Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 4 877
Page 21 of 488-
NEXT-scASV: a Nextflow pipeline for allele-specific variant calling from single-cell RNA-seq data.
The rapid accumulation of single-cell sequencing data presents major computational challenges in reproducibility, scaling, and handling data characteristics such as sparsity and technical variations, which complicate even basic analyses. The next level of complexity is the allele-specific analysis, focused on identifying differential gene expression or regulation between homologous chromosomes by estimating the allelic imbalance of read counts at individual single-nucleotide variants. Here, we present NEXT-scASV, a scalable Nextflow pipeline for calling allele-specific variants (ASVs) from 5' single-cell RNA sequencing data. NEXT-scASV automates the entire process-from read alignment and quality control to variant calling and statistical evaluation of the allelic imbalance-within a containerized environment, ensuring reproducibility and ease of deployment across platforms. NEXT-scASV is able to perform de novo ASV detection from single-cell sequencing data without prior genotyping. Its modular design allows for massive parallelization, efficiently handling the scale of modern atlas-level studies. We validate NEXT-scASV on a dataset of 135,000 peripheral blood mononuclear cells from 57 donors, demonstrating that it processes large-scale data efficiently, completing analysis in a week on a cluster with one node and 100 threads. Crucially, the pipeline reliably identifies ASVs even in rare cell populations (e.g., gdT GZMBhi and memory B IGHMhi cells), which remains elusive for bulk analyses. We also successfully detect allele-specific regulation of long non-coding RNAs and other lowly expressed, cell type-specific genes. Genes linked to detected ASVs show a high concordance (80%) with previously reported eQTLs. This strong validation confirms that NEXT-scASV produces biologically relevant results, making it a powerful tool for uncovering allele-specific regulation in large-scale, complex single-cell studies.
-
Tree shrew immune cell atlas identifies NR1H3⁺ tissue macrophages with conserved anti-inflammatory function.
Rodents are widely used in immunology but do not always recapitulate human immune functions. The tree shrew (Tupaia belangeri) is phylogenetically closer to primates than rodents and may help bridge this gap, yet its immune system has not been comprehensively characterised at single-cell resolution. Here, we present a single-cell transcriptomic atlas of the tree shrew immune system, profiling 39 cell types across 12 tissues. We uncover human-like tonsillar structures and two transcriptionally distinct splenic macrophage subsets: an NR1H3+ immunoregulatory subset and an IRF8+ pro-inflammatory subset. During acute Epstein-Barr virus infection, NR1H3+ macrophages undergo pronounced inflammatory reprogramming and emerge as a major intercellular signalling hub. Cross-species integration of single-cell datasets from seven vertebrate species indicates that NR1H3+ macrophage gene programmes are evolutionarily conserved. Functional analyses further support a shared anti-inflammatory programme mediated by NR1H3-dependent suppression of the non-canonical NF-κB pathway in both tree shrew and human macrophages. Together, these findings provide a reference resource for tree shrew immunology and support the tree shrew as a complementary model alongside rodent and primate systems.
-
Multiplatform single-cell and spatial transcriptomics reveal sex-specific malignant states and architecture in male breast cancer.
Male breast cancer (MBC) is rare and remains largely managed using knowledge derived from female breast cancer. Here, we generated a multiplatform single-cell and spatial transcriptomic atlas integrating scRNA-seq, SeekSpace, Visium HD, Xenium In Situ, and multiplex immunohistochemistry data from male and female breast cancer cohorts, covering more than 660,000 cells. We identified male-specific tumor cells (MSTCs) that were enriched in MBC, rare in female breast cancer, and associated with poorer disease-free survival. MSTCs exhibited neural transcriptional programs, increased transcriptome-inferred copy number variation burden, and spatial coupling with fatty acid metabolism signals. MSTC-enriched regions showed reduced antigen-presentation signatures and spatial association with APOE+/CD163+ macrophages, with GRN-SORT1 emerging as a candidate macrophage-tumor interaction axis. These findings define an MBC-associated malignant state and its immune-metabolic spatial context.
-
Single-cell multi-omics dissects transcript isoform and immune repertoire dynamics in human immunosenescence.
Immunosenescence, a major hallmark of systemic aging, refers to the progressive functional decline of the immune system. This decline not only compromises host defense and immunological memory but also fuels chronic inflammation and tissue degeneration (collectively known as inflammaging). While single-cell RNA sequencing (scRNA-seq) has revealed transcriptomic alterations associated with immune aging, analyses restricted to transcript abundance fail to capture deeper regulatory layers, such as transcript isoform diversity and the remodeling of immune receptor repertoires. To address this limitation, we present a human peripheral immune single-cell multi-omics atlas that integrates gene expression, transcript isoform diversity, and immune receptor repertoires. By combining single-cell full-length transcriptome sequencing (scCycloneSEQ), short-read scRNA-seq, and single-cell immune receptor sequencing (scTCR/BCR-seq), we systematically profiled peripheral blood mononuclear cells (PBMCs) from healthy donors aged 30-40 and 60-70 years. Our analyses uncovered extensive age-related remodeling of immune cell composition, functional states, and TCR/BCR diversity. Notably, we found that CD4+ effector memory T cells exhibited widespread differential isoform usage (DIU), 3'UTR length variation, and a marked reshaping of cytotoxic T lymphocyte (CTL) clonotypes-all of which were closely associated with aging-related inflammation and cellular senescence. This multi-omics atlas delineates key molecular features of immunosenescence and provides a high-resolution resource for deciphering the regulatory architecture underlying immune aging.
-
Attention-based deep clustering method for scRNA-seq cell type identification.
Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.
-
Single-cell transcriptomics unveils the immunologic landscape of anti-PD-1-associated indirect drug-induced liver injury.
BACKGROUND: Immune-related adverse events (irAEs) caused by immune checkpoint inhibitors (ICIs) have become a bottleneck limiting their widespread use in anti-cancer therapy. Among them, immune checkpoint inhibitor-associated liver injury (ILICI) is a unique entity of drug-induced liver injury (DILI) whose clinical management has become a notable emerging challenge in cancer therapy. Nonetheless, the exact pathological mechanisms of ILICI are still poorly understood. METHODS: We established a Lewis lung adenocarcinoma-bearing mouse model in C57BL/6 J mice treated with anti-PD-1 (αPD-1). Subsequently, liver tissues from the two mouse groups were collected for single-cell RNA sequencing (scRNA-seq), and the core pathogenic mechanisms of αPD-1-induced liver injury were subsequently validated using both animal tissues and patient specimens. RESULTS: Our model revealed that αPD-1 treatment induced a hepatic phenotype characterized by focal inflammatory infiltration and fibrosis, concomitant with elevated serum aminotransferase (ALT) and pro-inflammatory cytokine levels. Further scRNA-seq analysis demonstrated that hepatocytes in the αPD-1 group exhibited features of disrupted lipid metabolism and enhanced NETosis, along with expanded CD8⁺ T cells displaying increased cytotoxicity and expansion of pro-inflammatory Ly6Chigh monocytes. These Ly6Chigh monocyte-derived macrophages robustly recruited and activated neutrophils via the Cxcl2-Cxcr2 and C3-(Itgam/Itgb2) signaling axes. Corroborated by validation in both animal and patient tissues, we identified neutrophil extracellular traps (NETs) as a pivotal event in αPD-1-associated liver injury. CONCLUSION: This study provides the first single-cell atlas of the hepatic microenvironment in a mouse model of αPD-1-induced liver injury under tumor-bearing conditions, revealing the concomitant metabolic reprogramming and profibrotic phenotype. Furthermore, our findings propose that the activation of NETosis is closely associated with the progression of liver injury, suggesting it may play a significant role in this pathological process.
-
Single-Cell Transcriptomics Identifies BST2 as an Oncogenic Driver and Immunotherapy Biomarker in Lung Adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality worldwide. The tumor microenvironment (TME) plays a pivotal role in LUAD progression, but the specific molecular mechanisms driving malignancy and immune evasion remain incompletely understood. Bone marrow stromal cell antigen 2 (BST2) has been implicated in other cancers, yet its functional role and therapeutic potential in LUAD require further elucidation. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data from primary LUAD tissues and normal lung tissues with bulk RNA-seq data from The Cancer Genome Atlas (TCGA)-LUAD and GEO cohorts. Malignant epithelial cells were identified using inferCNV analysis. Cellular trajectory and cell-cell communication analyses were systematically performed to delineate the malignant transformation process and its interactions with the TME. High-dimensional weighted gene coexpression network analysis further identified key functional modules within malignant subpopulations. The oncogenic role of BST2 was comprehensively validated through differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and in vitro cellular assays, including RT‑qPCR, MTT, colony formation, Transwell invasion, and wound healing experiments. In addition, we employed the tumor immune dysfunction and exclusion (TIDE) algorithm to evaluate its association with immunotherapy response and performed drug screening via the Clue.io platform to explore its therapeutic potential. RESULTS: scRNA-seq analysis revealed significant heterogeneity in the TME and identified two distinct subpopulations of malignant epithelial cells. Trajectory analysis uncovered a specific lineage (Lineage 4) driving the normal-to-malignant transition, while cell communication analysis highlighted interactions between malignant cells and tumor-associated macrophages (TAMs) mediated by MIF and APP signaling pathways. High-dimensional weighted gene coexpression network analysis (hdWGCNA) identified a coexpression gene module (Module 1) specifically enriched in malignant subpopulations. By intersecting with a set of immunoregulatory genes, BST2 was ultimately determined as a key candidate oncogene. BST2 was significantly upregulated in malignant epithelial cells and TAMs, and its high expression was closely associated with poor patient prognosis. GSEA demonstrated that high BST2 expression was linked to the activation of crucial oncogenic pathways, including oxidative phosphorylation, Kras signaling, and epithelial-mesenchymal transition (EMT). In vitro validation further confirmed that BST2 knockdown suppressed LUAD cell proliferation, migration, and invasion. Furthermore, elevated BST2 expression was associated with reduced efficacy of immune checkpoint blockade (ICB) therapy. CONCLUSION: Our study unveils BST2 as a critical oncogene in LUAD, promoting tumor progression and influencing the TME, particularly via TAM recruitment. BST2 expression predicts patient prognosis and immunotherapy response, positioning it as a promising biomarker and therapeutic target.
-
Platelet heterogeneity in ageing and cancer: A single-cell atlas reveals transcriptional state-specific associations with metastasis.
Platelets have been increasingly recognized as versatile regulators of ageing, immunity, and cancer, yet their functional heterogeneity has remained poorly defined. We performed the first large-scale single-cell RNA sequencing of 28,192 platelets from healthy, aged, metastatic, and treated mice using the BD Rhapsody platform. Our analysis revealed four conserved and functionally distinct platelet transcriptional states: haemostatic platelet (HP), neural gene-enriched platelet (NEP), platelet-leukocyte aggregate (PLA) and platelet-erythrocyte aggregate (PEA). Among these states, Tpm2-high HP is linked to ageing-associated lung metastasis and is characterized by cytoskeletal remodelling gene signatures. The PLA state was predicted to be a signalling hub for immunothrombosis, with a PLA-Bridge subpopulation coordinating immune-adherent platelets via the Ppbp-Cxcr2 and Thbs1-Cd47 checkpoint axes. Strikingly, AAV-mPf4 gene therapy was associated with a NEP-associated transcriptional program, which mitigates age-related functional decline. This study provides a single-cell transcriptomic atlas of murine platelets under ageing and metastasis conditions and reveals transcriptional state-specific heterogeneity. This study also proposes PF4-based interventions and cytoskeletal candidates for diagnosis and therapy.
-
Single-cell RNA sequencing unveils CD8+ T cell heterogeneity in the diffuse large B-cell lymphoma microenvironment: A systematic review.
BACKGROUND: The heterogeneous response to immunotherapy in diffuse large B-cell lymphoma (DLBCL) is largely attributable to the diverse functional states of CD8⁺ T cells within the tumor microenvironment. Although single-cell RNA sequencing (scRNA-seq) has revolutionized cellular resolution, a systematic synthesis of this evidence to map CD8⁺ T cell heterogeneity and its clinical implications in DLBCL is currently lacking. METHODS: Following the PRISMA guidelines and a PROSPERO-registered protocol (CRD420261282336), we conducted a qualitative systematic review (without meta-analysis) of 18 eligible scRNA-seq studies published up to September 2025. Dual reviewers independently performed study selection, data extraction, and quality assessment using a self-designed scRNA-seq checklist combined with the AMSTAR-2 tool. RESULTS: We constructed a dynamic exhaustion trajectory atlas of CD8⁺ T cells in DLBCL, revealing a hierarchical continuum comprising multiple functionally distinct subclusters. Key findings include: ① the progenitor exhausted T cell (Tpex) subset is associated with favorable prognosis and holds therapeutic targetability, whereas the terminally exhausted (Tex-term) subset correlates with poor prognosis; ② the CXCR5⁺TCF7⁺ S1 subset predicts sensitivity to RB-CHOP chemotherapy, and CD58 pathway impairment is linked to CAR-T resistance; and ③ the recently identified CD8‑fit cells show promise as a synergistic immunotherapy target. CONCLUSION: This review provides a comprehensive single-cell atlas of CD8⁺ T cells in DLBCL, advocating a shift toward cell‑state‑guided precision immunotherapy. The identified biomarkers facilitate pre‑treatment patient stratification and reveal novel combinatorial targets. Future multicenter studies should focus on validating these targets (e.g., CD58, CD8‑fit) and standardizing scRNA‑seq analytical frameworks to translate these findings into clinical practice.
-
Single-cell atlas of neuroglial dynamics in SNCA-A53T Parkinson's disease mouse model.
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder characterized by progressive degeneration of midbrain substantia nigra dopaminergic neurons, resulting in striatal dopamine depletion and motor dysfunction. While this pathological cascade is well-established, its underlying mechanisms remain elusive. METHODS: To further investigate the pathological mechanisms of PD, we performed single-cell RNA sequencing of the midbrain and striatum from Hua-Syn (SNCA*A53T) transgenic (A53T) mice as a PD model. RESULTS: Analysis of 22 865 midbrain and 32 117 striatal cells revealed cell-type-specific risk association. Glial populations (astrocytes, microglia, oligodendrocytes) showed significant enrichment for PD-risk genes. Variance-based clustering identified PD-enriched subclusters exhibiting upregulated inflammatory pathways, apoptotic pathways, proteostasis disruption, glutamatergic signaling dysregulation, and mitochondrial respiratory chain defects. Transcriptional regulation analysis identified genes associated with PD specific activity, including Rorb and Foxc1 in the midbrain and Dbx2 and Klf13 in the striatum. Cell-cell interactions showed that cell-to-cell signaling was enhanced, and the SEMA and CCL neuroinflammatory axes were specifically activated in the PD group. CONCLUSIONS: Our integrative analysis delineates the cellular and molecular architecture of the pathological process triggered by the expression of A53T mutant α-synuclein, and provides a framework for targeted therapeutic development.