a779
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41587-021-00870-2DOIModular, efficient and constant-memory single-cell RNA-seq preprocessing. (Article adapted for Chapter 3)
https://doi.org/10.1038/s41586-021-03500-8DOIA transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. (Referenced in Published Content and Contributions)
https://doi.org/10.1038/s41586-021-03950-0DOIA multimodal cell census and atlas of the mammalian primary motor cortex. (Referenced in Published Content and Contributions)
https://doi.org/10.1038/s41551-021-00754-5DOIMassively scaled-up testing for SARS-CoV-2 RNA via next-generation sequencing of pooled and barcoded nasal and saliva samples. (Referenced in Published Content and Contributions)
https://doi.org/10.1038/s41598-019-48815-9DOIPrinciples of open source bioinstrumentation applied to the poseidon syringe pump system. (Article adapted for Chapter 2)
https://doi.org/10.1101/2020.08.09.20171223DOIMarkedly heterogeneous COVID-19 testing plans among US colleges and universities. (Referenced in Published Content and Contributions)
https://doi.org/10.1101/2020.04.02.021451DOIDecrease in ACE2 mRNA expression in aged mouse lung. (Referenced in Published Content and Contributions)
https://doi.org/10.1038/s41586-021-03969-3DOIIsoform cell-type specificity in the mouse primary motor cortex. (Article adapted for Chapter 4)
https://doi.org/10.1093/bioinformatics/btab085DOINormalization of single-cell RNA-seq counts by log (x+ 1) or log (1+ x). (Article adapted for Chapter 3)
https://doi.org/10.1038/s41598-020-78942-7DOIReliable and accurate diagnostics from highly multiplexed sequencing assays. (Referenced in Published Content and Contributions)
https://doi.org/10.1101/2021.01.25.428188DOIBenchmarking of lightweight-mapping based single-cell RNA-seq pre-processing. (Referenced in Published Content and Contributions)
https://doi.org/10.1016/j.ohx.2021.e00201DOILow-cost, scalable, and automated fluid sampling for fluidics applications. (Article adapted for Chapter 2)
https://doi.org/10.1101/2022.05.06.490859DOIDepth normalization for single-cell genomics count data. (Referenced in Published Content and Contributions)
ORCID:
AuthorORCID
Booeshaghi, Ali Sina0000-0002-6442-4502
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14649
Collection:CaltechTHESIS
Deposited By: Ali Booeshaghi
Deposited On:02 Jun 2022 19:54
Last Modified:09 Jun 2022 19:44

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