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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↗
PubMed2024

SIMS: A deep-learning label transfer tool for single-cell RNA sequencing analysis.

Cell atlases serve as vital references for automating cell labeling in new samples, yet existing classification algorithms struggle with accuracy. Here we introduce SIMS (scalable, interpretable machine learning for single cell), a low-code data-efficient pipeline for single-cell RNA classification. We benchmark SIMS against datasets from different tissues and species. We demonstrate SIMS's efficacy in classifying cells in the brain, achieving high accuracy even with small training sets (<3,500 cells) and across different samples. SIMS accurately predicts neuronal subtypes in the developing brain, shedding light on genetic changes during neuronal differentiation and postmitotic fate refinement. Finally, we apply SIMS to single-cell RNA datasets of cortical organoids to predict cell identities and uncover genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

Single-Cell Analysis↗
PubMed2025

CAFs activated by YAP1 upregulate cancer matrix stiffness to mediate hepatocellular carcinoma progression.

BACKGROUND: The stiffness of the matrix is closely related to the progression of hepatocellular carcinoma (HCC). Although direct targeting of stromal rigidity in HCC remains a clinical challenge, cancer-associated fibroblasts (CAFs) are considered key contributors to this process. Given the heterogeneity of CAFs, this study explored the relationship between specific CAF subsets and liver cancer matrix stiffness, aiming to identify novel therapeutic targets for HCC patients. METHODS: Single-cell sequencing datasets were leveraged to identify cell types within liver cancer and characterize the transcriptomic profiles of CAFs. Prognostic analysis, utilizing the Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) liver cancer datasets, assessed the correlation between matrix stiffness-related genes and HCC patient outcomes. Pseudo-time analysis was applied to trace the developmental trajectories of CAFs. By calculating intercellular communication probabilities and analyzing transcription factor activity, the functions and interactions of different CAF subsets were elucidated. Gene Ontology (GO) analysis was used to explore the functional roles of CAFs in distinct Yes-associated protein 1 (YAP1) groups. Finally, cellular experiments and animal experiments were further conducted to validate the hypotheses of this study. RESULTS: This study identified CAF subpopulations based on single-cell sequencing data and analyzed transcriptional changes within these subpopulations. Key findings include the identification of collagen type I alpha 1 (COL1A1), collagen type III alpha 1 (COL3A1), and lysyloxidase (LOX) as pivotal node genes during CAF development. Moreover, the expression of matrix stiffness-related genes was inversely correlated with the prognosis of HCC patients. Notably, the YAP1-positive CAF subpopulation emerged as the primary contributor to matrix stiffness in liver cancer. This subpopulation upregulates the expression of matrix stiffness-related genes and promotes tumor progression by activating signaling pathways such as autophagy and GTPase activity regulation. Cellular experiments and animal studies further validated this conclusion. CONCLUSION: This single-cell analysis uncovered the functional roles of CAFs in liver cancer. The YAP1-positive CAF subpopulation, in particular, was shown to contribute to matrix stiffness by upregulating the expression of relevant genes and promoting tumor progression through the activation of specific signaling pathways.

Carcinoma, Hepatocellular↗
PubMed2025

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Humans↗
PubMed2025

scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing.

MOTIVATION: Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement. Though these methods now exist, the limited availability of modular and portable workflows remains a challenge. RESULTS: Here, we present, nf-core/scnanoseq, a secondary analysis pipeline for long-read single-cell and single-nuclei RNA that delivers gene and transcript-level quantification. The scnanoseq pipeline is implemented using Nextflow and is built upon the nf-core framework, enabling portability across computational environments, scalability and reproducibility of results across pipeline runs. The nf-core/scnanoseq workflow follows best practices for analyzing single-cell and single-nuclei data, performing barcode detection and correction, genome and transcriptome read alignment, unique molecular identifier deduplication, gene and transcript quantification, and extensive quality control reporting. AVAILABILITY AND IMPLEMENTATION: The source code, and detailed documentation are freely available at https://github.com/nf-core/scnanoseq and https://nf-co.re/scnanoseq under the MIT License. Documentation for the version of nf-core/scnanoseq used for this paper, including default parameters and descriptions of output files are available at https://nf-co.re/scnanoseq/1.1.0.

Single-Cell Analysis↗
PubMed2026

Beyond Bulk: Cell-Type-Resolved Epigenomics as the Path Forward in Alzheimer's Disease Research.

Alzheimer's disease (AD) is a complex neurodegenerative disorder in which most risk variants are noncoding and are enriched at gene regulatory regions, implicating epigenetic mechanisms as central mediators of disease pathogenesis. For most of the history of AD epigenetics research, bulk tissue analysis has dominated, obscuring the fundamentally distinct epigenomic landscapes of individual brain cell types and masking cell-type-specific contributions to disease. Advances in single-cell and single-nucleus sequencing, fluorescence-activated nuclei sorting and multiplexed epigenomic platforms have transformed this landscape, enabling cell-type-resolved profiling of chromatin accessibility, DNA methylation, histone modifications and transcription across the major neuronal, glial and neurovascular populations of the human brain. Here, we review these advances, structured around the argument that cell-type resolution is not a methodological refinement but a conceptual necessity. We describe the distinct epigenomic programs disrupted in neurons, microglia, astrocytes, oligodendrocytes and neurovascular cells in AD, highlighting how each cell type responds to pathology. We discuss the discovery of epigenomic erosion, the progressive loss of cell-type-specific epigenomic identity across virtually all brain cell populations as AD advances, as a unifying disease mechanism linking chromatin dysregulation to cognitive decline. Finally, we identify critical gaps in current knowledge, including the near-complete absence of cell-type-resolved histone modification and DNA methylation data for most brain cell types, the underrepresentation of rare populations in standard preparations and the untapped potential of metabolic acylation marks as indicators of the epigenome-metabolism interface in neurodegeneration.

Humans↗
PubMed2026

A pan-cancer single-cell atlas uncovers the role of sex hormones and chromosomes in sex-divergent reprogramming of the tumor microenvironment.

BACKGROUND: Sex bias is pervasive in tumors; however, how sex chromosomes and hormone-responsive signaling shape the tumor microenvironment (TME) remains insufficiently characterized. Considering the critical impact of the TME on tumor progression and response to immunotherapy, a pan-cancer investigation of sex-specific and cancer-context-dependent TME features is warranted. METHOD: Based on stringent inclusion criteria, we constructed a high-resolution pan-cancer single-cell sequencing atlas by integrating 31 publicly available single-cell RNA-seq datasets, comprising a total of 1,831,436 cells by integrating 468 samples from eight types of non-sex-specific solid tumors (282 males and 186 females). After correcting for batch effects, we identified major and minor cellular subsets. Multiple computational approaches were applied to investigate sex-associated differences in cellular composition, gene expression, pathway activity, malignant cell states and intercellular communication. RESULTS: We systematically compared sex-specific TME features across eight common solid malignancies. Male-biased CD8+ T cell exhaustion emerged as a recurrent but non-uniform feature, with its magnitude varying across cancer types and being modified by tissue-specific contexts. This pattern was associated with androgen-response signature scores and expression-based loss of the Y chromosome (LOY) scores. M2-like macrophage polarization showed a more cancer-type-dependent pattern; although female-biased enrichment was observed in selected malignancies, it did not represent a uniform pan-cancer feature. Expression-based X chromosome inactivation (XCI)/XCI escape-related programs, estrogen-response signature scores and stromal components, including fibroblasts and endothelial cells, were associated with macrophage and immune-regulatory states in specific tumor contexts. Tumor cells of male origin displayed higher genomic instability and more aggressive phenotypes, with androgen-response signatures and LOY contributing to the development of a male biased malignant state. Furthermore, expression-based LOY scores in malignant cells were associated with CD8+ T cell exhaustion based on transcriptomic proxies. CONCLUSION: Our study uncovers extensive but heterogeneous sex-specific differences in the TME across multiple cancer types. We propose a regulatory framework linking sex chromosomes, hormone-responsive signaling and TME interactions, which is consistent with recurrent male-biased CD8&#x207a; T cell exhaustion and context-dependent M2-like macrophage polarization. Importantly, the magnitude and, in some cancers, the direction of these sex-biased features are modified by tissue-specific contexts. These findings underscore the need to include sex chromosome and hormone status as essential biological variables in studies of the tumor microenvironment and the design of immunotherapies.

Tumor Microenvironment↗
PubMed1993

Neural activity in the caudate nucleus of monkeys during spatial sequencing.

Single cell activity was recorded from the monkey caudate nucleus. The animal had to execute motor and oculomotor sequences based on memorized information. In each trial, the monkey had to remember the order of illumination of three fixed spatial targets. After a delay, the animal had to press the targets in the same sequence. The "task-related" cells were activated by onset of the targets and on execution of saccades or arm movements. In a majority of cells, activation did not depend only on the retinal position of the stimuli or on the spatial parameters of gaze and arm movements, but was contingent on the particular sequence in which the targets were illuminated or the movements were performed.

Animals↗
PubMed2023

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Journal Article↗
PubMed2025

FOSB is a key factor in the genetic link between inflammatory bowel disease and acute myocardial infarction: multiple bioinformatics analyses and validation.

BACKGROUND: Inflammatory Bowel Disease (IBD), which includes Crohn's disease and ulcerative colitis, is associated with an increased risk of Acute Myocardial Infarction (AMI). The genetic mechanisms underlying this link are not well understood. METHODS: We downloaded IBD and AMI-related microarray datasets from the NCBI Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and analyzed using enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Machine learning techniques, including LASSO, random forest, and Boruta, were employed to screen for hub genes. These genes were validated through qRT-PCR and Western blotting. Single-cell sequencing was used to confirm findings. Additionally, potential therapeutic targets were identified using the Connectivity Map (CMap) database. RESULTS: Five key hub genes-THBD, FOSB, ADGPR3, IL1R2, and PLAUR-were identified as significantly involved in both IBD and AMI pathogenesis. A diagnostic model for AMI constructed using these hub genes demonstrated high predictive accuracy. Single-cell sequencing analysis and several potential drugs targeting these hub genes were identified, offering new therapeutic avenues. CONCLUSION: This study highlights the crucial role of FOSB and other hub genes in the comorbidity of IBD and AMI. The findings provide novel insights for early diagnosis and potential therapeutic strategies, emphasizing the importance of further investigation into these genetic links.

Humans↗
PubMed2024

Gene expression profiles of endothelium, microglia and oligodendrocytes in hippocampus of post-stroke depression rat at single cell resolution.

Post-stroke depression (PSD) is a common but severe mental complication after stroke. However, the cellular and molecular understanding of PSD is still yet to be illustrated. In current study, we prepared PSD rat model (MD) via unilateral middle cerebral artery occlusion (MCAO) and chronic stress stimulation (DEPR), and isolated hippocampal tissues for single cell sequencing of 10x Genomics Chromium. First, we determined the presence of the increased cell population of endothelium and microglia and the compromised oligodendrocytes in MD compared to NC, MCAO and DEPR. The enriched functions of highly variable genes (HVGs) of endothelium and microglia suggested a reinforced blood-brain barrier in MD. Next, cell clusters of endothelium, microglia and oligodendrocytes were individually analyzed, and the subtypes with distinct functions were identified. The presence of expression profiles, intercellular communications and signaling pathways of these three cell populations of PSD displayed a similar but more aggressive appearance with DEPR compared to MCAO and NC. Taken together, this study characterized the specific gene profile of endothelium, microglia and oligodendrocytes of hippocampal PSD by single cell sequencing, emphasizing the crosstalk among them to provide theoretical basis for the in-depth mechanism research and drug therapy of PSD.

Animals↗
PubMed2025

Surgery/anesthesia may cause monocytes to promote tumor development.

BACKGROUND: The immune system of patients undergoing major surgery usually has obvious immune responses during the perioperative period, and the patient's immune status would affect the patient's prognosis. In this study single-cell sequencing technology was used to investigate the effect of surgery/anesthesia on peripheral blood mononuclear cells (PBMCs) in depth during the perioperative period. METHODS: We performed an in-depth analysis of our previously published data, which included a total of 4 patients were recruited in this study. Their peripheral blood samples were collected pre operation, 0, 24, and 48&#xa0;h post operation, and then PBMCs were extracted, followed by single cell sequencing. The results of sequencing were analyzed with R packages seurat and scSTAR. Finally, RT-PCR technology was used to verify the expression of key genes in monocyte. RESULTS: The ratio of CD4+ and CD8+ T cells and Tregs showed little change, and the function of CD4+ and CD8+ T cells recovered soon. The function of Treg had not been restored 48&#xa0;h post operation. Non-classical monocyte was impressed after surgery and showed no recovery trend within 48&#xa0;h. Similar to scRNA-seq, the expression levels of MDM2 and SESN1 in patients with tumor increased significantly after surgery. CONCLUSIONS: Surgery/anesthesia had little effect on CD4+ and CD8+ T cells, and continued to affect the functional changes of Treg. It had more impact on monocytes, which may cause them to promote tumor development to a certain extent.

Humans↗
PubMed1993

Co-amplification of the cystic fibrosis delta F508 mutation with the HLA DQA1 sequence in single cell PCR: implications for improved assessment of polar bodies and blastomeres in preimplantation diagnosis.

We have developed a heminested PCR (polymerase chain reaction) method, performed on single cells, for the analysis of the most common cystic fibrosis (CF) mutation (delta F508). As a quality control, the polymorphic exon 2 of the HLA DQA1 locus was co-amplified from the same cell. With a non-radioactive reverse dot-blot assay, the genotype of these two loci could be determined. Experiments on 98 single fibroblasts, heterozygous for the CFTR and the DQA1 locus, showed that amplification of either locus could be obtained in 97 per cent of the cases, but only 90 per cent showed heterozygosity for CF, 75 per cent showed heterozygosity for DQA1, and 74 per cent showed heterozygosity for both CF and DQA1. Contaminations detected only after DQA1 typing occurred in 3 per cent of our samples. Error rate calculations based on our experimental PCR data indicate that single blastomere diagnosis would lead to unacceptable errors, i.e., an affected fetus, in less than 1 per cent of the cases. The risk of undetected crossing-over or the dubious results that crossing-over could generate, would make isolated polar body diagnosis at the present time very difficult. The combined approach of PCR on polar bodies followed by confirmation of the diagnosis on blastomeres, however, should give a solid base for preimplantation diagnosis of monogenic disorders.

Base Sequence↗
PubMed2006

Sequencing genomes from single cells by polymerase cloning.

Genome sequencing currently requires DNA from pools of numerous nearly identical cells (clones), leaving the genome sequences of many difficult-to-culture microorganisms unattainable. We report a sequencing strategy that eliminates culturing of microorganisms by using real-time isothermal amplification to form polymerase clones (plones) from the DNA of single cells. Two Escherichia coli plones, analyzed by Affymetrix chip hybridization, demonstrate that plonal amplification is specific and the bias is randomly distributed. Whole-genome shotgun sequencing of Prochlorococcus MIT9312 plones showed 62% coverage of the genome from one plone at a sequencing depth of 3.5x, and 66% coverage from a second plone at a depth of 4.7x. Genomic regions not revealed in the initial round of sequencing are recovered by sequencing PCR amplicons derived from plonal DNA. The mutation rate in single-cell amplification is <2 x 10(5), better than that of current genome sequencing standards. Polymerase cloning should provide a critical tool for systematic characterization of genome diversity in the biosphere.

Chromosome Mapping↗
PubMed2025

scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.

MOTIVATION: Identifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data. RESULTS: This article proposes single-cell meta-Path based Omics Embedding (scPOEM), a novel embedding method that jointly projects chromatin accessibility peaks and expressed genes into a shared low-dimensional space. By integrating the relationships among peak-peak, peak-gene, and gene-gene interactions, scPOEM assigns closer representations in the embedding space to related peak-gene pairs. Our experiments demonstrate that scPOEM generates stable representations of peaks and genes, outperforms existing methods in recovering biologically meaningful peak-gene regulatory relationships and enables new insights in subgroup and differential analysis of gene regulation. These results highlight its potential to uncover gene regulatory mechanisms and enhance the understanding of transcriptional regulation at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: The source code of scPOEM is available at https://github.com/Houyt23/scPOEM. The datasets can be obtained from the 10&#xd7; Genomics (https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0) and GEO database under access codes GSE194122 and GSE239916.

Gene Expression Regulation↗
PubMed2025

scafari: exploring scDNA-seq data.

SUMMARY: Recent advances in single-cell sequencing made it possible to not just analyze a cell's individual expression pattern, but to gain insights into a single cell's genome using the cutting-edge technology single-cell DNA sequencing. Mission Bio is, with the Tapestri platform, one of the few providers of this technology. So far, however, there is only little open-source software available for user-friendly processing and quality analysis of this data type. With scafari, we present a tool that offers easy-to-use data quality control as well as explorative variant analyses and visualization. AVAILABILITY AND IMPLEMENTATION: scafari is implemented as an R Bioconductor package featuring a shiny application and is available at https://bioconductor.org/packages/scafari.

Software↗