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A new computational approach enables integrative analysis of disparate single-cell RNA–sequencing data sets by identifying shared patterns of variation between cell subpopulations. Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat ( http://satijalab.org/seurat/ ), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

Multimodal single-cell assays provide high-resolution snapshots of complex cell populations, but are mostly limited to transcriptome plus an additional modality. Here, we describe expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq) for the high-throughput characterization of at least five modalities of information from each single cell. We demonstrate application of ECCITE-seq to multimodal CRISPR screens with robust direct single-guide RNA capture and to clonotype-aware multimodal phenotyping of cancer samples.ECCITE-seq combines the single-cell analysis of multiple modalities, for example transcriptome, immune cell receptors, cell surface proteins and single-guide RNAs.

Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells. Chromatin accessibility, gene expression and protein levels are measured in the same single cell.

Hematopoietic stem cells (HSCs) give rise to diverse cell types in the blood system, yet our molecular understanding of the early trajectories that generate this enormous diversity in humans remains incomplete. Here, we leverage Drop‐seq, a massively parallel single‐cell RNA sequencing (scRNA‐seq) approach, to individually profile 20,000 progenitor cells from human cord blood, without prior enrichment or depletion for individual lineages based on surface markers. Our data reveal a transcriptional compendium of progenitor states in human cord blood, representing four committed lineages downstream from HSC, alongside the transcriptional dynamics underlying fate commitment. We identify intermediate stages that simultaneously co‐express “primed” programs for multiple downstream lineages, and also observe striking heterogeneity in the early molecular transitions between myeloid subsets. Integrating our data with a recently published scRNA‐seq dataset from human bone marrow, we illustrate the molecular similarity between these two commonly used systems and further explore the chromatin dynamics of “primed” transcriptional programs based on ATAC‐seq. Finally, we demonstrate that Drop‐seq data can be utilized to identify new heterogeneous surface markers of cell state that correlate with functional output. Synopsis Single‐cell transcriptome profiling of hematopoietic progenitors collected from human cord blood provides molecular evidence for multi‐lineage transcriptomic priming in early progenitors, which correlates with epigenetic state, surface marker expression, and functional output. Unsupervised reconstruction of transcriptomic cell states in human CD34 + hematopoietic progenitors from cord blood is performed using Drop‐seq. “Primed” and “ de novo ” programs that accompany specification into four downstream lineages are identified. Integration of cord blood and bone marrow single cell datasets reveals strong conservation of molecular programs. Heterogeneous surface markers are identified within early lymphoid‐primed multipotent progenitors (LMPPs) and their expression correlates with transcriptomic state and functional potential. Graphical Abstract Single‐cell transcriptome profiling of hematopoietic progenitors collected from human cord blood provides molecular evidence for multi‐lineage transcriptomic priming in early progenitors, which correlates with epigenetic state, surface marker expression, and functional output.

Advances in single-cell RNA sequencing (scRNA-seq) have allowed for comprehensive analysis of the immune system. In this Review, we briefly describe the available scRNA-seq technologies together with their corresponding strengths and weaknesses. We discuss in depth how scRNA-seq can be used to deconvolve immune system heterogeneity by identifying novel distinct immune cell subsets in health and disease, characterizing stochastic heterogeneity within a cell population and building developmental 'trajectories' for immune cells. Finally, we discuss future directions of the field and present integrated approaches to complement molecular information from a single cell with studies of the environment, epigenetic state and cell lineage.

Continuous supply of immune cells throughout life relies on the delicate balance in the hematopoietic stem cell (HSC) pool between long-term maintenance and meeting the demands of both normal blood production and unexpected stress conditions. Here we identified distinct subsets of human long-term (LT)-HSCs that responded differently to regeneration-mediated stress: an immune checkpoint ligand CD112 lo subset that exhibited a transient engraftment restraint (termed latency) before contributing to hematopoietic reconstitution and a primed CD112 hi subset that responded rapidly. This functional heterogeneity and CD112 expression are regulated by INKA1 through direct interaction with PAK4 and SIRT1, inducing epigenetic changes and defining an alternative state of LT-HSC quiescence that serves to preserve self-renewal and regenerative capacity upon regeneration-mediated stress. Collectively, our data uncovered the molecular intricacies underlying HSC heterogeneity and self-renewal regulation and point to latency as an orchestrated physiological response that balances blood cell demands with preserving a stem cell reservoir. Dick and colleagues identify human LT-HSC subsets with distinct quiescent states. They link these differences to INKA1-mediated downregulation of the transmembrane protein CD112 and its interaction with the protein deacetylase SIRT1. INKA1 is inversely correlated with the histone H4K16Ac mark, which then distinguishes ‘latent’ CD112 lo LT-HSCs from CD112 hi LT-HSCs that are more readily activated in response to hematopoietic stress.

The expression of inhibitory immune checkpoint molecules, such as programmed death-ligand (PD-L)1, is frequently observed in human cancers and can lead to the suppression of T cell–mediated immune responses. Here, we apply expanded CRISPR-compatible (EC)CITE-seq, a technology that combines pooled CRISPR screens with single-cell mRNA and surface protein measurements, to explore the molecular networks that regulate PD-L1 expression. We also develop a computational framework, mixscape, that substantially improves the signal-to-noise ratio in single-cell perturbation screens by identifying and removing confounding sources of variation. Applying these tools, we identify and validate regulators of PD-L1 and leverage our multimodal data to identify both transcriptional and post-transcriptional modes of regulation. Specifically, we discover that the Kelch-like protein KEAP1 and the transcriptional activator NRF2 mediate the upregulation of PD-L1 after interferon (IFN)-γ stimulation. Our results identify a new mechanism for the regulation of immune checkpoints and present a powerful analytical framework for the analysis of multimodal single-cell perturbation screens. ECCITE-seq, which combines pooled CRISPR screens with single-cell mRNA and surface protein measurements, and the computational framework mixscape identify new regulation mechanisms of PD-L1 expression.

Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq), which leverages the RNA-targeting CRISPR–Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling. CaRPool-seq encodes multiple perturbations on a cleavable CRISPR array that is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes. We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells. Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes. This work introduces CaRPool-seq that utilizes the RNA-targeting CRISPR–Cas13d system to perform combinatorial perturbations in single-cell screens.

BAP1 regulation of EZH2 provides therapeutic opportunities in cancer. The tumor suppressors BAP1 and ASXL1 interact to form a polycomb deubiquitinase complex that removes monoubiquitin from histone H2A lysine 119 (H2AK119Ub). However, BAP1 and ASXL1 are mutated in distinct cancer types, consistent with independent roles in regulating epigenetic state and malignant transformation. Here we demonstrate that Bap1 loss in mice results in increased trimethylated histone H3 lysine 27 (H3K27me3), elevated enhancer of zeste 2 polycomb repressive complex 2 subunit ( Ezh2 ) expression, and enhanced repression of polycomb repressive complex 2 (PRC2) targets. These findings contrast with the reduction in H3K27me3 levels seen with Asxl1 loss. Conditional deletion of Bap1 and Ezh2 in vivo abrogates the myeloid progenitor expansion induced by Bap1 loss alone. Loss of BAP1 results in a marked decrease in H4K20 monomethylation (H4K20me1). Consistent with a role for H4K20me1 in the transcriptional regulation of EZH2, expression of SETD8—the H4K20me1 methyltransferase—reduces EZH2 expression and abrogates the proliferation of BAP1 -mutant cells. Furthermore, mesothelioma cells that lack BAP1 are sensitive to EZH2 pharmacologic inhibition, suggesting a novel therapeutic approach for BAP1 -mutant malignancies.

The immune system is our body’s defense mechanism against a variety of foreign threats. It is composed of many different cell types, each one serving a unique function. To understand how each cell type contributes to pathogen elimination we need to extensively characterize the molecular pathways that drive their behavior. Doing so will open new therapeutic avenues for treating diseases such cancer. Recent advances in single-cell genomics methods have made it possible to explore cell type heterogeneity in complex tissues. Meanwhile, the ever expanding CRISPR tool kit offers a unique opportunity to understand how each gene participates in shaping cell identity and function. In this work, I use scRNA-seq and CRISPR to study multiple aspects of the immune system. I start with studying hematopoiesis, a developmental process that generates all immune subsets and move on to dissect the regulation of inhibitory immune checkpoints, molecules that are important for establishing self tolerance and at the same time assist tumors in escaping immune surveillance. Furthermore, I develop a new computational method for the analysis of single-cell pooled CRISPR screens to remove technical and CRISPR noise and discover new biology. In the first study presented here (Chapter 2) we used scRNA-seq to characterize all hematopoietic stem and progenitor cell (HSPCs) populations found in human cord blood. After cell type annotation, we identify transcriptional signatures to reconstruct the developmental trajectory of hematopoiesis and identify key transcriptional dynamics that drive hematopoietic fate decisions. Moreover, we propose CD52 and CSFR3 as new surface protein markers for the isolation of early lymphoid-primed and neutrophil/monocyte-primed progenitors using flow cytometry. This work lays a foundation for characterizing heterogeneous populations of cells and understanding fate choice decisions in developmental systems. Finally, it inspires the creation of single-cell methods to simultaneously test and validate the role of each gene in cell fate decisions and cell function. In the second study (Chapter 3) we develop a new single-cell sequencing technology to simultaneously capture the transcriptomes, surface protein expression, CRISPR perturbations and, T and B cell receptor clonotypes from single cells (ECCITE-seq). We showed ECCITE-seq can reliably capture the gRNA each cell received and confirmed robust down regulation of target gene expression both at the RNA and surface protein expression level. Furthermore, we used clonotype and surface protein expression measurements to define various T cell subsets in healthy and cutaneous T cell lymphoma and identified distinct gene expression signatures that define malignant cells. This multimodal single-cell method is vital for studying gene function as it overcomes existing limitations of pooled CRISPR screens discussed in the introduction. In the third study (Chapter 4) we set out to uncover the regulators of the immune checkpoint protein PD-L1 using ECCITE-seq. To analyze this new dataset type, we build a new computational framework (mixscape) that removes technical and biological noise while preserving the perturbation-specific signatures in the data. Having protein and RNA measurements of PD-L1, we are able to deconvolve transcriptional and post-translational modes of regulation. Specifically, we show that the CUL3-KEAP1 complex controls PD-L1 expression at the transcriptional level, while the CUL3-SPOP complex modulates PD-L1 protein stability. Contrary to other studies, we also demonstrate that BRD4, a bromodomain reader, negatively regulates PD-L1 expression. Overall, this study demonstrates the power of multimodal methods in dissecting precisely how each gene works as part of a molecular pathway to control other genes and thereby influence cellular behavior. Finally, while cell context plays a role in gene expression and regulation we believe that core pathway signatures are conserved across different cell types. This realization inspired the preliminary work described in Chapter 5 where we explore how a set of perturbations affect interferon beta pathway expression across cell types from different tissues. While we find many signatures to be conserved, we also identify smaller subsets of genes that are unique to each tissue. In part these differences can be attributed to cell type-specific genetic backgrounds and chromatin states although further experiments are needed to validate this statement. We envision applying this approach to study how different pathway components contribute to the regulation of the pathway itself with the goal being to create a comprehensive atlas of pathway and transcription factor signatures that can serve as a reference to understand pathway dysregulation in various disease contexts.