Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Understanding the molecular programs that guide differentiation during development is a major challenge. Here, we introduce Waddington-OT, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315,000 single-cell RNA sequencing (scRNA-seq) profiles, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent, extra-embryonic, and neural cells, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

The mammalian brain consists of millions to billions of cells that are organized into many cell types with specific spatial distribution patterns and structural and functional properties1-3. Here we report a comprehensive and high-resolution transcriptomic and spatial cell-type atlas for the whole adult mouse brain. The cell-type atlas was created by combining a single-cell RNA-sequencing (scRNA-seq) dataset of around 7 million cells profiled (approximately 4.0 million cells passing quality control), and a spatial transcriptomic dataset of approximately 4.3 million cells using multiplexed error-robust fluorescence in situ hybridization (MERFISH). The atlas is hierarchically organized into 4 nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We present an online platform, Allen Brain Cell Atlas, to visualize the mouse whole-brain cell-type atlas along with the single-cell RNA-sequencing and MERFISH datasets. We systematically analysed the neuronal and non-neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell-type organization in different brain regions-in particular, a dichotomy between the dorsal and ventral parts of the brain. The dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. Our study also uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types. Finally, we found that transcription factors are major determinants of cell-type classification and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole mouse brain transcriptomic and spatial cell-type atlas establishes a benchmark reference atlas and a foundational resource for integrative investigations of cellular and circuit function, development and evolution of the mammalian brain.

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets.

COVID-19, which is caused by SARS-CoV-2, can result in acute respiratory distress syndrome and multiple organ failure1-4, but little is known about its pathophysiology. Here we generated single-cell atlases of 24 lung, 16 kidney, 16 liver and 19 heart autopsy tissue samples and spatial atlases of 14 lung samples from donors who died of COVID-19. Integrated computational analysis uncovered substantial remodelling in the lung epithelial, immune and stromal compartments, with evidence of multiple paths of failed tissue regeneration, including defective alveolar type 2 differentiation and expansion of fibroblasts and putative TP63+ intrapulmonary basal-like progenitor cells. Viral RNAs were enriched in mononuclear phagocytic and endothelial lung cells, which induced specific host programs. Spatial analysis in lung distinguished inflammatory host responses in lung regions with and without viral RNA. Analysis of the other tissue atlases showed transcriptional alterations in multiple cell types in heart tissue from donors with COVID-19, and mapped cell types and genes implicated with disease severity based on COVID-19 genome-wide association studies. Our foundational dataset elucidates the biological effect of severe SARS-CoV-2 infection across the body, a key step towards new treatments.

STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization.

Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.

Massively parallel single-cell and single-nucleus RNA sequencing has opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so is the need for computational pipelines for scaled analysis. Here we developed Cumulus-a cloud-based framework for analyzing large-scale single-cell and single-nucleus RNA sequencing datasets. Cumulus combines the power of cloud computing with improvements in algorithm and implementation to achieve high scalability, low cost, user-friendliness and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

High-definition spatial transcriptomics for in situ tissue profiling.

Spatial and molecular characteristics determine tissue function, yet high-resolution methods to capture both concurrently are lacking. Here, we developed high-definition spatial transcriptomics, which captures RNA from histological tissue sections on a dense, spatially barcoded bead array. Each experiment recovers several hundred thousand transcript-coupled spatial barcodes at 2-µm resolution, as demonstrated in mouse brain and primary breast cancer. This opens the way to high-resolution spatial analysis of cells and tissues.

A Distinct Transcriptional Program in Human CAR T Cells Bearing the 4-1BB Signaling Domain Revealed by scRNA-Seq.

T cells engineered to express chimeric antigen receptors (CARs) targeting CD19 have produced impressive outcomes for the treatment of B cell malignancies, but different products vary in kinetics, persistence, and toxicity profiles based on the co-stimulatory domains included in the CAR. In this study, we performed transcriptional profiling of bulk CAR T cell populations and single cells to characterize the transcriptional states of human T cells transduced with CD3ζ, 4-1BB-CD3ζ (BBζ), or CD28-CD3ζ (28ζ) co-stimulatory domains at rest and after activation by triggering their CAR or their endogenous T cell receptor (TCR). We identified a transcriptional signature common across CARs with the CD3ζ signaling domain, as well as a distinct program associated with the 4-1BB co-stimulatory domain at rest and after activation. CAR T cells bearing BBζ had increased expression of human leukocyte antigen (HLA) class II genes, ENPP2, and interleukin (IL)-21 axis genes, and decreased PD1 compared to 28ζ CAR T cells. Similar to previous studies, we also found BBζ CAR CD8 T cells to be enriched in a central memory cell phenotype and fatty acid metabolism genes. Our data uncovered transcriptional signatures related to costimulatory domains and demonstrated that signaling domains included in CARs uniquely shape the transcriptional programs of T cells.

The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices.

MOTIVATION: Facilitated by technological improvements, pharmacologic and genetic perturbational datasets have grown in recent years to include millions of experiments. Sharing and publicly distributing these diverse data creates many opportunities for discovery, but in recent years the unprecedented size of data generated and its complex associated metadata have also created data storage and integration challenges. RESULTS: We present the GCTx file format and a suite of open-source packages for the efficient storage, serialization and analysis of dense two-dimensional matrices. We have extensively used the format in the Connectivity Map to assemble and share massive datasets currently comprising 1.3 million experiments, and we anticipate that the format's generalizability, paired with code libraries that we provide, will lower barriers for integrated cross-assay analysis and algorithm development. AVAILABILITY AND IMPLEMENTATION: Software packages (available in Python, R, Matlab and Java) are freely available at https://github.com/cmap. Additional instructions, tutorials and datasets are available at clue.io/code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion.

Drug resistance presents a challenge to the treatment of cancer patients. Many studies have focused on cell-autonomous mechanisms of drug resistance. By contrast, we proposed that the tumour micro-environment confers innate resistance to therapy. Here we developed a co-culture system to systematically assay the ability of 23 stromal cell types to influence the innate resistance of 45 cancer cell lines to 35 anticancer drugs. We found that stroma-mediated resistance is common, particularly to targeted agents. We characterized further the stroma-mediated resistance of BRAF-mutant melanoma to RAF inhibitors because most patients with this type of cancer show some degree of innate resistance. Proteomic analysis showed that stromal cell secretion of hepatocyte growth factor (HGF) resulted in activation of the HGF receptor MET, reactivation of the mitogen-activated protein kinase (MAPK) and phosphatidylinositol-3-OH kinase (PI(3)K)-AKT signalling pathways, and immediate resistance to RAF inhibition. Immunohistochemistry experiments confirmed stromal cell expression of HGF in patients with BRAF-mutant melanoma and showed a significant correlation between HGF expression by stromal cells and innate resistance to RAF inhibitor treatment. Dual inhibition of RAF and either HGF or MET resulted in reversal of drug resistance, suggesting RAF plus HGF or MET inhibitory combination therapy as a potential therapeutic strategy for BRAF-mutant melanoma. A similar resistance mechanism was uncovered in a subset of BRAF-mutant colorectal and glioblastoma cell lines. More generally, this study indicates that the systematic dissection of interactions between tumours and their micro-environment can uncover important mechanisms underlying drug resistance.

Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing.

To gain insight into the genomic basis of diffuse large B-cell lymphoma (DLBCL), we performed massively parallel whole-exome sequencing of 55 primary tumor samples from patients with DLBCL and matched normal tissue. We identified recurrent mutations in genes that are well known to be functionally relevant in DLBCL, including MYD88, CARD11, EZH2, and CREBBP. We also identified somatic mutations in genes for which a functional role in DLBCL has not been previously suspected. These genes include MEF2B, MLL2, BTG1, GNA13, ACTB, P2RY8, PCLO, and TNFRSF14. Further, we show that BCL2 mutations commonly occur in patients with BCL2/IgH rearrangements as a result of somatic hypermutation normally occurring at the IgH locus. The BCL2 point mutations are primarily synonymous, and likely caused by activation-induced cytidine deaminase-mediated somatic hypermutation, as shown by comprehensive analysis of enrichment of mutations in WRCY target motifs. Those nonsynonymous mutations that are observed tend to be found outside of the functionally important BH domains of the protein, suggesting that strong negative selection against BCL2 loss-of-function mutations is at play. Last, by using an algorithm designed to identify likely functionally relevant but infrequent mutations, we identify KRAS, BRAF, and NOTCH1 as likely drivers of DLBCL pathogenesis in some patients. Our data provide an unbiased view of the landscape of mutations in DLBCL, and this in turn may point toward new therapeutic strategies for the disease.

Prognostic gene expression signature for patients with hepatitis C-related early-stage cirrhosis.

BACKGROUND & AIMS: Cirrhosis affects 1% to 2% of the world population and is the major risk factor for hepatocellular carcinoma (HCC). Hepatitis C cirrhosis-related HCC is the most rapidly increasing cause of cancer death in the United States. Noninvasive methods have been developed to identify patients with asymptomatic early-stage cirrhosis, increasing the burden of HCC surveillance, but biomarkers are needed to identify patients with cirrhosis who are most in need of surveillance. We investigated whether a liver-derived 186-gene signature previously associated with outcomes of patients with HCC is prognostic for patients with newly diagnosed cirrhosis but without HCC. METHODS: We performed gene expression profile analysis of formalin-fixed needle biopsy specimens from the livers of 216 patients with hepatitis C-related early-stage (Child-Pugh class A) cirrhosis who were prospectively followed up for a median of 10 years at an Italian center. We evaluated whether the 186-gene signature was associated with death, progression of cirrhosis, and development of HCC. RESULTS: Fifty-five (25%), 101 (47%), and 60 (28%) patients were classified as having poor-, intermediate-, and good-prognosis signatures, respectively. In multivariable Cox regression modeling, the poor-prognosis signature was significantly associated with death (P = .004), progression to advanced cirrhosis (P < .001), and development of HCC (P = .009). The 10-year rates of survival were 63%, 74%, and 85% and the annual incidence of HCC was 5.8%, 2.2%, and 1.5% for patients with poor-, intermediate-, and good-prognosis signatures, respectively. CONCLUSIONS: A 186-gene signature used to predict outcomes of patients with HCC is also associated with outcomes of patients with hepatitis C-related early-stage cirrhosis. This signature might be used to identify patients with cirrhosis in most need of surveillance and strategies to prevent the development of HCC.

Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer.

A comprehensive understanding of the molecular vulnerabilities of every type of cancer will provide a powerful roadmap to guide therapeutic approaches. Efforts such as The Cancer Genome Atlas Project will identify genes with aberrant copy number, sequence, or expression in various cancer types, providing a survey of the genes that may have a causal role in cancer. A complementary approach is to perform systematic loss-of-function studies to identify essential genes in particular cancer cell types. We have begun a systematic effort, termed Project Achilles, aimed at identifying genetic vulnerabilities across large numbers of cancer cell lines. Here, we report the assessment of the essentiality of 11,194 genes in 102 human cancer cell lines. We show that the integration of these functional data with information derived from surveying cancer genomes pinpoints known and previously undescribed lineage-specific dependencies across a wide spectrum of cancers. In particular, we found 54 genes that are specifically essential for the proliferation and viability of ovarian cancer cells and also amplified in primary tumors or differentially overexpressed in ovarian cancer cell lines. One such gene, PAX8, is focally amplified in 16% of high-grade serous ovarian cancers and expressed at higher levels in ovarian tumors. Suppression of PAX8 selectively induces apoptotic cell death of ovarian cancer cells. These results identify PAX8 as an ovarian lineage-specific dependency. More generally, these observations demonstrate that the integration of genome-scale functional and structural studies provides an efficient path to identify dependencies of specific cancer types on particular genes and pathways.

Structure and transcription of integrated HPV DNA in vulvar carcinomas.

HPV infections are associated with a fraction of vulvar cancers. Through hybridization capture and DNA sequencing, HPV DNA was detected in five of thirteen vulvar cancers. HPV16 DNA was integrated into human DNA in three of the five. The insertions were in introns of human NCKAP1, C5orf67, and LRP1B. Integrations in NCKAP1 and C5orf67 were flanked by short direct repeats in the human DNA, consistent with HPV DNA insertions at sites of abortive, staggered, endonucleolytic incisions. The insertion in C5orf67 was present as a 36 kbp, human-HPV-hetero-catemeric DNA as either an extrachromosomal circle or a tandem repeat within the human genome. The human circularization/repeat junction was defined at single nucleotide resolution. The integrated viral DNA segments all retained an intact upstream regulatory region and the adjacent viral E6 and E7 oncogenes. RNA sequencing revealed that the only HPV genes consistently transcribed from the integrated viral DNAs were E7 and E6*I. The other two HPV DNA+ tumors had coinfections, but no evidence for integration. HPV-positive and HPV-negative vulvar cancers exhibited contrasting human, global gene expression patterns partially overlapping with previously observed differences between HPV-positive and HPV-negative cervical and oropharyngeal cancers. A substantial fraction of the differentially expressed genes involved immune system function. Thus, transcription and HPV DNA integration in vulvar cancers resemble those in other HPV-positive cancers. This study emphasizes the power of hybridization capture coupled with DNA and RNA sequencing to identify a broad spectrum of HPV types, determine human genome integration status of viral DNAs, and elucidate their structures.

Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView.

Optical pooled screening (OPS) is a scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture due to limitations associated with in situ sequencing of perturbation barcodes. Here, we develop PerturbView, an OPS technology that leverages in vitro transcription to amplify barcodes before in situ sequencing, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in induced pluripotent stem cell-derived neurons, primary immune cells and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF-κB signaling. Furthermore, we combine PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in situ screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain.

The mammalian brain is composed of millions to billions of cells that are organized into numerous cell types with specific spatial distribution patterns and structural and functional properties. An essential step towards understanding brain function is to obtain a parts list, i.e., a catalog of cell types, of the brain. Here, we report a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain. The cell type atlas was created based on the combination of two single-cell-level, whole-brain-scale datasets: a single-cell RNA-sequencing (scRNA-seq) dataset of ~7 million cells profiled, and a spatially resolved transcriptomic dataset of ~4.3 million cells using MERFISH. The atlas is hierarchically organized into five nested levels of classification: 7 divisions, 32 classes, 306 subclasses, 1,045 supertypes and 5,200 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The results reveal unique features of cell type organization in different brain regions, in particular, a dichotomy between the dorsal and ventral parts of the brain: the dorsal part contains relatively fewer yet highly divergent neuronal types, whereas the ventral part contains more numerous neuronal types that are more closely related to each other. We also systematically characterized cell-type specific expression of neurotransmitters, neuropeptides, and transcription factors. The study uncovered extraordinary diversity and heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types across the brain, suggesting they mediate a myriad of modes of intercellular communications. Finally, we found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. The whole-mouse-brain transcriptomic and spatial cell type atlas establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cell type and circuit function, development, and evolution of the mammalian brain.

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.

A single-cell and spatial atlas of autopsy tissues reveals pathology and cellular targets of SARS-CoV-2.

The SARS-CoV-2 pandemic has caused over 1 million deaths globally, mostly due to acute lung injury and acute respiratory distress syndrome, or direct complications resulting in multiple-organ failures. Little is known about the host tissue immune and cellular responses associated with COVID-19 infection, symptoms, and lethality. To address this, we collected tissues from 11 organs during the clinical autopsy of 17 individuals who succumbed to COVID-19, resulting in a tissue bank of approximately 420 specimens. We generated comprehensive cellular maps capturing COVID-19 biology related to patients' demise through single-cell and single-nucleus RNA-Seq of lung, kidney, liver and heart tissues, and further contextualized our findings through spatial RNA profiling of distinct lung regions. We developed a computational framework that incorporates removal of ambient RNA and automated cell type annotation to facilitate comparison with other healthy and diseased tissue atlases. In the lung, we uncovered significantly altered transcriptional programs within the epithelial, immune, and stromal compartments and cell intrinsic changes in multiple cell types relative to lung tissue from healthy controls. We observed evidence of: alveolar type 2 (AT2) differentiation replacing depleted alveolar type 1 (AT1) lung epithelial cells, as previously seen in fibrosis; a concomitant increase in myofibroblasts reflective of defective tissue repair; and, putative TP63+ intrapulmonary basal-like progenitor (IPBLP) cells, similar to cells identified in H1N1 influenza, that may serve as an emergency cellular reserve for severely damaged alveoli. Together, these findings suggest the activation and failure of multiple avenues for regeneration of the epithelium in these terminal lungs. SARS-CoV-2 RNA reads were enriched in lung mononuclear phagocytic cells and endothelial cells, and these cells expressed distinct host response transcriptional programs. We corroborated the compositional and transcriptional changes in lung tissue through spatial analysis of RNA profiles in situ and distinguished unique tissue host responses between regions with and without viral RNA, and in COVID-19 donor tissues relative to healthy lung. Finally, we analyzed genetic regions implicated in COVID-19 GWAS with transcriptomic data to implicate specific cell types and genes associated with disease severity. Overall, our COVID-19 cell atlas is a foundational dataset to better understand the biological impact of SARS-CoV-2 infection across the human body and empowers the identification of new therapeutic interventions and prevention strategies.