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.

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.

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.

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.

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.

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.

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.

Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling.

Drug resistance in cancer is often linked to changes in tumor cell state or lineage, but the molecular mechanisms driving this plasticity remain unclear. Using murine organoid and genetically engineered mouse models, we investigated the causes of lineage plasticity in prostate cancer and its relationship to antiandrogen resistance. We found that plasticity initiates in an epithelial population defined by mixed luminal-basal phenotype and that it depends on increased Janus kinase (JAK) and fibroblast growth factor receptor (FGFR) activity. Organoid cultures from patients with castration-resistant disease harboring mixed-lineage cells reproduce the dependency observed in mice by up-regulating luminal gene expression upon JAK and FGFR inhibitor treatment. Single-cell analysis confirms the presence of mixed-lineage cells with increased JAK/STAT (signal transducer and activator of transcription) and FGFR signaling in a subset of patients with metastatic disease, with implications for stratifying patients for clinical trials.

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.

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.

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.

Fully defined human pluripotent stem cell-derived microglia and tri-culture system model C3 production in Alzheimer's disease.

Aberrant inflammation in the CNS has been implicated as a major player in the pathogenesis of human neurodegenerative disease. We developed a new approach to derive microglia from human pluripotent stem cells (hPSCs) and built a defined hPSC-derived tri-culture system containing pure populations of hPSC-derived microglia, astrocytes, and neurons to dissect cellular cross-talk along the neuroinflammatory axis in vitro. We used the tri-culture system to model neuroinflammation in Alzheimer's disease with hPSCs harboring the APPSWE+/+ mutation and their isogenic control. We found that complement C3, a protein that is increased under inflammatory conditions and implicated in synaptic loss, is potentiated in tri-culture and further enhanced in APPSWE+/+ tri-cultures due to microglia initiating reciprocal signaling with astrocytes to produce excess C3. Our study defines the major cellular players contributing to increased C3 in Alzheimer's disease and presents a broadly applicable platform to study neuroinflammation in human disease.

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.

Regenerative lineages and immune-mediated pruning in lung cancer metastasis.

Developmental processes underlying normal tissue regeneration have been implicated in cancer, but the degree of their enactment during tumor progression and under the selective pressures of immune surveillance, remain unknown. Here we show that human primary lung adenocarcinomas are characterized by the emergence of regenerative cell types, typically seen in response to lung injury, and by striking infidelity among transcription factors specifying most alveolar and bronchial epithelial lineages. In contrast, metastases are enriched for key endoderm and lung-specifying transcription factors, SOX2 and SOX9, and recapitulate more primitive transcriptional programs spanning stem-like to regenerative pulmonary epithelial progenitor states. This developmental continuum mirrors the progressive stages of spontaneous outbreak from metastatic dormancy in a mouse model and exhibits SOX9-dependent resistance to natural killer cells. Loss of developmental stage-specific constraint in macrometastases triggered by natural killer cell depletion suggests a dynamic interplay between developmental plasticity and immune-mediated pruning during metastasis.

Author Correction: Characterization of cell fate probabilities in single-cell data with Palantir.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

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.

Characterization of cell fate probabilities in single-cell data with Palantir.

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

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.