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A single cell atlas of the human liver tumor microenvironment.
Malignant cell growth is fueled by interactions between tumor cells and the stromal cells composing the tumor microenvironment. The human liver is a major site of tumors and metastases, but molecular identities and intercellular interactions of different cell types have not been resolved in these pathologies. Here, we apply single cell RNA-sequencing and spatial analysis of malignant and adjacent non-malignant liver tissues from five patients with cholangiocarcinoma or liver metastases. We find that stromal cells exhibit recurring, patient-independent expression programs, and reconstruct a ligand-receptor map that highlights recurring tumor-stroma interactions. By combining transcriptomics of laser-capture microdissected regions, we reconstruct a zonation atlas of hepatocytes in the non-malignant sites and characterize the spatial distribution of each cell type across the tumor microenvironment. Our analysis provides a resource for understanding human liver malignancies and may expose potential points of interventions.
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A longitudinal single-cell atlas of treatment response in pediatric AML.
Pediatric acute myeloid leukemia (pAML) is characterized by heterogeneous cellular composition, driver alterations and prognosis. Characterization of this heterogeneity and how it affects treatment response remains understudied in pediatric patients. We used single-cell RNA sequencing and single-cell ATAC sequencing to profile 28 patients representing different pAML subtypes at diagnosis, remission and relapse. At diagnosis, cellular composition differed between genetic subgroups. Upon relapse, cellular hierarchies transitioned toward a more primitive state regardless of subtype. Primitive cells in the relapsed tumor were distinct compared to cells at diagnosis, with under-representation of myeloid transcriptional programs and over-representation of other lineage programs. In some patients, this was accompanied by the appearance of a B-lymphoid-like hierarchy. Our data thus reveal the emergence of apparent subtype-specific plasticity upon treatment and inform on potentially targetable processes.
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Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women.
UNLABELLED: The human uterus is a complex and dynamic organ whose lining grows, remodels, and regenerates in every menstrual cycle or upon tissue damage. Here we applied single-cell RNA sequencing to profile more the 50,000 uterine cells from both the endometrium and myometrium of 5 healthy premenopausal individuals, and jointly analyzed the data with a previously published dataset from 15 subjects. The resulting normal uterus cell atlas contains more than 167K cells representing the lymphatic endothelium, blood endothelium, stromal, ciliated epithelium, unciliated epithelium, and immune cell populations. Focused analyses within each major cell type and comparisons with subtype labels from prior studies allowed us to document supporting evidence, resolve naming conflicts, and to propose a consensus annotation system of 39 subtypes. We release their gene expression centroids, differentially expressed genes, and mRNA patterns of literature-based markers as a shared community resource. We find many subtypes show dynamic changes over different phases of the cycle and identify multiple potential progenitor cells: compartment-wide progenitors for each major cell type, transitional cells that are upstream of other subtypes, and potential cross-lineage multipotent stromal progenitors that may be capable of replenishing the epithelial, stromal, and endothelial compartments. When compared to the healthy premenopausal samples, a postpartum and a postmenopausal uterus sample revealed substantially altered tissue composition, involving the rise or fall of stromal, endothelial, and immune cells. The cell taxonomy and molecular markers we report here are expected to inform studies of both basic biology of uterine function and its disorders. SIGNIFICANCE: We present single-cell RNA sequencing data from seven individuals (five healthy pre-menopausal women, one post-menopausal woman, and one postpartum) and perform an integrated analysis of this data alongside 15 previously published scRNA-seq datasets. We identified 39 distinct cell subtypes across four major cell types in the uterus. By using RNA velocity analysis and centroid-centroid comparisons we identify multiple computationally predicted progenitor populations for each of the major cell compartments, as well as potential cross-compartment, multi-potent progenitors. While the function and interactions of these cell populations remain to be validated through future experiments, the markers and their "dual characteristics" that we describe will serve as a rich resource to the scientific community. Importantly, we address a significant challenge in the field: reconciling multiple uterine cell taxonomies being proposed. To achieve this, we focused on integrating historical and contemporary knowledge across multiple studies. By providing detailed evidence used for cell classification we lay the groundwork for establishing a stable, consensus cell atlas of the human uterus.
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Alzheimer DataLENS: An Open Data Analytics Portal for Alzheimer's Disease Research.
BACKGROUND: Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. OBJECTIVE: Create an online portal of public omics datasets for AD research. METHODS: We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. RESULTS: Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. CONCLUSIONS: Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.
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Genomic evolution reshapes cell-type diversification in the amniote brain.
Over 320 million years of evolution, amniotes have developed complex brains and cognition through largely unexplored genetic and gene expression mechanisms. We created a comprehensive single-cell atlas of over 1.3 million cells from the telencephalon and cerebellum of turtles, zebra finches, pigeons, mice, and macaques, employing single-cell resolution spatial transcriptomics to validate gene expression patterns across species. Our study identifies significant species-specific variations in cell types, highlighting their conservation and diversification in evolution. We found pronounced differences in telencephalon excitatory neurons (EXs) and cerebellar cell types between birds and mammals. Birds predominantly express SLC17A6 in EX, whereas mammals express SLC17A7 in the neocortex and SLC17A6 elsewhere, possibly due to loss of function of SLC17A7 in birds. Additionally, we identified a bird-specific Purkinje cell subtype (SVIL+), implicating the lysine-specific demethylase 11 (LSD1)/KDM1A pathway in learning and circadian rhythms and containing numerous positively selected genes, which suggests an evolutionary optimization of cerebellar functions for ecological and behavioral adaptation. Our findings elucidate the complex interplay between genetic evolution and environmental adaptation, underscoring the role of genetic diversification in the development of specialized cell types across amniotes.
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Single-cell analysis identifies PI3+S100A7+keratinocytes in early cervical squamous cell carcinoma with HPV infection.
BACKGROUND: Cervical squamous cell carcinoma (CESC), the most common subtype of cervical cancer, is primarily caused by the high-risk human papillomavirus (HPV) infection and genetic susceptibility. Single-cell RNA sequencing (scRNA-seq) has been widely used in CESC research to uncover the diversity of cell types and states within tumor tissues, enabling a detailed study of the tumor microenvironment (TME). This technology allows precise mapping of HPV infection in cervical tissues, providing valuable insights into the initiation and progression of HPV-mediated malignant transformation. METHODS: We performed the scRNA-seq to characterize gene expression in tumor tissues and paired adjacent para-cancerous tissues from four patients with early-stage CESC using the 10× Genomics platform. The HPV infection and its subtypes were identified using the scRNA data and viral sequence mapping, and trajectory analyses were performed using HPV+ or HPV- cells. Interactions between different types of keratinized cells and their interactions with other cell types were identified, and pathways and specificity markers were screened for proliferating keratinized cells. The Cancer Genome Atlas (TCGA) dataset was used to verify the prognostic correlation between tumor-specific PI3+S100A7+ keratinocyte infiltration and CESC, and the localization relationship between PI3+S100A7+ keratinocytes and macrophages was verified by immunofluorescence staining. RESULTS: Various types of keratinocytes and fibroblasts were the two cell types with the most significant differences in percentage between the tumor tissue samples and paired adjacent non-cancerous tissue samples in the early stages of CESC. We found that PI3+S100A7+ keratinocytes were associated with early HPV-positive CESC, and PI3+S100A7+ keratinocytes were more abundant in tumors than in adjacent normal tissues in the TCGA-CESC dataset. Analysis of clinical information revealed that the infiltration of PI3+S100A7+ keratinocytes was notably higher in tumors with poor prognosis than in those with good prognosis. Additionally, multiplex immunofluorescence analysis showed a specific increase in PI3+S100A7+ expression within tumor tissues, with PI3+S100A7+ keratinocytes and CD163+ macrophages being spatially very close to each other. In the analysis of cell-cell interactions, macrophages exhibited strong crosstalk with PI3+S100A7+ proliferating keratinocytes in HPV-positive CESC tumors, mediated by tumor necrosis factor (TNF), CCL2, CXCL8, and IL10, highlighting the dynamic and tumor-specific enhancement of macrophage-keratinocyte interactions, which are associated with poor prognosis and immune modulation. Using CIBERSORTx, we discovered that patients with high infiltration of both PI3+S100A7+ proliferating keratinocytes and macrophages had the shortest overall survival. In the analysis of cell-cell interactions, PI3+S100A7+ proliferating keratinocytes and macrophages were found to be involved in highly active pathways that promote differentiation and structure formation, including cytokine receptor interactions, the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway, and TNF signaling pathway regulation. Further subtyping of fibroblast populations identified four subtypes. The C1 group, characterized by its predominance in tumor tissues, is a subtype enriched with cancer-associated fibroblasts (CAFs), whereas the C3 group is primarily enriched in adjacent non-cancerous tissues and consists of undifferentiated cells. Moreover, the distinct molecular and cellular differences between HPV16- and HPV66-associated tumors were demonstrated, emphasizing the unique tumor-promoting mechanisms and microenvironmental influences driven by each HPV subtype. CONCLUSIONS: We discovered a heterogeneous population of keratinocytes between tumor and adjacent non-cancerous tissues caused by HPV infection and identified macrophages and specific CAFs that play a crucial role during the early stage in promoting the inflammatory response and remodeling the cancer-promoting TME. Our findings provide new insights into the transcriptional landscape of early-stage CESC to understand the mechanism of HPV-mediated malignant transformation in cervical cancer.
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Combining single-cell and bulk RNA sequencing data to create a reliable prognostic model for predicting clear-cell renal cell carcinoma progression.
BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is the most common pathological type of kidney cancer and is characterized by a low survival rate. Accurate prediction of the occurrence and progression of ccRCC is crucial for diagnosis and treatment. This study aimed to integrate multiple publicly available bulk sequencing and single-cell datasets on ccRCC to establish a novel prognostic model for reliable and precise predictions of ccRCC development. METHODS: We used data from five ccRCC samples from the Gene Expression Omnibus (GEO) database to identify 1,303 overlapping differentially expressed genes (DEGs). Through pseudotime analysis of single-cell ccRCC data sourced from the GEO database, we identified 4,002 genes that were highly associated with cancer progression. By using machine learning to screen reliable prognostic genes, we constructed a prognostic model for renal cancer with The Cancer Genome Atlas-Kidney Renal Clear-Cell Carcinoma Collection (TCGA-KIRC) dataset and validated its effectiveness using a novel GEO renal cancer dataset. Finally, we explored the role of these prognostic genes in the progression of ccRCC through in vivo and in vitro experiments. RESULTS: The five ccRCC sequencing datasets exhibited significant heterogeneity. Therefore, we screened 211 DEGs that were highly associated with the development and prognosis of renal cancer. By exploring the biological functions of these genes, we found that they closely influenced the prognosis of patients with cancer. Upon screening, a reliable set of renal cancer-related DEGs was obtained from multiple samples. The prognostic model accurately identified renal cancer stages and predicted outcomes. In both in vivo and in vitro experimental results, we found that intervening in the expression of these prognostic genes can significantly slow down the progression of ccRCC. CONCLUSIONS: We developed a valuable predictive tool for ccRCC progression, which can help estimate the survival period of patients with renal cancer and aid in the clinical diagnosis and targeted therapy of tumors.
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Identification and validation of a signature based on myofibroblastic cancer-associated fibroblast marker genes for predicting prognosis, immune infiltration, and therapeutic response in bladder cancer.
PURPOSE: Myofibroblastic cancer-associated fibroblasts (myCAFs) are important components of the tumor microenvironment closely associated with tumor stromal remodeling and immunosuppression. This study aimed to explore myCAFs marker gene biomarkers for clinical diagnosis and therapy for patients with bladder cancer (BC). MATERIALS AND METHODS: BC single-cell RNA sequencing (scRNA-seq) data were obtained from the National Center for Biotechnology Information Sequence Read Archive. Transcriptome and clinical data were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Subsequently, univariate Cox and LASSO (Least Absolute Shrinkage and Selection Operator regression) regression analyses were performed to construct a prognostic signature. Immune cell activity was estimated using single-sample gene set enrichment analysis whilst the TIDE (tumor immune dysfunction and exclusion) method was employed to assess patient response to immunotherapy. The chemotherapy response of patients with BC was evaluated using genomics of drug sensitivity in cancer. Furthermore, Immunohistochemistry was used to verify the correlation between MAP1B expression and immunotherapy efficacy. The scRNA-seq data were analyzed to identify myCAFs marker genes. RESULTS: Combined with bulk RNA-sequencing data, we constructed a two-gene (COL6A1 and MAP1B) risk signature. In patients with BC, the signature demonstrated outstanding prognostic value, immune infiltration, and immunotherapy response. This signature served as a crucial guide for the selection of anti-tumor chemotherapy medications. Additionally, immunohistochemistry confirmed that MAP1B expression was significantly correlated with immunotherapy efficacy. CONCLUSIONS: Our findings revealed a typical prognostic signature based on myCAF marker genes, which offers patients with BC a novel treatment target alongside theoretical justification.
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Deciphering cell states and the cellular ecosystem to improve risk stratification in acute myeloid leukemia.
Acute myeloid leukemia (AML) demonstrates significant cellular heterogeneity in both leukemic and immune cells, providing valuable insights into clinical outcomes. Here, we constructed an AML single-cell transcriptome atlas and proposed sciNMF workflow to systematically dissect underlying cellular heterogeneity. Notably, sciNMF identified 26 leukemic and immune cell states that linked to clinical variables, mutations, and prognosis. By examining the co-existence patterns among these cell states, we highlighted a unique AML cellular ecosystem (ACE) that signifies aberrant tumor milieu and poor survival, which is confirmed by public RNA-seq cohorts. We further developed the ACE signature (ACEsig), comprising 12 genes, which accurately predicts AML prognosis, and outperforms existing signatures. When applied to cytogenetically normal AML or intensively treated patients, the ACEsig continues to demonstrate strong performance. Our results demonstrate that large-scale systematic characterization of cellular heterogeneity has the potential to enhance our understanding of AML heterogeneity and contribute to more precise risk stratification strategy.
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A single-cell atlas of the peripheral immune response in patients with severe COVID-19.
There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, which has infected more than three million people worldwide1. Approximately 20% of patients with COVID-19 develop severe disease and 5% of patients require intensive care2. Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines3,4 that may be produced by a subset of inflammatory monocytes5,6, lymphopenia7,8 and T cell exhaustion9,10. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from seven patients hospitalized for COVID-19, four of whom had acute respiratory distress syndrome, and six healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene signature, HLA class II downregulation and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19.