Spatial CRISPR genomics identifies regulators of the tumor microenvironment.

While CRISPR screens are helping uncover genes regulating many cell-intrinsic processes, existing approaches are suboptimal for identifying extracellular gene functions, particularly in the tissue context. Here, we developed an approach for spatial functional genomics called Perturb-map. We applied Perturb-map to knock out dozens of genes in parallel in a mouse model of lung cancer and simultaneously assessed how each knockout influenced tumor growth, histopathology, and immune composition. Moreover, we paired Perturb-map and spatial transcriptomics for unbiased analysis of CRISPR-edited tumors. We found that in Tgfbr2 knockout tumors, the tumor microenvironment (TME) was converted to a fibro-mucinous state, and T cells excluded, concomitant with upregulated TGFß and TGFß-mediated fibroblast activation, indicating that TGFß-receptor loss on cancer cells increased TGFß bioavailability and its immunosuppressive effects on the TME. These studies establish Perturb-map for functional genomics within the tissue at single-cell resolution with spatial architecture preserved and provide insight into how TGFß responsiveness of cancer cells can affect the TME.

Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies.

While regulatory T (Treg) cells are traditionally viewed as professional suppressors of antigen presenting cells and effector T cells in both autoimmunity and cancer, recent findings of distinct Treg cell functions in tissue maintenance suggest that their regulatory purview extends to a wider range of cells and is broader than previously assumed. To elucidate tumoral Treg cell 'connectivity' to diverse tumor-supporting accessory cell types, we explored immediate early changes in their single-cell transcriptomes upon punctual Treg cell depletion in experimental lung cancer and injury-induced inflammation. Before any notable T cell activation and inflammation, fibroblasts, endothelial and myeloid cells exhibited pronounced changes in their gene expression in both cancer and injury settings. Factor analysis revealed shared Treg cell-dependent gene programs, foremost, prominent upregulation of VEGF and CCR2 signaling-related genes upon Treg cell deprivation in either setting, as well as in Treg cell-poor versus Treg cell-rich human lung adenocarcinomas. Accordingly, punctual Treg cell depletion combined with short-term VEGF blockade showed markedly improved control of PD-1 blockade-resistant lung adenocarcinoma progression in mice compared to the corresponding monotherapies, highlighting a promising factor-based querying approach to elucidating new rational combination treatments of solid organ cancers.

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.

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.

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.

Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity.

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.

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 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.

Distinct Cellular Mechanisms Underlie Anti-CTLA-4 and Anti-PD-1 Checkpoint Blockade.

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.

An Immune Atlas of Clear Cell Renal Cell Carcinoma.

Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.

Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses.

To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single-cell analysis of the tumor, non-involved lung, and blood cells, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor-infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single-cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. VIDEO ABSTRACT.

Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance.

Large-scale genomic studies have identified multiple somatic aberrations in breast cancer, including copy number alterations and point mutations. Still, identifying causal variants and emergent vulnerabilities that arise as a consequence of genetic alterations remain major challenges. We performed whole-genome small hairpin RNA (shRNA) "dropout screens" on 77 breast cancer cell lines. Using a hierarchical linear regression algorithm to score our screen results and integrate them with accompanying detailed genetic and proteomic information, we identify vulnerabilities in breast cancer, including candidate "drivers," and reveal general functional genomic properties of cancer cells. Comparisons of gene essentiality with drug sensitivity data suggest potential resistance mechanisms, effects of existing anti-cancer drugs, and opportunities for combination therapy. Finally, we demonstrate the utility of this large dataset by identifying BRD4 as a potential target in luminal breast cancer and PIK3CA mutations as a resistance determinant for BET-inhibitors.

Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.

Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development.

Tissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and "corrupted" developmental processes including cancer.

Integration of genomic data enables selective discovery of breast cancer drivers.

Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.

STING inhibits the reactivation of dormant metastasis in lung adenocarcinoma.

Metastasis frequently develops from disseminated cancer cells that remain dormant after the apparently successful treatment of a primary tumour. These cells fluctuate between an immune-evasive quiescent state and a proliferative state liable to immune-mediated elimination1-6. Little is known about the clearing of reawakened metastatic cells and how this process could be therapeutically activated to eliminate residual disease in patients. Here we use models of indolent lung adenocarcinoma metastasis to identify cancer cell-intrinsic determinants of immune reactivity during exit from dormancy. Genetic screens of tumour-intrinsic immune regulators identified the stimulator of interferon genes (STING) pathway as a suppressor of metastatic outbreak. STING activity increases in metastatic progenitors that re-enter the cell cycle and is dampened by hypermethylation of the STING promoter and enhancer in breakthrough metastases or by chromatin repression in cells re-entering dormancy in response to TGFß. STING expression in cancer cells derived from spontaneous metastases suppresses their outgrowth. Systemic treatment of mice with STING agonists eliminates dormant metastasis and prevents spontaneous outbreaks in a T cell- and natural killer cell-dependent manner-these effects require cancer cell STING function. Thus, STING provides a checkpoint against the progression of dormant metastasis and a therapeutically actionable strategy for the prevention of disease relapse.

A unified atlas of CD8 T cell dysfunctional states in cancer and infection.

CD8 T cells play an essential role in defense against viral and bacterial infections and in tumor immunity. Deciphering T cell loss of functionality is complicated by the conspicuous heterogeneity of CD8 T cell states described across experimental and clinical settings. By carrying out a unified analysis of over 300 assay for transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-seq) experiments from 12 studies of CD8 T cells in cancer and infection, we defined a shared differentiation trajectory toward dysfunction and its underlying transcriptional drivers and revealed a universal early bifurcation of functional and dysfunctional T cell states across models. Experimental dissection of acute and chronic viral infection using single-cell ATAC (scATAC)-seq and allele-specific single-cell RNA (scRNA)-seq identified state-specific drivers and captured the emergence of similar TCF1+ progenitor-like populations at an early branch point, at which functional and dysfunctional T cells diverge. Our atlas of CD8 T cell states will facilitate mechanistic studies of T cell immunity and translational efforts.

Natural Genetic Variation Reveals Key Features of Epigenetic and Transcriptional Memory in Virus-Specific CD8 T Cells.

Stable changes in chromatin states and gene expression in cells of the immune system form the basis for memory of infections and other challenges. Here, we used naturally occurring cis-regulatory variation in wild-derived inbred mouse strains to explore the mechanisms underlying long-lasting versus transient gene regulation in CD8 T cells responding to acute viral infection. Stably responsive DNA elements were characterized by dramatic and congruent chromatin remodeling events affecting multiple neighboring sites and required distinct transcription factor (TF) binding motifs for their accessibility. Specifically, we found that cooperative recruitment of T-box and Runx family transcription factors to shared targets mediated stable chromatin remodeling upon T cell activation. Our observations provide insights into the molecular mechanisms driving virus-specific CD8 T cell responses and suggest a general mechanism for the formation of transcriptional and epigenetic memory applicable to other immune and non-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.

CellRank 2: unified fate mapping in multiview single-cell data.

Single-cell RNA sequencing allows us to model cellular state dynamics and fate decisions using expression similarity or RNA velocity to reconstruct state-change trajectories; however, trajectory inference does not incorporate valuable time point information or utilize additional modalities, whereas methods that address these different data views cannot be combined or do not scale. Here we present CellRank 2, a versatile and scalable framework to study cellular fate using multiview single-cell data of up to millions of cells in a unified fashion. CellRank 2 consistently recovers terminal states and fate probabilities across data modalities in human hematopoiesis and endodermal development. Our framework also allows combining transitions within and across experimental time points, a feature we use to recover genes promoting medullary thymic epithelial cell formation during pharyngeal endoderm development. Moreover, we enable estimating cell-specific transcription and degradation rates from metabolic-labeling data, which we apply to an intestinal organoid system to delineate differentiation trajectories and pinpoint regulatory strategies.

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.