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PubMed2026

A single-nucleus transcriptome atlas of soybean anthers.

Anther development is crucial for plant sexual reproduction. However, a high-resolution, cell-type-specific transcriptomic atlas of this process is lacking for the legume crop soybean (Glycine max). Here, we construct a comprehensive transcriptional atlas of developing soybean anthers using single-nucleus RNA sequencing (snRNA-seq). We identify and characterize nine distinct cell types spanning both somatic and reproductive lineages. Our analysis reveals robust transcriptional continuity across anther developmental stages and dynamic reprogramming during key transitions. Notably, the shift from diploid meiocytes to haploid unicellular microspores is marked by the induction of previously inactive genes, despite an overall reduction in transcript abundance. Subsequently, within bicellular microspores, generative and vegetative cell lineages exhibit sharply divergent transcriptional programs: generative cells specialize in mRNA export and turnover, whereas vegetative cells up-regulate translational machinery. Evolutionary analysis further indicates that generative-cell-specific genes are subject to more relaxed purifying selection compared to those specific to vegetative cells. Functional validation using mutants generated by CRISPR/Cas9-mediated genome editing and EMS mutagenesis reveals the essential roles of OSD1A and PKSA in pollen development and fertility. This high-resolution atlas provides fundamental insights into the transcriptional regulation of soybean anther development and serves as a valuable resource for manipulating male fertility to advance hybrid breeding programs. The data are available at https://databases.genedenovo.com/pollen.

Glycine max↗
PubMed2026

Unveiling the power of TIIC: A prognostic tool for esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) remains a lethal malignancy with limited prognostic tools for guiding immunotherapy. Tumor-infiltrating immune cells (TIICs) play a critical role in EAC prognosis and treatment response. METHODS: We integrated single-cell RNA sequencing and bulk transcriptome data from TCGA and GEO databases. TIIC-specific RNAs were identified via tissue specificity index calculation combined with machine learning feature selection. Twenty machine learning algorithms were benchmarked to construct an optimal TIIC signature score (TIIC-Score) based on the comprehensive C-index. Immunotherapy response, genomic mutation, and copy number variation were analyzed. Summary-data-based Mendelian randomization (SMR) and two-sample Mendelian randomization (MR) were performed to explore genetic associations. Core prognostic TIIC-related genes were functionally validated in esophageal cancer cell lines through loss-of-function assays. RESULTS: The TIIC-Score demonstrated robust prognostic value for 1-, 2-, and 3-year overall survival across multiple cohorts, outperforming 22 published models. High TIIC-Score was associated with poor survival and increased chromosomal instability. Mutation profiling revealed high frequencies of TP53 (78.2%), TTN (48.7%), and SYNE1 (30.8%). MR analysis identified a significant association between gastro-oesophageal reflux and EAC risk at SNP rs8130507. Functionally, CCNI was upregulated in esophageal cancer cells, and its knockdown suppressed malignant phenotypes while promoting apoptosis, supporting its pro-tumorigenic role. CONCLUSION: The TIIC-Score provides a novel prognostic framework for EAC that effectively stratifies patient risk and may help identify individuals most likely to benefit from immunotherapy.

Esophageal adenocarcinoma↗
PubMed2026

Hox/Meis-dependent gene-regulatory transition underlies cardiopharyngeal neural crest diversification.

Neural crest cells (NCCs) are multipotent migratory cells essential for cardiac development, yet the lineage trajectories and gene regulatory networks underlying their differentiation in the cardiopharyngeal region remain unclear. Here, we integrate single-cell RNA-seq, spatial transcriptomics, and multiomic analyses to construct a comprehensive map of NCC lineages in developing mouse cardiopharyngeal tissues. We identify a transition from Hox-positive pharyngeal NCCs to Hox-negative intracardiac populations associated with the outflow tract cushion, accompanied by a shift in Meis transcription factor binding and gene-regulatory network architecture. By contrast, NCCs forming the aorticopulmonary septum and great vessel smooth muscle retain distinct Hox-codes. A Meis2-Sox9-Scx gene-regulatory network defines a skeletogenic progenitor-like intermediate state that gives rise to coronary artery smooth muscle and semilunar valves. Our findings suggest that the loss of Hox-dependent regional identity enables pharyngeal NCCs to acquire new fates upon entering the cardiac cushion, providing insight into the developmental origins of coronary and valvular calcification.

Journal Article↗
PubMed2024

Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing. METHODS: Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}. RESULTS: Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of highly mutated genes in both HCC and Non-HCC revealed the presence of highly mutated genes in HCC (AHNAK2, MUC6, MUC16, TTN, ZNF17, FLG, MUC12, OBSCN, PDE4DIP, MUC5b, and HYDIN). Among the 26 significantly mutated HCC genes-identified across 10 genome sequencing studies-in addition to TCGA, APOB and RP1L1 showed the highest number of mutations in both HCC and Non-HCC tissues. Tier 1, Tier 2 variants in TCGA SMGs in HCC and Non-HCC (TP53, PIK3CA, CDKN2A, and BAP1). Cancer Genome Landscape analysis revealed Tier 1 and Tier 2 variants in HCC (MSH2) and in Non-HCC (KMT2D and ATM). For KEGG analysis, the significantly annotated clusters in HCC were Notch signaling, Wnt signaling, PI3K-AKT pathway, Hippo signaling, Apelin signaling, Hedgehog (Hh) signaling, and MAPK signaling, in addition to ECM-receptor interaction, focal adhesion, and calcium signaling. Tier 1 and Tier 2 variants KIT, KMT2D, NOTCH1, KMT2C, PIK3CA, KIT, SMARCA4, ATM, PTEN, MSH2, and PTCH1 were low frequency variants in both HCC and Non-HCC. CONCLUSION: Our results are in accordance with previous studies in HCC regarding highly mutated genes, TCGA and specifically enriched pathways in HCC. Analysis for clinical interpretation of variants revealed the presence of Tier 1 and Tier 2 variants that represent potential clinically actionable targets. The use of sequencing techniques to detect structural variants and novel techniques as single cell sequencing together with multiomics transcriptomics, metagenomics will integrate the molecular pathogenesis of HCC in Egyptian patients.

Humans↗
PubMed2026

Microplastics and nanoplastics-related genes signature predicts prognosis in pancreatic ductal adenocarcinoma and functional validation of interleukin 1 alpha.

BACKGROUND: Microplastics and nanoplastics (MNPs), as emerging environmental pollutants, have garnered significant attention from the global scientific community due to their potential threats to human health, particularly their association with the occurrence and development of cancer. The goal of our study is to create a predictive marker for pancreatic ductal adenocarcinoma (PAAD) based on MNPs-related genes, with the purposes of predicting survival outcomes and assessing the tumor immune microenvironment. METHODS: Using multi-cohort data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC), we assessed the association between MNPs and PAAD prognosis through the Xiantao Academic (https://www.xiantao.love/). The development of a prognostic signature was followed by an assessment of its significance through the Kaplan-Meier method, time-dependent receiver operating characteristic (ROC), and decision curve analysis (DCA). The validity of the risk model was confirmed through the ICGC and GSE71729 cohorts. The model was then assessed for levels of tumor immune infiltration. To explore MNPs-related genes expression characteristics within immune cells in PAAD, we performed single-cell RNA sequencing and spatial transcriptomics analysis through the Sparkle Platform (https://grswsci.top/). Finally, in vitro experiments were conducted to investigate the biological function of interleukin 1 alpha (IL1A). RESULTS: A four-gene signature comprising XDH, IL1A, KIF20A, and ASPM, based on MNPs, was developed to stratify PAAD patients into two distinct risk groups. The high-risk group showed a significantly poorer prognosis. A similar trend was verified in the external cohorts ICGC and GSE71729. The signature risk score affected immune cell infiltration in the PAAD microenvironment. The infiltration of B cells, CD8+ T cells, cytotoxic cells, immature dendritic cells (iDCs), mast cells, plasmacytoid dendritic cell (pDC), T cells, Tem cells, T follicular helper (TFH) cells, and T helper 17 (Th17) cells had a positive correlation with the low-risk group. In contrast, high-risk patients tended to have increased number of T helper (Th2) cells and higher expression of SIGLEC15, CD274, IGSF8. Knockdown of IL1A in PAAD cells inhibited their tumor proliferation ability in vitro. CONCLUSIONS: Using MNPs-related genes, we built a prognostic model for PAAD, revealing that patients with high-risk scores are likely to have a worse prognosis. This model is designed to develop personalized treatment strategies tailored to the specific needs of each patient, thereby improving clinical outcomes for PAAD patients. Furthermore, IL1A could be a promising therapeutic candidate for PAAD.

Microplastics↗
PubMed2026

scBaseCount: An AI agent-curated, standardized, auto-updated single-cell data repository.

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata. Here, we introduce scBaseCount, a database that leverages an AI agent to automate discovery and metadata extraction and standardize data processing. Built by mining all 10x Genomics datasets, scBaseCount is the largest public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues. It offers an unbiased view of the data landscape within the SRA and enables the training of more performant computational models through access to broader phenotypic diversity. Uniform processing enables measurement of both intronic and exonic reads and non-coding gene expression and improves alignment across experiments. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to autonomously curate biological data repositories.

Single-Cell Analysis↗
PubMed2026

A reproducible computational transcriptomic framework for cell-type-resolved fibroinflammatory-AKT remodeling in human heart failure.

BACKGROUND: Human heart failure involves multicellular transcriptional remodeling, but public transcriptomic studies often remain disconnected from cell-type localization and perturbational interpretation. METHODS: We developed a reproducible computational workflow integrating human left-ventricular bulk transcriptomes, donor-level cell-type pseudobulk results from a human heart-failure single-cell/single-nucleus atlas, external snRNA-seq support, curated module scoring, focused ligand-receptor prioritization and LINCS/L1000 perturbational matching. RESULTS: Cross-cohort analysis identified 14,358 same-direction HF-associated genes, including 1633 replicated HF-up and 785 replicated HF-down genes. Donor-level pseudobulk analysis localized disease remodeling to cardiomyocyte, fibroblast and myeloid compartments. Activated fibroblast and inflammatory myeloid programs defined a fibroinflammatory remodeling axis connected to context-dependent AKT-associated transcriptional shifts. External snRNA-seq support was strongest for fibroblast activation and AKT-associated remodeling, with etiology-dependent heterogeneity across validation resources. L1000FWD screening prioritized safety-aware perturbational hypotheses, including glimepiride and simvastatin as interpretable candidates requiring experimental validation. CONCLUSIONS: This study provides a computational transcriptomic framework linking reproducible human HF signatures, cell-type-resolved fibroinflammatory remodeling and perturbational genomic prioritization without claiming drug efficacy or AKT causality.

Humans↗
PubMed2026

Oncogenic Mutations and Tumor Microenvironment Alterations in Diffuse Large B-Cell Lymphoma With Bulky Disease.

BACKGROUND: Bulky disease represents a clinically aggressive subset of diffuse large B-cell lymphoma (DLBCL) associated with adverse clinical outcomes. The aim of this study was to investigate the influence of oncogenic mutations and tumor microenvironment alterations on bulky disease in DLBCL. METHODS: We analyzed a cohort of 939 patients with newly diagnosed DLBCL. Using DNA (n = 934) and RNA (n = 524) sequencing, we compared oncogenic mutations and tumor microenvironment (TME) alterations based on tumor diameter, with cutoff values at 5.0 cm and 10.0 cm. Further stratification by mutations in key genes (CD58, STAT6, EBF1) correlated with tumor diameter revealed distinct transcriptomic and immunologic profiles. Subsequent single-cell RNA sequencing, guided by these mutational signatures, resolved the cellular heterogeneity within the TME. RESULTS: Integrative analysis revealed that tumor diameter correlated with increased incidence of mutations in CD58, STAT6, and EBF1; adverse genetic subtypes such as EZB-like MYC+ and TP53Mut; activation of oncogenic pathways (JAK/STAT, BCR, PI3K, and MYC); and an immunosuppressive tumor microenvironment. Notably, immune checkpoint molecules varied across the bulky stages, with CTLA-4, TIGIT, ICOS, and CD28 expression inversely correlated with tumor diameter, while CD70 and 4-1BBL expression positively correlated. Single-cell RNA sequencing further revealed mutation-specific tumor microenvironment insights. CD58-mutated tumor exhibited a profoundly immune-deserted microenvironment dominated by malignant B cells with minimal immune infiltration, whereas STAT6-mutated tumor was associated with increased fibroblasts and CD4 + T cells, particularly regulatory T cells (Treg) and Th1-like cells; EBF1-mutated tumor was characterized by increased proportions of malignant B cells. CONCLUSIONS: Collectively, our findings highlight the biological complexity of bulky disease, identifying candidate molecular targets and providing a biological framework for future therapeutic hypothesis generation in this clinically aggressive subset of DLBCL.

Humans↗
PubMed2026

Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

MOTIVATION: Advances in single-cell sequencing have enabled multiomics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. RESULTS: We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. AVAILABILITY AND IMPLEMENTATION: This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material.

Multiomics↗
PubMed2025

Spatial mutual nearest neighbors for spatial transcriptomics data.

MOTIVATION: Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. RESULTS: Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-nearest neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N = 31 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. AVAILABILITY AND IMPLEMENTATION: Our software package is available on GitHub (https://github.com/Pixel-Dream/spatialMNN). The code is available on Zenodo (https://doi.org/10.5281/zenodo.15073963).

Algorithms↗
PubMed2026

Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.

Single-cell and spatial omics (SPOs) technologies have advanced how healthcare physicians characterise brain tumours by enabling detailed understanding of their cellular architecture, functional states, and microenvironmental dynamics. These approaches provide high-resolution detection of tumour heterogeneity and allow precise analysis of the brain tumour microenvironment. Their application has also led to the discovery of novel biomarkers used for early brain tumour detection, prognosis, and improved tumour stratification. Furthermore, integrative multi-omic analyses have revealed new therapeutic targets, clarified mechanisms of drug resistance, and uncovered molecular pathways underpinning treatment failure. By bridging cellular-level insights with spatial context, SPOs hold significant promise for advancing personalised diagnostics, predicting therapeutic response, and guiding the development of targeted interventions for brain tumours. Despite these advances, several limitations constrain the full translational potential of SPOs, including high experimental costs, substantial computational demands, lack of standardised protocols, and challenges in data integration and reproducibility. Addressing these barriers through scalable bioinformatic pipelines, consensus experimental frameworks, and cost-effective platforms remains critical for broadening accessibility and enabling clinical adoption.

Brain Neoplasms↗
PubMed2026

Single-cell RNA sequencing provides further insights into the immunostimulatory action of freeze-dried Lactiplantibacillus plantarum on Penaeus vannamei shrimp.

Immunostimulation through dietary interventions opened new avenues in developing disease control and prevention tools for shrimp aquaculture. We have previously shown that feeding with freeze-dried Lactiplantibacillus plantarum (LAB) increased disease resistance of Penaeus vannamei against both Vibrio parahaemolyticus and white spot syndrome virus (WSSV) based on bulk RNA sequencing of shrimp gills. This tissue participates in ion transport and serves as a first line of defense against environmental stressors and pathogenic infections. However, characterization of their cell composition and functions remains limited. Here, we implemented a single-cell RNA sequencing approach to further gather insights into how feeding with freeze-dried LAB modulates host immunity which may not be evident with bulk RNA sequencing approach. A total of five clusters with unique transcriptional signatures were identified, corresponding to pillar cells, septal cells, and sessile hemocytes. Pseudo-bulk analyses at global- and cluster-levels showed differential expression of genes related to host immunity and metabolism. We further revealed how overall transcriptomic changes are not exclusively caused by gene expression changes but may also be driven by cell population dynamics. This study highlighted how single-cell RNA sequencing approach may shed light on the mechanisms of action of immunostimulants which may be masked in bulk transcriptome analyses.

Animals↗
PubMed2026

Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease.

Allogeneic hematopoietic cell transplantation cures hematologic diseases but is limited by acute graft‑versus‑host disease. How human T cell clones drive epithelial injury remains poorly mapped. We studied 31 transplant recipients, integrating longitudinal T cell antigen receptor (TCR) profiling with single-cell RNA sequencing/TCR sequencing and spatial transcriptomics to track T cell clonal dynamics. We developed DecompTCR to resolve temporal dynamics and adapted computational tools to map clone phenotypes and niches in tissue. Our analyses revealed that cyclophosphamide selectively depletes alloreactive clones, although insufficient early expansion leads to incomplete depletion and severe disease. Severe graft‑versus‑host disease is marked by persistent expansion of alloreactive clones, rewiring of homeostatic cell types and diversification of donor-derived CD8+ clonotypes that acquire Hobit (ZNF683)+ tissue‑resident memory T (TRM) cell programs during migration to epithelium. Spatial deconvolution identified CD8+ effector/Hobit+ TRM hubs near intestinal stem‑cell-rich crypt bases and crypt‑loss regions. This clonotype‑resolved framework links tissue‑instructed TRM cell remodeling to localized epithelial injury, nominating early-repertoire dynamics and spatial hub burden as biomarkers.

Journal Article↗
PubMed2025

Advances in tumor subclone formation and mechanisms of growth and invasion.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Humans↗
PubMed2026

Integrated Genomic and Proteomic Analysis Reveals T-B Lymphocyte Signatures in the MYCN Driven "Immune Desert" of Specific Neuroblastoma Subtypes.

AIMS: This study aims to systematically dissect how MYCN amplification shapes the immunosuppressive tumor microenvironment (TME) in high-risk neuroblastoma, elucidating key mechanisms underlying immune evasion. METHODS: We performed an integrated multi-omics analysis of bulk RNA-seq (n = 721), single-cell RNA-seq (n = 9), proteomic data (n = 49) and spatial transcriptomics (Visium, with external validation in melanoma). Analyses included unsupervised clustering, cell-cell communication inference, transcriptional regulatory network reconstruction, and spatial proximity assessment to map the immune landscape. RESULTS: A distinct molecular subtype (Class C), defined by MYCN amplification and poor prognosis, exhibited a comprehensive "immune desert" phenotype characterized by low immune scores and minimal leukocyte infiltration. Single-cell analysis confirmed significant depletion of T and B lymphocytes within the Class C TME. Dysregulated transcriptional networks were identified, including upregulation of REL and EOMES in T cells-with EOMES potentially driving exhaustion via regulation of Transient Receptor Potential (TRP) genes, and REL inhibition enhancing cytotoxic function in vitro. A unique immunosuppressive B-cell subset (B7) engaged in enhanced crosstalk with exhausted T cells and harbored a MYC-centered network linked to cell cycle dysregulation and poor survival. Spatial transcriptomics revealed significant proximity between B7-active regions and Treg/exhaustion-enriched areas, externally validated in melanoma. Proteomic data validated elevated REL expression in MYCN-amplified tumors. CONCLUSION: This work delineates the immunosuppressive architecture of MYCN-driven neuroblastoma, revealing novel regulatory nodes within specific lymphocyte compartments. Integrating single-cell, spatial, and proteomic evidence, we propose REL inhibition as a therapeutic candidate, the EOMES/TRP axis as a bioinformatically supported hypothesis, and the B7/MYC hub as a hypothesis supported by transcriptomic and spatial evidence.

Humans↗
PubMed2026

Multi-omics approaches in idiopathic pulmonary fibrosis: from molecular mechanisms to therapeutic targets and precision medicine.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with limited therapeutic options and marked molecular heterogeneity. Despite available antifibrotic therapies, disease progression remains poorly predictable, highlighting the need for improved mechanistic understanding and therapeutic targeting. This review summarizes recent advances in multi-omics research to elucidate the molecular mechanisms underlying IPF and to identify potential biomarkers and pharmacological targets. Multi-omics studies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiling, and single-cell sequencing, have revealed key pathogenic mechanisms in IPF. Genetic susceptibility factors such as MUC5B promoter variants and telomere-related genes contribute to disease risk. Epigenetic regulation, including DNA methylation, histone modifications, and non-coding RNAs, plays a central role in fibrotic remodeling. Transcriptomic and proteomic analyses have identified dysregulated signaling pathways, including TGF-β, mTOR, cellular senescence, and extracellular matrix remodeling. Metabolomic alterations indicate disrupted lipid and amino acid metabolism. Importantly, integration of multi-omics datasets enables the identification of molecular endotypes, candidate biomarkers, and potential therapeutic targets. However, challenges including data integration, tissue heterogeneity, limited cohort size, and the need for functional validation remain important barriers to clinical translation. Continued development of multi-omics approaches may facilitate more accurate disease classification and support the development of personalized therapeutic strategies for IPF.

biomarkers↗
PubMed2021

Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Animals↗
PubMed2026

Transcriptome changes in circulating immune cells of critical COVID-19 patients predict a specific metabolic and epigenetic imprint.

BACKGROUND: The progression to critical COVID-19 arises predominantly from a dysregulated host immune response although the underlying regulatory mechanisms still remain partially elusive. This limits a prompt prediction of the disease progression, reduces the therapeutic options and restrains our understanding of “long COVID”. METHODS: Here, we analyzed the transcriptome of peripheral blood mononuclear cells (PBMCs) collected from COVID-19 patients experiencing different degrees of the disease (mild and critical), and control patients enrolled in the clinical trial COntAGIouS as well as independent bulk RNA-seq, single-cell RNA-seq and proteomic datasets. RESULTS: In critical COVID-19 patients, the integrative analysis of transcriptomic data revealed an altered regulatory network involving microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and coding genes that control mRNA translation-related genes, epigenetics, and metabolism. In parallel, we observed an upregulation of tRNA aminoacylation genes in critical COVID-19 patients by the analysis of either bulk or single-cell RNA-seq data from publicly available independent cohorts. Additionally, we found increased expression of coding genes enriched for the cognate amino acids (glycine, alanine, isoleucine and tyrosine), all related to protein localization, post-translational modifications, and cell metabolism in our cohort. Similar alterations in amino acid frequency were found in an independent proteomic dataset. CONCLUSIONS: Collectively, our findings indicate a broad perturbation of the gene expression landscape that characterizes the aberrant host immune response in critical COVID-19 patients and is potentially coordinated by miRNA and tRNA metabolism alterations. TRIAL REGISTRATION: COntAGIouS, NCT04327570. Registered 26 March 2020, https://clinicaltrials.gov/ct2/show/NCT04327570 .

Female↗