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Scaling deep identifiable models enables zero-shot characterization of single-cell biological states.
How to identify true biological differences across samples while overcoming batch effects has been a persistent challenge in single-cell RNA-seq data analysis, hindering analyses across datasets for transferable biological findings. In this work, we show that scaling up deep identifiable models leads to a surprisingly effective solution for this challenging task. We developed scShift, a deep variational inference framework with theoretical support in disentangling batch-dependent and independent variations. By training the model with compendiums of scRNA-seq atlases, scShift shows remarkable zero-shot capabilities in revealing representations of cell types and biological states in single-cell data while overcoming batch effects. We employed scShift to systematically compare lung fibrosis states across different datasets, tissues and experimental systems. scShift uniquely extrapolates lung fibrosis states to previously unseen post-COVID-19 fibrosis, characterizing universal myeloid-fibrosis signatures, potential repurposing drug targets and fibrosis-associated cell interactions. Evaluations of over 200 trained scShift models demonstrate emergent zero-shot capabilities and a scaling law beyond a transition threshold, with respect to dataset diversity. With its scaling performance on massive single-cell compendiums and exceptional zero-shot capabilities, scShift represents an important advance toward next-generation computational models for single-cell analysis.
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Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.
Background: Colon adenocarcinoma (COAD) is a heterogeneous tumor and senescence is crucial in the occurrence of cancer. This study aimed to identify senescence-based subtypes and construct a prognostic signature to predict the prognosis and guide immunotherapy or chemotherapy decisions for COAD patients. Methods: Based on the single-cell RNA sequencing (scRNA-seq) data of 13 samples from the Gene Expression Omnibus (GEO) database, we assessed cellular senescence characteristics. Transcriptome data, copy number variations (CNVs) and single nucleotide variations (SNVs) data were obtained from The Cancer Genome Atlas (TCGA) database. GSE39582 and GSE17537 were used for validation. Senescence subtypes were identified using unsupervised consensus clustering analysis, and a prognostic signature was developed using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO). Response of risk groups to chemotherapy was predicted using the half-maximal inhibitory concentration (IC50) values. We further analyzed the relationship between risk gene expression and methylation level. The prediction performance was assessed by nomogram. Results: Senescence-related pathways were highly enriched in malignant cells and bulk RNA-seq verified cellular senescence. Three senescence subtypes were identified, in which patients in clust3 had poorest prognosis and higher T stage, accompanied with higher tumor mutation burden (TMB) and mutations, activated inflammatory response, more immune cell infiltration, and higher immune escape tendency. A senescence-based signature using 11 genes (MFNG, GPRC5B, TNNT1, CCL22, NOXA1, PABPC1L, PCOLCE2, MID2, CPA3, HSPA1A, and CALB1) was established, and accurately predicted a lower prognosis in high risk patients. Its robustness was validated by external cohort. Low risk patients were more sensitive to small molecule drugs including Erlotinib, Sunitinib, MG-132, CGP-082996, AZ628, Sorafenib, VX-680, and Z-LLNle-CHO. Risk score was an independent prognostic factor and nomogram confirmed its reliability. Four risk genes (CALB1, CPA3, NOXA1, and TNNT1) had significant positive correlation with their methylation level, while six genes (CCL22, GPRC5B, HSPA1A, MFNG, PABPC1L, and PCOLCE2) were negatively correlated with their methylation level. Conclusion: This study provides novel understanding of heterogeneity in COAD from the perspective of senescence, and develops signatures for prognosis prediction in COAD.
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Genetic regulation of gene expression across multiple tissues in chickens.
The chicken is a valuable model for understanding fundamental biology and vertebrate evolution and is a major global source of nutrient-dense and lean protein. Despite being the first non-mammalian amniote to have its genome sequenced, a systematic characterization of functional variation on the chicken genome remains lacking. Here, we integrated bulk RNA sequencing (RNA-seq) data from 7,015 samples, single-cell RNA-seq data from 127,598 cells and 2,869 whole-genome sequences to present a pilot atlas of regulatory variants across 28 chicken tissues. This atlas reveals millions of regulatory effects on primary expression (protein-coding genes, long non-coding RNA and exons) and post-transcriptional modifications (alternative splicing and 3'-untranslated region alternative polyadenylation). We highlighted distinct molecular mechanisms underlying these regulatory variants, their context-dependent behavior and their utility in interpreting genome-wide associations for 39 chicken complex traits. Finally, our comparative analyses of gene regulation between chickens and mammals demonstrate how this resource can facilitate cross-species gene mapping of complex traits.
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Applying transcriptomics to studyglycosylation at the cell type level.
The complex multi-step process of glycosylation occurs in a single cell, yet current analytics generally cannot measure the output (the glycome) of a single cell. Here, we addressed this discordance by investigating how single cell RNA-seq data can be used to characterize the state of the glycosylation machinery and metabolic network in a single cell. The metabolic network involves 214 glycosylation and modification enzymes outlined in our previously built atlas of cellular glycosylation pathways. We studied differential mRNA regulation of enzymes at the organ and single cell level, finding that most of the general protein and lipid oligosaccharide scaffolds are produced by enzymes exhibiting limited transcriptional regulation among cells. We predict key enzymes within different glycosylation pathways to be highly transcriptionally regulated as regulatable hotspots of the cellular glycome. We designed the Glycopacity software that enables investigators to extract and interpret glycosylation information from transcriptome data and define hotspots of regulation.
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[Hepatic T cell subtypes and functional analysis among alveolar echinococcosis patients using single-cell RNA sequencing].
OBJECTIVE: To investigate T cell subtypes and their functions in liver immune microenvironments among patients with alveolar echinococcosis (AE) using single-cell RNA sequencing (scRNA-seq). METHODS: Four AE patients that were admitted to Qinghai Provincial People's Hospital in 2023 for hepatic surgery for the first time were enrolled, and liver specimens were sampled 1 cm (peri-lesion, PL group) and > 5 cm from AE lesions (distal lesion, DL group) among each patient. Finally, a total of eight liver specimens were sampled from four AE patients for scRNA-seq analysis. Genome and transcriptome data of liver specimens were processed using the software Cell Ranger and R package. Differentially expressed genes (DEGs) and their biological functions were analyzed using gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and the primary intercellular communication patterns and interaction mechanisms were identified among T cell subtypes in liver specimens using the CellChat package. In addition, the developmental stages of T cells were subjected to trajectory analysis with the monocle package to investigate the expression of genes associated with cell growth and tumor transformation, and to predict the developmental trajectories of T cells. RESULTS: All four AE patients were female, with a mean age of (25.00 ± 9.06) years, and there were three cases from Jiuzhi County, Golog Tibetan Autonomous Prefecture and one case from Chengduo County, Yushu Tibetan Autonomous Prefecture, Qinghai Province. The viability of single-cell samples from eight liver specimens was 90.41% to 96.33%, and a total of 81 763 cells were analyzed, with 19 cell types annotated. Of these cell types, 13 were immune cells (87.60%), and T cells (33.13%), neutrophils (15.40%), and natural killer cells (11.92%) were the three most common cell types. Re-clustering of 27 752 T cells and proliferative T cells identified 10 distinct T cell subtypes, with CD8+ cytotoxic T cells (23.43%), CD8+ naive T cells (12.80%), and CD4+ effector memory T cells (17.73%) as dominant cell types. The proportions of T helper 2 (Th2) cells (5.19% vs. 3.63%; χ2 = 38.35, P < 0.01) and CD4+ effector memory T cells (21.59% vs. 13.67%; χ2 = 244.70, P < 0.01) were significantly higher in liver specimens in the PL group than in the DL group, and the proportion of CD4+ helper T cells was significantly lower in the PL group than in the DL group (7.50% vs. 14.75%; χ2 = 330.52, P < 0.01). KEGG pathway analysis revealed that Th2 cells were significantly enriched in cell apoptosis and multiple cancer-associated pathways, and CD4+ effector memory T cells were significantly enriched in the regulation of cytokines and chronic inflammation, while CD4+ helper T cells were significantly enriched in immune responses regulation. Trajectory analysis of T cells showed that CD4+ helper T cells were at an earlier developmental stage relative to Th2 cells and CD4+ effector memory T cells, and the expression of inhibitor of DNA binding 3 (ID3), thioredoxin interacting protein (TXNIP), Bcl2-associated athanogene 3 (BAG3) and heat shock protein family B (small) member 1 (HSPB1) genes appeared a tendency towards a decline over time. CONCLUSIONS: CD4+ effector memory T cells and CD8+ cytotoxic T cells are primary interacting cells in the liver specimens of AE patients. Reduced expression of Th2 cells and CD4+ helper T cells contributes to an inhibitory immune microenvironment, which promotes immune evasion by Echinococcus multilocularis, and Th2 cells are significantly enriched in multiple cancer-associated pathways, which may be linked to the invasive growth of E. multilocularis.
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Single-cell atlas of grass carp ( Ctenopharyngodon idella) peripheral blood IgM + B cells provides insights into B cell-mediated immune responses in teleost fish.
Teleost peripheral blood contains a remarkably high proportion of B cells, accounting for 15%-50% of circulating lymphocytes. However, their immune responses to bacterial infection are yet to be elucidated. In the present study, 10× Genomics single-cell RNA sequencing (scRNA-seq) was employed to characterize the transcriptomic landscape of peripheral blood IgM + B cells in grass carp ( Ctenopharyngodon idella) following challenge with Aeromonas hydrophila, a major aquatic pathogen. Six transcriptionally distinct IgM + B cell subpopulations were identified, including (im)mature B cells, innate B cells, proliferating B cells, IgD high B cells, and two infection-induced subsets denoted as infection Ⅰ and Ⅱ B cells. Bacterial infection altered the cellular heterogeneity of IgM + B cells, triggered metabolic reprogramming in (im)mature and innate B cell subpopulations, and enhanced the immunological activation of circulating B cells. Notably, infection Ⅰ B cells demonstrated robust induction of interferon φ1 ( IFNφ1), a type I IFN, following A. hydrophila exposure. This induction was further validated through in vitro bacterial stimulation, indicating that teleost B cells actively contribute to innate antibacterial responses through IFN signaling. Additionally, the IgD high B cell subpopulation remained consistently present in peripheral blood across both infected and uninfected states, pointing to a constitutive and likely mature phenotype. These findings significantly advance our understanding of the heterogeneity of peripheral blood IgM + B cells and provide new insights into IgM + B cell-mediated immune responses in teleost fish.
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Integrating multiple omics and machine learning to reveal the prognostic value of endoplasmic reticulum stress gene FKBP10 in gastric cancer.
BACKGROUND: Stomach adenocarcinoma (STAD) remains a leading cause of cancer-related mortality worldwide, with limited prognostic biomarkers and heterogeneous responses to immunotherapy. Endoplasmic reticulum stress (ERS) plays a critical role in tumor progression and immune modulation, yet its comprehensive prognostic value in STAD has not been systematically characterized. This study aims to identify ERS-related genes with prognostic significance and elucidate their role in the tumor microenvironment. METHODS: RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects were integrated to identify differentially expressed ERS-related genes. Univariate Cox regression and consensus clustering were applied to define molecular subtypes. A prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression and validated in independent Gene Expression Omnibus (GEO) cohorts (GSE66229, GSE14208). Immune infiltration, functional enrichment, and mutation landscapes were analyzed. Single-cell RNA sequencing (scRNA-seq) (GSE183904) and CellChat were used to explore FKBP10 expression and intercellular communication. Immunohistochemistry and in vitro assays [Cell Counting Kit-8 (CCK-8), flow cytometry] were performed to validate FKBP10 function. RESULTS: We identified 33 prognostic ERS-related genes, which classified STAD patients into two subtypes (C1 and C2) with distinct survival outcomes and immune infiltration profiles. A 14-gene risk model was constructed and stratified patients into high- and low-risk groups with significant survival differences [area under the curve (AUC) =0.702]. Risk scores correlated with age, tumor (T), metastasis (M), and overall stage. Single-cell analysis revealed FKBP10 was predominantly upregulated in fibroblasts within the tumor microenvironment, and high FKBP10 expression enhanced intercellular communication, particularly between fibroblasts and epithelial cells. Immunohistochemistry confirmed FKBP10 overexpression in STAD tissues, and FKBP10 silencing suppressed proliferation and induced S-phase arrest in HGC27 and MKN1 cells. CONCLUSIONS: This study establishes a robust ERS-related prognostic signature for STAD and highlights FKBP10 as a key regulator in the tumor microenvironment. FKBP10 promotes gastric cancer (GC) cell proliferation and reshapes cellular communication, offering a potential biomarker and therapeutic target. These findings provide a foundation for personalized prognostic assessment and immunotherapy strategies in STAD.
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Single-cell multi-omic profiling of heterogeneous PROCR+ cells in mouse mammary gland and its implication for breast cancer.
Protein C receptor (PROCR) marks a rare population of mammary epithelial cells with stem-like properties, but their low abundance has hindered characterization of their functional diversity and regulatory programs. Here, we integrate targeted enrichment with single-cell transcriptomic and chromatin accessibility profiling to construct a multimodal atlas of PROCR+ cells across development. We identify heterogeneous PROCR+ subpopulations, including a Procr +, Cdh5 -, Col1a1 + population with features consistent with a mammary stem cell (MaSC) state, such as high developmental potency, an EMT-associated program, and an early differentiation position. Integrative analyses reveal dynamic transcriptional regulatory circuits underlying stemness and lineage commitment. Cross-species comparisons uncover analogous PROCR+ populations in human mammary epithelium, and this stem cell-like signature is enriched in triple-negative and claudin-low tumors. Together, these findings refine the mammary epithelial hierarchy, define regulatory programs underlying stemness and lineage bifurcation, and establish a molecular link between a distinct MaSC state and tumor aggressiveness.
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Tracing tumorigenesis in a solid tumor model at single-cell resolution.
Characterizing the complex composition of solid tumors is fundamental for understanding tumor initiation, progression and metastasis. While patient-derived samples provide valuable insight, they are heterogeneous on multiple molecular levels, and often originate from advanced tumor stages. Here, we use single-cell transcriptome and epitope profiling together with pathway and lineage analyses to study tumorigenesis from a developmental perspective in a mouse model of salivary gland squamous cell carcinoma. We provide a comprehensive cell atlas and characterize tumor-specific cells. We find that these cells are connected along a reproducible developmental trajectory: initiated in basal cells exhibiting an epithelial-to-mesenchymal transition signature, tumorigenesis proceeds through Wnt-differential cancer stem cell-like subpopulations before differentiating into luminal-like cells. Our work provides unbiased insights into tumor-specific cellular identities in a whole tissue environment, and emphasizes the power of using defined genetic model systems.
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The role of tumor-infiltrating B cells in the tumor microenvironment of hepatocellular carcinoma and its prognostic value: a bioinformatics analysis.
BACKGROUND: Accumulating evidence indicates that tumor heterogeneity is characterized by distinct immunosubtypes. However, prior studies have mainly focused on the functions of T cells. The role of tumor-infiltrating B cells in the microenvironment of hepatocellular carcinoma (HCC) requires further investigation. METHODS: We conducted an integrative analysis of single cell RNA sequencing (scRNA-seq) datasets in HCC tumor samples from Gene Expression Omnibus database. We analyzed the features of B cells in normal liver tissue and HCC. Additionally, we conducted a deconvolution analysis using the matrix of scRNA-seq datasets and the RNA-seq datasets in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database. The survival analyses of the TCGA-LIHC cohort with different B cell infiltration rates and was further validated. Finally, we performed immunohistochemistry analysis of primary tumor tissue of HCC patients using antibodies against CD79A and validated the impact of tumor-infiltrating B cells in the prognosis of LIHC. RESULTS: We identified several subtypes of B cells in the microenvironment of HCC, including the plasma cells and naïve B cells. The relative ratio of B cells, but not the plasma cells, was significantly decreased in HCC as compared to the normal liver tissue (P<0.05). In addition, genes related to antigen presentation and cell proliferation were decreased in tumor-infiltrating B cells (P<0.05). The observation of B cell infiltration was further validated with the TCGA-LIHC cohort. The overall survival and disease-free survival in HCC patients with higher B-cell infiltration rate were significantly longer than those in the lower infiltration group (P<0.05) in the TCGA-LIHC cohort. Moreover, we demonstrated higher infiltration rates of B cells were significantly associated with a better prognosis of HCC in our cohort. CONCLUSIONS: Tumor-infiltrating B cells potentially exert a tumor-suppressive function in the microenvironment of HCC and the higher levels of B cell infiltration are associated with a favorable outcome of HCC. Targeted activation of B cells may improve the tumor immune-targeted therapy.