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A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations.
Single-cell RNA-sequencing (scRNA-seq) facilitates the unbiased reconstruction of multicellular tissue systems in health and disease. Here, we present a curated scRNA-seq dataset of human muscle samples from 10 adult donors with diverse anatomical locations. We integrated ~ 22,000 single-cell transcriptomes using Scanorama to account for technical and biological variation and resolved 16 distinct populations of muscle-resident cells using unsupervised clustering of the data compendium. These cell populations included muscle stem/progenitor cells (MuSCs), which bifurcated into discrete "quiescent" and "early-activated" MuSC subpopulations. Differential expression analysis identified transcriptional profiles altered in the activated MuSCs including genes associated with aging, obesity, diabetes, and impaired muscle regeneration, as well as long non-coding RNAs previously undescribed in human myogenic cells. Further, we modeled ligand-receptor cell-communication interactions and observed enrichment of the TWEAK-FN14 pathway in activated MuSCs, a characteristic signature of muscle wasting diseases. In contrast, the quiescent MuSCs have enhanced expression of the EGFR receptor, a recognized human MuSC marker. This work provides a new benchmark reference resource to examine human muscle tissue heterogeneity and identify potential targets in MuSC diversity and dysregulation in disease contexts.
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Single-cell RNA sequencing of the carotid artery and femoral artery of rats exposed to hindlimb unloading.
BACKGROUND: Prolonged spaceflight is known to cause vascular deconditioning and remodeling. Tail suspension, a widely used spaceflight analog, is reported to result in vascular remodeling of rats. However, little is known about the cellular atlas of the heterogeneous cells of CA and FA from hindlimb-unloaded rats. METHODS: Firstly, we leveraged scRNA-seq to perform clustering analysis to identify diverse cell populations and sub-clusters within CA and FA from rats subjected to 3 months of hindlimb unloading. The dysregulated genes specific for artery types and cell types in HU group compared to Con were unraveled. Then R package "Cellchat" was used to reveal ligand-receptor cellular communication. At last, the TF network analysis was performed using the SCENIC R package to predict the pivotal TFs in rat artery remodeling induced by hindlimb unloading. RESULTS: Clustering analysis identified ECs, SMCs, fibroblasts, and a spectrum of immune cells, as well as neuronal and stem cells. Notably, an increased percentage of ECs in the CA and a diminished proportion of SMCs in both CA and FA were observed following tail suspension. Intersection of dysregulated genes specific for artery type and cell type after tail suspension revealed several gene sets involved in ECM remodeling, inflammation, vasoconstriction, etc. Fibroblasts, in particular, exhibited the most significant gene expression variability, highlighting their plasticity. Subclustering within ECs, SMCs and fibroblasts revealed specialized subsets engaged in processes such as EndoMT and cell cycle checkpoint regulation. Additionally, enhanced intercellular interactions among major cell types, especially between SMC and fibroblast, underscored the importance of cell communication in vascular remodeling. Several TFs were identified as potentially influential in the vascular remodeling process under simulated microgravity conditions. CONCLUSIONS: This study presents the first cellular atlas of the conductive arteries in hindlimb-unloaded rats, revealing a spectrum of dysregulated gene profiles. The identification of the subclusters of ECs, SMCs and fibroblasts, cellular communication analysis and transcription factors prediction are also included in this work. The findings provide a reference for future research on vascular deconditioning following long-duration spaceflight.
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Single-cell heterogeneity and dynamic evolution of Ph-like acute lymphoblastic leukemia patient with novel TPR-PDGFRB fusion gene.
Philadelphia chromosome-like acute lymphoblastic leukemia (Ph-like ALL) is a refractory and recurrent subtype of B-cell ALL enriched with kinase-activating rearrangements. Incomplete understanding of the heterogeneity within the tumor cells presents a major challenge for the diagnosis and therapy of Ph-like ALL. Here, we exhibited a comprehensive cell atlas of one Ph-like ALL patient with a novel TPR-PDGFRB fusion gene at diagnosis and relapse by using single-cell RNA sequencing (scRNA-seq). Twelve heterogeneous B-cell clusters, four with strong MKI67 expression indicating highly proliferating B cells, were identified. A relapse-enriched B-cell subset associated with poor prognosis was discovered, implicating the transcriptomic evolution during disease progression. Integrative single-cell analysis was performed on Ph-like ALL and Ph+ ALL patients, and revealed Ph-like specific B-cell subpopulations and shared malignant B cells characterized by the ectopic expression of the inhibitory receptor CLEC2D. Collectively, scRNA-seq of Ph-like ALL with a novel TPR-PDGFRB fusion gene provides valuable insights into the underlying heterogeneity associated with disease progression and offers useful information for the development of immunotherapeutic techniques in the future.
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Identification and Validation of Senescence-Related Signature by Combining Single Cell and Bulk Transcriptome Data Analysis to Predict the Prognosis and Identify the Key Gene CAV1 in Pancreatic Cancer.
BACKGROUND: The role of cellular senescence in the tumor microenvironment of pancreatic cancer (PC) remains unclear, particularly regarding its impact on prognosis and immunotherapy outcomes. METHODS: We utilized single-cell sequencing datasets (GSE155698 and GSE154778) for pancreatic cancer from the Gene Expression Omnibus (GEO) database and bulk RNA-seq data from the University of California, Santa Cruz (UCSC) and International Cancer Genome Consortium (ICGC) repositories, creating three patient cohorts: The Cancer Genome Atlas (TCGA) cohort, PAAD-AU cohort, and PAAD-CA cohort. Dimensionality reduction cluster analysis processed the single-cell data, while weighted gene co-expression network analysis (WGCNA) and differential expression gene analysis were applied to bulk RNA-seq data. Prognostic models were developed using Cox proportional hazards (COX) and least absolute shrinkage and selection operator (LASSO) regression, with validation through survival analysis, decision curve analysis, and principal component analysis (PCA). Tumor mutation data were analyzed using the "maftools" package, and the immune microenvironment was assessed with TIMER2 data. RESULTS: We developed a senescence-related (SENR) six-gene prognostic signature for PC, which stratifies patients by risk, with high-risk groups showing poorer prognoses. This model also offers predictive insights into tumor mutations and immune microenvironment characteristics. Caveolin-1 (CAV1) emerged as a significant prognostic biomarker, with functional validation showing its role in promoting cancer cell proliferation and migration, highlighting its potential as a therapeutic target. CONCLUSION: This study provides a novel senescence-related prognostic tool for PC, enhancing patient stratification for prognosis and immunotherapy, and identifies CAV1 as a key gene with clinical significance for targeted interventions.
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Graph-based deep learning for integrating single-cell and bulk transcriptomic data to identify clinical cancer subtypes.
The integration of single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic data has become essential for deciphering the complex heterogeneity of cancer and identifying clinical cancer subtypes. However, the inherent challenges posed by the high dimensionality, sparsity, and noise characteristics of scRNA-seq data have significantly hindered its widespread clinical translation. To address these limitations, we introduce single-cell and bulk transcriptomic graph deep learning, a graph-based deep learning method that synergistically integrates scRNA-seq and bulk transcriptomic data to precisely identify cancer subtypes and predict clinical outcomes. scBGDL constructs sample-specific gene graphs modeling complex gene-gene interactions and cellular relationships. The architecture employs Graph Attention Networks for feature aggregation, MinCutPool layers for dimensionality reduction, and Transformer modules to capture high-order biological dependencies. Independently validated in each of 16 distinct The Cancer Genome Atlas cancer types, scBGDL significantly outperformed existing methods in prognostic accuracy (mean C-index: 0.7060 versus 0.6709 max competitor), demonstrating robustness and generalizability to diverse transcriptional architectures. To demonstrate clinical versatility, we further evaluated scBGDL in three therapeutic contexts using multicenter cohorts: lung adenocarcinoma survival prediction (n = 1099), epithelial ovarian cancer platinum-based chemotherapy response (n = 762), skin cutaneous melanoma immunotherapy outcome (n = 305). scBGDL consistently delivered robust risk stratification (log-rank P < 0.05 across cohorts), identified key driver edges, and uncovered clinically relevant biological interpretations. By enabling multimodal data integration and interpretable biological insights, scBGDL advances precision oncology for prognosis prediction, therapy optimization, and biomarker discovery. The source code for scBGDL model is available online (https://github.com/NEFLab/scBGDL).
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Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis.
Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.
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Single-Cell RNA Sequencing Reveals an Atlas of Hezuo Pig Testis Cells.
Spermatogenesis is a complex biological process crucial for male reproduction and is characterized by intricate interactions between testicular somatic cells and germ cells. Due to the cellular heterogeneity of the testes, investigating different cell types across developmental stages has been challenging. Single-cell RNA sequencing (scRNA-seq) has emerged as a valuable approach for addressing this limitation. Here, we conducted an unbiased transcriptomic study of spermatogenesis in sexually mature 4-month-old Hezuo pigs using 10× Genomics-based scRNA-seq. A total of 16,082 cells were collected from Hezuo pig testes, including germ cells (spermatogonia (SPG), spermatocytes (SPCs), spermatids (SPTs), and sperm (SP)) and somatic cells (Sertoli cells (SCs), Leydig cells (LCs), myoid cells (MCs), endothelial cells (ECs), and natural killer (NK) cells/macrophages). Pseudo-time analysis revealed that LCs and MCs originated from common progenitors in the Hezuo pig. Functional enrichment analysis indicated that the differentially expressed genes (DEGs) in the different types of testicular germ cells were enriched in the PI3K-AKT, Wnt, HIF-1, and adherens junction signaling pathways, while the DEGs in testicular somatic cells were enriched in ECM-receptor interaction and antigen processing and presentation. Moreover, genes related to spermatogenesis, male gamete generation, sperm part, sperm flagellum, and peptide biosynthesis were expressed throughout spermatogenesis. Using immunohistochemistry, we verified several stage-specific marker genes (such as UCHL1, WT1, SOX9, and ACTA2) for SPG, SCs, and MCs. By exploring the changes in the transcription patterns of various cell types during spermatogenesis, our study provided novel insights into spermatogenesis and testicular cells in the Hezuo pig, thereby laying the foundation for the breeding and preservation of this breed.
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Single-cell analysis of the miRNA activities in tuberculous meningitis (TBM) model mice injected with the BCG vaccine.
BACKGROUND: Our previous study revealed the transcriptome atlas of specific cell types in tuberculous meningitis (TBM) model mice injected with the BCG vaccine via scRNA sequencing. However, the activities of miRNAs in TBM at single-cell resolution remain to be explored. METHOD: Cell type-specific miRNA activities were investigated by using motif enrichment analyses (miReact) on the transcriptome data of 15 cell types. The target mRNAs of miRNAs were predicted and subjected to enrichment analysis. Furthermore, miRNAs and their target mRNAs with opposite expression trends were chosen to construct functional networks. Besides, qRT-PCR and RNA scope were performed to verify the expression level of representative miRNA. RESULTS: The tSNE dimensionality reduction presented 15 cell types in TBM model mice, in which microglia and endothelial cells accounted for the majority. Target mRNAs of each cell type were predicted for verification or network construction. The immune and inflammation-related miRNA-mRNA networks of macrophages and microglia, oxidative phosphorylation-related miRNA-mRNA networks of neurons, ion and protein transport-related networks of epididymal cells, and angiogenesis-related miRNA-mRNA networks of VSMCs were constructed. The miRNA activity analysis revealed that miR-21a-3p activity was increased in microglia, macrophages, neurons and epididymal cells. The result of qRT-PCR and RNA scope indicate that miR-21a-3p was significantly higher-expressed in TBM brain tissue compared with normal brain tissue. CONCLUSION: In our study, an in-depth exploration of the mRNA expression and miRNA activity of macrophages, microglia, epididymal cells, neurons and vascular smooth muscle cells during TBM progression was conducted using scRNA-Seq, which provided novel insights into the immune cell engagement in TBM patients.
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Single-cell transcriptomics reveals cellular and genetic mechanisms of alpine adaptation in Rosa sericea.
INTRODUCTION: Plant development is shaped by environmental conditions, and its adaptation to climate change is crucial for biodiversity conservation. The extreme climate of the Qinghai-Tibet Plateau makes it an ideal system for studying plant adaptive strategies. Rosa sericea, a dominant alpine shrub, exhibits remarkable morphological plasticity, but its molecular and cellular adaptation mechanisms are still unclear. In this study, we integrated single-nucleus RNA sequencing (snRNA-seq) with high-dimensional weighted gene co-expression network analysis (hdWGCNA), gene ontology (GO) enrichment, gene set enrichment analysis (GSEA), pseudotime trajectory inference, and gene overexpression techniques to profile 31,796 cells from R. sericea leaves. METHODS: We constructed a draft single-cell transcriptional atlas with putative annotation of 11 leaf cell types and identified eight co-expression gene modules linked to key cell types. RESULTS: The leaf development spatiotemporal dynamics uncovered a developmental continuum from cell proliferation to photosynthetically specialized maturation. Furthermore, we identified several developmental and physiological features potentially associated with high-altitude adaptation, including presence of transcriptionally active nuclear-encoded genes involved in chloroplast function in epidermal pavement cells, the potential role of SPL7-mediated copper homeostasis, and a putative RO6G37307-TTG2-TCP4 regulatory module associated with trichome development. DISCUSSION: Together, this study provides the first single-cell-resolved transcriptional framework for R. sericea leaves and suggests adaptive developmental mechanisms at the cellular and genetic levels, enhancing our understanding of how alpine plants respond to climate change.
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scREAD: A Single-Cell RNA-Seq Database for Alzheimer's Disease.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder of the brain and the most common form of dementia among the elderly. The single-cell RNA-sequencing (scRNA-Seq) and single-nucleus RNA-sequencing (snRNA-Seq) techniques are extremely useful for dissecting the function/dysfunction of highly heterogeneous cells in the brain at the single-cell level, and the corresponding data analyses can significantly improve our understanding of why particular cells are vulnerable in AD. We developed an integrated database named scREAD (single-cell RNA-Seq database for Alzheimer's disease), which is as far as we know the first database dedicated to the management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. scREAD provides comprehensive analysis results for 73 data sets from 10 brain regions, including control atlas construction, cell-type prediction, identification of differentially expressed genes, and identification of cell-type-specific regulons.