Animal\u2013microbe symbioses are fundamental to the biology of nearly all multicellular organisms, yet many questions remain regarding how hosts recognize beneficial microbes and establish stable associations. The symbiosis between the Hawaiian bobtail squid, Euprymna scolopes, and the bioluminescent bacterium Vibrio fischeri provides a powerful model for investigating these processes.
\r\n\r\nIn this dissertation, I examined host\u2013microbe interactions across multiple biological scales, from bacterial genetic variation and tissue-level responses to cellular mechanisms of symbiosis. I demonstrate that bacterial strain identity influences host transcriptional responses. I further identify the skin as an early site of host\u2013microbe interaction, revealing that hosts respond to symbiont exposure prior to stable colonization of the light organ. Finally, I establish methodologies for single-cell and single-nucleus transcriptomics in E. scolopes, providing a foundation for investigating symbiosis at cellular resolution.
\r\n\r\nTogether, these findings show that host responses to symbiotic bacteria emerge from interconnected processes spanning bacterial genetics, host tissues, and individual cell types. This work advances our understanding of how beneficial host\u2013microbe partnerships are established and maintained and provides new tools for studying symbiosis in cephalopods and other animal systems.
", "doi": "10.7907/q2b1-xn16", "publication_date": "2027", "thesis_type": "phd", "thesis_year": "2027" }, { "id": "thesis:17880", "collection": "thesis", "collection_id": "17880", "cite_using_url": "https://resolver.caltech.edu/CaltechThesis:02102026-091429391", "primary_object_url": { "basename": "Thesis_final_CF.pdf", "content": "final", "filesize": 8081793, "license": "other", "mime_type": "application/pdf", "url": "/17880/1/Thesis_final_CF.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Biophysical Modeling for Gene Expression and Evolution", "author": [ { "family_name": "Felce", "given_name": "Catherine E.", "orcid": "0009-0009-9909-6711", "clpid": "Felce-Catherine-E" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Phillips", "given_name": "Robert B.", "orcid": "0000-0003-3082-2809", "clpid": "Phillips-R" }, { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Pennell", "given_name": "Matthew", "orcid": "0000-0002-2886-3970", "clpid": "Pennell-Matthew" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_pma" } ], "abstract": "Principled biophysical modeling is a necessary foundation for analyzing RNA sequencing data. In recent years, higher quality data for other data modalities at single-cell resolution have become available. I present joint biophysical models combining two of these modalities, chromatin accessibility measurements (ATAC-seq) and protein counts, individually with single-cell transcriptomic data, and give preliminary data results. I consider the extension of biophysically motivated models to the field of phylogenetics. I present competing mechanistic hypotheses for gene expression evolution and test them via parametrized single-cell cross-species data. I also consider a physics-inspired model for population-level evolution via maternal effects and interacting subpopulations.", "doi": "10.7907/chmp-kt37", "publication_date": "2026", "thesis_type": "phd", "thesis_year": "2026" }, { "id": "thesis:18729", "collection": "thesis", "collection_id": "18729", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06012026-033232974", "primary_object_url": { "basename": "Thesis_Carilli_Maria.pdf", "content": "final", "filesize": 40294216, "license": "cc_by_nc_nd", "mime_type": "application/pdf", "url": "/18729/2/Thesis_Carilli_Maria.pdf", "version": "v6.0.0" }, "type": "thesis", "title": "Genetic Interrogation of Expression Regulation", "author": [ { "family_name": "Carilli", "given_name": "Maria Theresa Natalina", "orcid": "0000-0002-8977-7224", "clpid": "Carilli-Maria-Theresa-Natalina" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Phillips", "given_name": "Robert B.", "orcid": "0000-0003-3082-2809", "clpid": "Phillips-R" }, { "family_name": "Yue", "given_name": "Yisong", "orcid": "0000-0001-9127-1989", "clpid": "Yue-Yisong" }, { "family_name": "Engelhardt", "given_name": "Barbara E.", "orcid": "0000-0002-6139-7334", "clpid": "Engelhardt-Barbara-E" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "Understanding the effects of variation in the genome on organisms' phenotypes is a central goal of biology. For the past several decades, this has been done by performing large-scale association tests to identify links between genetic variants across the genome and particular diseases or traits. As most variants are in non-coding regions of the genome and act only in specific contexts, there arose the intermediate step of associating variants with gene expression patterns in particular tissues using bulk RNA-sequencing data and, with the recent widespread adoption of single-cell RNA-sequencing, particular cell types. However, associative tests are generally restricted to variants within some sequence distance of the gene, as testing tens of millions of distal variants against tens of thousands of genes in hundreds of contexts is computationally and statistically burdensome. Moreover, associating these proximal variants to changes in average gene expression using scRNA-seq data is far from pinpointing the cellular process they may affect. To move beyond analysis of averages, recent work has advocated for a mechanistic approach to analyzing scRNA-seq by modeling the underlying biological processes of transcription, splicing, and degradation.
\r\n\r\nIn this thesis, we first show how biophysical models are useful for identifying changes in biophysical processes not accessible at the level of mean expression. We next develop accelerated inference procedures for these models to make feasible their application at the scale required for genetic association tests. We then propose a framework for testing for the presence or absence of proximal or distal gene regulation using homozygous crosses. Finally, we couple the genetic testing framework and biophysical models to identify regulatory strategies of biophysical processes across cell types in eight tissues of eight genetically diverse mouse strains.
", "doi": "10.7907/qnzh-rf18", "publication_date": "2026", "thesis_type": "phd", "thesis_year": "2026" }, { "id": "thesis:17344", "collection": "thesis", "collection_id": "17344", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06012025-204136107", "primary_object_url": { "basename": "Methods_for_long_read_RNA_seq_transcriptomics_Loving_Rebekah_2025.pdf", "content": "final", "filesize": 51348381, "license": "other", "mime_type": "application/pdf", "url": "/17344/1/Methods_for_long_read_RNA_seq_transcriptomics_Loving_Rebekah_2025.pdf", "version": "v5.0.0" }, "type": "thesis", "title": "Methods for Long Read RNA-Seq Transcriptomics", "author": [ { "family_name": "Loving Ngo", "given_name": "Rebekah Kiana", "orcid": "0000-0001-8725-0376", "clpid": "Loving-Ngo-Rebekah-Kiana" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" }, { "family_name": "Perona", "given_name": "Pietro", "orcid": "0000-0002-7583-5809", "clpid": "Perona-P" }, { "family_name": "Mortazavi", "given_name": "Ali", "orcid": "0000-0002-4259-6362", "clpid": "Mortazavi-Ali" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "While short read RNA-seq dominated the field for decades, long read RNA-seq is particularly useful for isoform-level expression analysis, genome annotation, detecting novelly splicing transcripts, identifying exact breakpoints in gene fusions, and discovering chimeric RNAs. Long read RNA-seq has rapidly scaled to the point of producing terabytes of data from a single set of experiments. Technological advances in RNA and DNA sequencing library preparation, chemistry used in the Oxford nanopores, and basecalling algorithms have reduced long read sequencing error rates to sub-1% error. Further, the cost of long read sequencing has dropped to about one hundred US dollars per human genome. These two factors have lead to the mass production of high-throughput, long read, and single-cell RNA-seq data. While recent tools for long read RNA-seq have been developed, they have not kept pace in scalability and accuracy with long read RNA-seq in the fashion that short read RNA-seq tools have met computational scalability and accuracy challenges. To address this, in this thesis, we leverage long k-mers and pseudoalignment for mapping and quantifying long reads in the novel algorithm implemented within lr-kallisto, which yields both efficiency and higher accuracy for long read mapping and quantification than previous tools. We demonstrate that long read RNA-seq has reached sufficient depth and accuracy to yield accurate quantification of isoform-level expression for differential expression analysis. Furthermore, we explore the feasibilty of also utilizing long k-mers and pseudoalignment in both transcript discovery in dn-kallisto and gene fusion and immune receptor sequence discovery with fugi with measured success. Thus, our tools will enable a more complete, accurate, and scalable analysis of single-cell and bulk RNA-seq than has hitherto been possible in both quantifications and differential expression analysis as well as investigation of gene fusions, chimeric RNAs, and immune receptor sequences without bias.", "doi": "10.7907/3nz8-3c83", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" }, { "id": "thesis:17389", "collection": "thesis", "collection_id": "17389", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06032025-002120461", "primary_object_url": { "basename": "Thesis.pdf", "content": "final", "filesize": 25197916, "license": "other", "mime_type": "application/pdf", "url": "/17389/1/Thesis.pdf", "version": "v5.0.0" }, "type": "thesis", "title": "A Biophysical Approach to Normalization and Trajectory Inference in Single-Cell RNA Sequencing Data Analysis", "author": [ { "family_name": "Fang", "given_name": "Meichen", "orcid": "0000-0002-8217-0710", "clpid": "Fang-Meichen" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" }, { "family_name": "Bois", "given_name": "Justin S.", "orcid": "0000-0001-7137-8746", "clpid": "Bois-J-S" }, { "family_name": "Chong", "given_name": "Shasha", "orcid": "0000-0002-5372-311X", "clpid": "Chong-Shasha" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "Single-cell genomics assays, particularly single-cell RNA sequencing that enables genome-wide profiling of gene expression, have been driven forward by a combination of technological and computational advances. While producing extraordinary large amounts of data for biological discovery, methods for mining results currently rely heavily on heuristics and lack of modeling has resulted in limited mechanistic biological insight. This thesis presents two models for normalization and trajectory inference in single-cell RNA sequencing analysis to demonstrate how biophysical modeling, when combined with principled statistical inference, can yield interpretable insights grounded in rigorous theoretical frameworks.
\r\n\r\nWe begin by explaining the two cultures in single-cell RNA sequencing analysis. Next, we present the chemical master equation, which forms the theoretical foundation for biophysically informed stochastic models of gene expression, and explore an existing gap in developing uniform approximations over time under the large-volume limit. Returning to single-cell RNA sequencing data analysis, we introduce two mechanistic models for normalization and trajectory inference, which are essential components of single-cell RNA sequencing analysis.
", "doi": "10.7907/asek-t904", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" }, { "id": "thesis:17266", "collection": "thesis", "collection_id": "17266", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05232025-094543832", "primary_object_url": { "basename": "caltech-thesis-final-delaney-sullivan.pdf", "content": "final", "filesize": 19916923, "license": "other", "mime_type": "application/pdf", "url": "/17266/3/caltech-thesis-final-delaney-sullivan.pdf", "version": "v6.0.0" }, "type": "thesis", "title": "Software, Tools, and Methods Development for Single-Cell Transcriptomics", "author": [ { "family_name": "Sullivan", "given_name": "Delaney Kalcey", "orcid": "0000-0002-8359-6705", "clpid": "Sullivan-Delaney-Kalcey" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" }, { "family_name": "Guttman", "given_name": "Mitchell", "orcid": "0000-0003-4748-9352", "clpid": "Guttman-M" } ], "thesis_committee": [ { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" }, { "family_name": "Pimentel", "given_name": "Harold", "orcid": "0000-0001-8556-2499", "clpid": "Pimentel-Harold" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" }, { "family_name": "Guttman", "given_name": "Mitchell", "orcid": "0000-0003-4748-9352", "clpid": "Guttman-M" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "Advances in transcriptomics have transformed the study of gene expression, enabling a shift from low-throughput bulk RNA measurements to high-resolution, large-scale single-cell RNA-sequencing (scRNA-seq). This work refines existing methodologies and introduces new strategies for achieving precise, versatile, and scalable transcriptomic analyses across a broad spectrum of assays and biological contexts.
\r\n\r\nOn the computational front, this dissertation introduces new methods for adaptable preprocessing of sequencing reads, enabling the handling of very complex read structures. It refines existing strategies for efficiently querying large-scale transcriptomic datasets and enhances approaches for quantifying nascent and mature RNA species. A general framework is introduced for discovering and organizing biologically informative sequences directly from raw sequencing data, facilitating the detection of sample-specific or condition-specific variation. On the experimental front, a novel single-cell RNA sequencing method is presented that is cost-effective, open source, and scalable, supporting large-scale studies with substantial cell numbers and high per-cell resolution.
\r\n\r\nThese developments collectively expand the toolkit for transcriptomics, enabling more efficient and comprehensive exploration of RNA biology.
", "doi": "10.7907/kee5-ty36", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" }, { "id": "thesis:17185", "collection": "thesis", "collection_id": "17185", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:04292025-233607669", "primary_object_url": { "basename": "KilYeokyoung2025Thesis.pdf", "content": "final", "filesize": 8595745, "license": "other", "mime_type": "application/pdf", "url": "/17185/1/KilYeokyoung2025Thesis.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Engineering and Computational Tools for Salivary Biomedicine", "author": [ { "family_name": "Kil", "given_name": "Yeokyoung (Anne)", "orcid": "0000-0002-1235-7379", "clpid": "Kil-Yeokyoung-Anne" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Gao", "given_name": "Wei", "orcid": "0000-0002-8503-4562", "clpid": "Gao-Wei" }, { "family_name": "Burdick", "given_name": "Joel Wakeman", "orcid": "0000-0002-3091-540X", "clpid": "Burdick-J-W" }, { "family_name": "Wyllie", "given_name": "Anne L", "orcid": "0000-0001-6015-0279", "clpid": "Wyllie-A-L" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "Saliva is emerging as a powerful biofluid for noninvasive diagnostics, offering a window into human health through its diverse biomolecular composition. This dissertation advances the field of salivary biomedicine by addressing critical challenges in saliva collection, processing, and analysis. First, a comparative analysis of five saliva collection devices highlighted key usability factors, informing the development of SalivaStraw--a novel device designed to improve collection efficiency and minimize leakage. Next, colosseum, a low-cost, open-source fraction collector, was designed and developed to facilitate scalable saliva processing and improve biomarker isolation. Finally, a computational framework leveraging spline regression was applied to longitudinal salivary transcriptomic data, enabling the identification of temporally regulated genes and underscoring saliva\u2019s potential for dynamic health monitoring. Collectively, this work contributes new tools and methodologies that strengthen the foundation of saliva-based diagnostics, broadening its applications in precision medicine and beyond.", "doi": "10.7907/8wdh-5v36", "publication_date": "2025", "thesis_type": "phd", "thesis_year": "2025" }, { "id": "thesis:16280", "collection": "thesis", "collection_id": "16280", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:01162024-161326655", "primary_object_url": { "basename": "TDilanyan_Thesis.pdf", "content": "final", "filesize": 7266593, "license": "other", "mime_type": "application/pdf", "url": "/16280/4/TDilanyan_Thesis.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Open-Source Custom Beads for Single-Cell Transcriptomics", "author": [ { "family_name": "Dilanyan", "given_name": "Taleen Gaied", "orcid": "0000-0002-3131-3259", "clpid": "Dilanyan-Taleen-Gaied" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Rees", "given_name": "Douglas C.", "orcid": "0000-0003-4073-1185", "clpid": "Rees-D-C" }, { "family_name": "Shapiro", "given_name": "Mikhail G.", "orcid": "0000-0002-0291-4215", "clpid": "Shapiro-M-G" }, { "family_name": "Hsieh-Wilson", "given_name": "Linda C.", "orcid": "0000-0001-5661-1714", "clpid": "Hsieh-Wilson-L-C" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_chem" } ], "abstract": "Open-source single-cell genomics technologies have helped democratize single-cell genomics and expedite method development. Methods such as inDrops and Drop-seq for single-cell RNA-seq preceded popular technologies such as the 10x Genomics\u2019 Chromium platform, however despite initial enthusiasm for open-source methods, their popularity has waned. A major reason has been the lack of availability of low-cost, customizable beads, which are essential for microfluidics based single-cell RNA-seq. We address this challenge by introducing a new method for producing barcoded hydrogel beads for single-cell RNA-seq called HiPER (High-throughput PER-barcoded hydrogel beads) that allows for increasing the diversity of barcode sequences, reducing manufacturing cost, and that can be readily adapted to custom applications. HiPER barcodes are decoupled from the capture sequences and can therefore be configured to capture RNA, DNA, or tailored for specific-gene enrichment.", "doi": "10.7907/v52p-gf80", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" }, { "id": "thesis:16368", "collection": "thesis", "collection_id": "16368", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05042024-011724418", "type": "thesis", "title": "Complexity of Transcriptomic Data Analysis and Implications for Biological Discovery", "author": [ { "family_name": "Luebbert", "given_name": "Laura", "orcid": "0000-0003-1379-2927", "clpid": "Luebbert-Laura" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Van Valen", "given_name": "David A.", "orcid": "0000-0001-7534-7621", "clpid": "Van-Valen-D" }, { "family_name": "Murray", "given_name": "Richard M.", "orcid": "0000-0002-5785-7481", "clpid": "Murray-R-M" }, { "family_name": "Bjorkman", "given_name": "Pamela J.", "orcid": "0000-0002-2277-3990", "clpid": "Bjorkman-P-J" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "3MT Competition (Caltech)" }, { "literal": "div_bbe" } ], "abstract": "Over the past decade, the advancement of \u2018omics\u2019 technologies has ushered in a new era for the life sciences. Given the high-throughput nature of omics technologies, this era is characterized by unique computational challenges pertaining to data size and dimensionality, and technical and biological noise. Concurrently, it offers opportunities, as global, untargeted, and parallel measurement of large amounts of information often captures unexpected insights.
\r\n\r\nThis thesis describes challenges inherent to the omics era of life sciences, particularly highlighting the increasing importance of merging expertise in biology and computer science. It describes the development of multiple software tools designed to address several of these challenges, which were immediately adopted and widely implemented in transcriptomics and proteomics research. Additionally, it contains three chapters focused on unraveling previously unquantifiable information, including the interpretation of sequencing data from organisms with low-quality reference genome assemblies and workflows for identifying novel viruses using single-cell RNA sequencing data already massively generated in research, healthcare, and agriculture.
", "doi": "10.7907/xnw5-v914", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" }, { "id": "thesis:16438", "collection": "thesis", "collection_id": "16438", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05292024-221741183", "primary_object_url": { "basename": "Thesis_Rev_TaraChari.pdf", "content": "final", "filesize": 11821855, "license": "cc_by_nc_nd", "mime_type": "application/pdf", "url": "/16438/1/Thesis_Rev_TaraChari.pdf", "version": "v6.0.0" }, "type": "thesis", "title": "Perturbing the Genome: From Bench to Biophysics", "author": [ { "family_name": "Chari", "given_name": "Tara Varada", "orcid": "0000-0002-6953-4313", "clpid": "Chari-Tara-Varada" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Qian", "given_name": "Lulu", "orcid": "0000-0003-4115-2409", "clpid": "Qian-Lulu" }, { "family_name": "Murray", "given_name": "Richard M.", "orcid": "0000-0002-5785-7481", "clpid": "Murray-R-M" }, { "family_name": "Anderson", "given_name": "David J.", "orcid": "0000-0001-6175-3872", "clpid": "Anderson-D-J" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "In single-cell genomics, we can simultaneously assay hundreds of thousands of cells, their molecular contents, and how they respond to perturbation, from genetic knockouts to environmental changes. This thesis focuses on how to merge experimental and computational techniques to generate and analyze large-scale perturbation data for high-resolution systems biology. Beginning at the bench, we demonstrate how combining large-scale cell atlas surveys with multi-condition experimentation can illuminate the diversity of cell types across whole organisms and cellular strategies in response to environmental changes and perturbations. We then investigate the limitations of current practice in exploratory analysis, and strategies for determining preservation or distortion of biological insight by these data transformation and dimensionality reduction techniques. To address these limitations, we demonstrate how stochastic biophysical models can rewrite the way we interpret complex perturbation data, taking greater advantage of the diverse molecular measurements to develop biological hypotheses about DNA and RNA regulation in cellular function, development, and disease.
", "doi": "10.7907/5drv-ma07", "publication_date": "2024", "thesis_type": "phd", "thesis_year": "2024" }, { "id": "thesis:15247", "collection": "thesis", "collection_id": "15247", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05312023-213322223", "primary_object_url": { "basename": "moses_lambda_2023.pdf", "content": "final", "filesize": 48841902, "license": "other", "mime_type": "application/pdf", "url": "/15247/1/moses_lambda_2023.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Computation Foundations of Spatial Transcriptomics", "author": [ { "family_name": "Moses", "given_name": "Lambda", "orcid": "0000-0002-7092-9427", "clpid": "Moses-Lambda" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Van Valen", "given_name": "David A.", "orcid": "0000-0001-7534-7621", "clpid": "Van-Valen-D" }, { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" }, { "family_name": "Pimentel", "given_name": "Harold", "orcid": "0000-0001-8556-2499", "clpid": "Pimentel-Harold" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "Single-cell and spatial transcriptomics have come of age in the past few years; datasets and data analysis software packages have proliferated. With the increasing sizes of datasets, proliferating new data collection technologies, and mainstreaming of high-throughput technologies, the software can be improved for better speed and memory efficiency, standardized and consistent user interface for multiple technologies, and in documentation to onboard new users. First, I collected a database of spatial transcriptomics literature and analyzed the data on trends and sociology in this field. Based on the database and data analyses, I wrote a comprehensive book both qualitatively and quantitatively documenting the history of the field since the 1960s and reviewing more recent developments, which informed the software and methods I later developed. Then, to address the challenges with the pre-processing large datasets, we developed \\texttt{kallisto} \\texttt{bustools} for fast and modular pseudoalignment of sequencing reads to the transcriptome in single-cell RNA-seq (scRNA-seq), giving consistent results with the established and much more computationally demanding alignment method Cell Ranger. Briefly summarized are my attempt to map dissociated cells in scRNA-seq to a spatial gene expression reference and to build a image processing pipeline for image based spatial transcriptomics data analysis. Finally, to address the challenges in downstream analyses of spatial -omics data, I first wrote the new \\texttt{SpatialFeatureExperiment} (SFE) data structure to represent and operate on geometries in spatial transcriptomics data and to organize results from spatial analyses. Based on SFE, I wrote Voyager, which brings decades of research in geospatial data analysis to spatial transcriptomics, to better utilize the opportunities from spatial information to gain novel biological insights. To reduce user learning curve, Voyager conforms to SCE styles and conventions and has a comprehensive documentation website and consistent user interface to many geospatial methods.
", "doi": "10.7907/rt24-pq60", "publication_date": "2023", "thesis_type": "phd", "thesis_year": "2023" }, { "id": "thesis:15105", "collection": "thesis", "collection_id": "15105", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:02122023-103759689", "primary_object_url": { "basename": "Ph_D__thesis.pdf", "content": "final", "filesize": 11301290, "license": "other", "mime_type": "application/pdf", "url": "/15105/3/Ph_D__thesis.pdf", "version": "v8.0.0" }, "type": "thesis", "title": "Graph Modeling for Genomics and Epidemiology", "author": [ { "family_name": "Eldjarn Hjoerleifsson", "given_name": "Kristjan", "orcid": "0000-0002-7851-1818", "clpid": "Eldjarn-Hjoerleifsson-Kristjan" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Wold", "given_name": "Barbara J.", "orcid": "0000-0003-3235-8130", "clpid": "Wold-B-J" }, { "family_name": "Wierman", "given_name": "Adam C.", "orcid": "0000-0002-5923-0199", "clpid": "Wierman-A-C" }, { "family_name": "Melsted", "given_name": "Pall", "orcid": "0000-0002-8418-6724", "clpid": "Melsted-Pall" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_eng" } ], "abstract": "The last decades have seen great leaps made in the development of RNA sequencing technologies, yielding lower cost and greater throughput of experiments, to the point where the scale of the data produced on a daily basis is staggering. While computational hardware is also continuously improving, famously (or perhaps infamously) described by Gordon Moore (Moore, 1965), the rate at which data are produced eclipses advances on the hardware front. Over the last few years, many new methods have been proposed for bridging that ever-widening chasm, more than a few of which harness the latent graphical structure of genomic data to reduce the number of calculations required and pack the data tighter in memory. This body of work continues this development on three different, but related, fronts. Firstly, I present developments that greatly improve upon the efficiency of state-of-the-art methods for the quantification of RNA-seq reads, and describe a method that improves the accuracy of quantification without substantially increasing the computational over- head. Secondly, I introduce a procedure for the discovery of associations between novel gene isoforms and phenotypes, without prior knowledge of those isoforms. Lastly, I present the largest reconstruction of the transmission tree of a viral outbreak to date, modeled from viral genome sequences, contact tracing, and symptom data. I then use the reconstructed transmission tree to assess the efficacy of different vaccination strategies.", "doi": "10.7907/s32c-a211", "publication_date": "2023", "thesis_type": "phd", "thesis_year": "2023" }, { "id": "thesis:16062", "collection": "thesis", "collection_id": "16062", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06022023-194724728", "type": "thesis", "title": "Stochastic Foundations for Single-Cell RNA Sequencing", "author": [ { "family_name": "Gorin", "given_name": "Gennady", "orcid": "0000-0001-6097-2029", "clpid": "Gorin-Gennady" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Shapiro", "given_name": "Mikhail G.", "orcid": "0000-0002-0291-4215", "clpid": "Shapiro-M-G" }, { "family_name": "Wang", "given_name": "Zhen-Gang", "orcid": "0000-0002-3361-6114", "clpid": "Wang-Zhen-Gang" }, { "family_name": "Chong", "given_name": "Shasha", "orcid": "0000-0002-5372-311X", "clpid": "Chong-Shasha" }, { "family_name": "Ismagilov", "given_name": "Rustem F.", "orcid": "0000-0002-3680-4399", "clpid": "Ismagilov-R-F" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_chem" } ], "abstract": "Single-cell RNA sequencing, which quantifies cell transcriptomes, has seen widespread adoption, accompanied by proliferation of analysis methods. However, there has been relatively little systematic investigation of its best practices and their underlying assumptions, leading to challenges and discrepancies in interpretation. I present a set of generic, principled strategies for modeling the biological and technical components of sequencing experiments and use case studies to motivate their application to sequencing data.
", "doi": "10.7907/jn6n-x368", "publication_date": "2023", "thesis_type": "phd", "thesis_year": "2023" }, { "id": "thesis:16081", "collection": "thesis", "collection_id": "16081", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:06042023-195408313", "primary_object_url": { "basename": "galvezmerchan_angel_2023_thesis.pdf", "content": "final", "filesize": 15283976, "license": "other", "mime_type": "application/pdf", "url": "/16081/1/galvezmerchan_angel_2023_thesis.pdf", "version": "v4.0.0" }, "type": "thesis", "title": "Studies of mRNA Expression and Degradation", "author": [ { "family_name": "G\u00e1lvez Merch\u00e1n", "given_name": "\u00c1ngel", "orcid": "0000-0001-7420-8697", "clpid": "G\u00e1lvez-Merch\u00e1n-\u00c1ngel" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" }, { "family_name": "Voorhees", "given_name": "Rebecca M.", "orcid": "0000-0003-1640-2293", "clpid": "Voorhees-R-M" } ], "thesis_committee": [ { "family_name": "Aravin", "given_name": "Alexei A.", "orcid": "0000-0002-6956-8257", "clpid": "Aravin-A-A" }, { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" }, { "family_name": "Voorhees", "given_name": "Rebecca M.", "orcid": "0000-0003-1640-2293", "clpid": "Voorhees-R-M" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "Part 1: Protein degradation coupled to Nonsense-mediated mRNA decay
\r\n\r\nTranslation of mRNAs containing premature termination codons (PTCs) results in truncated protein products with deleterious effects. Nonsense-mediated decay (NMD) is a surveillance pathway responsible for detecting PTC containing transcripts. While the molecular mechanisms governing mRNA degradation have been extensively studied, the fate of the nascent protein product remains largely uncharacterized. In part 1 of this thesis, we use a fluorescent reporter system in mammalian cells to reveal a selective degradation pathway specifically targeting the protein product of an NMD mRNA. We show that this process is post-translational, and dependent on the ubiquitin proteasome system. To systematically uncover factors involved in NMD-linked protein quality control, we conducted genome-wide flow cytometry-based screens. Our screens recovered known NMD factors, but suggested protein degradation did not depend on the canonical ribosome-quality control (RQC) pathway. A subsequent arrayed screen demonstrated that protein and mRNA branches of NMD rely on a shared recognition event. Our results establish the existence of a targeted pathway for nascent protein degradation from PTC containing mRNAs, and provides a reference for the field to identify and characterize required factors.
\r\n\r\nPart 2: The Commons Cell Atlas
\r\n\r\nCurrent cell atlas projects aim to curate representative datasets, cell-types, and marker genes for tissues across an organism. Despite their ubiquity, atlas projects rely on duplicated and manual effort to curate marker genes and annotate cell-types. Importantly, the lack of data-compatible tools and a fixed representation of the atlas make their reanalysis near-impossible. To overcome these challenges, we present a collection of data, algorithms, and tools to automate cataloging and analyzing cell-types across all tissues in an organism. We leveraged this work to build a Human Commons Cell Atlas comprising 2.9 million cells across 27 tissues that can be easily updated and that is structured to facilitate custom analyses. To showcase the flexibility of the atlas, we demonstrate that it can be used for isoform analyses. In particular, we study cell-type specificity of isoforms of OAS1, which has recently been shown to offer SARS-CoV-2 protection in certain individuals that display higher expression of the p46 isoform. Using our Commons Cell Atlas, we localize the OAS1 p44b isoform to the testis, and find that it is specific to germ line cells. By virtue of enabling customized analyses via a modular and dynamic atlas structure, the Commons Cell Atlas should be useful for exploratory analyses that are intractable within the rigid framework of current gene-centric static atlases.
", "doi": "10.7907/esxk-ch24", "publication_date": "2023", "thesis_type": "phd", "thesis_year": "2023" }, { "id": "thesis:14649", "collection": "thesis", "collection_id": "14649", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:05292022-204424650", "type": "thesis", "title": "Foundations and Applications of Single-Cell RNA Sequencing", "author": [ { "family_name": "Booeshaghi", "given_name": "Ali Sina", "orcid": "0000-0002-6442-4502", "clpid": "Booeshaghi-Ali-Sina" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Greer", "given_name": "Julia R.", "orcid": "0000-0002-9675-1508", "clpid": "Greer-J-R" }, { "family_name": "Colonius", "given_name": "Tim", "orcid": "0000-0003-0326-3909", "clpid": "Colonius-T" }, { "family_name": "Melsted", "given_name": "P\u00e1ll", "orcid": "0000-0002-8418-6724", "clpid": "Melsted-P\u00e1ll" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "local_group": [ { "literal": "div_eng" } ], "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.
\r\n\r\nThis 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.
\r\n\r\nThe 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.
\r\n\r\nThe 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.
", "doi": "10.7907/ptbp-a779", "publication_date": "2022", "thesis_type": "phd", "thesis_year": "2022" }, { "id": "thesis:11226", "collection": "thesis", "collection_id": "11226", "cite_using_url": "https://resolver.caltech.edu/CaltechTHESIS:10102018-143313907", "type": "thesis", "title": "Statistical Methods for Gene Differential Expression Analysis of RNA-Sequencing", "author": [ { "family_name": "Yi", "given_name": "Lynn Donglin", "orcid": "0000-0003-4575-0158", "clpid": "Yi-Lynn-Donglin" } ], "thesis_advisor": [ { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" } ], "thesis_committee": [ { "family_name": "Chan", "given_name": "David C.", "orcid": "0000-0002-0191-2154", "clpid": "Chan-D-C" }, { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Pachter", "given_name": "Lior S.", "orcid": "0000-0002-9164-6231", "clpid": "Pachter-L" }, { "family_name": "Chandrasekaran", "given_name": "Venkat", "clpid": "Chandrasekaran-V" } ], "local_group": [ { "literal": "div_bbe" } ], "abstract": "RNA-Sequencing (\"RNA-Seq\") is performed to measure gene expression, often to ask the question of what genes are differentially expressed across various biological conditions. Statistical methods have been used to model RNA-Seq quantifications in order to determine differential expression, and have traditionally be divided into gene-level methods and transcript-level methods. There has been little attempt to connect the statistical divide, although transcript expression and gene expression are biologically inextricably linked. In this thesis, we provide a case study of a comparative differential expression analysis, demonstrating that many differential expression events happen on the isoform-level, and that performing an analysis using only summarized gene quantifications would fail to capture these events. Furthermore, we develop statistical methods that unify the transcript-level and gene-level analysis. In bulk RNA-Seq, by using p-value aggregation methods, we are able to translate transcript-level results into gene-level results under a unified framework. For single cell RNA-Seq, we propose using multiple logistic regression, leveraging the high dimensionality of the data in order to determine if the transcript quantifications pertaining to a gene are able to constitute a linear discriminant for cell type. This method combines differential transcript expression analysis and differential gene expression analysis into a unified framework which we call \u201cgene differential expression.\u201d Lastly, we demonstrate that our methods could be used on transcript compatibility counts instead of transcript quantifications in order to bypass ambiguous read assignment and improve accuracy. We show that transcript compatibility counts obtained via transcriptome pseudoalignment are comparable in quantification accuracy to quantifications from genome alignment methods.
", "doi": "10.7907/0YE6-2217", "publication_date": "2019", "thesis_type": "phd", "thesis_year": "2019" } ]