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Background The advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge. Methods To address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods. Results By integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified. Conclusions We developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.

Background Exosomes secreted from stem cells exerted salutary effects on the fibrotic liver. Herein, the roles of exosomes derived from human embryonic stem cell (hESC) in anti-fibrosis were extensively investigated. Compared with two-dimensional (2D) culture, the clinical and biological relevance of three-dimensional (3D) cell spheroids were greater because of their higher regeneration potential since they behave more like cells in vivo. In our study, exosomes derived from 3D human embryonic stem cells (hESC) spheroids and the monolayer (2D) hESCs were collected and compared the therapeutic potential for fibrotic liver in vitro and in vivo. Results In vitro, PKH26 labeled-hESC-Exosomes were shown to be internalized and integrated into TGFβ-activated-LX2 cells, and reduced the expression of profibrogenic markers, thereby regulating cellular phenotypes. TPEF imaging indicated that PKH26-labeled-3D-hESC-Exsomes possessed an enhanced capacity to accumulate in the livers and exhibited more dramatic therapeutic potential in the injured livers of fibrosis mouse model. 3D-hESC-Exosomes decreased profibrogenic markers and liver injury markers, and improved the level of liver functioning proteins, eventually restoring liver function of fibrosis mice. miRNA array revealed a significant enrichment of miR-6766-3p in 3D-hESC-Exosomes, moreover, bioinformatics and dual luciferase reporter assay identified and confirmed the TGFβRII gene as the target of miR-6766-3p. Furthermore, the delivery of miR-6766-3p into activated-LX2 cells decreased cell proliferation, chemotaxis and profibrotic effects, and further investigation demonstrated that the expression of target gene TGFβRII and its downstream SMADs proteins, especially phosphorylated protein p-SMAD2/3 was also notably down-regulated by miR-6766-3p. These findings unveiled that miR-6766-3p in 3D-hESC-Exosomes inactivated SMADs signaling by inhibiting TGFβRII expression, consequently attenuating stellate cell activation and suppressing liver fibrosis. Conclusions Our results showed that miR-6766-3p in the 3D-hESC-Exosomes inactivates smads signaling by restraining TGFβRII expression, attenuated LX2 cell activation and suppressed liver fibrosis, suggesting that 3D-hESC-Exosome enriched-miR-6766-3p is a novel anti-fibrotic therapeutics for treating chronic liver disease. These results also proposed a significant strategy that 3D-Exo could be used as natural nanoparticles to rescue liver injury via delivering antifibrotic miR-6766-3p. Graphical Abstract

Reactive oxygen species (ROS) are critically implicated in ischemic stroke (IS), yet the transcriptional networks and predictive biomarkers underlying ROS dysregulation remain incompletely understood. We integrated two independent microarray cohorts (GSE58294 and GSE16561) to comprehensively analyze ROS-related pathways in IS. Single-sample gene set enrichment analysis (ssGSEA) was used to quantify pathway activity, and weighted gene co-expression network analysis (WGCNA) identified modules associated with ROS dysregulation. Functional enrichment and protein-protein interaction (PPI) network analyses characterized the biological functions of module genes. Elastic Net regression modeling, receiver operating characteristic (ROC) analysis, calibration, and decision curve analysis (DCA) were employed to construct and validate a predictive risk score model. SHapley Additive exPlanations (SHAP) analysis was further applied to interpret gene contributions. Immune cell infiltration was assessed using multiple algorithms, and CLEC4E, the top-ranked gene, was functionally investigated through single-gene GSEA. The OGD/R-treated SH-SY5Y cells and mouse ischemia-reperfusion (I/R) models were established for in vivo and in vitro validation. ROS-related pathways were consistently upregulated in IS across both cohorts. WGCNA revealed a robust ROS-associated module (brown module), enriched in immune activation and inflammatory signaling processes. Elastic Net regression identified seven key genes (CLEC4E, SLC8A1, HIST1H4H, BMX, MCEMP1, KREMEN1, ZFP36L2) with strong predictive ability (AUC = 0.81-0.86 across datasets). SHAP analysis highlighted CLEC4E as the most influential contributor, positively associated with IS risk. Immune deconvolution indicated that CLEC4E expression was negatively correlated with B- and T-cell infiltration, while functional analysis linked it to MAPK signaling, RNA degradation, and neutrophil activation pathways. Finally, CLEC4E was significantly elevated in IS. Knockdown of CLEC4E could alleviate the effect of OGD/R on SH-SY5Y cells. Our study demonstrates pervasive activation of ROS-related transcriptional programs in IS and identifies a novel seven-gene signature predictive of disease risk. Among these, CLEC4E emerges as a key mediator connecting ROS dysregulation to immune infiltration and inflammatory signaling, providing new insights into IS pathophysiology and potential therapeutic targets.

Background Pathway enrichment analysis is a crucial method for the biological interpretation of metabolomic data by identifying associations between altered metabolites and biological pathways. However, such traditional approaches often rely on a limited set of predefined metabolic pathways, resulting in a low likelihood of discovering pathways associated with a given metabolic profile. To overcome this limitation, we extended our previously developed iDMET methodology to incorporate a broader range of metabolite sets, including those derived from differential metabolomic profiles. This enhanced approach, termed iDMET+, significantly expands dataset diversity and size, increasing the likelihood of discovering associated metabolite sets for a given metabolic profile, thereby enables more biological insights to be obtained from the metabolic profile. Results We validated iDMET+ through case studies on three diseases: clear cell renal cell carcinoma, colorectal cancer, and small cell lung cancer. First, using a clear cell renal cell carcinoma study as input, iDMET+ correctly identified another study of the same disease that involved metabolomic analysis. This pair of studies was identified as relevant in our previous iDMET results, showing the consistency between iDMET+ and iDMET. Second, using the metabolomic profile of colorectal cancer as input, iDMET+ identified not only another metabolomic study of the same cancer but, surprisingly, also metabolomic studies on prostate cancer and a high-fat diet. These studies focused on MYC-driven metabolic reprogramming, which was also a major focus of the input study. In both case studies, related studies were enriched because the differential metabolomic profiles of directly associated studies were part of the metabolite set. In contrast, the small cell lung cancer study highlighted limitations in dataset coverage—the absence of directly relevant differential metabolomic profiles resulted in fewer enriched metabolite sets. Nevertheless, the analysis of commonly altered metabolites still yielded some meaningful results. Metabolite alterations associated with inhibition of the purine salvage pathway were observed, suggesting potential involvement in tumor metabolic reprogramming. Conclusions These results demonstrate that iDMET+ offers broader biologically relevant information than the conventional pathway-based approaches and has the potential to uncover biologically significant findings by searching across diverse datasets. This work also identifies areas of improvements for iDMET+.

Further research is needed to investigate the association between netosis and clear cell renal cell carcinoma (ccRCC). We developed a prognostic framework for netosis using univariate, Lasso, and multivariate Cox regression analyses. The CIBERSORT algorithm was employed to compute immune infiltration metrics for The Cancer Genome Atlas (TCGA) dataset. These scores, combined with Cox regression analysis and patient survival data, contribute to the establishment of a prognostic model for the tumor microenvironment (TME). A combined prognostic model incorporating netosis and TME was then developed, stratifying patients based on median results. Further evaluation of the variations in the pathways within the model was conducted using Fast Gene Set Enrichment Analysis (FGSEA) and Weighted Correlation Network Analysis (WGCNA). Additionally, single-cell data integration allowed us to examine netosis-related genes in the context of cell communication and tumor development using the CellChat and Monocle packages. Netosis and TME scores exhibited a high degree of predictive power for patient survival, as illustrated by Kaplan-Meier (KM) curves. Gene set enrichment analysis (GSEA) revealed significant disparities in pathways associated with tumor occurrence between netosis and TME scores. A combined prognostic model incorporating both netosis and TME scores showed excellent performance in the validation set and TCGA data. FGSEA and WGCNA revealed significant differences in pathways associated with traditional tumor development and occurrence within distinct groups of the combined model. Furthermore, single-cell data analysis revealed substantial variations in intercellular communication levels among groups of netosis model genes with high and low expression. Pseudotime analysis highlighted increased expression of EREG, LYZ, S100A8, and S100A9. The combined netosis and TME prognostic model demonstrated high accuracy and efficacy, underscoring its potential value in guiding the treatment and prognosis of future ccRCC patients.

Muscle stem cells (MSCs) play a crucial role in muscle growth, repair, and regeneration, offering potential applications in cell-mediated therapy, tissue engineering, and alternative food production. Despite significant advancements in isolating and enriching MSCs from mammalian tissues, research on fish MSCs remains limited. This study aimed to establish an optimized protocol for isolating, enriching, and propagating black sea bream ( Acanthopagrus schlegelii ) MSCs for potential biotechnological applications. Skeletal muscle tissues were enzymatically dissociated using various enzymes, with collagenase type II and pronase identified as the most effective combination for cell isolation and tissue debris removal. Differential plating (DP) on collagen type I effectively enriched MSCs, as evidenced by a significant increase in Pax7 expression in non-adhesive cells. Among several adhesion substrates tested, Matrigel-coated dishes best supported the maintenance and differentiation potential of enriched MSCs, enabling robust myotube formation. To mitigate the high cost of Matrigel, cells were transitioned to laminin- or gelatin-coated dishes after the early passages. Notably, Matrigel-conditioned cells maintained their survival and differentiation capacities on these more cost-effective substrates. After long-term culture on gelatin-coated dishes, the cell lines were stably maintained for more than 25 passages, and their myogenic differentiation potentials were well preserved, with variations observed between the cell lines. These findings provide a foundational framework for the efficient isolation, enrichment, and culture of fish MSCs, contributing to the development of scalable and cost-effective protocols for their application in muscle biology and biotechnology.

Background Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation. Results We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed. Conclusions Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.

Background Clear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction. Method Gene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments. Results SRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells. Conclusion We uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.

Type 2 diabetes mellitus (T2DM) is characterized by β-cell dysfunction and insulin resistance, yet the early molecular drivers remain elusive. This study set out with the aim of identifying blood-pancreas shared long non-coding RNAs (lncRNAs) as potential systemic biomarkers in treatment-naïve patients with new-onset T2DM. We integrated transcriptome sequencing of peripheral blood from 8 T2DM patients and 8 controls with single-cell RNA sequencing (scRNA-seq) of pancreatic islets from an independent cohort. Differential expression analysis revealed 1,709 dysregulated lncRNAs in peripheral blood, of which 257 were identified as high-priority candidate through weighted gene co-expression network analysis (WGCNA). Further intersection with scRNA-seq data from 17 T2DM donors identified 157 β-cell-specific mRNAs co-expressed with 135 blood-derived lncRNAs. Functional enrichment analysis implicated these genes in chromatin remodeling, focal adhesion, and neurodegenerative pathways. Validation in an expanded cohort (85 T2DM vs. 85 controls) confirmed significant downregulation of ENST00000473095, MSTRG.90147.1 and ENST00000531992 in T2DM. The combined ROC-AUC value of these three lncRNAs was 0.73, which exceeds the AUCs of each individual lncRNA (0.61–0.64). Our findings tentatively suggest that blood-derived lncRNAs as early biomarkers reflecting β-cell stress and systemic dysregulation. These lncRNAs may potentially bridge peripheral blood biomarkers with tissue-specific pathophysiology in T2DM.

Renal cell carcinoma (RCC), accounting for 90% of all kidney cancer, is categorized into clear cell RCC (ccRCC) and non-clear cell RCC (non-ccRCC) for treatment based on the current NCCN Guidelines. Thus, the classification will be associated with therapeutic implications. This study aims to identify novel biomarkers to differentiate ccRCC from non-ccRCC using bioinformatics and machine learning. The gene expression profiles of ccRCC and non-ccRCC subtypes (including papillary RCC (pRCC) and chromophobe RCC (chRCC)), were obtained from TCGA. Differential expression genes (DEGs) were identified, and specific DEGs for ccRCC and non-ccRCC were explored using a Venn diagram. Gene Ontology and pathway enrichment analysis were performed using DAVID. The top ten expressed genes in ccRCC were then selected for machine learning analysis. Feature selection was operated to identify a minimum highly effective gene set for constructing a predictive model. The expression of best-performing gene set was validated on tissue samples from RCC patients using immunohistochemistry techniques. Subsequently, machine learning models for diagnosing RCC were developed using H-scores. There were 910, 415, and 835 genes significantly specific for DEGs in ccRCC, pRCC, and chRCC, respectively. Specific DEGs in ccRCC enriched in PD-1 signaling, immune system, and cytokine signaling in the immune system, whereas TCA cycle and respiratory, signaling by insulin receptor, and metabolism were enriched in chRCC. Feature selection based on Decision Tree Classifier revealed that the model with two genes, including NDUFA4L2 and DAT, had an accuracy of 98.89%. Supervised classification models based on H-score of NDUFA4L2, and DAT revealed that Decision Tree models showed the best performance with 82% accuracy and 0.9 AUC. NDUFA4L2 expression was associated with lymphovascular invasion, pathologic stage and pT stage in ccRCC. Using integrated bioinformatics and machine learning analysis, NDUFA4L2 and DAT were identified as novel biomarkers to differential diagnosis ccRCC from non-ccRCC.