Comprehensive Integration of Single-Cell Data

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors

Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

The diverse malignant, stromal, and immune cells in tumors affect growth, metastasis, and response to therapy. We profiled transcriptomes of ∼6,000 single cells from 18 head and neck squamous cell carcinoma (HNSCC) patients, including five matched pairs of primary tumors and lymph node metastases. Stromal and immune cells had consistent expression programs across patients. Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-to-mesenchymal transition (p-EMT). Cells expressing the p-EMT program spatially localized to the leading edge of primary tumors. By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined HNSCC subtypes by their malignant and stromal composition and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features. Our results provide insight into the HNSCC ecosystem and define stromal interactions and a p-EMT program associated with metastasis.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

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.

Single-Cell RNA-Seq Reveals the Transcriptional Landscape and Heterogeneity of Aortic Macrophages in Murine Atherosclerosis

RATIONALE

It is assumed that atherosclerotic arteries contain several macrophage subsets endowed with specific functions. The precise identity of these subsets is poorly characterized as they have been defined by the expression of a restricted number of markers.

OBJECTIVE

We have applied single-cell RNA sequencing as an unbiased profiling strategy to interrogate and classify aortic macrophage heterogeneity at the single-cell level in atherosclerosis.

METHOD AND RESULTS

We performed single-cell RNA sequencing of total aortic CD45⁺ cells extracted from the nondiseased (chow fed) and atherosclerotic (11 weeks of high-fat diet) aorta of low-density lipoprotein receptor-deficient (Ldlr^-/-) mice. Unsupervised clustering singled out 13 distinct aortic cell clusters. Among the myeloid cell populations, resident-like macrophages with a gene expression profile similar to aortic resident macrophages were found in healthy and diseased aortas, whereas monocytes, monocyte-derived dendritic cells, and 2 populations of macrophages were almost exclusively detectable in atherosclerotic aortas, comprising inflammatory macrophages showing enrichment in Il1b and previously undescribed TREM2^hi (triggered receptor expressed on myeloid cells 2) macrophages showing enrichment in Trem2. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these 3 macrophage subsets and monocyte-derived dendritic cells and uncovered putative functions of each cell type. Notably, TREM2^hi macrophages seemed to be endowed with specialized functions in lipid metabolism and catabolism and presented a gene expression signature reminiscent of osteoclasts, suggesting a role in lesion calcification. TREM2 expression was moreover detected in human lesional macrophages. Importantly, these macrophage populations were present also in advanced atherosclerosis and in Apoe^-/- aortas, indicating relevance of our findings in different stages of atherosclerosis and mouse models.

CONCLUSIONS

These data unprecedentedly uncovered the transcriptional landscape and phenotypic heterogeneity of aortic macrophages and monocyte-derived dendritic cells in atherosclerotic and identified previously unrecognized macrophage populations and their gene expression signature, suggesting specialized functions. Our findings will open up novel opportunities to explore distinct myeloid cell populations and their functions in atherosclerosis.

A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation

The isocortex and hippocampal formation (HPF) in the mammalian brain play critical roles in perception, cognition, emotion, and learning. We profiled ∼1.3 million cells covering the entire adult mouse isocortex and HPF and derived a transcriptomic cell-type taxonomy revealing a comprehensive repertoire of glutamatergic and GABAergic neuron types. Contrary to the traditional view of HPF as having a simpler cellular organization, we discover a complete set of glutamatergic types in HPF homologous to all major subclasses found in the six-layered isocortex, suggesting that HPF and the isocortex share a common circuit organization. We also identify large-scale continuous and graded variations of cell types along isocortical depth, across the isocortical sheet, and in multiple dimensions in hippocampus and subiculum. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation and begins to shed light on its underlying relationship with the development, evolution, connectivity, and function of these two brain structures.

Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy

Clinical benefits of cytokine blockade in ileal Crohn's disease (iCD) are limited to a subset of patients. Here, we applied single-cell technologies to iCD lesions to address whether cellular heterogeneity contributes to treatment resistance. We found that a subset of patients expressed a unique cellular module in inflamed tissues that consisted of IgG plasma cells, inflammatory mononuclear phagocytes, activated T cells, and stromal cells, which we named the GIMATS module. Analysis of ligand-receptor interaction pairs identified a distinct network connectivity that likely drives the GIMATS module. Strikingly, the GIMATS module was also present in a subset of patients in four independent iCD cohorts (n = 441), and its presence at diagnosis correlated with failure to achieve durable corticosteroid-free remission upon anti-TNF therapy. These results emphasize the limitations of current diagnostic assays and the potential for single-cell mapping tools to identify novel biomarkers of treatment response and tailored therapeutic opportunities.

Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution

Microglia, the tissue-resident macrophages in the brain, are damage sensors that react to nearly any perturbation, including neurodegenerative diseases such as Alzheimer’s disease (AD). Here, using single-cell RNA sequencing, we determined the transcriptome of more than 1,600 individual microglia cells isolated from the hippocampus of a mouse model of severe neurodegeneration with AD-like phenotypes and of control mice at multiple time points during progression of neurodegeneration. In this neurodegeneration model, we discovered two molecularly distinct reactive microglia phenotypes that are typified by modules of co-regulated type I and type II interferon response genes, respectively. Furthermore, our work identified previously unobserved heterogeneity in the response of microglia to neurodegeneration, discovered disease stage-specific microglia cell states, revealed the trajectory of cellular reprogramming of microglia in response to neurodegeneration, and uncovered the underlying transcriptional programs.

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

Hepatocellular carcinoma (HCC) has high relapse and low 5-year survival rates. Single-cell profiling in relapsed HCC may aid in the design of effective anticancer therapies, including immunotherapies. We profiled the transcriptomes of ∼17,000 cells from 18 primary or early-relapse HCC cases. Early-relapse tumors have reduced levels of regulatory T cells, increased dendritic cells (DCs), and increased infiltrated CD8+ T cells, compared with primary tumors, in two independent cohorts. Remarkably, CD8+ T cells in recurrent tumors overexpressed KLRB1 (CD161) and displayed an innate-like low cytotoxic state, with low clonal expansion, unlike the classical exhausted state observed in primary HCC. The enrichment of these cells was associated with a worse prognosis. Differential gene expression and interaction analyses revealed potential immune evasion mechanisms in recurrent tumor cells that dampen DC antigen presentation and recruit innate-like CD8+ T cells. Our comprehensive picture of the HCC ecosystem provides deeper insights into immune evasion mechanisms associated with tumor relapse.

Microglia Heterogeneity in the Single-Cell Era

Microglia are resident immune cells in the central nervous system (CNS) that are capable of carrying out prominent and various functions during development and adulthood under both homeostatic and disease conditions. Although microglia are traditionally thought to be heterogeneous populations, which potentially allows them to achieve a wide range of responses to environmental changes for the maintenance of CNS homeostasis, a lack of unbiased and high-throughput methods to assess microglia heterogeneity has prevented the study of spatially and temporally distributed microglia subsets. The recent emergence of novel single-cell techniques, such as cytometry by time-of-flight mass spectrometry (CyTOF) and single-cell RNA sequencing, enabled scientists to overcome such limitations and reveal the surprising context-dependent heterogeneity of microglia. In this review, we summarize the current knowledge about the spatial, temporal, and functional diversity of microglia during development, homeostasis, and disease in mice and humans.

RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes

Pancreatic islet cells are critical for maintaining normal blood glucose levels, and their malfunction underlies diabetes development and progression. We used single-cell RNA sequencing to determine the transcriptomes of 1,492 human pancreatic α, β, δ, and PP cells from non-diabetic and type 2 diabetes organ donors. We identified cell-type-specific genes and pathways as well as 245 genes with disturbed expression in type 2 diabetes. Importantly, 92% of the genes have not previously been associated with islet cell function or growth. Comparison of gene profiles in mouse and human α and β cells revealed species-specific expression. All data are available for online browsing and download and will hopefully serve as a resource for the islet research community.

Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) of metastatic lung cancer was performed using 49 clinical biopsies obtained from 30 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNA-seq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell-state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap-junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNA-seq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

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scRNA-seq is feasible in metastatic human NSCLCs and reveals a rich tumor ecosystem

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Individual tumors and cancer cells exhibit substantial molecular diversity

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Cancer and tumor microenvironment cells exhibit marked therapy-induced plasticity

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scRNA-seq of metastatic NSCLCs unveils new opportunities to improve clinical outcomes

Imbalance of Regulatory and Cytotoxic SARS-CoV-2-Reactive CD4+ T Cells in COVID-19

The contribution of CD4+ T cells to protective or pathogenic immune responses to SARS-CoV-2 infection remains unknown. Here, we present single-cell transcriptomic analysis of >100,000 viral antigen-reactive CD4+ T cells from 40 COVID-19 patients. In hospitalized patients compared to non-hospitalized patients, we found increased proportions of cytotoxic follicular helper cells and cytotoxic T helper (TH) cells (CD4-CTLs) responding to SARS-CoV-2 and reduced proportion of SARS-CoV-2-reactive regulatory T cells (TREG). Importantly, in hospitalized COVID-19 patients, a strong cytotoxic TFH response was observed early in the illness, which correlated negatively with antibody levels to SARS-CoV-2 spike protein. Polyfunctional TH1 and TH17 cell subsets were underrepresented in the repertoire of SARS-CoV-2-reactive CD4+ T cells compared to influenza-reactive CD4+ T cells. Together, our analyses provide insights into the gene expression patterns of SARS-CoV-2-reactive CD4+ T cells in distinct disease severities.

Establishing Cerebral Organoids as Models of Human-Specific Brain Evolution

Direct comparisons of human and non-human primate brains can reveal molecular pathways underlying remarkable specializations of the human brain. However, chimpanzee tissue is inaccessible during neocortical neurogenesis when differences in brain size first appear. To identify human-specific features of cortical development, we leveraged recent innovations that permit generating pluripotent stem cell-derived cerebral organoids from chimpanzee. Despite metabolic differences, organoid models preserve gene regulatory networks related to primary cell types and developmental processes. We further identified 261 differentially expressed genes in human compared to both chimpanzee organoids and macaque cortex, enriched for recent gene duplications, and including multiple regulators of PI3K-AKT-mTOR signaling. We observed increased activation of this pathway in human radial glia, dependent on two receptors upregulated specifically in human: INSR and ITGB8. Our findings establish a platform for systematic analysis of molecular changes contributing to human brain development and evolution.

Single-Cell Genomics Reveals a Novel Cell State During Smooth Muscle Cell Phenotypic Switching and Potential Therapeutic Targets for Atherosclerosis in Mouse and Human

BACKGROUND

Smooth muscle cells (SMCs) play significant roles in atherosclerosis via phenotypic switching, a pathological process in which SMC dedifferentiation, migration, and transdifferentiation into other cell types. Yet how SMCs contribute to the pathophysiology of atherosclerosis remains elusive.

METHODS

To reveal the trajectories of SMC transdifferentiation during atherosclerosis and to identify molecular targets for disease therapy, we combined SMC fate mapping and single-cell RNA sequencing of both mouse and human atherosclerotic plaques. We also performed cell biology experiments on isolated SMC-derived cells, conducted integrative human genomics, and used pharmacological studies targeting SMC-derived cells both in vivo and in vitro.

RESULTS

We found that SMCs transitioned to an intermediate cell state during atherosclerosis, which was also found in human atherosclerotic plaques of carotid and coronary arteries. SMC-derived intermediate cells, termed "SEM" cells (stem cell, endothelial cell, monocyte), were multipotent and could differentiate into macrophage-like and fibrochondrocyte-like cells, as well as return toward the SMC phenotype. Retinoic acid (RA) signaling was identified as a regulator of SMC to SEM cell transition, and RA signaling was dysregulated in symptomatic human atherosclerosis. Human genomics revealed enrichment of genome-wide association study signals for coronary artery disease in RA signaling target gene loci and correlation between coronary artery disease risk alleles and repressed expression of these genes. Activation of RA signaling by all-trans RA, an anticancer drug for acute promyelocytic leukemia, blocked SMC transition to SEM cells, reduced atherosclerotic burden, and promoted fibrous cap stability.

CONCLUSIONS

Integration of cell-specific fate mapping, single-cell genomics, and human genetics adds novel insights into the complexity of SMC biology and reveals regulatory pathways for therapeutic targeting of SMC transitions in atherosclerotic cardiovascular disease.

Pathogenic Mechanisms Underlying Idiopathic Pulmonary Fibrosis

The pathogenesis of idiopathic pulmonary fibrosis (IPF) involves a complex interplay of cell types and signaling pathways. Recurrent alveolar epithelial cell (AEC) injury may occur in the context of predisposing factors (e.g., genetic, environmental, epigenetic, immunologic, and gerontologic), leading to metabolic dysfunction, senescence, aberrant epithelial cell activation, and dysregulated epithelial repair. The dysregulated epithelial cell interacts with mesenchymal, immune, and endothelial cells via multiple signaling mechanisms to trigger fibroblast and myofibroblast activation. Recent single-cell RNA sequencing studies of IPF lungs support the epithelial injury model. These studies have uncovered a novel type of AEC with characteristics of an aberrant basal cell, which may disrupt normal epithelial repair and propagate a profibrotic phenotype. Here, we review the pathogenesis of IPF in the context of novel bioinformatics tools as strategies to discover pathways of disease, cell-specific mechanisms, and cell-cell interactions that propagate the profibrotic niche.

Single-Cell Transcriptomic Atlas of Primate Ovarian Aging

Molecular mechanisms of ovarian aging and female age-related fertility decline remain unclear. We surveyed the single-cell transcriptomic landscape of ovaries from young and aged non-human primates (NHPs) and identified seven ovarian cell types with distinct gene-expression signatures, including oocyte and six types of ovarian somatic cells. In-depth dissection of gene-expression dynamics of oocytes revealed four subtypes at sequential and stepwise developmental stages. Further analysis of cell-type-specific aging-associated transcriptional changes uncovered the disturbance of antioxidant signaling specific to early-stage oocytes and granulosa cells, indicative of oxidative damage as a crucial factor in ovarian functional decline with age. Additionally, inactivated antioxidative pathways, increased reactive oxygen species, and apoptosis were observed in granulosa cells from aged women. This study provides a comprehensive understanding of the cell-type-specific mechanisms underlying primate ovarian aging at single-cell resolution, revealing new diagnostic biomarkers and potential therapeutic targets for age-related human ovarian disorders.

CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons

CRISPR/Cas9-based functional genomics have transformed our ability to elucidate mammalian cell biology. However, most previous CRISPR-based screens were conducted in cancer cell lines rather than healthy, differentiated cells. Here, we describe a CRISPR interference (CRISPRi)-based platform for genetic screens in human neurons derived from induced pluripotent stem cells (iPSCs). We demonstrate robust and durable knockdown of endogenous genes in such neurons and present results from three complementary genetic screens. First, a survival-based screen revealed neuron-specific essential genes and genes that improved neuronal survival upon knockdown. Second, a screen with a single-cell transcriptomic readout uncovered several examples of genes whose knockdown had strikingly cell-type-specific consequences. Third, a longitudinal imaging screen detected distinct consequences of gene knockdown on neuronal morphology. Our results highlight the power of unbiased genetic screens in iPSC-derived differentiated cell types and provide a platform for systematic interrogation of normal and disease states of neurons. VIDEO ABSTRACT.

Single-Cell RNA Sequencing Analysis Reveals Sequential Cell Fate Transition during Human Spermatogenesis

Spermatogenesis generates mature male gametes and is critical for the proper transmission of genetic information between generations. However, the developmental landscapes of human spermatogenesis remain unknown. Here, we performed single-cell RNA sequencing (scRNA-seq) analysis for 2,854 testicular cells from donors with normal spermatogenesis and 174 testicular cells from one nonobstructive azoospermia (NOA) donor. A hierarchical model was established, which was characterized by the sequential and stepwise development of three spermatogonia subtypes, seven spermatocyte subtypes, and four spermatid subtypes. Further analysis identified several stage-specific marker genes of human germ cells, such as HMGA1, PIWIL4, TEX29, SCML1, and CCDC112. Moreover, we identified altered gene expression patterns in the testicular somatic cells of one NOA patient via scRNA-seq analysis, paving the way for further diagnosis of male infertility. Our work allows for the reconstruction of transcriptional programs inherent to sequential cell fate transition during human spermatogenesis and has implications for deciphering male-related reproductive disorders.

Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing

High-throughput single-cell RNA sequencing (scRNA-seq) is becoming a cornerstone of developmental research, providing unprecedented power in understanding dynamic processes. Here, we present a high-resolution scRNA-seq expression atlas of the Arabidopsis root composed of thousands of independently profiled cells. This atlas provides detailed spatiotemporal information, identifying defining expression features for all major cell types, including the scarce cells of the quiescent center. These reveal key developmental regulators and downstream genes that translate cell fate into distinctive cell shapes and functions. Developmental trajectories derived from pseudotime analysis depict a finely resolved cascade of cell progressions from the niche through differentiation that are supported by mirroring expression waves of highly interconnected transcription factors. This study demonstrates the power of applying scRNA-seq to plants and provides an unparalleled spatiotemporal perspective of root cell differentiation.

Single-Cell RNA Sequencing of Lymph Node Stromal Cells Reveals Niche-Associated Heterogeneity

Stromal cells (SCs) establish the compartmentalization of lymphoid tissues critical to the immune response. However, the full diversity of lymph node (LN) SCs remains undefined. Using droplet-based single-cell RNA sequencing, we identified nine peripheral LN non-endothelial SC clusters. Included are the established subsets, Ccl19hi T-zone reticular cells (TRCs), marginal reticular cells, follicular dendritic cells (FDCs), and perivascular cells. We also identified Ccl19lo TRCs, likely including cholesterol-25-hydroxylase+ cells located at the T-zone perimeter, Cxcl9+ TRCs in the T-zone and interfollicular region, CD34+ SCs in the capsule and medullary vessel adventitia, indolethylamine N-methyltransferase+ SCs in the medullary cords, and Nr4a1+ SCs in several niches. These data help define how transcriptionally distinct LN SCs support niche-restricted immune functions and provide evidence that many SCs are in an activated state.

Tissue-resident FOLR2+ macrophages associate with CD8+ T cell infiltration in human breast cancer

Macrophage infiltration is a hallmark of solid cancers, and overall macrophage infiltration correlates with lower patient survival and resistance to therapy. Tumor-associated macrophages, however, are phenotypically and functionally heterogeneous. Specific subsets of tumor-associated macrophage might be endowed with distinct roles on cancer progression and antitumor immunity. Here, we identify a discrete population of FOLR2+ tissue-resident macrophages in healthy mammary gland and breast cancer primary tumors. FOLR2+ macrophages localize in perivascular areas in the tumor stroma, where they interact with CD8+ T cells. FOLR2+ macrophages efficiently prime effector CD8+ T cells ex vivo. The density of FOLR2+ macrophages in tumors positively correlates with better patient survival. This study highlights specific roles for tumor-associated macrophage subsets and paves the way for subset-targeted therapeutic interventions in macrophages-based cancer therapies.

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

The tumor immune microenvironment plays a critical role in cancer progression and response to immunotherapy in clear cell renal cell carcinoma (ccRCC), yet the composition and phenotypic states of immune cells in this tumor are incompletely characterized. We performed single-cell RNA and T cell receptor sequencing on 164,722 individual cells from tumor and adjacent non-tumor tissue in patients with ccRCC across disease stages: early, locally advanced, and advanced/metastatic. Terminally exhausted CD8⁺ T cells were enriched in metastatic disease and were restricted in T cell receptor diversity. Within the myeloid compartment, pro-inflammatory macrophages were decreased, and suppressive M2-like macrophages were increased in advanced disease. Terminally exhausted CD8⁺ T cells and M2-like macrophages co-occurred in advanced disease and expressed ligands and receptors that support T cell dysfunction and M2-like polarization. This immune dysfunction circuit is associated with a worse prognosis in external cohorts and identifies potentially targetable immune inhibitory pathways in ccRCC.

Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Tumor ecosystems are composed of multiple cell types that communicate by ligand-receptor interactions. Targeting ligand-receptor interactions (for instance, with immune checkpoint inhibitors) can provide significant benefits for patients. However, our knowledge of which interactions occur in a tumor and how these interactions affect outcome is still limited. We present an approach to characterize communication by ligand-receptor interactions across all cell types in a microenvironment using single-cell RNA sequencing. We apply this approach to identify and compare the ligand-receptor interactions present in six syngeneic mouse tumor models. To identify interactions potentially associated with outcome, we regress interactions against phenotypic measurements of tumor growth rate. In addition, we quantify ligand-receptor interactions between T cell subsets and their relation to immune infiltration using a publicly available human melanoma dataset. Overall, this approach provides a tool for studying cell-cell interactions, their variability across tumors, and their relationship to outcome.

Tumors are composed of cancer cells and many non-malignant cell types, such as immune and stromal cells. To better understand how all cell types in a tumor cooperate to facilitate malignant growth, Kumar et al. studied communication between cells via ligand and receptor interactions using single-cell data and computational modeling.

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena-from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.