Single-Cell Transcriptomics Reveals Early Emergence of Liver Parenchymal and Non-parenchymal Cell Lineages.

The cellular complexity and scale of the early liver have constrained analyses examining its emergence during organogenesis. To circumvent these issues, we analyzed 45,334 single-cell transcriptomes from embryonic day (E)7.5, when endoderm progenitors are specified, to E10.5 liver, when liver parenchymal and non-parenchymal cell lineages emerge. Our data detail divergence of vascular and sinusoidal endothelia, including a distinct transcriptional profile for sinusoidal endothelial specification by E8.75. We characterize two distinct mesothelial cell types as well as early hepatic stellate cells and reveal distinct spatiotemporal distributions for these populations. We capture transcriptional profiles for hepatoblast specification and migration, including the emergence of a hepatomesenchymal cell type and evidence for hepatoblast collective cell migration. Further, we identify cell-cell interactions during the organization of the primitive sinusoid. This study provides a comprehensive atlas of liver lineage establishment from the endoderm and mesoderm through to the organization of the primitive sinusoid at single-cell resolution.

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment.

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph nodes, using single-cell RNA-seq. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer. Our results have important implications for characterizing tumor-infiltrating immune cells.

Negative Co-stimulation Constrains T Cell Differentiation by Imposing Boundaries on Possible Cell States.

Co-stimulation regulates T cell activation, but it remains unclear whether co-stimulatory pathways also control T cell differentiation. We used mass cytometry to profile T cells generated in the genetic absence of the negative co-stimulatory molecules CTLA-4 and PD-1. Our data indicate that negative co-stimulation constrains the possible cell states that peripheral T cells can acquire. CTLA-4 imposes major boundaries on CD4+ T cell phenotypes, whereas PD-1 subtly limits CD8+ T cell phenotypes. By computationally reconstructing T cell differentiation paths, we identified protein expression changes that underlied the abnormal phenotypic expansion and pinpointed when lineage choice events occurred during differentiation. Similar alterations in T cell phenotypes were observed after anti-CTLA-4 and anti-PD-1 antibody blockade. These findings implicate negative co-stimulation as a key regulator and determinant of T cell differentiation and suggest that checkpoint blockade might work in part by altering the limits of T cell phenotypes.

Signals that control MAIT cell function in healthy and inflamed human tissues.

Mucosal-associated invariant T (MAIT) cells have a semi-invariant T-cell receptor that allows recognition of antigen in the context of the MHC class I-related (MR1) protein. Metabolic intermediates of the riboflavin synthesis pathway have been identified as MR1-restricted antigens with agonist properties. As riboflavin synthesis occurs in many bacterial species, but not human cells, it has been proposed that the main purpose of MAIT cells is antibacterial surveillance and protection. The majority of human MAIT cells secrete interferon-gamma (IFNg) upon activation, while some MAIT cells in tissues can also express IL-17. Given that MAIT cells are present in human barrier tissues colonized by a microbiome, MAIT cells must somehow be able to distinguish colonization from infection to ensure effector functions are only elicited when necessary. Importantly, MAIT cells have additional functional properties, including the potential to contribute to restoring tissue homeostasis by expression of CTLA-4 and secretion of the cytokine IL-22. A recent study provided compelling data indicating that the range of human MAIT cell functional properties is explained by plasticity rather than distinct lineages. This further underscores the necessity to better understand how different signals regulate MAIT cell function. In this review, we highlight what is known in regards to activating and inhibitory signals for MAIT cells with a specific focus on signals relevant to healthy and inflamed tissues. We consider the quantity, quality, and the temporal order of these signals on MAIT cell function and discuss the current limitations of computational tools to extrapolate which signals are received by MAIT cells in human tissues. Using lessons learned from conventional CD8 T cells, we also discuss how TCR signals may integrate with cytokine signals in MAIT cells to elicit distinct functional states.

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon.

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

Epigenomic-Guided Mass Cytometry Profiling Reveals Disease-Specific Features of Exhausted CD8 T Cells.

Exhausted CD8 T (Tex) cells are immunotherapy targets in chronic infection and cancer, but a comprehensive assessment of Tex cell diversity in human disease is lacking. Here, we developed a transcriptomic- and epigenetic-guided mass cytometry approach to define core exhaustion-specific genes and disease-induced changes in Tex cells in HIV and human cancer. Single-cell proteomic profiling identified 9 distinct Tex cell clusters using phenotypic, functional, transcription factor, and inhibitory receptor co-expression patterns. An exhaustion severity metric was developed and integrated with high-dimensional phenotypes to define Tex cell clusters that were present in healthy subjects, common across chronic infection and cancer or enriched in either disease, linked to disease severity, and changed with HIV therapy. Combinatorial patterns of immunotherapy targets on different Tex cell clusters were also defined. This approach and associated datasets present a resource for investigating human Tex cell biology, with implications for immune monitoring and immunomodulation in chronic infections, autoimmunity, and cancer.

CellRank for directed single-cell fate mapping.

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank ( https://cellrank.org ) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

The emergent landscape of the mouse gut endoderm at single-cell resolution.

Here we delineate the ontogeny of the mammalian endoderm by generating 112,217 single-cell transcriptomes, which represent all endoderm populations within the mouse embryo until midgestation. We use graph-based approaches to model differentiating cells, which provides a spatio-temporal characterization of developmental trajectories and defines the transcriptional architecture that accompanies the emergence of the first (primitive or extra-embryonic) endodermal population and its sister pluripotent (embryonic) epiblast lineage. We uncover a relationship between descendants of these two lineages, in which epiblast cells differentiate into endoderm at two distinct time points-before and during gastrulation. Trajectories of endoderm cells were mapped as they acquired embryonic versus extra-embryonic fates and as they spatially converged within the nascent gut endoderm, which revealed these cells to be globally similar but retain aspects of their lineage history. We observed the regionalized identity of cells along the anterior-posterior axis of the emergent gut tube, which reflects their embryonic or extra-embryonic origin, and the coordinated patterning of these cells into organ-specific territories.

MicroRNA 28 controls cell proliferation and is down-regulated in B-cell lymphomas.

Burkitt lymphoma (BL) is a highly aggressive B-cell non-Hodgkin lymphoma (B-NHL), which originates from germinal center (GC) B cells and harbors translocations deregulating v-myc avian myelocytomatosis viral oncogene homolog (MYC). A comparative analysis of microRNAs expressed in normal and malignant GC B cells identified microRNA 28 (miR-28) as significantly down-regulated in BL, as well as in other GC-derived B-NHL. We show that reexpression of miR-28 impairs cell proliferation and clonogenic properties of BL cells by modulating several targets including MAD2 mitotic arrest deficient-like 1, MAD2L1, a component of the spindle checkpoint whose down-regulation is essential in mediating miR-28-induced proliferation arrest, and BCL2-associated athanogene, BAG1, an activator of the ERK pathway. We identify the oncogene MYC as a negative regulator of miR-28 expression, suggesting that its deregulation by chromosomal translocation in BL leads to miR-28 suppression. In addition, we show that miR-28 can inhibit MYC-induced transformation by directly targeting genes up-regulated by MYC. Overall, our data suggest that miR-28 acts as a tumor suppressor in BL and that its repression by MYC contributes to B-cell lymphomagenesis.

Integrated genomic profiling identifies microRNA-92a regulation of IQGAP2 in locally advanced rectal cancer.

Locally advanced rectal cancer (LARC) is treated with chemoradiation prior to surgical excision, leaving residual tumors altered or completely absent. Integrating layers of genomic profiling might identify regulatory pathways relevant to rectal tumorigenesis and inform therapeutic decisions and further research. We utilized formalin-fixed, paraffin-embedded pre-treatment LARC biopsies (n=138) and compared copy number, mRNA, and miRNA expression with matched normal rectal mucosa. An integrative model was used to predict regulatory interactions to explain gene expression changes. These predictions were evaluated in vitro using multiple colorectal cancer cell lines. The Cancer Genome Atlas (TCGA) was also used as an external cohort to validate our genomic profiling and predictions. We found differentially expressed mRNAs and miRNAs that characterize LARC. Our integrative model predicted the upregulation of miR-92a, miR-182, and miR-221 expression to be associated with downregulation of their target genes after adjusting for the effect of copy number alterations. Cell line studies using miR-92a mimics and inhibitors demonstrate that miR-92a expression regulates IQGAP2 expression. We show that endogenous miR-92a expression is inversely associated with endogenous KLF4 expression in multiple cell lines, and that this relationship is also present in rectal cancers of TCGA. Our integrative model predicted regulators of gene expression change in LARC using pre-treatment FFPE tissues. Our methodology implicated multiple regulatory interactions, some of which are corroborated by independent lines of study, while others indicate new opportunities for investigation.

SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.

Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/.

Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma.

Large-scale cancer genomics projects are profiling hundreds of tumors at multiple molecular layers, including copy number, mRNA and miRNA expression, but the mechanistic relationships between these layers are often excluded from computational models. We developed a supervised learning framework for integrating molecular profiles with regulatory sequence information to reveal regulatory programs in cancer, including miRNA-mediated regulation. We applied our approach to 320 glioblastoma profiles and identified key miRNAs and transcription factors as common or subtype-specific drivers of expression changes. We confirmed that predicted gene expression signatures for proneural subtype regulators were consistent with in vivo expression changes in a PDGF-driven mouse model. We tested two predicted proneural drivers, miR-124 and miR-132, both underexpressed in proneural tumors, by overexpression in neurospheres and observed a partial reversal of corresponding tumor expression changes. Computationally dissecting the role of miRNAs in cancer may ultimately lead to small RNA therapeutics tailored to subtype or individual.

Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon.

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.

Molecular heterogeneity of C. elegans glia across sexes.

A comprehensive description of nervous system function, and sex dimorphism within, is incomplete without clear assessment of the diversity of its component cell types, neurons and glia. C. elegans has an invariant nervous system with the first mapped connectome of a multicellular organism and single-cell atlas of component neurons. Here we present single nuclear RNA-seq evaluation of glia across the entire adult C. elegans nervous system, including both sexes. Machine learning models enabled us to identify both sex-shared and sex-specific glia and glial subclasses. We have identified and validated molecular markers in silico and in vivo for these molecular subcategories. Comparative analytics also reveals previously unappreciated molecular heterogeneity in anatomically identical glia between and within sexes, indicating consequent functional heterogeneity. Furthermore, our datasets reveal that while adult C. elegans glia express neuropeptide genes, they lack the canonical unc-31/CAPS-dependent dense core vesicle release machinery. Thus, glia employ alternate neuromodulator processing mechanisms. Overall, this molecular atlas, available at www.wormglia.org, reveals rich insights into heterogeneity and sex dimorphism in glia across the entire nervous system of an adult animal.

Single-cell analysis of bidirectional reprogramming between early embryonic states reveals mechanisms of differential lineage plasticities.

Two distinct fates, pluripotent epiblast (EPI) and primitive (extra-embryonic) endoderm (PrE), arise from common progenitor cells, the inner cell mass (ICM), in mammalian embryos. To study how these sister identities are forged, we leveraged embryonic (ES) and eXtraembryonic ENdoderm (XEN) stem cells - in vitro counterparts of the EPI and PrE. Bidirectional reprogramming between ES and XEN coupled with single-cell RNA and ATAC-seq analyses uncovered distinct rates, efficiencies and trajectories of state conversions, identifying drivers and roadblocks of reciprocal conversions. While GATA4-mediated ES-to-iXEN conversion was rapid and nearly deterministic, OCT4, KLF4 and SOX2-induced XEN-to-iPS reprogramming progressed with diminished efficiency and kinetics. The dominant PrE transcriptional program, safeguarded by Gata4, and globally elevated chromatin accessibility of EPI underscored the differential plasticities of the two states. Mapping in vitro trajectories to embryos revealed reprogramming in either direction tracked along, and toggled between, EPI and PrE in vivo states without transitioning through the ICM.

SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data.

Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.

DUX4 expression in cancer induces a metastable early embryonic totipotent program.

The transcription factor DUX4 regulates a portion of the zygotic gene activation (ZGA) program in the early embryo. Many cancers express DUX4 but it is unknown whether this generates cells similar to early embryonic stem cells. Here we identified cancer cell lines that express DUX4 and showed that DUX4 is transiently expressed in a small subset of the cells. DUX4 expression activates the DUX4-regulated ZGA transcriptional program, the subsequent 8C-like program, and markers of early embryonic lineages, while suppressing steady-state and interferon-induced MHC class I expression. Although DUX4 was expressed in a small number of cells under standard culture conditions, DNA damage or changes in growth conditions increased the fraction of cells expressing DUX4 and its downstream programs. Our demonstration that transient expression of endogenous DUX4 in cancer cells induces a metastable early embryonic stem cell program and suppresses antigen presentation has implications for cancer growth, progression, and immune evasion.

Tissue Schematics: A framework to decipher tissue architecture and assembly.

High-parameter spatial proteomics provide unprecedented opportunities to investigate how tissue architectures are assembled. In an article in this issue of Cell Systems, Bhate et al. present "Tissue Schematics," a conceptual framework and computational approach to decipher the rules of tissue assembly.

CUT&Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells.

Cleavage Under Targets and Tagmentation (CUT&Tag) is an antibody-directed transposase tethering strategy for in situ chromatin profiling in small samples and single cells. We describe a modified CUT&Tag protocol using a mixture of an antibody to the initiation form of RNA polymerase II (Pol2 Serine-5 phosphate) and an antibody to repressive Polycomb domains (H3K27me3) followed by computational signal deconvolution to produce high-resolution maps of both the active and repressive regulomes in single cells. The ability to seamlessly map active promoters, enhancers, and repressive regulatory elements using a single workflow provides a complete regulome profiling strategy suitable for high-throughput single-cell platforms.

Defining cellular population dynamics at single-cell resolution during prostate cancer progression.

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigate prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in an in vivo murine model. We observe an expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is partially androgen responsive. Androgen-independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity, which is inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition, which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.