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Found: 4 872
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A pan-cancer single-cell RNA-seq atlas of intratumoral B cells.
Tumor-infiltrating B cells play a significant role in tumor development, progression, and prognosis, yet a comprehensive classification system is lacking. To address this gap, we present a pan-cancer single-cell RNA sequencing (scRNA-seq) atlas of tumor-infiltrating B and plasma cells across a large sample cohort. We identify key B cell subset signatures, revealing distinct subpopulations and highlighting the heterogeneity and functional diversity of these cells in the tumor microenvironment. We explore associations between B cell subsets and checkpoint inhibitor therapy responses, finding subset-specific effects on overall response. Additionally, we examine B and T cell crosstalk, identifying unique ligand-receptor pairs for specific B cell subsets, spatially validated. This comprehensive dataset serves as a valuable resource, providing a detailed atlas that enhances the understanding of B cell complexity in tumors and opens new avenues for research and therapeutic strategies.
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Single-cell atlas of the human brain vasculature across development, adulthood and disease.
A broad range of brain pathologies critically relies on the vasculature, and cerebrovascular disease is a leading cause of death worldwide. However, the cellular and molecular architecture of the human brain vasculature remains incompletely understood1. Here we performed single-cell RNA sequencing analysis of 606,380 freshly isolated endothelial cells, perivascular cells and other tissue-derived cells from 117 samples, from 68 human fetuses and adult patients to construct a molecular atlas of the developing fetal, adult control and diseased human brain vasculature. We identify extensive molecular heterogeneity of the vasculature of healthy fetal and adult human brains and across five vascular-dependent central nervous system (CNS) pathologies, including brain tumours and brain vascular malformations. We identify alteration of arteriovenous differentiation and reactivated fetal as well as conserved dysregulated genes and pathways in the diseased vasculature. Pathological endothelial cells display a loss of CNS-specific properties and reveal an upregulation of MHC class II molecules, indicating atypical features of CNS endothelial cells. Cell-cell interaction analyses predict substantial endothelial-to-perivascular cell ligand-receptor cross-talk, including immune-related and angiogenic pathways, thereby revealing a central role for the endothelium within brain neurovascular unit signalling networks. Our single-cell brain atlas provides insights into the molecular architecture and heterogeneity of the developing, adult/control and diseased human brain vasculature and serves as a powerful reference for future studies.
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Single-cell RNA-seq with spatial transcriptomics to create an atlas of human diabetic kidney disease.
Diabetic kidney disease (DKD) develops in ~40% of patients with diabetes and is the leading cause of chronic kidney disease worldwide. We used single-cell RNA-sequencing and spatial transcriptomic analyses of kidney specimens from patients with DKD. Unsupervised clustering revealed distinct cell clusters, including epithelial cells and fibroblasts. We also identified differentially expressed genes (DEGs) and assessed enrichment, and cell-cell interactions. Specific enrichment of DKD was evident in venous endothelial cells (VECs) and fibroblasts with elevated CCL19 expression. The DEGs in most kidney parenchymal cells in DKD were primarily enriched in inflammatory signaling pathways. Intercellular crosstalk revealed that most cell interactions in DKD are associated with chemokines. Spatial transcriptomics revealed that VECs co-localized with fibroblasts, with most immune cells being enriched in areas of renal fibrosis. These results provided insight into the cell populations, intercellular interactions, and signaling pathways underlying the pathogenesis and potential targets for treating DKD.
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An integrated single-cell RNA-seq atlas of the mouse hypothalamic paraventricular nucleus links transcriptomic and functional types.
The hypothalamic paraventricular nucleus (PVN) is a highly complex brain region that is crucial for homeostatic regulation through neuroendocrine signaling, outflow of the autonomic nervous system, and projections to other brain areas. In the past years, single-cell datasets of the hypothalamus have contributed immensely to the current understanding of the diverse hypothalamic cellular composition. While the PVN has been adequately classified functionally, its molecular classification is currently still insufficient. To address this, we created a detailed atlas of PVN transcriptomic cell types by integrating various PVN single-cell datasets into a recently published hypothalamus single-cell transcriptome atlas. Furthermore, we functionally profiled transcriptomic cell types, based on relevant literature, existing retrograde tracing data, and existing single-cell data of a PVN-projection target region. Finally, we validated our findings with immunofluorescent stainings. In our PVN atlas dataset, we identify the well-known different neuropeptide types, each composed of multiple novel subtypes. We identify Avp-Tac1, Avp-Th, Oxt-Foxp1, Crh-Nr3c1, and Trh-Nfib as the most important neuroendocrine subtypes based on markers described in literature. To characterize the preautonomic functional population, we integrated a single-cell retrograde tracing study of spinally projecting preautonomic neurons into our PVN atlas. We identify these (presympathetic) neurons to cocluster with the Adarb2+ clusters in our dataset. Further, we identify the expression of receptors for Crh, Oxt, Penk, Sst, and Trh in the dorsal motor nucleus of the vagus, a key region that the pre-parasympathetic PVN neurons project to. Finally, we identify Trh-Ucn3 and Brs3-Adarb2 as some centrally projecting populations. In conclusion, our study presents a detailed overview of the transcriptomic cell types of the murine PVN and provides a first attempt to resolve functionality for the identified populations.
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Single-cell RNA-seq reveals keratinocyte and fibroblast heterogeneity and their crosstalk via epithelial-mesenchymal transition in psoriasis.
The pathogenesis of psoriasis, a chronic inflammatory autoimmune skin disease with a high global prevalence, remains unclear. We performed a high-resolution single-cell RNA sequencing analysis of 94,759 cells from 13 samples, including those from psoriasis model mice and wild-type mice. We presented a single-cell atlas of the skin of imiquimod-induced mice with psoriasis and WT mice, especially the heterogeneity of keratinocytes and fibroblasts. More interestingly, we discovered that special keratinocyte subtypes and fibroblast subtypes could interact with each other through epithelial-mesenchymal transition and validated the results with drug verification. Moreover, we conducted a tentative exploration of the potential pathways involved and revealed that the IL-17 signalling pathway may be the most relevant pathway. Collectively, we revealed the full-cycle landscape of key cells associated with psoriasis and provided a more comprehensive understanding of the pathogenesis of psoriasis.
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A pan-cancer single-cell transcriptomic atlas of human bone metastases.
Bone is a common site for cancer metastasis, yet the mechanisms driving human spinal bone metastases (BMs) are poorly understood. Here, we obtained a single-cell RNA sequencing (scRNA-seq) atlas of 62 BMs across 13 different origins, along with paired primary tumor and normal bone marrow. Unsupervised clustering of cancer cell functional programs revealed 3 groups with distinct prognosis and tumor microenvironment. Integrative analysis with large-scale primary and metastatic pan-cancer and healthy bone marrow scRNA-seq datasets revealed SELE-positive endothelial cells, osteoblasts, and osteoclasts associated with cancer cell proliferation. We also identified BM-enriched exhausted CD8+ T cells with increased expression of immune checkpoint genes. In bone metastatic mouse models, a combined anti-PD-1/TIGIT immune therapy effectively suppressed tumor cell proliferation and significantly enhanced the cytotoxic activity of CD8+ T cells. Our results provide a systematic view of the molecular basis of BM and suggest future avenues for immunotherapy optimization for BM patients.
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Mapping the spatial atlas of the human bone tissue integrating spatial and single-cell transcriptomics.
Bone is a multifaceted tissue requiring orchestrated interplays of diverse cells within specialized microenvironments. Although significant progress has been made in understanding cellular and molecular mechanisms of component cells of bone, revealing their spatial organization and interactions in native bone tissue microenvironment is crucial for advancing precision medicine, as they govern fundamental signaling pathways and functional dependencies among various bone cells. In this study, we present the first integrative high-resolution map of human bone and bone marrow, using spatial and single-cell transcriptomics profiling from femoral tissue. This multi-modal approach discovered a novel bone formation-specialized niche enriched with osteoblastic lineage cells and fibroblasts and unveiled critical cell-cell communications and co-localization patterns between osteoblastic lineage cells and other cells. Furthermore, we discovered a novel spatial gradient of cellular composition, gene expression and signaling pathway activities radiating from the trabecular bone. This comprehensive atlas delineates the intricate bone cellular architecture and illuminates key molecular processes and dependencies among cells that coordinate bone metabolism. In sum, our study provides an essential reference for the field of bone biology and lays the foundation for advanced mechanistic studies and precision medicine approaches in bone-related disorders.
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Single-cell atlas of the transcriptome and chromatin accessibility in the human retina.
Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here we present an integrated dual-modal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 3.9 million cells from 125 donors of diverse ancestral backgrounds, including 8 published studies and 2.7 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) with more than 130 cell types identified. We annotated each cluster, identified marker genes and characterized cis-regulatory elements and gene regulatory networks. Our analysis uncovered differences in transcriptome, chromatin and gene regulatory networks across cell types. We modeled changes in gene expression and chromatin accessibility across age, ancestry and tissue region. This integrated atlas enhanced the fine-mapping of genome-wide association study and expression quantitative trait loci variants. Accessible through interactive browsers, this multimodal multidonor and multilab HRCA can facilitate a better understanding of retinal function and pathology.
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Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux.
Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.
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Comprehensive analysis of scRNA-seq and bulk RNA-seq reveals the non-cardiomyocytes heterogeneity and novel cell populations in dilated cardiomyopathy.
BACKGROUND: Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Infiltration and alterations in non-cardiomyocytes of the human heart involve crucially in the occurrence of DCM and associated immunotherapeutic approaches. METHODS: We constructed a single-cell transcriptional atlas of DCM and normal patients. Then, the xCell algorithm, EPIC algorithm, MCP counter algorithm, and CIBERSORT method were applied to identify DCM-related cell types with a high degree of precision and specificity using RNA-seq datasets. We further analyzed the heterogeneity among cell types, performed trajectory analysis, examined transcription factor regulatory networks, investigated metabolic heterogeneity, and conducted intercellular communication analysis. Finally, we used bulk RNA-seq data to confirm the roles of M2-like2 subpopulations and GAS6 in DCM. RESULTS: We integrated and analyzed Single-cell sequencing (scRNA-seq) data from 7 DCM samples and 3 normal heart tissue samples, totaling 70,958 single-cell data points. Based on gene-specific expression and prior marker genes, we identified 9 distinct subtypes, including fibroblasts, endothelial cells, myeloid cells, pericytes, T/NK cells, smooth muscle cells, neuronal cells, B cells, and cardiomyocytes. Using machine learning methods to quantify bulk RNA-seq data, we found significant differences in fibroblasts, T cells, and macrophages between DCM and normal samples. Further analysis revealed high heterogeneity in tissue preference, gene expression, functional enrichment, immunodynamics, transcriptional regulatory factors, metabolic changes, and communication patterns in fibroblasts and myeloid cells. Among fibroblast subpopulations, proliferative F3 cells were implicated in the fibroblast transition process in DCM, while myofibroblast F6 cells promoted the fibroblast transition to a late cell state in DCM. Additionally, two subpopulations of M2 macrophages, M2-like1 and M2-like2, were identified with distinct features. The M2-like2 cell subpopulation, which was enriched in glycolysis and fatty acid metabolism, involved in inflammation inhibition and fibrosis promotion. Cell‒cell communication analysis indicated the GAS6-MERTK axis might exhibit interaction between M2 macrophage and M2-like1 macrophage. Furthermore, deconvolution analysis for bulk RNA-seq data revealed a significant increase in M2-like2 subpopulations in DCM, suggesting a more important role for this cell population in DCM. CONCLUSIONS: We revealed the heterogeneity of non-cardiomyocytes in DCM and identified subpopulations of myofibroblast and macrophages engaged in DCM, which suggested a potential significance of non-cardiomyocytes in treatment of DCM.