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Integrative analysis of single-cell sequencing identifies CD8+ TIM3+ CD101+ T cell-associated genes as prognostic biomarkers in breast cancer.
BACKGROUND: Breast cancer is a prevalent and deadly malignancy that significantly impacts women's quality of life and imposes financial burdens. Despite therapeutic advancements, tumour heterogeneity and frequent relapses remain major challenges. Accordingly, this study aimed to characterize immune features associated with CD8+ TIM3+ CD101+ T cells and develop a prognostic signature for breast cancer. METHODS: This study integrated single-cell and bulk transcriptomic datasets to characterize CD8+ TIM3+ CD101+ T cell (CCT)-related immune features and construct a prognostic signature in breast cancer. Single-cell RNA-seq data were sourced from the Gene Expression Omnibus (GEO) repository, and bulk transcriptomic data were from The Cancer Genome Atlas (TCGA) and GEO databases. Analytical methods included pseudo-time trajectory reconstruction (Monocle2), intercellular signalling analysis (CellChat), functional enrichment (ClusterProfiler), and immune profiling (ssGSEA). Prognostic modeling was conducted using least absolute shrinkage and selection operator (LASSO) Cox regression, with validation via Kaplan-Meier and time-dependent receiver operating characteristic (ROC) analyses. RESULTS: Single-cell analysis identified 17 clusters spanning seven cell types, including T cells, myeloid cells, and epithelial cells. T-cell sub-clustering revealed four subtypes. Pseudotime analysis suggested a potential state-transition relationship between CD8+ CD101- TIM3+ and CD8+ CD101+ TIM3+ T-cell states. A total of 121 differentially expressed genes were enriched in vital biological processes. An 11-gene prognostic model showed strong predictive power across cohorts. Single-cell T-cell reclustering identified a CD8+ CD101+ TIM3+ T-cell subpopulation, which was primarily characterized by the expression of markers such as CD101 and HAVCR2/TIM3. CONCLUSIONS: This study maps cellular heterogeneity and molecular networks in breast cancer, offering insights for targeted therapy and improved prognosis.
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A cellular atlas of Pitx2-dependent cardiac development.
The Pitx2 gene encodes a homeobox transcription factor that is required for mammalian development. Disruption of PITX2 expression in humans causes congenital heart diseases and is associated with atrial fibrillation; however, the cellular and molecular processes dictated by Pitx2 during cardiac ontogeny remain unclear. To characterize the role of Pitx2 during murine heart development we sequenced over 75,000 single cardiac cell transcriptomes between two key developmental timepoints in control and Pitx2 null embryos. We found that cardiac cell composition was dramatically altered in mutants at both E10.5 and E13.5. Interestingly, the differentiation dynamics of both anterior and posterior second heart field-derived progenitor cells were disrupted in Pitx2 mutants. We also uncovered evidence for defects in left-right asymmetry within atrial cardiomyocyte populations. Furthermore, we were able to detail defects in cardiac outflow tract and valve development associated with Pitx2 Our findings offer insight into Pitx2 function and provide a compilation of gene expression signatures for further detailing the complexities of heart development that will serve as the foundation for future studies of cardiac morphogenesis, congenital heart disease and arrhythmogenesis.
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Identification of Poor Prognosis-Associated Fibroblast Subpopulation Signature Genes Utilizing the Scissor Algorithm to Classify Colorectal Cancer Subtypes and Evaluate the Immune Landscape.
BACKGROUND/AIMS: Colorectal cancer (CRC) shows high heterogeneity. Conventional bulk transcriptome-based classification methods fail to capture the complex tumor microenvironment of CRC, limiting precision therapy advances. The Scissor algorithm integrates single-cell/bulk transcriptomic data with clinical information to identify prognosis-linked cell subpopulations, offering new insights into tumor heterogeneity resolution. METHODS: We integrated bulk RNA-seq data, single-cell RNA-seq data, and clinical information from The Cancer Genome Atlas and Gene Expression Omnibus databases. The Scissor algorithm was employed to screen fibroblast subpopulations strongly associated with prognosis. Multidimensional analyses, including signature gene identification, functional enrichment analysis, Gene Set Variation Analysis (GSVA)-based subtyping, survival analysis, immune landscape assessment, cell-cell communication, mutational profiling, and drug sensitivity prediction, were conducted. RESULTS: We identified Scissor+ fibroblast subpopulations significantly correlated with prognosis, whose signature genes were associated with pro-metastatic pathways such as extracellular matrix remodeling and transforming growth factor-beta signaling. GSVA scoring stratified samples into Scissor_high and Scissor_low subtypes, with the former being associated with worse survival outcomes, immunosuppressive microenvironment features (including Treg and M2 macrophage enrichment), and stronger immune evasion tendencies. Cell communication analysis revealed that the Scissor_high subtype strongly interacted with many cell types, with remarkable enrichment in the ANNEXIN, SPP1, and NIF signaling pathways. Drug sensitivity predictions suggested that patients with this cell subtype may respond better to specific anticancer agents (e.g., AZD3759, Erlotinib, Gefitinib). CONCLUSIONS: This study was the first to identify Scissor+ fibroblast subpopulations markedly associated with poor prognosis in CRC, revealing their ability to activate pro-metastatic pathways and immune-activated but functionally exhausted characteristics. Moreover, their predictive value in CRC therapy was revealed. The study provided new perspectives for CRC prognosis evaluation and personalized immune-targeted combination therapies.
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DISCO: a database of Deeply Integrated human Single-Cell Omics data.
The ability to study cellular heterogeneity at single cell resolution is making single-cell sequencing increasingly popular. However, there is no publicly available resource that offers an integrated cell atlas with harmonized metadata that users can integrate new data with. Here, we present DISCO (https://www.immunesinglecell.org/), a database of Deeply Integrated Single-Cell Omics data. The current release of DISCO integrates more than 18 million cells from 4593 samples, covering 107 tissues/cell lines/organoids, 158 diseases, and 20 platforms. We standardized the associated metadata with a controlled vocabulary and ontology system. To allow large scale integration of single-cell data, we developed FastIntegration, a fast and high-capacity version of Seurat Integration. We also developed CELLiD, an atlas guided automatic cell type identification tool. Employing these two tools on the assembled data, we constructed one global atlas and 27 sub-atlases for different tissues, diseases, and cell types. DISCO provides three online tools, namely Online FastIntegration, Online CELLiD, and CellMapper, for users to integrate, annotate, and project uploaded single-cell RNA-seq data onto a selected atlas. Collectively, DISCO is a versatile platform for users to explore published single-cell data and efficiently perform integrated analysis with their own data.
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Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks.
Single cell RNA-seq (scRNA-seq) profiles conceal temporal and spatial tissue developmental information. De novo reconstruction of single cell temporal trajectory has been fairly addressed, but reverse engineering single cell 3D spatial tissue organization is hitherto landmark based, and de novo spatial reconstruction is a compelling computational open problem. Here it is shown that a proposed algorithm for de novo coalescent embedding (D-CE) of oligo/single cell transcriptomic networks can help to address this problem. Relying on the spatial information encoded in the expression patterns of genes, it is found that D-CE of cell-cell association transcriptomic networks, by preserving mesoscale network organization, captures spatial domains, identifies spatially expressed genes, reconstructs cell samples' 3D spatial distribution, and uncovers spatial domains and markers necessary for understanding the design principles on spatial organization and pattern formation. Comparison to the novoSpaRC and CSOmap (the only available de novo 3D spatial reconstruction methods) on 14 datasets and 497 reconstructions, reveals a significantly superior performance of D-CE.
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Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures.
Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq data set to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. Altogether, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.
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Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.
Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.
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Respiratory epithelial cell types, states and fates in the era of single-cell RNA-sequencing.
Standalone and consortia-led single-cell atlases of healthy and diseased human airways generated with single-cell RNA-sequencing (scRNA-seq) have ushered in a new era in respiratory research. Numerous discoveries, including the pulmonary ionocyte, potentially novel cell fates, and a diversity of cell states among common and rare epithelial cell types have highlighted the extent of cellular heterogeneity and plasticity in the respiratory tract. scRNA-seq has also played a pivotal role in our understanding of host-virus interactions in coronavirus disease 2019 (COVID-19). However, as our ability to generate large quantities of scRNA-seq data increases, along with a growing number of scRNA-seq protocols and data analysis methods, new challenges related to the contextualisation and downstream applications of insights are arising. Here, we review the fundamental concept of cellular identity from the perspective of single-cell transcriptomics in the respiratory context, drawing attention to the need to generate reference annotations and to standardise the terminology used in literature. Findings about airway epithelial cell types, states and fates obtained from scRNA-seq experiments are compared and contrasted with information accumulated through the use of conventional methods. This review attempts to discuss major opportunities and to outline some of the key limitations of the modern-day scRNA-seq that need to be addressed to enable efficient and meaningful integration of scRNA-seq data from different platforms and studies, with each other as well as with data from other high-throughput sequencing-based genomic, transcriptomic and epigenetic analyses.
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Lessons From Transcriptome Analysis of Autoimmune Diseases.
Various immune cell types, including monocytes, macrophages, and adaptive immune T and B cells, play major roles in inflammation in systemic autoimmune diseases. However, the precise contribution of these cells to autoimmunity remains elusive. Transcriptome analysis has added a new dimension to biology and medicine. It enables us to observe the dynamics of gene expression in different cell types in patients with diverse diseases as well as in healthy individuals, which cannot be achieved with genomic information alone. In this review, we summarize how transcriptome analysis has improved our understanding of the pathological roles of immune cells in autoimmune diseases with a focus on the ImmuNexUT database we reported. We will also discuss the common experimental and analytical design of transcriptome analyses. Recently, single-cell RNA-seq analysis has provided atlases of infiltrating immune cells, such as pro-inflammatory monocytes and macrophages, peripheral helper T cells, and age or autoimmune-associated B cells in various autoimmune disease lesions. With the integration of genomic data, expression quantitative trait locus (eQTL) analysis can help identify candidate causal genes and immune cells. Finally, we also mention how the information obtained from these analyses can be used practically to predict patient prognosis.
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Integration of spatial and single-cell transcriptomic analysis uncovers cellular and molecular alterations in the hypertensive brain.
AIMS: Hypertension is a major global health burden, and the central nervous system (CNS) plays a pivotal role in regulating blood pressure. However, the underlying cellular and molecular mechanisms remain incompletely understood. This study aimed to characterize the spatial and single-cell transcriptomic profiles of CNS regions implicated in hypertension. MATERIALS AND METHODS: Spatial transcriptomic sequencing and single-cell RNA sequencing were performed on the hypothalamus and medulla oblongata of spontaneously hypertensive rats and normotensive Wistar-Kyoto controls at 4 and 10 weeks of age. Transcriptomic findings were integrated with histological analyses and validated using human brain tissues. KEY FINDINGS: Spatial mapping identified previously unrecognized brain regions within the hypothalamus and medulla oblongata that may contribute to the initiation and progression of hypertension. Distinct neuronal subsets, including Ano5+, Oxt+, and Zeb2+ neurons, were characterized, exhibiting potential crosstalk with microglia, astrocytes, and oligodendrocytes. Further integrated analyses revealed a significant upregulation of Eno1 in hypertensive brains, implicating it in neural regulation of blood pressure. Findings in human samples were consistent with the results obtained from rats. SIGNIFICANCE: This study provides a comprehensive spatial transcriptomic atlas of the hypertensive brain, uncovering multidimensional molecular mechanisms underlying CNS-mediated blood pressure regulation. These results advance our understanding of neurogenic hypertension and may inform future diagnostic and therapeutic strategies.