In 1926, Haim Nachman Bialik, the premier poet and leading intellectual light of the Zionist movement, sailed for New York on a five-month-long fundraising mission on behalf of the yishuv, the pre-statehood Jewish settlement in Palestine. After his return, the poet gave a long speech in Tel Aviv, recounting his impressions of the United States before an audience of thousands. The America that Bialik presented to his listeners, this essay begins by arguing, should be read as tissue of widely circulating tropes and mythemes, which the poet had absorbed during his formative years in Europe as well as in the course of his 1926 tour. The essay then proceeds to discuss the uses to which the poet puts this (largely borrowed) narrative of American difference, focusing in particular on Bialik’s ambivalent response to the futural (largely Emersonian) ethos to which he returns time and again in his speech, and which he seems to simultaneously endorse and reject. The main part of the essay’s argument is devoted to making sense of this ambivalence, which I attribute to the diverging “temporal imaginaries” that underwrite Zionist and American exceptionalisms.

CD8+ T cells fight viral infections and other intracellular pathogens by identifying and destroying infected cells. CD8+ T cells, like all cells, take on different cell states which determine their behavior. This dissertation examines how epigenetic mechanisms such as chromatin accessibility and transcription factor binding regulate transcription, and consequently CD8+ T cell state. The particular CD8+ T cell states that I examine in this dissertation are: Naive (quiescent cells), Effector (cells actively fighting infection), Memory (cells that remain after infection is cleared, ready to fight recurrence of same infection), and Exhausted (cells with weakened cytotoxic function due to prolonged encounter with a particular antigen).

Chapter One introduces CD8+ T cells, regulation of transcription, and some of the high-throughput sequencing assays used later in the text to study CD8+ T cells. Chapter Two describes some of the computational tools I developed to process and infer useful information from high-throughput sequencing data. Chapter Three presents evidence that the chromatin of Exhausted CD8+ T cells differs from that of normal T cells, and demonstrates that these differences have functional consequences by validating an exhaustion-specific enhancer for PD-1, an important immunotherapy target. Chapter Four presents evidence that Batf and Irf4 work together to remodel chromatin and affect the binding of key CD8+ T cell transcription factors during the transition from naive to effector cell state.

cis-regulatory elements (CREs) are non-coding segments of the genome which regulate the transcription of nearby genes. They can be broadly divided to two categories: 1) promoters, positioned directly upstream of their target gene, and 2) enhancers, positioned distally to their target gene. Enhancers are thought to be the main drivers of cell type-specific and state-specific transcription, and regulate gene expression by fine-tuning the rate of transcription, as opposed to the more binary (on or off) regulatory function that promoters typically have. Understanding how enhancers function is therefore crucially important to understanding how cells obtain and maintain certain fates and determine response to stimuli. Despite their importance, much is still unknown about the roles enhancers play in many biological processes, and how their sequence determines their regulatory function.

The first part of this dissertation deals with single-cell chromatin accessibility data (e.g as produced by single-cell ATAC-seq) as a means for systemically studying heterogeneity of CREs, and specifically enhancers. In chapter 2 this is demonstrated in the innate immune system's response to vaccination: in a subset of cells, a distinct state of chromatin accessibility maintains long-term epigenetic changes that prime these cells to a different response to stimuli, and provides non-specific viral protection.However promising, the unique properties of this data modality poses significant challenges. These are addressed in chapter 3, which introduced PeakVI, a deep generative model that provides a comprehensive statistical framework for analyzing data generated by scATAC-seq assays. Recent advances in sequencing technologies now enable obtaining these measurements alongside gene expression measurements (i.e single cell RNA-seq), providing the ability to directly measure the relationship between the heterogeneity of the chromatin landscape and that of the transcriptional profile. Chapter 4 introduces MultiVI, a general framework for the joint analysis of multi-modal single-cell data, using single-cell ATAC-seq and single-cell RNA-seq as the main example. These models enable exploration of cis-regulatory programs, identification of putative key enhancers, and generating hypotheses about their regulatory functions.

The second part of this dissertation focuses on analyzing high-throughput functional data produces by massively parallel reporter assays (MPRAs). These assays enable direct functional characterization of thousands of synthetically generated candidate regulatory sequences. However, these assays include both DNA-seq and RNA-seq observations, and require controlling for various technical confounders within both assays, posing substantial computational challenges. Chapter 5 describes MPRAnalyze, a nested generalized linear model that provides a comprehensive statistical framework for analyzing MPRA data. Chapter 6 then uses MPRAnalyze extensively to identify key enhancers and novel trancription factors involved in early neural differentiation. In chapter 7, systemic perturbation of binding sites in the identified enhancers reveal the specific sequence features that determine enhancer function, and elucidates how multiple functional sites interact in a single enhancer sequence to reach the desired functional output.

The classification of cell type is one of the first steps in scRNAseq analysis for translating observed transcriptional variation to biological insights. The same cell types can be sampled from different environment and using different technologies and their transcriptional profile can differ. Thus, defining cell types in scRNAseq data is much more than a matter of identifying clusters of cells that are similar to each other. In chapter 1, we developed a simulation method SymSim in order to understand the different facets of variability in scRNAseq. In Chapter 2, we applied a Bayesian Variational Inference method scVI for the harmonization scRNAseq datasets and propose a new method scANVI in the same frame work for the annotation of these datasets. We tested the performance of scVI and scANVI using both SymSim and experimental data. In Chapter 3 we applied our data harmonization method scVI to a Multiple Sclerosis (MS) case-control study using scRNAseq data to profile immune cells. We identified cellular changes associated with MS in tissue-specific cell type abundance and transcriptional changes after being able to identify shared cell types in both blood and CSF in multiple donors. In Chapter 4 we apply a number of scRNAseq harmonization and annotation including scVI and scANVI to a large consortium cell atlas project Tabula Sapiens. Tabula Sapiens aims to provide a comprehensive reference scRNAseq dataset for the scientific community. We developed an automatic annotation pipeline named PopularVote to facilitate the in-house data annotation process, and to be published for using as a public tool for other scientists to annotate their own data. This dissertation presents a set of tools that we developed or used in cell type annotation in a diverse set of scRNAseq applications (identifying rare cell types, comparing cell types across conditions, generating automatic data annotations). The potential of scRNAseq is best realized by generating a well-curated dataset that everyone in the research community can use and contribute to, and the ability to classify cells in an automatic manner will enable such efforts in the future.