Search Results (732)

Single-cell RNA sequencing (scRNA-seq) provides a comprehensive understanding of cellular complexity; however, its requirement for fresh or frozen samples limits its flexibility. To overcome this limitation to effectively leverage clinical samples, Chromium Fixed RNA Profiling on formalin-fixed paraffin-embedded (FFPE) tissue blocks (scFFPE-seq) was developed to perform single-nucleus RNA sequencing from nuclei isolated from FFPE. In this study, we utilized fresh tissue samples from colon, ileum, and skin to assess the viability of scFFPE-seq compared to these fresh samples. We were able to recover unique cell types from challenging FFPE tissues and validated scFFPE-seq findings through Hematoxylin and Eosin (H&E) images. The results demonstrated that scFFPE-seq effectively captured the single-cell transcriptome in FFPE tissues, obtaining comparable cell abundance, cell type annotation, and pathway characterization to those in fresh tissues. Overall, the study presents strong evidence of the potential of scFFPE-seq to enhance scientific knowledge by enabling the generation of high-quality, sensitive single-nucleus RNA-seq data from preserved tissue samples. This technique unlocks the vast archives of FFPE samples for extensive retrospective genomic studies. Full article

RNA sequencing (scRNA-seq) has emerged as a transformative technology for dissecting cellular heterogeneity and immune complexity in livestock species. This review summarizes recent advances in the application of single-cell transcriptomics to livestock health, with a particular focus on immune system organization and host–pathogen interactions in cattle, pigs, poultry, and small ruminants. We highlight the development of large-scale, multi-tissue cell atlases—such as the Cattle Cell Atlas and resources generated through the Farm Animal Genotype-Tissue Expression (FarmGTEx) consortium—that provide foundational reference frameworks for livestock genomics. These atlases have enabled the identification of tissue- and species-specific immune cell populations, clarified cellular tropism of major bacterial and viral pathogens, and revealed distinctive immunological features, including the prominent role of γδ T cells in ruminant immunity. We discuss how single-cell immune receptor sequencing has advanced monoclonal antibody discovery and informed rational vaccine design. Key technical and analytical challenges, including incomplete genome annotations, tissue processing constraints, and cross-platform data integration, are critically assessed. Finally, we outline future directions integrating spatial transcriptomics and multi-omics approaches to further resolve immune function within tissue contexts. Collectively, these advances position single-cell transcriptomics as a central framework for improving disease resistance, vaccine efficacy, and translational research in livestock health. Full article

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for accurate prediction of ICI response. We propose a deep learning framework, ICIsc, to accurately predict ICI response by integrating single-cell RNA sequencing (scRNA-seq) data with protein large language models. Specifically, patient representations are constructed using transcriptomic profiles and immune-related gene set scores as latent embedding features, while drug representations are derived from amino acid sequences of ICI encoded by the Evolutionary Scale Modeling 2 (ESM2). For bulk data, ICIsc employs a bilinear attention module to fuse patient and drug embeddings for response prediction. For scRNA-seq data, ICIsc infers cell–cell interactions using a single-sample network (SSN) approach and applies GATv2 to model immune microenvironment heterogeneity at the single-cell level. Benchmark evaluations and independent validation demonstrate that ICIsc consistently outperforms baseline models and exhibits robust generalization performance. SHAP-based interpretability analysis further identifies key genes (e.g., GAPDH) associated with immunotherapy response and patient prognosis. Overall, ICIsc provides an accurate and interpretable framework for predicting immunotherapy outcomes and elucidating underlying mechanisms. Full article

We present the single-cell transcriptomic analysis of peripheral blood mononuclear cells (PBMC) from individuals during acute HIV-1 infection caused by viral strains circulating in Russia and the Former Soviet Union (FSU) countries. Using 10x Genomics single-cell RNA sequencing (scRNA-seq) on the Illumina NextSeq 550 platform, we have analyzed scRNA-seq data from three treatment-naive patients (viral load > 1 × 10⁶ copies/mL, estimated infection duration ≤ 4 weeks) and three healthy donors. Data integration (Seurat, Harmony), automated cell-type annotation (CellTypist), and GeneOntology (GO) enrichment analysis for highly expressed and low-expressed genes revealed a profound reorganization of transcriptional programs across key immune populations, including memory CD4⁺ and CD8⁺ T cells, non-classical monocytes and natural killer cells (NK-cells). We observed signatures of hyperactivation of pro-inflammatory pathways (NF-kB, TNF, and type I/II interferon signaling), upregulation of genes associated with cellular migration (CXCR4, CCR7) and metabolic adaptation (oxidative phosphorylation components), alongside a mixed pro- and anti-apoptotic expression profile. Notably, our data pointed to a pronounced dysregulation of the TGF-β and mTOR signaling cascades, disrupted intercellular communication networks—particularly between cytotoxic cells and their regulators—altered expression of genes implicated in disease progression (OLR1, SERPINB2, COPS9) and viral persistence control (NEAT1, NAF1). This work provides an initial single-cell transcriptional atlas characterizing early immune responses to HIV-1 sub-subtypes A6 and CRF63_02A6, the predominant drivers of the HIV epidemic across the FSU region. Full article

Colorectal cancer (CRC) progression is strongly shaped by the tumor microenvironment (TME), where complex interactions between epithelial, immune, and stromal cells orchestrate immune suppression and tumor evolution. To dissect these relationships at single-cell resolution, we analyzed CRC scRNA-seq datasets using Seurat for data integration and CellChat for ligand–receptor inference. We identified extensive cellular heterogeneity within the TME, dominated by CMS2/CMS3 epithelial states, SPP1⁺ tumor-associated macrophages, diverse T-cell subsets, and CXCR4⁺ B cells. Communication analysis revealed MIF-centered signaling—including MIF–CD74–CXCR4 and MIF–CD74–CD44—as the predominant axis linking tumor epithelial cells with T cells, B cells, and macrophage subpopulations. CMS3 epithelial cells displayed particularly strong connectivity to SPP1⁺ macrophages and cytotoxic lymphocytes through both MIF- and APP–CD74-mediated pathways. Differential gene expression confirmed elevated levels of MIF, CD74, CD44, and SPP1 in tumor tissues, while pathway enrichment analyses highlighted cytokine signaling, antigen presentation, and chemokine-regulated immune modulation as key biological processes. Collectively, our study provides a high-resolution map of CRC intercellular communication and identifies MIF-CD74-associated signaling as a central immunoregulatory hub with potential relevance for therapeutic targeting and biomarker development. Full article

Single-cell transcriptomics has redefined our understanding of cancer by exposing the complexity of tumor ecosystems and their therapeutic vulnerabilities. scRNA-seq studies have identified lineage hierarchies, immune evasion programs, and resistance-associated states across solid and liquid tumors, informing biomarker development and drug discovery. Advanced computational frameworks integrate these data with longitudinal profiling, RNA velocity, and network diffusion to prioritize targets and predict therapeutic response. Emerging multi-omics approaches further expand the scope of precision oncology by linking genetic alterations, protein-level markers, and spatial context to functional states. This narrative review aims to synthesize current applications of single-cell transcriptomics for target discovery, highlight computational frameworks that translate high-dimensional data into actionable insights, and explore how multi-omics integration is shaping future directions. By bridging molecular complexity with target prioritization, these approaches hold promise for translating single-cell insights into clinically actionable biomarkers and therapeutic strategies for personalized cancer treatment and rational drug development. Full article

Background/Objectives: Given the lack of clarity regarding how maternal overnutrition during pregnancy regulates offspring metabolic health, our study intends to explore the specific influences of maternal Western diet (WD) exposure on neonatal islet cell development and heterogeneity. Methods: Using a WD-induced gestational diabetes mellitus (GDM) rat model, we assessed glucose homeostasis via blood glucose and serum insulin levels. Target protein expression and islet function were evaluated using immunofluorescence and insulin secretion assays, respectively. To delineate alterations in cellular heterogeneity, we subsequently performed single-cell RNA sequencing (scRNA-seq) on isolated islet cells. Results: Maternal WD exposure induced significant glucose intolerance and insulin resistance, confirming GDM establishment. Their neonatal offspring consequently displayed disrupted glucose homeostasis, characterized by concurrent hypoglycemia, hyperinsulinemia, and enhanced insulin secretion. ScRNA-seq analysis further identified the enhanced endocrine cells in GDM-offspring islets, with imbalanced α/β-cell subsets—specifically, reduced immature α1/β1 subsets and expanded mature α2/β2/β3/β4 subsets, alongside upregulated expression of insulin- and glucagon-related genes (Ins1, Ins2, Gcg). Notably, β cells in GDM offspring displayed metabolic hyperactivity (enriched ribosomal and glycolytic pathways) with multiple organelle dysfunction, including mitochondrial swelling, cristae reduction, decreased membrane potential, and severe endoplasmic reticulum stress. Conclusions: The metabolic dysregulation of WD-induced GDM in maternal rats is transmitted to offspring, leading to disrupted neonatal α/β-cell subset balance and accelerated islet maturation. However, such excessive development comes at the cost of organelle damage in β cells. Our findings provide a molecular basis for mitigating the intergenerational transmission of diabetes through early nutritional interventions. Full article

Cell adhesion molecules (CAMs) are key regulators of tissue structural integrity and functional coordination, yet their specific role in the adaptation of yak lung tissue to high-altitude hypoxia remains unelucidated. Thus, we employed transcriptomic sequencing (RNA-seq), molecular biology assays, and single-cell RNA-seq (scRNA-seq) to analyze the expression characteristics of CAMs in yak lung tissues at high and low altitudes. Trypsin or collagenase digestion showed higher cell counts in high-altitude yak lungs (p < 0.05). RNA-seq analysis revealed significant enrichment of differentially expressed genes (DEGs) in adhesion-related pathways. Inductively coupled plasma mass spectrometry detected elevated Ca²⁺ levels in high-altitude yak lungs (p < 0.05). Quantitative real-time PCR (qRT-PCR) detection of key genes from five major families of CAMs revealed the downregulation of cadherin and integrin family-related genes, and upregulation of immunoglobulin superfamily-related genes, in high-altitude yak lungs (p < 0.05), corroborated by immunohistochemical (IHC) staining. A 10× scRNA-seq revealed adhesion changes in 9 of 15 lung cell subpopulations, with differentially expressed CAMs involving integrins. This study demonstrates that yak lung tissue establishes a sophisticated adhesive homeostasis through differential CAMs regulation. This strategy optimizes pulmonary immune responses and energy allocation, ensures structural integrity and functional coordination, and thereby facilitates superior acclimatization to higher-altitude hypoxia. Full article

Background: Several studies have attempted to identify the initiating drivers of small intestinal neuroendocrine tumor (SI-NET) development and the molecular mechanisms underlying their progression and metastatic spread. Previous gene expression studies have used bulk microarrays or RNA sequencing to compare tumor tissue with normal intestinal mucosa. However, the intestine comprises multiple distinct cell types, and bulk analyses are limited by this cellular heterogeneity, which can confound tumor-specific signals. Methods: We performed single-cell RNA sequencing on primary SI-NETs and paired normal mucosa from two patients to directly compare tumor cells with their cells of origin, the enterochromaffin (EC) cells. To minimize type I errors, we applied a two-step validation strategy by overlapping differentially expressed genes with an external single-cell dataset and cross-referencing candidate genes for enteroendocrine expression in the Human Protein Atlas. Results: For further distinction and characterization, ECs were subdivided into serotonergic and non-serotonergic clusters. This analysis revealed that the SI-NET cells are transcriptionally more similar to serotonergic ECs, consistent with serum metabolite profiles derived from clinical parameters. Our analyses uncovered a loss-of-expression program characterized by regulators of epithelial differentiation and in parallel, a gain-of-expression program displayed neuronal signaling gene induction, implicating functional reprogramming toward neuronal-like properties. Together, these specific losses and gains suggest that our patient-derived SI-NETs undergo adaptation through both loss of enteroendocrine functions and acquisition of neurobiological-promoting signaling pathways. Conclusions: These findings nominate candidate drivers for further functional validation and highlight potential therapeutic strategies in our patient cohort, including restoring suppressed Notch signaling and targeting aberrant neuronal signaling networks. However, even with a two-step validation procedure, the modest cohort size limits statistical power and generalizability, particularly for the proposed association to a serotonergic phenotype. Larger, multi-patient single-cell studies are required to confirm these mechanisms and establish their clinical relevance. Full article

Transposable elements (TEs) are key regulators of immunity in both health and disease. It has been proven that the activity and transcriptional expression levels of TEs increase during viral infections, correlating with the antiviral response. This study investigates non-LTR TE (LINE, SINE, and SVA) transcriptomic signatures in human PBMCs during infections caused by influenza A virus, HIV, and SARS-CoV-2 (Delta/Omicron variants) using single-cell RNA sequencing (scRNA-seq) data from 98 patients. In the HIV and SARS-CoV-2 patient cohorts, unique cell-specific TE expression patterns were identified that allow for the differentiation of disease severity, prediction of disease progression, and assessment of the therapy’s efficacy. The expression of LINE elements was found to be more dependent on the nature and course of the disease than that of SINE elements. The most variable TE expression profile was observed in precursor cytotoxic T-lymphocytes (T CD8+ Naive cells), which depended on the virus type and the severity of the viral disease. For this cell type, a bioinformatic analysis of the co-expression regulation of TE transcriptional networks and transcription factors during viral infections was performed. This analysis identified key players among those most involved in virus-specific responses, which could serve as diagnostic biomarkers or therapeutic targets for treating diseases caused by influenza A virus, HIV, and SARS-CoV-2. This work confirms the involvement of non-LTR TEs in mediating antiviral responses. Further research into the mechanisms of TE participation in antiviral defense is necessary to recommend them as potential biomarkers for the diagnosis, monitoring, and assessment of antiviral therapy, or as therapeutic targets for viral infections of various origins. Full article

Background and Objective: Colorectal cancer (CRC) liver metastasis (CRLM) represents a major clinical challenge, and acquired resistance to radiotherapy (RT) significantly limits therapeutic efficacy. A deep and comprehensive understanding of the cellular and molecular mechanisms driving RT resistance is urgently required to develop effective combination strategies. Here, we aimed to dissect the dynamic cellular landscape of the tumor microenvironment (TME) and identify key epigenetic regulators mediating radioresistance in CRLM by integrating cutting-edge single-cell and spatial omics technologies. Methods and Results: We performed integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on matched pre- and post-radiotherapy tumor tissues collected from three distinct CRLM patients. Employing a robust machine-learning framework on the multi-omics data, we successfully identified MORF4L1 (Mortality Factor 4 Like 1), an epigenetic reader, as a critical epigenetic mediator of acquired radioresistance. High-resolution scRNA-seq analysis of the tumor cell compartment revealed that the MORF4L1-high subpopulation exhibited significant enrichment in DNA damage repair (DDR) pathways, heightened activity of multiple pro-survival metabolic pathways, and robust signatures of immune evasion. Pseudotime trajectory analysis further confirmed that RT exposure drives tumor cells toward a highly resistant state, marked by a distinct increase in MORF4L1 expression. Furthermore, cell–cell communication inference demonstrated a pronounced, systemic upregulation of various immunosuppressive signaling axes within the TME following RT. Crucially, high-resolution ST confirmed these molecular and cellular interactions in their native context, revealing a significant spatial co-localization of MORF4L1-expressing tumor foci with multiple immunosuppressive immune cell types, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs), thereby underscoring its role in TME-mediated resistance. Conclusions: Our comprehensive spatial and single-cell profiling establishes MORF4L1 as a pivotal epigenetic regulator underlying acquired radioresistance in CRLM. These findings provide a compelling mechanistic rationale for combining radiotherapy with the targeted inhibition of MORF4L1, presenting a promising new therapeutic avenue to overcome treatment failure and improve patient outcomes in CRLM. Full article

The establishment and development of single-cell RNA-sequencing (scRNA-seq) technology has accelerated the analysis of cell genome characteristics down to the single-cell level. Despite the rapid development of scRNA-seq technology, we cannot obtain a complete gene expression matrix in the biological experiments, and the scRNA-seq data obtained from experiments also have a high dropout rate. Unfortunately, gene expression analysis and clustering tools require a complete matrix of gene expression values for classification or clustering calculations. Most imputation methods focus on the impact of the imputed high-dimensional expression matrix on clustering and cannot obtain the low-dimensional representation matrix, which may have an even better guiding effect on clustering. To this end, we designed an iterative imputation pipeline called scIRT to estimate dropout events for scRNA-seq and achieve dimensionality reduction simultaneously by combining the synthetic minority over-sampling technique (SMOTE) and non-negative matrix factorization (NMF). The adaptation of SMOTE effectively imputes missing data, while NMF performs dimensionality reduction and feature extraction on high-dimensional data. Using several scRNA-seq datasets, we demonstrated that this new approach achieved better and more robust performance than the existing approaches. We also compared the different effects of the imputed matrix and the low-dimensional representation matrix on clustering. ScIRT is a tool that can be used to preprocess scRNA-seq data. It can effectively recover missing data from scRNA-seq to facilitate downstream analyses such as cell type clustering and visualization. Full article

Skeletal muscle orchestrates a remarkable journey from embryonic formation to age-related decline, yet its cellular intricacies in goats remain largely uncharted. We present the first single-cell RNA sequencing (scRNA-seq) atlas of the longissimus dorsi muscle from goats, profiling 120,944 cells across 14 developmental stages from embryonic day 30 (E30) to 11 years postnatal (Y11). We focused on skeletal muscle satellite cells (MuSCs) and fibro-adipogenic progenitors (FAPs), identifying a unique MuSCs_ACT1_high subpopulation in early embryogenesis and a senescence-associated MuSCs_CDKN1A_high subpopulation in later developmental stages. In FAPs, we characterized the early-stage FAPs_MDFI_high subpopulation with differentiation potential, which further exhibited the capacity to commit to both adipogenic and fibrogenic lineages. Transcription factor analysis revealed strikingly similar regulatory profiles between MuSCs and FAPs, suggesting that these two cell types are governed by shared signaling pathways during development. Cell–cell interaction analysis demonstrated that the DLK1-NOTCH3 ligand-receptor pair plays a critical role in enabling early embryonic FAPs to maintain the quiescent state of MuSCs. This dynamic single-cell transcriptomic atlas, spanning 14 developmental stages of skeletal muscle in ruminants for the first time, provides a valuable theoretical foundation for further elucidating the differentiation of skeletal muscle satellite cells and fibro-adipogenic progenitors in ruminants. Full article

Micronutrients, particularly boron (B), iron (Fe), manganese (Mn), and zinc (Zn), are pivotal for cotton (Gossypium spp.) growth, reproductive success, and fiber quality. However, their critical roles are often overlooked in fertility programs focused primarily on macronutrients. This review synthesizes recent advances in the physiological, molecular, and agronomic understanding of B, Fe, Mn, and Zn in cotton production. The overarching goal is to elucidate their impact on cotton nutrient use efficiency (NUE). Drawing from the peer-reviewed literature, we highlight how these micronutrients regulate essential processes, including photosynthesis, cell wall integrity, hormone signaling, and stress remediation. These processes directly influence root development, boll retention, and fiber quality. As a result, deficiencies in these micronutrients contribute to significant yield gaps even when macronutrients are sufficiently supplied. Key genes, including Boron Transporter 1 (BOR1), Iron-Regulated Transporter 1 (IRT1), Natural Resistance-Associated Macrophage Protein 1 (NRAMP1), Zinc-Regulated Transporter/Iron-Regulated Transporter-like Protein (ZIP), and Gossypium hirsutum Zinc/Iron-regulated transporter-like Protein 3 (GhZIP3), are crucial for mediating micronutrient uptake and homeostasis. These genes can be leveraged in breeding for high-yielding, nutrient-efficient cotton varieties. In addition to molecular hacks, advanced phenotyping technologies, such as unmanned aerial vehicles (UAVs) and single-cell RNA sequencing (scRNA-seq; a technology that measures gene expression at single-cell level, enabling the high-resolution analysis of cellular diversity and the identification of rare cell types), provide novel avenues for identifying nutrient-efficient genotypes and elucidating regulatory networks. Future research directions should include leveraging microRNAs, CRISPR-based gene editing, and precision nutrient management to enhance the use efficiency of B, Fe, Mn, and Zn. These approaches are essential for addressing environmental challenges and closing persistent yield gaps within sustainable cotton production systems. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has a wide range of clinical manifestations modulated by genetic factors. The aim of this study was to identify genetic determinants of severe COVID-19 affecting protein sequence to gain insight into disease pathogenesis. Variants prioritized in two patients requiring lung transplant were tested in the Milan FOGS cohort (487/869 cases/controls), highlighting an independent association between the p.F8S low-frequency variant of interferon alpha receptor 2 gene (IFNAR2) and severe disease (OR = 1.73 [1.24–2.42], p = 0.001), replicated in the COVID-19 Host Genetics Initiative cohort (26,167/2,061,934 cases/controls). In the FOGS cohort, the p.F8S variant was linked to higher circulating IL-6 levels. In keeping, bulk transcriptomic analysis in PBMCs at the peak of infection (n = 57) showed that carriers of the p.F8S variant had upregulation of immune signaling and pathogens response (p < 0.05). Functional flow cytometry experiments in healthy donors (n = 12) revealed that membrane IFNAR2 protein expression was reduced in B lymphocytes, but higher in dendritic cells (p < 0.05). Finally, by interrogating a public scRNAseq resource of PBMC of people with COVID-19, we showed that p.F8S carriers had upregulation of immune pathways specifically in dendritic cells (p < 0.05). These results suggest that the p.F8S variant may influence COVID-19 severity by enhancing adaptive immune response, thereby favoring inflammation. Full article