Despite recent advances in single-cell genomic, transcriptional, and mass-cytometric profiling, it remains a challenge to collect highly multiplexed measurements of secreted proteins from single cells for comprehensive analysis of functional states. Herein, we combine spatial and spectral encoding with polydimethylsiloxane (PDMS) microchambers for codetection of 42 immune effector proteins secreted from single cells, representing the highest multiplexing recorded to date for a single-cell secretion assay. Using this platform to profile differentiated macrophages stimulated with lipopolysaccharide (LPS), the ligand of Toll-like receptor 4 (TLR4), reveals previously unobserved deep functional heterogeneity and varying levels of pathogenic activation. Uniquely protein profiling on the same single cells before and after LPS stimulation identified a role for macrophage inhibitory factor (MIF) to potentiate the activation of LPS-induced cytokine production. Advanced clustering analysis identified functional subsets including quiescent, polyfunctional fully activated, partially activated populations with different cytokine profiles. This population architecture is conserved throughout the cell activation process and prevails as it is extended to other TLR ligands and to primary macrophages derived from a healthy donor. This work demonstrates that the phenotypically similar cell population still exhibits a large degree of intrinsic heterogeneity at the functional and cell behavior level. This technology enables full-spectrum dissection of immune functional states in response to pathogenic or environmental stimulation, and opens opportunities to quantify deep functional heterogeneity for more comprehensive and accurate immune monitoring.

Signaling from the T-cell receptor (TCR) conditions T-cell differentiation and activation, requiring exquisite sensitivity and discrimination. Using mass cytometry, a high-dimensional technique that can probe multiple signaling nodes at the single-cell level, we interrogate TCR signaling dynamics in control C57BL/6 and autoimmunity-prone nonobese diabetic (NOD) mice, which show ineffective ERK activation after TCR triggering. By quantitating signals at multiple steps along the signaling cascade and parsing the phosphorylation level of each node as a function of its predecessors, we show that a small impairment in initial pCD3ζ activation resonates farther down the signaling cascade and results in larger defects in activation of the ERK1/2-S6 and IκBα modules. This nonlinear property of TCR signaling networks, which magnifies small initial differences during signal propagation, also applies in cells from B6 mice activated at different levels of intensity. Impairment in pCD3ζ and pSLP76 is not a feedback consequence of a primary deficiency in ERK activation because no proximal signaling defect was observed in Erk2 KO T cells. These defects, which were manifest at all stages of T-cell differentiation from early thymic pre-T cells to memory T cells, may condition the imbalanced immunoregulation and tolerance in NOD T cells. More generally, this amplification of small initial differences in signal intensity may explain how T cells discriminate between closely related ligands and adopt strongly delineated cell fates.

Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts.

Regulatory T (Treg) cells prevent autoimmunity by limiting immune responses and inflammation in the secondary lymphoid organs and nonlymphoid tissues. While unique subsets of Treg cells have been described in some nonlymphoid tissues, their relationship to Treg cells in secondary lymphoid organs and circulation remains unclear. Furthermore, it is possible that Treg cells from similar tissue types share largely similar properties. We have identified a short-lived effector Treg cell subset that expresses the α2 integrin, CD49b, and exhibits a unique tissue distribution, being abundant in peripheral blood, vasculature, skin, and skin-draining lymph nodes, but uncommon in the intestines and in viscera-draining lymph nodes. CD49b+ Treg cells, which display superior functionality revealed by in vitro and in vivo assays, appear to develop after multiple rounds of cell division and TCR-dependent activation. Accordingly, single-cell RNA-seq analysis placed these cells at the apex of the Treg developmental trajectory. These results shed light on the identity and development of a functionally potent subset of mature effector Treg cells that recirculate through and survey peripheral tissues.

Immune-checkpoint blockade is able to achieve durable responses in a subset of patients; however, we lack a satisfying comprehension of the underlying mechanisms of anti-CTLA-4- and anti-PD-1-induced tumor rejection. To address these issues, we utilized mass cytometry to comprehensively profile the effects of checkpoint blockade on tumor immune infiltrates in human melanoma and murine tumor models. These analyses reveal a spectrum of tumor-infiltrating T cell populations that are highly similar between tumor models and indicate that checkpoint blockade targets only specific subsets of tumor-infiltrating T cell populations. Anti-PD-1 predominantly induces the expansion of specific tumor-infiltrating exhausted-like CD8 T cell subsets. In contrast, anti-CTLA-4 induces the expansion of an ICOS+ Th1-like CD4 effector population in addition to engaging specific subsets of exhausted-like CD8 T cells. Thus, our findings indicate that anti-CTLA-4 and anti-PD-1 checkpoint-blockade-induced immune responses are driven by distinct cellular mechanisms.

Tissue damage increases the risk of cancer through poorly understood mechanisms1. In mouse models of pancreatic cancer, pancreatitis associated with tissue injury collaborates with activating mutations in the Kras oncogene to markedly accelerate the formation of early neoplastic lesions and, ultimately, adenocarcinoma2,3. Here, by integrating genomics, single-cell chromatin assays and spatiotemporally controlled functional perturbations in autochthonous mouse models, we show that the combination of Kras mutation and tissue damage promotes a unique chromatin state in the pancreatic epithelium that distinguishes neoplastic transformation from normal regeneration and is selected for throughout malignant evolution. This cancer-associated epigenetic state emerges within 48 hours of pancreatic injury, and involves an 'acinar-to-neoplasia' chromatin switch that contributes to the early dysregulation of genes that define human pancreatic cancer. Among the factors that are most rapidly activated after tissue damage in the pre-malignant pancreatic epithelium is the alarmin cytokine interleukin 33, which recapitulates the effects of injury in cooperating with mutant Kras to unleash the epigenetic remodelling program of early neoplasia and neoplastic transformation. Collectively, our study demonstrates how gene-environment interactions can rapidly produce gene-regulatory programs that dictate early neoplastic commitment, and provides a molecular framework for understanding the interplay between genetic and environmental cues in the initiation of cancer.

Dendritic cells (DCs) play a critical role in orchestrating adaptive immune responses due to their unique ability to initiate T cell responses and direct their differentiation into effector lineages. Classical DCs have been divided into two subsets, cDC1 and cDC2, based on phenotypic markers and their distinct abilities to prime CD8 and CD4 T cells. While the transcriptional regulation of the cDC1 subset has been well characterized, cDC2 development and function remain poorly understood. By combining transcriptional and chromatin analyses with genetic reporter expression, we identified two principal cDC2 lineages defined by distinct developmental pathways and transcriptional regulators, including T-bet and RORγt, two key transcription factors known to define innate and adaptive lymphocyte subsets. These novel cDC2 lineages were characterized by distinct metabolic and functional programs. Extending our findings to humans revealed conserved DC heterogeneity and the presence of the newly defined cDC2 subsets in human cancer.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in protein abundance and confirmation. However, typical computational approaches treat them as static interaction networks derived from a single time point. Here, we provide methods for learning the dynamic modulation of relationships between proteins from static single-cell data. We demonstrate our approach using TGFß induced epithelial-to-mesenchymal transition (EMT) in murine breast cancer cell line, profiled with mass cytometry. We take advantage of the asynchronous rate of transition to EMT in the data and derive a pseudotime EMT trajectory. We propose methods for visualizing and quantifying time-varying edge behavior over the trajectory, and a metric of edge dynamism to predict the effect of drug perturbations on EMT.

Drugs that inhibit the MAPK pathway have therapeutic benefit in melanoma, but responses vary between patients, for reasons that are still largely unknown. Here we aim at explaining this variability using pre- and post-MEK inhibition transcriptional profiles in a panel of melanoma cell lines. We found that most targets are context specific, under the influence of the pathway in only a subset of cell lines. We developed a computational method to identify context-specific targets, and found differences in the activity levels of the interferon pathway, driven by a deletion of the interferon locus. We also discovered that IFNα/β treatment strongly enhances the cytotoxic effect of MEK inhibition, but only in cell lines with low activity of interferon pathway. Taken together, our results suggest that the interferon pathway plays an important role in, and predicts, the response to MAPK inhibition in melanoma. Our analysis demonstrates the value of system-wide perturbation data in predicting drug response.

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.

Radial glia (RG) cells are the first neural stem cells to appear during embryonic development. Adult human glioblastomas harbor a subpopulation of RG-like cells with typical RG morphology and markers. The cells exhibit the classic and unique mitotic behavior of normal RG in a cell-autonomous manner. Single-cell RNA sequencing analyses of glioblastoma cells reveal transcriptionally dynamic clusters of RG-like cells that share the profiles of normal human fetal radial glia and that reside in quiescent and cycling states. Functional assays show a role for interleukin in triggering exit from dormancy into active cycling, suggesting a role for inflammation in tumor progression. These data are consistent with the possibility of persistence of RG into adulthood and their involvement in tumor initiation or maintenance. They also provide a putative cellular basis for the persistence of normal developmental programs in adult tumors.

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.

Cellular diversity of the lung endothelium has not been systematically characterized in humans. We provide a reference atlas of human lung endothelial cells (ECs) to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium.

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.

The advent of cost-effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug-free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene-drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.

The mammary gland is a very dynamic organ that undergoes continuous remodeling. The critical regulators of this process are not fully understood. Here we identify the microRNA cluster miR-424(322)/503 as an important regulator of epithelial involution after pregnancy. Through the generation of a knockout mouse model, we found that regression of the secretory acini of the mammary gland was compromised in the absence of miR-424(322)/503. Mechanistically, we show that miR-424(322)/503 orchestrates cell life and death decisions by targeting BCL-2 and IGF1R (insulin growth factor-1 receptor). Furthermore, we demonstrate that the expression of this microRNA cluster is regulated by TGF-β, a well-characterized regulator of mammary involution. Overall, our data suggest a model in which activation of the TGF-β pathway after weaning induces the transcription of miR-424(322)/503, which in turn down-regulates the expression of key genes. Here, we unveil a previously unknown, multilayered regulation of epithelial tissue remodeling coordinated by the microRNA cluster miR-424(322)/503.

Recent single-cell analysis technologies offer an unprecedented opportunity to elucidate developmental pathways. Here we present Wishbone, an algorithm for positioning single cells along bifurcating developmental trajectories with high resolution. Wishbone uses multi-dimensional single-cell data, such as mass cytometry or RNA-Seq data, as input and orders cells according to their developmental progression, and it pinpoints bifurcation points by labeling each cell as pre-bifurcation or as one of two post-bifurcation cell fates. Using 30-channel mass cytometry data, we show that Wishbone accurately recovers the known stages of T-cell development in the mouse thymus, including the bifurcation point. We also apply the algorithm to mouse myeloid differentiation and demonstrate its generalization to additional lineages. A comparison of Wishbone to diffusion maps, SCUBA and Monocle shows that it outperforms these methods both in the accuracy of ordering cells and in the correct identification of branch points.