Genomics, proteomics & bioinformatics最新文献

Deciphering the cellular and molecular mechanisms of type 1 diabetes (T1D) has long been a central goal in immunology. Here, we perform a large-scale single-cell transcriptomic analysis of peripheral blood mononuclear cells from children with T1D, their first-degree relatives, and healthy controls, enabling high-resolution profiling of immune dynamics across disease stages. We identify distinct immune signatures associated with pathogenesis: an expansion of non-mature regulatory T cells (Tregs) marks new-onset T1D, coinciding with clinical manifestation. During active autoimmunity, we observe increased Th22 cells linked to tumor necrosis factor (TNF) and interleukin (IL)-6 upregulation, reduced mucosal-associated invariant T (MAIT) cells with altered functional activity, and elevated ADAM10 and ADAM17 expression, promoting proinflammatory intercellular signaling. In contrast, the later phase of disease is characterized by Th17 cell accumulation and enhanced signalling through TGF-β1 and IL-12. Transcriptional regulatory network analysis highlights BACH2 as a key regulator of Treg maturation, implicating its dysregulation in immune tolerance breakdown. Dynamic shifts in CD4+ T cell subsets and cell-cell communication reveal stage-specific immunological trajectories. These findings provide a comprehensive map of systemic immune remodelling in T1D and uncover potential biomarkers and therapeutic targets for stage-stratified intervention.

Endothelial cells (ECs) play complex roles across tissues and vessel types. Yet, systematic investigations of EC heterogeneity in the combined context of vessel type and tissue microenvironment are still largely lacking. We integrated over three million single cells from single-cell RNA-seq datasets in 15 human tissues. We found that ECs in some tissues (e.g., heart and kidney) exhibited greater tissue specificity, whereas others showed greater vessel specificity. We developed a computational pipeline to analyze cell-cell communications (CCC) mediated by metabolites or proteins to explore microenvironmental regulation. Interestingly, our results showed that CCC events involving ECs varied vastly across tissues, highlighting tissue-specific EC interactions. Using topic modeling, we identified CCC patterns, termed CCC topics, representing metabolite- and protein-mediated interactions between ECs and other tissue-resident cells. Most CCC topics showed high tissue specificity, potentially explaining the microenvironmental regulation of EC heterogeneity. The work systematically investigates EC heterogeneity and provides insights into its regulation across diverse tissue microenvironments. The script to reproduce all analyses in this study is available at https://github.com/zhuzimoo/EC_project.

While lineage plasticity is a well-established driver of therapy resistance in prostate cancer, the role of tumor-infiltrating immune cells in mediating phenotype switching remains poorly understood. Here, we employed single-cell multi-omics to systematically characterize immune infiltration dynamics, transcriptional reprogramming, and intercellular communication networks during prostate cancer progression. Our analysis revealed that granulin (GRN)-expressing macrophages orchestrate the transition from adenocarcinoma (Adeno) to a therapy-resistant multilineage state exhibiting vimentin (VIM)+ mesenchymal and stem-like features through GRN/ tumor necrosis factor receptor superfamily member 1A (TNFRSF1A) interaction and the subsequent activation of the nuclear factor kappa-B (NF-κB) pathway. Intriguingly, these plastic tumor subclones reciprocally enhanced GRN expression in macrophages via colony stimulating factor 1 (CSF1) and CSF1 receptor (CSF1R) receptor-ligand axis, establishing a feedforward signaling loop that sustains lineage plasticity. Functional validation demonstrated GRN's critical role in driving epithelial-mesenchymal transition in vitro and conferring resistance to enzalutamide (ENZ) in patient-derived organoids. Therapeutic intervention studies in transgenic Adeno of the mouse prostate (TRAMP) models showed that CSF1R inhibition disrupted this vicious cycle, reducing GRN  + macrophages and suppressing multilineage subclone emergence. Spatial mapping revealed direct physical interactions between VIM  + tumor cells and GRN  + macrophages, while single-cell proteomics in castration-resistant patients confirmed the clinical relevance of this axis. Furthermore, we identified three novel stromal populations [decorin (DCN)+ endothelial cells, C-C motif chemokine ligand 7 (CCL7)+ fibroblasts, and interferon-induced protein with tetratricopeptide repeats 1 (IFIT1)+ neutrophils associated with disease relapse. These findings illuminate the tumor-immune crosstalk underlying treatment resistance and unveil promising therapeutic targets for overcoming lineage plasticity-driven resistance in advanced prostate cancer.

Infantile hemangioma (IH), a benign vascular tumor of infancy, is characterized by rapid postnatal growth and subsequent spontaneous resolution. Although propranolol, initially developed for cardiovascular disorders, is the primary treatment for IH, its precise molecular mechanisms remain incompletely elucidated. This study employed single-cell RNA sequencing (scRNA-seq) to generate a comprehensive cellular atlas comprising eight tissues from three IH infants, sampled both before and after propranolol treatment, alongside two normal infant skin samples, yielding 103,082 cells. Concurrently, tumor-normal tissue paired whole-genome sequencing (WGS) was conducted on samples from 13 IH infants. To provide a comprehensive genomic landscape, we also characterized germline and somatic variations. scRNA-seq analysis identified two distinct propranolol-targeted IH-specific cell subtypes: APLN  + hemangioma endothelial cells (HemECs), associated with angiogenesis, and CENPF  + hemangioma pericytes (Hem-Pericytes), associated with IH proliferation. WGS revealed that the majority of mutations resided in intronic regions, somatic copy number alterations changed weakly, and that most somatic mutations were clonal. Functional validation demonstrated that propranolol treatment suppressed APLN transcription. Overall, this integrative study delineated two distinct IH-specific cell subtypes, with detailed characterization of germline/somatic mutations, copy number variations, and clonal evolution in IH.

As the most abundant internal RNA modification, N  6-methyladenosine (m6A) affects the fate of RNA through various mechanisms and regulates essential biological processes. In this study, we developed exomePeak2, a novel computational tool for the comprehensive analysis of the m6A epitranscriptome using data generated by Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq), the most widely adopted method for transcriptome-wide profiling of m6A RNA methylation. With a novel statistical model that efficiently addresses the common GC content bias and the variable immunoprecipitation (IP) efficiency in MeRIP-seq data, exomePeak2 achieves state-of-the-art performance in m6A site detection (or peak calling) and differential methylation analysis compared to competing approaches. Additionally, exomePeak2 provides a number of critical functions for MeRIP-seq analysis, such as unraveling the dynamics of RNA methylation in an absolute sense, handling strand-specific libraries for clear discrimination of anti-sense transcripts, performing peak calling with or without a reference transcriptome, and motif-based methylation level quantification at near base-resolution. These capabilities enable more reliable and comprehensive epitranscriptome analysis of MeRIP-seq data. exomePeak2 is also applicable to techniques implementing similar working principles for other modifications, such as hMeRIP-seq for 5-hydroxymethylcytidine (hm5C) and acRIP-seq for N  4-acetylcytidine (ac4C). exomePeak2, together with a comprehensive MeRIP-seq analysis protocol, is freely available from GitHub: https://github.com/ZW-xjtlu/exomePeak2.

The thymus provides a crucial microenvironment for lymphocyte development, undergoes rapid growth by progressive atrophy, yet the underlying molecular dynamics remain largely unclear. Here, we present a comprehensive single-cell transcriptomic data resource of mouse thymus across four developmental stages (3, 9, 15, and 33 weeks). We identified eleven major immune cell types, including γδ​cells, B cells, NKT cells, Dendritic cells, immature single-positive-T cells, DN3a cells, double positive (DP)-T cells-further subdivided into DP-1, DP-2, and DP-3-as well as CD4+ and CD8+ T cells. Our analysis revealed dynamic remodeling of thymic cell composition and transcriptional profiles over time. Notably, the proportion of CD8+ T cells and the expression of Smad4 and Smad7, key regulators of lymphocyte development, declined during thymic involution. The spatial single-cell atlas revealed that DP-2 and DP-3 formed distinct clusters, with dendritic cells (DCs) located in closer proximity to DP-2. Notably, DC-DP-2 interactions were enriched via the MHC-II pathway, potentially promoting the differentiation of DP-T cells. Further spatial trajectory analysis delineated the developmental landscape of DP-2 cells. Finally, autoimmune disease-associated risk genes were preferentially enriched in CD4+ and NKT cells, displaying distinct spatiotemporal expression patterns. Collectively, this study provides a valuable resource for dissecting thymic architecture and immune system development across different ages.

Integrated multi-omics approaches have fundamentally redefined investigative framework in systems biology and precision medicine. Despite substantial technological progress, precise molecular characterization of pre-disease states remains a pivotal challenge in the disease management and proactive healthcare. High dimensionality, heterogeneity, and technical noise inherent to multi-omics datasets complicate the robust detection of the subtle pre-pathological signals. For instance, elucidating the critical tipping point between reversible metabolic dysregulation and irreversible Type 2 Diabetes requires distinguishing coherent synergistic deviations across transcriptomic and metabolomic layers from stochastic physiological noise. Furthermore, prevailing analytical methods often struggle to establish the causal, dynamic relationships necessary to predict transition and distinguish pre-disease from health. To address these challenges, this article proposes a pre-disease state centered research methodology, focusing on the cross-scale regulatory architectures and multivariate synergistic dynamics inherent to complex living systems. It aims to advance methodological approaches for the investigation of spatiotemporally resolved dynamic processes and to establish a theoretically grounded foundation for proactive health. We highlight how emerging conceptual, computational, and technological breakthroughs can be leveraged to decode the molecular architecture of pre-disease state. It provides a novel systems-level window to overcome the limitations in current multi-omics studies. Ultimately, we advocate for this paradigm as a critical bridge between multi-omics insight and interceptive medicine, shifting clinical action into the pre-symptomatic phase.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of phenotypic heterogeneity. Although the predominant focus of scRNA-seq analyses has been assessing gene expression changes, several approaches have been proposed in recent years to identify changes at the DNA level from scRNA-seq data. In this study, we evaluated the relative performance of six strategies for calling single-nucleotide variants from scRNA-seq data using 381 single-cell transcriptomes from five cancer patients. Specifically, we focused on the quality of the inferred genotypes and the resulting single-cell phylogenies. We found that scAllele, Monopogen, and Monovar consistently returned phylogenetically informative genotype calls, providing more precise signals of discrimination between tumor and normal cells within heterogeneous samples and among distinct subclonal lineages in longitudinal samples. In addition, we evaluated the evolution of gene expression along the cell phylogenies. While most transcriptomic variation was very plastic and did not correlate with the cell phylogeny, a group of genes associated with cell cycle processes showed a strong phylogenetic signal in one of the patients, underscoring a potential link between gene expression patterns and lineage-specific traits in the context of cancer progression. In summary, our study highlights the potential of scRNA-seq data for inferring cell phylogenies to decipher the evolutionary dynamics of cell populations.