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Gene-Length Bias and Compartment Non-Identifiability in Cell-Type Deconvolution of Sheep Skin: A Prospectively Specified and Timestamped Diagnostic Re-Analysis of Wool-Density Transcriptomes in Hetian Sheep.
Bulk skin transcriptomes cannot distinguish cell-intrinsic gene regulation from shifts in tissue-type composition, and computational deconvolution is widely applied to resolve this limitation. In this study, we performed deconvolution on published Hetian sheep skin RNA-seq data from three high-wool-density and three low-wool-density animals, using the available ovine skin single-cell reference. All hypotheses and analytical workflows were prospectively specified and timestamped prior to estimating cell-type fractions. Our work identifies two major methodological pitfalls for livestock tissue deconvolution. First, gene-length bias distorts deconvolution outputs derived from mixed bulk and 3' single-cell references. After bias correction, the estimated hair-follicle epithelial fraction changed substantially. This bias cannot be uncovered by standard benchmark workflows, highlighting a hidden failure mode for deconvolution. Second, limited signature separability within the reference prevents reliable discrimination between related epidermal compartments. This non-identifiability strongly disturbs apparent compartment-level effects. When analysed at the granularity the reference reliably supports, we found no compositional difference between high- and low-wool-density groups for the epithelial hypothesis. The dermal hypothesis could not be meaningfully evaluated due to incompatible reference compartment merging requirements. Group-level differential expression remained robust despite this null compositional outcome. We recovered a large set of differentially expressed genes consistent with the original study, including most qPCR-validated candidates. Adjusting for cell-type composition altered part of this gene list, as expected given negligible group-level compositional divergence. Co-expression modules correlated more strongly with inter-individual compositional variation than with wool-density phenotype. This indicates compositional heterogeneity shapes transcriptome co-expression even without measurable mean differences across experimental groups. To avoid misleading biological inference, we propose a set of practical diagnostic checks for deconvolution when developmental or limited single-cell atlases are used for livestock tissues. Collectively, these transcriptomic patterns support cell-state-driven regulation rather than detectable mean shifts in tissue compartment composition.
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An Anoikis-Related Gene Signature Associated With Stromal Remodeling and Clinical Outcome in Gastric Cancer.
Gastric cancer (GC) remains a highly aggressive malignancy with poor prognosis. Anoikis resistance is closely associated with tumor progression and metastasis, but the prognostic and functional relevance of anoikis-related genes (ARGs) in GC remains incompletely understood. Public transcriptomic, clinical, somatic mutation, and single-cell RNA sequencing (scRNA-seq) datasets were integrated to identify differentially expressed ARGs, develop an ARG-based risk model, and investigate its associations with tumor microenvironment (TME) features. In vitro experiments were performed to assess the effects of selected candidate genes on GC cell migration and invasion. Forty-five differentially expressed ARGs were identified in The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) cohort. An ARG-based risk model was developed using the TCGA-STAD cohort and evaluated in the GSE84437 cohort. The model was associated with overall survival and provided potential value for risk stratification, although its discriminative performance was modest. High-risk patients showed distinct immune, stromal, and somatic mutational features. Single-cell analysis suggested that ARG-related transcriptional programs were associated with stromal remodeling and endothelial-to-mesenchymal transition (EndMT)-associated features in the GC microenvironment. Among the core ARGs, EEF1A2 and TAGLN showed prognostic relevance and higher protein expression in GC tissues. Small interfering RNA (siRNA)-mediated knockdown of either gene reduced the migration and invasion of HGC-27 cells. These findings indicate that ARG-related molecular features are associated with GC prognosis and immune-stromal characteristics, while EEF1A2 and TAGLN may represent candidate genes related to GC cell migration and invasion.
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Single-cell RNA-seq transcriptomic landscape of human and mouse islets and pathological alterations of diabetes.
Single-cell RNA sequencing has paved the way for delineating the pancreatic islet cell atlas and identifying hallmarks of diabetes. However, pathological alterations of type 2 diabetes (T2D) remain unclear. We isolated pancreatic islets from control and T2D mice for single-cell RNA sequencing (scRNA-seq) and retrieved multiple datasets from the open databases. The complete islet cell landscape and robust marker genes and transcription factors of each endocrine cell type were identified. GLRA1 was restricted to beta cells, and beta cells exhibited obvious heterogeneity. The beta subcluster in the T2D mice remarkably decreased the expression of Slc2a2, G6pc2, Mafa, Nkx6-1, Pdx1, and Ucn3 and had higher unfolded protein response (UPR) scores than in the control mice. Moreover, we developed a Web-based interactive tool, creating new opportunities for the data mining of pancreatic islet scRNA-seq datasets. In conclusion, our work provides a valuable resource for a deeper understanding of the pathological mechanism underlying diabetes.
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Integrated analysis to identify biological features and molecular markers of poorly cohesive gastric carcinoma (PCC).
As one of the two main histologic subtypes of gastric cancer (GC), diffuse-type gastric cancer (DGC) containing poorly cohesive gastric carcinoma (PCC) components has a worse prognosis and does not respond well to typical therapies. Despite the large number of studies revealing the complex pathogenic network of DGC, the molecular heterogeneity of DGC is still not fully understood. We obtained single-cell RNA-seq data and bulk data from the tumor immune single cell hub, the public gene expression omnibus, and the cancer genome atlas databases. A series of bioinformatics analyses were performed using R software. Immunofluorescence staining, hematoxylin and eosin staining, western blot, and functional experiments were used for experimental validation. Caudin-3, -4 and -7 were lowly expressed in DGC and their expression levels were further reduced in PCC. The PCC components were mainly located in the deeper layers of the DGC and had a high level of hypoxic Wnt/β-catenin signaling and stemness. We further identified Insulin Like Growth Factor Binding Protein 7 (IGFBP7) as a marker for PCC components in the deep layer. IGFBP7 is stimulated by hypoxia and promotes cancer cell invasiveness and reduced claudin expression. In addition, programmed death-1 ligand (PD-L1) was specifically expressed in the deep layer, reflecting deep layer-specific immunosuppression. The PCC components are predominantly situated in the deeper layers of DGC. Initial molecular characterization of these PCC components revealed distinct features, including low expression of claudin-3, -4, and -7, high expression of IGFBP7, and the presence of PD-L1. These molecular traits may partially account for the pronounced tumor heterogeneity observed in GC.
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In Silico Evaluation of Natural Compounds for an Acidic Extracellular Environment in Human Breast Cancer.
The survival rates for breast cancer (BC) have improved in recent years, but resistance, metastasis, and recurrence still remain major therapeutic challenges for BC. The acidic tumor microenvironment (TME) has attracted attention because of its association with tumorigenesis, metastasis, drug resistance, and immune surveillance. In this study, we evaluated natural compounds from traditional herbal medicine used to treat cancer that selectively target genes regulated by extracellular acidosis. We integrated four transcriptomic data including BC prognostic data from The Cancer Genome Atlas database, gene expression profiles of MCF-7 cells treated with 102 natural compounds, patterns of gene profiles by acidic condition, and single-cell RNA-sequencing from BC patient samples. Bruceine D (BD) was predicted as having the highest therapeutic potential, having an information gain (IG) score of 0.24, to regulate reprogrammed genes driven by acidosis affecting the survival of BC patients. BD showed the highest IG on EMT (IG score: 0.11) and invasion (IG score: 0.1) compared to the other phenotypes with the CancerSEA database. BD also demonstrated therapeutic potential by interfering with the tumor cell-TME interactions by reducing the amyloid beta precursor protein and CD44 expression. Therefore, BD is a potential candidate to target the acidic TME induced metastatic process in BC.
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AEnet: a practical tool to construct the splicing-associated phenotype atlas at a single cell level.
Alternative splicing (AS), a crucial driver of proteomic diversity, is a fundamental source of cellular heterogeneity alongside gene expression levels. AS is closely linked to various physiological and pathological processes, including tumor progression and embryonic development. Single-cell RNA sequencing (scRNA-seq) technologies capture AS events through junction reads at cellular resolution, enabling the identification of core AS events that regulate specific cell types or states. However, single-cell sequencing technologies and their data are plagued by inherent limitations, such as shallow sequencing depth, high dropout rates, and batch effects. Furthermore, previous clustering approaches have overlooked the crucial interplay between AS and gene expression in defining distinct "cell types," posing ongoing challenges in this field. In this study, we present a novel method called Alternative Splicing-Gene Expression Network (AEnet), which combines gene expression levels with AS patterns to profile cellular heterogeneity and define what we term "cell subpopulations." AEnet also identifies key AS events and infers the regulatory mechanisms underlying these events. By applying AEnet to tumor cells, pan-cancer immune cells, and embryonic cells, we demonstrate enhanced cell clustering, the identification of novel AS events with potential functional importance, and the discovery of the key splicing factors involved in cell state transitions. The application of AEnet provides new insights into cellular heterogeneity and its role in both physiological and pathological processes.
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Single-cell transcriptomic profiling unveils insights into ovarian fibrosis in obese mice.
BACKGROUND: Adiposity profoundly impacts reproductive health in both humans and animals. However, the precise subpopulations contributing to infertility under obese conditions remain elusive. RESULTS: In this study, we established an obese mouse model through an eighteen-week high-fat diet regimen in adult female mice. Employing single-cell RNA sequencing (scRNA-seq), we constructed a comprehensive single-cell atlas of ovarian tissues from these mice to scrutinize the impact of obesity on the ovarian microenvironment. ScRNA-seq revealed notable alterations in the microenvironment of ovarian tissues in obese mice. Granulosa cells, stromal cells, T cells, and macrophages exhibited functional imbalances compared to the control group. We observed heightened interaction strength in the SPP1-CD44 pairing within lgfbp7+ granulosa cell subtypes and Il1bhigh monocyte subtypes in the ovarian tissues of obese mice. Moreover, the interaction strength between Il1bhigh monocyte subtypes and Pdgfrb+ stromal cell subtypes in the form of TNF - TNFrsf1α interaction was also enhanced subsequently to obesity, potentially contributing to ovarian fibrosis pathogenesis. CONCLUSIONS: We propose a model wherein granulosa cells secrete SPP1 to activate monocytes, subsequently triggering TNF-α secretion by monocytes, thereby activating stromal cells and ultimately leading to the development of ovarian fibrosis. Intervening in this process may represent a promising avenue for improving clinical outcomes in fertility treatments for obese women.
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A single-cell multiomics roadmap of zebrafish spermatogenesis reveals regulatory principles of male germline formation.
Spermatogenesis is the biological process by which male sperm cells (spermatozoa) are produced in the testes. Beyond facilitating the transmission of genetic information, spermatogenesis also provides a potential framework for inter- and transgenerational inheritance of gene-regulatory states. While extensively studied in mammals, our understanding of spermatogenesis in anamniotes remains limited. Here we present a comprehensive single-cell multiomics resource, combining single-cell RNA sequencing (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) profiling, with base-resolution DNA methylome (WGBS) analysis of sorted germ cell populations from zebrafish (Danio rerio) testes. We identify the major germ cell types involved in zebrafish spermatogenesis as well as key drivers associated with these transcriptional states. Moreover, we describe localised DNA methylation changes associated with spermatocyte populations, as well as local and global changes in chromatin accessibility leading to chromatin compaction in spermatids. Notably, we identify loci that evade global chromatin compaction, and which remain accessible, suggesting a potential mechanism for the intergenerational transmission of gene-regulatory states. In summary, this high-resolution atlas of zebrafish spermatogenesis provides a valuable resource for studying vertebrate germ cell development and epigenetic inheritance, while offering a robust framework for comparative analyses across diverse models of germ cell biology.
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Role of endothelial cell markers in prognosis of hepatocellular carcinoma: Integrating bioinformatics analysis and experimental validation.
BACKGROUND: Hepatocellular carcinoma (HCC) is a highly prevalent malignancy with poor prognosis. Endothelial cells (ECs) play a crucial role in HCC progression, yet their involvement at the single-cell level remains underexplored. This study aimed to identify ECs-specific markers and develop a prognostic multi-gene signature for HCC using single-cell RNA sequencing (scRNA-seq). MATERIALS AND METHODS: Single-cell transcriptomic data from 12 HCC samples were analyzed to identify EC-associated genes. A prognostic gene signature was constructed using Lasso-Cox regression analysis based on The Cancer Genome Atlas (TCGA) cohort and subsequently validated using an independent cohort from the International Cancer Genome Consortium (ICGC). Immunohistochemistry (IHC) and Western blotting were employed to experimentally validate gene expression in tissue samples. RESULTS: Five EC-specific genes-NDRG1, HBEGF, FKBP1A, KLRB1, and FDPS-were identified as prognostic markers. The resulting multi-gene signature effectively stratified patients into high- and low-risk groups, with significant differences in overall survival. Validation in the ICGC cohort confirmed the model's predictive performance. IHC and Western blotting results further confirmed the elevated expression of these genes in HCC tissues. CONCLUSIONS: This study established an EC-related prognostic signature that accurately predicts HCC prognosis. The identified markers may aid early diagnosis and serve as potential therapeutic targets for HCC treatment.
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Temporal analyses of postnatal liver development and maturation by single-cell transcriptomics.
The postnatal development and maturation of the liver, the major metabolic organ, are inadequately understood. We have analyzed 52,834 single-cell transcriptomes and identified 31 cell types or states in mouse livers at postnatal days 1, 3, 7, 21, and 56. We observe unexpectedly high levels of hepatocyte heterogeneity in the developing liver and the progressive construction of the zonated metabolic functions from pericentral to periportal hepatocytes, which is orchestrated with the development of sinusoid endothelial, stellate, and Kupffer cells. Trajectory and gene regulatory analyses capture 36 transcription factors, including a circadian regulator, Bhlhe40, in programming liver development. Remarkably, we identified a special group of macrophages enriched at day 7 with a hybrid phenotype of macrophages and endothelial cells, which may regulate sinusoidal construction and Treg-cell function. This study provides a comprehensive atlas that covers all hepatic cell types and is instrumental for further dissection of liver development, metabolism, and disease.