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Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition.
Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can decompose transcriptional heterogeneity into interpretable components-including patient-specific, dataset-specific and shared components relevant to disease subtypes-and that, in the presence of strong intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.
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Single-cell profiling reveals a novel CAF subpopulation linking stromal heterogeneity to immune suppression in breast cancer subtypes.
BACKGROUND: The tumor microenvironment critically influences breast cancer (BC) progression, immune surveillance, and therapeutic response. Cancer-associated fibroblasts (CAFs), a heterogeneous stromal population, are key regulators of these processes, yet their subtype-specific contributions in BC remain insufficiently defined. METHODS: We integrated three single-cell RNA sequencing datasets from 29 BC patients to characterize stromal populations. Bulk RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to assess correlations between CAF subsets and immune infiltration. Gene signatures were derived to identify subtype-specific CAF-immune interactions, prognostic markers, and potential predictors of chemotherapy response. RESULTS: Three conserved stromal populations (iCAFs, myCAFs, and pericytes) were identified, along with a previously unrecognized subset, the cluster 3 (CL3) CAF-like cells, referred as metabolic stressed CAF (msCAF). msCAF cells displayed transcriptional programs associated with antigen presentation, stress response, glycolysis, and extracellular matrix remodeling. Their abundance was inversely correlated with T-cell infiltration and function, in a subtype-specific manner: triple negative breast cancer (TNBC) was enriched for msCAFs in immune-infiltrated but functionally constrained microenvironments, whereas Luminal A tumors exhibited weaker immune infiltration with heterogeneous CAF-immune associations. msCAFs were characterized by a conserved gene signature (HLA-A, HLA-C, IL32, EMP3) and subtype-specific genes related to T-cell exhaustion. Several genes demonstrated prognostic relevance with distinct patterns in Luminal A (IER3, TIMP1, TBX3, SEC61G) and TNBC (ADM, C4orf3, LDHA) tumors, as well as shared biomarkers (FN1, LOXL2, P4HA1). Multiple msCAF genes also predicted chemotherapy response, suggesting utility as treatment stratification biomarkers. CONCLUSION: msCAFs represent a clinically relevant CAF subset that drives immune suppression, impacts subtype-specific prognosis, and influences therapy response in BC. These findings highlight msCAFs as promising targets for enhancing immunotherapy and personalizing treatment strategies.
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Single-Cell RNA Sequencing and Bulk RNA Sequencing Revealed the Interplay Between Intratumoral Heterogeneity and the Tumor Microenvironment in Breast Cancer.
OBJECTIVE: The study is to investigate differential signaling pathways within the tumor microenvironment across molecular subtypes of breast cancer (BC). METHODS: Single-cell RNA (scRNA-seq) sequencing data of BC samples were obtained from the Gene Expression Omnibus database. Cell types were identified using the SingleR package, in conjunction with the analysis of marker genes. Subsequently, Monocle was used for pseudotime analysis of epithelial cells, fibroblasts, and macrophages to characterize their differentiation states. CellChat was employed to study the ligand-receptor interactions among various cell types across different BC molecular subtypes. In addition, we used common bulk RNA sequencing data from The Cancer Genome Atlas to investigate the correlation between key signaling pathway factors identified by scRNA-seq and clinical outcomes. RESULTS: Inference of copy number variation analysis using T cells revealed significantly elevated copy number variation scores in epithelial cells and fibroblasts. In the communication between epithelial cells and fibroblasts, the ANGPTL pathway is critical in estrogen receptor-positive breast cancer (ER+BC), while the PTN pathway plays a key role in both ER+BC and human epidermal growth factor receptor 2-positive breast cancer (HER2+BC), and the GAS pathway is associated with poor prognosis in triple-negative breast cancer (TNBC). In the interaction between fibroblasts and macrophages, the macrophage subpopulation supporting tumor angiogenesis exhibits significant activity in ER+BC, with the associated SPP1 and GRN pathways strongly influencing tumor progression. The SEMA3 pathway mainly acts through dividing tumor-associated fibroblast clusters across all BC subtypes. When exploring the role of lymphocytes, the PTN pathway also plays a role in HER2+BC, while in TNBC, CXCL and CD70 pathways are significantly involved in immune response modulation. CONCLUSION: Our comprehensive analysis of cell-cell communication networks among epithelial cells, fibroblasts, macrophages, and lymphocytes across BC subtypes focuses on ligand-receptor interactions. This study revealed that certain molecules within these networks exhibit significant prognostic value and therapeutic promise.
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A single-cell atlas of alternative wing development in two hemipteran species.
Insect wing polyphenism enables a single genome to produce distinct wing morphs in response to environmental cues, yet its underlying cellular determinants remain elusive. Here, we perform single-cell RNA sequencing of long-winged- and short-winged-destined wing buds of Pyrrhocoris apterus and Nilaparvata lugens, identifying six conserved cell types with comparable proportions between the two morphs. RNA interference-mediated silencing of 51 marker genes indicates that wing-patterning genes En (epithelial-like cells) and bs (tracheal cells), and cell-cycle genes Anln, CycB3, and cdk1 (neuron cells), are essential for long-winged development, among which En exhibits a specific temporal requirement. Flow cytometry analysis shows that long-winged formation mainly relies on an extended duration of cell proliferation. Cross-species comparisons indicate shared wing cell identities. Our findings indicate that hemipteran short-winged morphs may evolve from ancestral long-winged forms via precise regulation of wing-patterning and cell-cycle gene expression in epithelial‑like, tracheal, and neuron cells. This provides insights into the developmental plasticity of insect tissues at single‑cell resolution.
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Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia.
BACKGROUND: Microglia, the brain’s resident immune cells, play a pivotal role in Alzheimer’s disease (AD) pathogenesis. These cells exhibit diverse transcriptional states in response to neuroinflammatory stimuli, and characterizing these states is essential for understanding AD mechanisms. METHODS: We integrated single-cell and spatial transcriptomic datasets from multiple cohorts and brain regions, encompassing both experimental models and human tissues. Findings were validated through immunostaining of human brain samples. RESULTS: Our comprehensive atlas revealed pronounced heterogeneity among microglial states, with disease-associated microglia (DAM) significantly enriched in AD brains compared to controls. Spatial transcriptomics and immunohistochemistry demonstrated that DAM predominantly localize to external cortical layers and near amyloid plaques, whereas homeostatic microglia are more abundant in internal cortical layers and distal regions. Immunofluorescence staining for P2RY12 and CD68 confirmed these spatial distributions. CONCLUSION: By harmonizing single-cell and spatial transcriptomics, we provide a detailed atlas of microglial diversity, highlighting regional and pathological specificity of microglial states in AD. SIGNIFICANCE: This work advances prior studies by mapping microglial transcriptional states across multiple human cohorts and experimental contexts, integrating spatial data to reveal novel anatomical patterns. The enrichment of DAM in external cortical layers and homeostatic microglia in internal layers represents a previously unreported finding with potential therapeutic implications. While not exhaustive of all microglial datasets, this study establishes the first integrated reference combining single-nucleus and spatial transcriptomics, underscoring the importance of linking microglial diversity to AD neuropathology and brain architecture.
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Single-cell RNA-sequencing profiles reveal the developmental landscape of hawthorn leaves.
Hawthorn (Crataegus spp.) is an economically and medicinally important genus in Rosaceae. Its leaves accumulate valuable secondary metabolites, including flavonoids, and have long been used in traditional Chinese medicine. Despite available chromosome-level genome assemblies and bulk transcriptome data, the cellular composition of hawthorn leaves, their developmental trajectories, and the cell-type-specific distribution of biosynthetic gene expression remain uncharacterized. Here we report the first single-cell transcriptomic atlas of hawthorn leaves, generated by integrating protoplast-based scRNA-seq and nucleus-based snRNA-seq from two species (C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder). From four libraries, we profiled 32,292 high-quality cells, resolved sixteen transcriptional clusters, and annotated nine canonical cell types: mesophyll, pavement, guard, xylem, metaxylem, phloem parenchyma, companion, sieve element, and meristematic cells. Cell-type-resolved interspecific comparison identified 642 differentially expressed genes between species, with divergent enrichment of photosynthesis and volatile biosynthesis processes in C. pinnatifida var. major and hypoxia-response processes in C. scabrifolia. Pseudotime analysis reconstructed branched developmental trajectories converging on mesophyll dominance at late stages, consistent with classical dicotyledonous leaf organogenesis. Cell-type-resolved profiling demonstrated spatial partitioning of flavonoid biosynthetic genes across multiple cell populations, with upstream enzymes concentrated in epidermal and phloem-associated cells and downstream enzymes in mesophyll and xylem cells. This atlas provides a cellular framework for mechanistic study of tissue‑specific secondary metabolite and for future functional and breeding applications in hawthorn.
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A Cross-Species Single-Cell Transcriptomic Atlas of Subcutaneous Adipose Tissue Reveals Conserved and Divergent Cellular Programs.
Adipose tissue is not only the primary site of energy storage but also an essential metabolic organ for maintaining energy homeostasis to regulate adipogenesis, metabolism, and chronic inflammation in mammals. However, the cross-species conservation and divergence of its cellular composition and transcriptional features remain unclear. To address this, we analyzed single-cell RNA sequencing datasets from cattle, pigs, and humans. Conserved cell types, including adipocytes, adipose stem and progenitor cells (ASPCs), macrophages, endothelial cells, and T cells, were identified across all three species. In contrast, pigs uniquely contained mesenchymal cells (COL1A1, COL1A2) and B cells (CD37), cattle possessed distinct preadipocyte populations (COL3A1, PNPLA2, GPAM), and humans exhibited abundant dendritic (HLA-DRA, HLA-DRB1) and mast cells (KIT). Focusing on adipocytes, we identified 537, 160, and 73 species-specific DEGs in cattle, pigs, and humans, respectively. Gene Ontology enrichment analysis of adipocyte-specific DEGs revealed that genes enriched in cattle were primarily involved in cytoskeletal organization and triglyceride biosynthesis (LPIN1, DGAT2), those enriched in pigs in oxidative phosphorylation and thermogenesis (LIPE, CIDEA), and those enriched in humans in lipid transport and immune signaling (ADIPOQ, LPL). Overall, this study establishes a cross-species single-cell atlas of subcutaneous adipose tissue, providing insights into conserved architectures and species-specific adaptations, and serving as a valuable resource for comparative biology and livestock research.
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A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.
Single-cell RNA-seq (scRNAseq) analyses performed at the cellular level aim to understand the cellular landscape of tissue sections, offer insights into rare cell-types, and identify marker genes for annotating distinct cell types. ScRNAseq analyses are widely applied to cancer research to understand tumor heterogeneity, disease progression, and resistance to therapy. Single-cell data processing is a challenging task due to its high-dimensionality, sparsity, and having imbalanced class(cell-type) distributions. An accurate cell-type identification is highly dependent on preprocessing and quality control steps. To address these issues, generative models have been widely used in recent years. Techniques frequently used include Variational Autoencoders (VAE), Generative Adversarial Networks (GANs), Gaussian-based methods, and, more recently, Flow-based (FB) generative models. We developed a Masked Affine Autoregressive transform-embedded FB (MAF-FB) model. Then, to improve MAF-FB further, we incorporated a mixture of experts (MOE) of attention mechanisms on top of it, resulting in our proposed MOE-FB model. We conducted a comparative analysis of fundamental generative models, aiming to serve as a preliminary guidance for developing novel automated scRNAseq data analysis systems. We performed a large-scale analysis by combiningfour datasets derived from pancreatic tissue sections and for further generalizability assessments, we employed Peripheral Blood Mononuclear Cells (PBMC68K and PBMC3K) and Human Cell Atlas Bone Marrow (HCA-BM10K) datasets. We utilized VAE, GAN, Gaussian Copula, and Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA), and compared them against our two novel FB models, MAF-FB and MOE-FB for ScRnaseq synthesis. To evaluate the performances of generative models, we used various discrepancy metrics and performed automated cell-type classification tasks. We also identified differentially expressed genes for each cell type, and inferred cell-cell interactions based on ligand-receptor bindings across distinct cell-type pairs. Among the generative models, FB models, especially MOE-FB, consistently outperformed others across all experimental setups in both discrepancy metrics with comparison to the baseline test set and cell-type classification tasks (with an F1-score of 0.90 precision of 0.89 and recall of 0.92 for the integrated pancreatic datasets). MOE-FB produced biologically more relevant synthetic data, and ligand-receptor-based cell-cell interactions inferred from the synthetic cells closely resemble the original data, achieving an RMSE of 0.65 against the corresponding pancreatic test set. These findings highlight the potential and promising use of FB models, especially MOE-FB, in scRNAseq analyses.
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Tracing the stemness and malignant transition in a heritable colorectal cancer Lynch Syndrome by single-cell RNA-seq analysis.
BACKGROUND: Lynch Syndrome (LS) is an autosomal dominant disease characterized by germline heterozygous mutations in DNA mismatch repair (MMR) genes. High-risk LS patients may proceed to colorectal cancer (CRC). However, the drivers or biomarkers of LS benign colon tissue approaching malignant CRC are not completely understood. This study aimed to understand the molecular and cellular changes during malignant transition in LS. METHODS: Single-cell RNA sequencing (scRNA-seq) was used to analyze paired fresh biopsy samples from 3 LS patients (carcinoma vs. para-carcinoma, labeled as LS-CA vs. LS-paraCA). Single-nuclear RNA sequencing (snRNA-seq) was used to analyze a frozen biopsy sample of a LS patient. Datasets of Healthy controls and patients diagnosed with sporadic CRC (without LS-related germline or somatic mutations; labeled as nonLS-CRC) were downloaded from the open source. Integrative computational analysis was performed to conclude potential drivers of the malignant transition. Immuno-histo-fluorescence staining (IHF) were also performed for validating the proposed three key markers. RESULTS: In the single-cell atlas, we observed an increase of primitive cancer stem-cells with high expression of biomarkers CEACAM5, BACE2, GPRC5A and OLFM4 in the epithelium of the LS. Both infiltration of immune cells and pathways related to DNA repair biological activity in LS are dramatically increased in carcinoma compared to para-carcinoma. The mutation burden in LS is fundamentally elevated compared to that in healthy controls. Furthermore, T cell and macrophage-related tumor immunity in LS is readily mobilized in carcinomas compared to para-carcinoma. CONCLUSIONS: This study provides single-cell transcriptomic resource using affected tissues from patients with Lynch Syndrome and describes an integrative profile covering the alterations of cancer stem cell markers, mutation burden, and tumor immunity during the malignant transition from latency state to Lynch Syndrome and to colorectal cancer at the single-cell level.
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Cluster replicability in single-cell and single-nucleus atlases of the mouse brain.
Single-cell RNA sequencing has advanced our understanding of cellular heterogeneity. Ensuring the replicability of identified cell clusters across studies is essential for determining their biological robustness. We assess the replicability of cell clusters identified in two large mouse brain atlases, one generated using single-cell RNA sequencing and the other with single nuclei. Both profile over 4 million cells and group them into over 5,000 clusters. Using transcriptome-wide neighbor voting, we identify 2,009 reciprocally matched cluster pairs with consistent spatial localization and coordinated gene expression, which were also observed in datasets from multiple species. Reciprocal clusters are enriched in the cerebellum, where lower diversity aids replicability, while the hypothalamus's heterogeneity limits agreement. Distinguishing close clusters is much more challenging than differentiating a cluster from most others, especially when using marker genes. By incorporating replicability data, we provide a stronger foundation for investigating the atlas-defined clusters and their biological meaning.