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At least 19 recordsLinked to original sources

PubMed2026

Cell type resolved MR based on brain single cell eQTLs corroborated by single cell RNA sequencing uncovers neuroimmune and vascular programs in intracerebral hemorrhage.

BACKGROUND: Intracerebral hemorrhage (ICH) lacks effective neuroprotective therapies. We integrated cell type–resolved genetic inference with single-cell profiling to map putative causal programs and multicellular circuitry relevant to ICH. METHODS: Cis-eQTLs from eight human brain cell types were used as instruments for two-sample Mendelian randomization (MR), with an ICH meta-analysis from large biobanks and a stroke consortium as the outcome. Instruments were LD-pruned and restricted to strong variants (F > 10). Inverse-variance weighting (IVW) was the primary estimator, supported by robustness methods, heterogeneity/pleiotropy diagnostics, and false discovery rate control. Experimental validation used mouse collagenase ICH single-cell RNA-seq at 24 h (n = 3 sham; n = 3 ICH) with Seurat integration, composition testing, Slingshot pseudotime, and CellChat. An independent mouse cohort underwent qRT–PCR for selected genes. RESULTS: The ICH meta-analysis showed acceptable genomic control, supporting downstream MR. We identified 524 nominal gene–cell type associations, with a glia-weighted signal landscape. Enrichment implicated autophagy/mitophagy, antigen processing, cytoskeletal and vesicular trafficking, endothelial matrix–adhesion programs, ferroptosis, and myelin stress pathways. In mouse scRNA-seq, disease-associated microglia expanded with reciprocal loss of homeostatic microglia and increased neutrophils and T cells. Prioritized genes showed directional concordance; qRT–PCR confirmed ARPC3 and EIF2AK2 upregulation and TBCK and SPECC1 downregulation in ICH versus sham. Pseudotime supported a shift toward disease-associated microglial states, and CellChat indicated increased network interaction strength with microglia and endothelium as hubs. CONCLUSIONS: Cell type–specific MR combined with single-cell validation highlights neuroimmune and neurovascular programs in ICH and links genetic signals to state transitions and inferred intercellular communication.

Animals↗

PubMed2025

Differentiation latency and dormancy signatures define fetal liver hematopoietic stem cells at single-cell resolution.

Decoding the mechanisms governing the self-renewal of hematopoietic stem cells (HSCs) during their expansion in the fetal liver (FL) could unlock novel therapeutic strategies to expand transplantable HSCs, a long-standing challenge. To explore intrinsic and extrinsic regulation of FL-HSC self-renewal at single-cell resolution, we engineered a culture platform replicating the FL endothelial niche that supports the amplification of serially engraftable HSCs. Leveraging this platform together with single-cell index flow cytometry, live imaging, transplantation assays, and single-cell RNA sequencing, we demonstrate that differentiation latency, cell-division symmetry, and transcriptional signatures of biosynthetic dormancy are distinguishing properties of rare FL-HSCs capable of serial multilineage hematopoietic reconstitution. Our findings support a paradigm in which intrinsic programs and niche-derived signals together facilitate the symmetric self-renewal of FL-HSCs while delaying their active participation in hematopoiesis. Our study also provides a resource for future investigations into intrinsic and extrinsic signaling pathways governing FL-HSC self-renewal.

Hematopoietic Stem Cells↗

PubMed2026

ELISA (Embedding-Linked Interactive Single-cell Agent): an interpretable hybrid generative Artificial Intelligence agent for expression-grounded discovery in single-cell genomics.

Translating single-cell RNA sequencing (scRNA-seq) data into mechanistic biological hypotheses remains a critical bottleneck, as agentic AI systems lack direct access to transcriptomic representations while expression foundation models remain opaque to natural language. Here, we introduce ELISA (Embedding-Linked Interactive Single-cell Agent), an interpretable framework that unifies single-cell generative pretrained transformer expression embeddings with biomedical bidirectional encoder representations from transformers-based semantic retrieval and large-language model (LLM)-mediated interpretation for interactive single-cell discovery. An automatic query classifier routes inputs to gene marker scoring, semantic matching, or reciprocal rank fusion pipelines depending on whether the query is a gene signature, natural language concept, or mixture of both. Integrated analytical modules perform pathway activity scoring across 60+ gene sets, ligand-receptor interaction prediction using 280+ curated pairs, condition-aware comparative analysis, and cell-type proportion estimation, all operating directly on embedded data without access to the original count matrix. Benchmarked across six diverse scRNA-seq datasets spanning inflammatory lung disease, pediatric and adult cancers, organoid models, healthy tissue, and neurodevelopment, ELISA significantly outperforms CellWhisperer, a classical lexical retriever (BM25), and a random baseline in cell type retrieval (combined permutation test, $p < 2\times 10^{-5}$ for each), with particularly large gains on gene-signature queries (Cohen's $d = 5.98$ for mean reciprocal rank). ELISA replicates published biological findings (mean composite score 0.88), and generates candidate hypotheses through grounded LLM reasoning, bridging the gap between transcriptomic data exploration and biological discovery.

Generative Artificial Intelligence↗

PubMed2026

scFANCL: Dual contrastive learning with false-negative correction at cell level for single-cell RNA-seq clustering.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables cellular characterization at single-cell resolution. However, its high dimensionality, sparsity, and noise make clustering challenging. Approaches utilizing contrastive learning and data augmentation have been introduced to improve representation quality for scRNA-seq clustering. In particular, dual contrastive frameworks combining instance- and cluster-level objectives can capture both cell-cell similarities and inter-cluster variations. However, existing dual contrastive frameworks focus primarily on discrete cluster boundaries, neglecting the biological continuity inherent in scRNA-seq data. METHODS: We propose scFANCL, a dual contrastive framework designed to capture biological continuity in scRNA data. Rather than treating all non-augmented samples as negatives, scFANCL applies a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, preserving continuous transcriptional relationships among them while maintaining inter-cluster separation. RESULTS: Extensive experiments across seven publicly available scRNA-seq datasets demonstrated that scFANCL achieves competitive clustering performance compared with existing baseline methods, consistently yielding high ARI and NMI scores across datasets of varying size and complexity. Ablation studies further confirmed the contribution of the false negative filtering component, showing measurable improvements over variants without filtering. Downstream analyses further suggest that the learned embeddings may reflect biologically meaningful transcriptional transitions, including continuous differentiation trajectories within related cell types. The source code is available at https://github.com/mjuailab/scFANCL . CONCLUSIONS: scFANCL addresses a key limitation of conventional contrastive learning by applying a cosine-similarity-based threshold to exclude cells of the same type from the negative pool, thereby preserving biological continuity within cell types while maintaining inter-cluster separation. Evaluations across seven benchmark scRNA-seq datasets demonstrate competitive clustering performance, with learned embeddings capturing biologically meaningful transcriptional structure and characteristics of rare cell populations.

Clustering Algorithms↗

PubMed2026

Multiomic study of cutaneous T-cell lymphoma reveals single-cell clonal evolution in progression and therapy resistance.

Cutaneous T-cell lymphoma (CTCL) remains a challenging disease due to its significant heterogeneity, therapy resistance, and relentless progression. Multiomics technologies offer the potential to provide uniquely precise views of disease progression and response to therapy. Here, we present a comprehensive multiomics view of CTCL clonal evolution, incorporating exome, whole-genome, epigenome, bulk, single-cell T-cell receptor, and single-cell RNA sequencing of 99 clinically annotated serial skin, peripheral blood, and lymph node samples from 34 patients with CTCL. We leveraged this extensive data set to define the molecular underpinnings of CTCL progression in individual patients at single-cell resolution with the goal of identifying clinically useful biomarkers and therapeutic targets. Our studies identified recurrent progression-associated clonal genomic alterations; we highlight mutation of CCR4, phosphoinositide 3-kinase inhibitor signaling, and programmed cell death protein 1 (PD-1) checkpoint pathways as evasion tactics deployed by malignant T cells. We identified a gain-of-function mutation in STAT3 (D661Y) and demonstrated, using cleavage under targets and release using nuclease (CUT&RUN) and RNA sequencing, that it enhances binding to and transcription of genes in Rho GTPase pathways. With our previous work implicating this pathway in histone deacetylase inhibitor-resistant CTCL, these data provide further support for a previously unrecognized role for Rho GTPase pathway dysregulation in CTCL progression. Recurrent progression-associated mutations were common in the epigenetic modifier EZH2, suggesting that EZH2 inhibition may benefit patients with CTCL. Our findings support an approach in which genomic analysis is widely used for improved disease monitoring, biomarker-informed clinical trial design, and genome-guided therapeutic decision-making. Moreover, these molecular changes present new opportunities for therapeutic targeting in this challenging and incurable cancer.

Multiomics↗

PubMed2026

scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.

Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.

Single-Cell Analysis↗

PubMed1976

Single cell studies on the antibody-forming potential of fractionated, hapten-specific B lymphocytes.

This study addresses itself to the problem of antibody formation in vitro by mouse splenic B lymphocytes enriched for reactivity to the hapten NIP by the hapten-gelatine binding and melting technique of Haas and Layton (1975). Small numbers of NIP-gelatine-bound B cells were placed in microcultures either by bulk dispensing of dilute cell suspensions, or by micromanipulation under direct microscopic visualization. Antibody formation was induced by the T cell-independent hapten-protein conjugate NIP-polymierized flagellin, using 10(4) thymus cells per microlitre as 'filler' cells. The frequency of precursors of NIP-specific antibody-forming cells among bound cells was about 2-2 X 10(-2) (one cell in forty-five) by both statistical and direct evaluation, after adjustment for a background frequency of 6-10 X 10(-8) precursors in the thymus filler cells. Single clones commenced antibody secretion asynchronously, as shown by the fact that the incidence of positive cultures continued to rise over the whole three days of culture, and that very small clones of one to four plaque-forming cells (PFC) were still found on day 3. The mean PFC number per positive culture rose from 1-2 at day 1 to 4-7 at day 2 and about 20 at day 3.

Animals↗

PubMed2026

Proteomic analysis of pancreatic endocrine cells by mechanistic single-cell isolation identifies membrane pathways.

To better understand diabetes and normoglycemia, pancreatic islet biology requires a precise molecular understanding of islet cell types at both the transcriptomic and proteomic levels. While transcriptomic analyses are well established, comprehensive proteomic characterization has been lacking, limiting our knowledge of islet molecular complexity. Here we introduce a nonenzymatic, mechanistic single-cell isolation technology using laser microdissection (LMD7), facilitating proteomic and transcriptomic analysis of physically isolated α-, β- and δ-cells from fresh-frozen, unfixed pancreatic tissue. This mechanistic approach avoids enzymatic digestion and chemical fixation, preserving the cells' native molecular state before processing. Given the limited existing proteomic data, we supplemented our findings with transcriptomic analysis generated using the same method and compared our results with data from enzymatically isolated cells, obtained by fluorescence-activated cell sorting and compiled by others. Our analysis revealed that enzymatic digestion alters gene expression patterns, particularly those of membrane-associated proteins, underscoring the impact of isolation techniques on biological outcomes. We identified cell-type-specific proteins typically underrepresented in pancreatic single-cell transcriptomic datasets. β-cells exhibited enrichment in vesicle trafficking proteins, α-cells displayed distinct calcium-dependent action potential machinery and δ-cells showed elevated expression of focal adhesion-related proteins. In addition, we report an inverse molecular relationship between β- and δ-cells, potentially driven by transcriptional regulators such as Mlxipl. By establishing robust molecular profiles directly from intact pancreatic tissue, this work provides a reference point for future pathological comparisons, offering a framework to investigate how diabetes and other endocrine disorders reshape islet cell biology.

Journal Article↗

PubMed2026

Differential cell signaling testing for cell-cell communication inference from single-cell data by dominoSignal.

MOTIVATION: Algorithms for ligand-receptor network inference have emerged as commonly used tools to estimate cell-cell communication from reference single-cell data. Many studies employ these algorithms to compare signaling between conditions and lack methods to statistically identify signals that are significantly different. We previously developed the cell communication inference algorithm Domino, which considers ligand and receptor gene expression in association with downstream transcription factor activity scoring. We developed the dominoSignal software to innovate upon Domino and extend its functionality to test statistically differential cellular signaling. RESULTS: This new functionality includes the compilation of active signals as linkages from multiple subjects in a single-cell data set and testing condition-dependent signaling linkage. The software is applicable for analysis of single-cell data sets with multiple subjects as biological replicates as well as with bootstrapped replicates from data sets with few or pooled subjects. We use simulation studies to benchmark the number of subjects in compared groups and cells within an annotated cell type sufficient to accurately identify differential linkages. We demonstrate the application of the Differential Cell Signaling Test (DCST) in the dominoSignal software to investigate consequences of cancer cell phenotypes and immunotherapy on cell-cell communication in tumor microenvironments. These applications in cancer studies demonstrate the ability of differential cell signaling analysis to infer changes to cell communication networks from therapeutic or experimental perturbations, which is broadly applicable across biological systems. AVAILABILITY: dominoSignal is available through Bioconductor at https://www.bioconductor.org/packages/release/bioc/html/dominoSignal.html.

Cell Communication↗

PubMed2025

Defining breast epithelial cell types in the single-cell era.

Single-cell studies on breast tissue have contributed to a change in our understanding of breast epithelial diversity that has, in turn, precipitated a lack of consensus on breast cell types. The confusion surrounding this issue highlights a possible challenge for advancing breast atlas efforts. In this perspective, we present our consensus on the identities, properties, and naming conventions for breast epithelial cell types and propose goals for future atlas endeavors. Our proposals and their underlying thought processes aim to catalyze the adoption of a shared model for this tissue and to serve as guidance for other investigators facing similar challenges.

Humans↗

PubMed2026

Post-Hoc Long-Read Sequencing Links Leukemic Mutation Status to Single-Cell Transcriptomes.

Single-cell RNA-sequencing-based characterization of cells that belong to the neoplastic clone is a major challenge in hematologic neoplasms, where malignant and normal cells coexist. Confident molecular profiling requires simultaneous analysis of gene expression and genetic mutations in individual cells, an ability that is not supported by the standard 10X Genomics workflow. Here, we systematically evaluated the potential and limitations of repurposing amplified cDNA generated during the 10X Genomics 3' workflow for post hoc genotyping of individual cells. We first established a mixed leukemic cell line system comprising one cell line with KIT point mutations and another with the BCR::ABL1 fusion gene. Targeted long-read PacBio sequencing enabled post hoc assignment of mutation data to transcriptionally profiled cells, but recovery differed between targets. Consistent with ambient RNA in microfluidics-based single-cell workflows, mutation-associated transcripts were detected in cells not expected to carry the corresponding mutations, illustrating how transcript recovery complicates cell-level genotype assignment. Target-specific thresholds mitigated this source of misclassification. In primary chronic myeloid leukemia samples, the post hoc approach detected BCR::ABL1-positive cells at diagnosis, but not during imatinib treatment. Together, we present a framework for adding mutation status to cells already profiled using the 10X Genomics workflow and highlight broader considerations for transcript-based single-cell genotyping.

BCR::ABL1↗

PubMed2026

Integrative evidence-knowledge marker selection enhances LLM-based cell type annotation in single-cell RNA-seq analysis.

BACKGROUND: Cell type annotation is essential for gaining biological insight from single-cell RNA sequencing data, yet manual labeling remains time-consuming and difficult to reproduce. Various computational approaches have been developed to automate this process, and recent studies suggest that large language models can infer cell types with promising accuracy in single-cell analysis. However, most workflows still rely on cluster-specific markers derived from gene expression alone or manual curation. As a result, marker selection can be sensitive to statistical criteria and dataset-dependent bias, which may lead to the selection of less informative genes or missing important markers, while providing limited biological context. RESULTS: To address this limitation, we introduce CELLIA, an LLM-based workflow for automated and robust cell type annotation. CELLIA employs an integrative evidence-knowledge marker selection strategy that combines statistical differential expression criteria with curated tissue-specific marker resources to identify informative marker genes. In benchmarking analyses of 102 cell types, this approach improved agreement with manual annotations. In addition, CELLIA achieved higher agreement in subtype-level analyses of closely related immune populations and was further evaluated in a non-immune stromal subtype setting, covering 25 cell types in total. CONCLUSION: By integrating evidence-knowledge from gene expression with curated biological prior knowledge, CELLIA provides a more stable marker selection and improves the reliability of LLM-cell type annotation.

Cell type annotation↗

PubMed2025

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals↗

PubMed2025

scMultiNODE: Integrative and Scalable Framework for Multi-Modal Temporal Single-Cell Data.

Measuring single-cell genomic profiles at different timepoints enables our understanding of cell development. This understanding is more comprehensive when we perform an integrative analysis of multiple measurements (or modalities) across various developmental stages. However, obtaining such measurements from the same set of single cells is resource-intensive, restricting our ability to study them jointly. We introduce scMultiNODE, an unsupervised integration model that combines gene expression and chromatin accessibility measurements in developing single cells, while preserving cell type variations and cellular dynamics. First, scMultiNODE uses a scalable, Quantized Gromov-Wasserstein optimal transport to align a large number of cells across different measurements. Next, it utilizes neural ordinary differential equations to explicitly model cell development with a regularization term to learn a dynamic latent space. Experiments on six real-world developmental single-cell datasets demonstrate that scMultiNODE can integrate temporally profiled multi-modal single-cell measurements more effectively than existing methods that focus on cell type variations and often overlook cellular dynamics. We also demonstrate that scMultiNODE's joint latent space facilitates several insightful downstream analyses of single-cell development, including the investigation of complex cell trajectories and the enabling of cross-modal label transfer. The data and code are publicly available at https://github.com/rsinghlab/scMultiNODE.

autoencoders↗

PubMed2026

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify transcriptional and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around silent genes prior to their expression in response to injury. These results provide evidence for a latent activated stem cell state in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Animals↗

PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗

PubMed2026

Identifying fate-determining transcription factors with single-cell omics.

Single-cell sequencing enables the systematic discovery of cell fate-determining transcription factors (TFs), or key TFs, that define cellular identity or drive cell state transitions. A wide range of computational methods have been developed for this goal, but they differ substantially in the input data and the biological questions they address. In this article, we systematically review computational approaches for key TF identification and organize them from three perspectives: whether they identify TFs defining cell state identity or driving state transitions, whether transitions are modeled as discrete or continuous processes, and whether TFs act individually or combinatorially. We summarize key features and application scenarios of relevant methods to guide tool selection and discuss emerging trends in this field toward programmable and active control of cell fate.

Transcription Factors↗

PubMed2025

Spatial transcriptomics-aided localization for single-cell transcriptomics with STALocator.

Single-cell RNA-sequencing (scRNA-seq) techniques can measure gene expression at single-cell resolution but lack spatial information. Spatial transcriptomics (ST) techniques simultaneously provide gene expression data and spatial information. However, the data quality of the spatial resolution or gene coverage is still much lower than the quality of the single-cell transcriptomics data. To this end, we develop a ST-Aided Locator for single-cell transcriptomics (STALocator) to localize single cells to corresponding ST data. Applications on simulated data showed that STALocator performed better than other localization methods. When applied to the human brain and squamous cell carcinoma data, STALocator could robustly reconstruct the relative spatial organization of critical cell populations. Moreover, STALocator could enhance gene expression patterns for Slide-seqV2 data and predict genome-wide gene expression data for fluorescence in situ hybridization (FISH) and Xenium data, leading to the identification of more spatially variable genes and more biologically relevant Gene Ontology (GO) terms compared with the raw data. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗