Cell genomics最新文献

Large-scale single-cell atlases have revealed many aging- and disease-associated cell types, yet these populations are often underrepresented in heterogeneous tissues, limiting detailed molecular analyses. To address this, we developed EnrichSci-a scalable, microfluidics-free platform that combines hybridization chain reaction RNA fluorescence in situ hybridization (FISH) with combinatorial indexing to profile single-nucleus transcriptomes of target cell types with full gene-body coverage. Applied to oligodendrocytes in the aging mouse brain, EnrichSci uncovered aging-associated molecular dynamics across distinct oligodendrocyte subtypes, revealing both shared and subtype-specific gene expression changes. Additionally, we identified aging-associated exon-level signatures missed by conventional gene-level analyses, highlighting post-transcriptional regulation as a critical dimension of cell-state dynamics in aging. By coupling transcript-guided enrichment with a scalable sequencing workflow, EnrichSci provides a versatile approach to decode dynamic regulatory landscapes in diverse cell types from complex tissues.

Genome-wide association studies (GWASs) have identified over 50 lung cancer risk loci; however, the precise cellular context of these genetic mechanisms remains unclear due to limitations in bulk tissue expression quantitative trait locus (eQTL) analyses. Here, we present the largest single-cell eQTL (sc-eQTL) atlas of human lung tissue to date, profiling 222 donors using multiplexed single-cell RNA sequencing (scRNA-seq). We identified 4,341 independent eQTLs across 17 cell types, with over 60% of sc-eQTLs and 51% of eGenes being cell-type specific, and fewer than 52% were detectable in paired bulk datasets. Integration with GWASs for non-small cell lung cancer highlighted epithelial and immune cells as key contributors to genetic susceptibility, identifying 28 candidate genes within known risk loci and 24 in novel regions. Notably, 47% of established non-small cell lung cancer (NSCLC) susceptibility loci exhibited cell-type-specific pleiotropic genetic regulation. This study provides a valuable resource of lung sc-eQTLs and illuminates how genetic variation modulates gene expression in a cell-type-specific fashion, contributing to lung cancer susceptibility.

In this issue of Cell Genomics, Xihao Li (X.L.), Zilin Li (Z.L.), and colleagues present the annotated genomic data structure (aGDS) format to streamline genomic analyses that use biobank-scale whole-genome sequencing data. Both authors have a research focus in statistical genetics/genomics, and here they highlight their latest work and the benefits of their aGDS approach.

In this issue of Cell Genomics, McKeever et al.¹ generate a single-nucleus transcriptomic atlas of ALS/FTLD brain and reveal widespread alternative polyadenylation changes. Their findings highlight 3' end RNA processing as a central integrator of stress responses, cell-type specificity, and disease susceptibility, offering new mechanistic insight and potential therapeutic directions.

Cohesin drives genome organization via loop extrusion, orchestrated by the dynamic exchange of multiple essential accessory proteins. Although these regulators bind the core cohesin complex only transiently, their disruption can dramatically alter loop-extrusion dynamics and chromosome morphology. Still, a quantitative theory of cohesin regulation and its interplay with genome folding is still elusive. Here, we derive a chemical-reaction network model of loop-extrusion regulation from first principles that is fully specified by available in vivo measurements. This "bursty extrusion model" untangles the distinct roles of regulators, whose exchange coincides with intermittent periods of motor activity. By incorporating bursty extrusion in polymer simulations, we reveal how variations in regulatory protein abundance can alter chromatin architecture across length and timescales. Our results are corroborated by in vivo and in vitro observations, bridging the gap between cohesin-regulator dynamics at the molecular scale and their genome-wide consequences on chromosome organization.

Aging is the main determinant of chronic diseases and mortality, yet organ-specific aging trajectories vary, and the molecular basis underlying this heterogeneity remains unclear. To elucidate this, we integrated genomic, epigenomic, transcriptomic, proteomic, and metabolomic data, employing post-genome-wide association study methodologies to systematically investigate the molecular mechanisms of nine organ-specific aging clocks and four blood-based epigenetic clocks. We uncovered genetic correlations and specific phenotypic clusters among these aging-related traits, identified prioritized genetic drug targets for heterogeneous aging, and elucidated downstream proteomic and metabolomic effects mediated by heterogeneous aging. We constructed a cross-layer molecular interaction network of heterogeneous aging across multiple organ systems and characterized detectable biomarkers of this heterogeneity. Integrating these findings, we developed an R/Shiny-based framework that provides a comprehensive multi-omic molecular landscape of heterogeneous aging, thereby advancing the understanding of aging heterogeneity and informing precision medicine strategies to delay organ-specific aging and prevent or treat its associated chronic diseases.

Ribosomal RNA (rRNA) genes are organized in tandem arrays known as ribosomal DNA (rDNA) on multiple chromosomes in Hominidae genomes. We measured copy number and transcriptional activity status of rRNA gene arrays across multiple individual genomes, revealing an identifiable fingerprint of rDNA copy number and activity. In some cases, entire arrays were transcriptionally silent, characterized by high DNA methylation across the rRNA gene, inaccessible chromatin, and the absence of transcription factors and transcripts. Silent arrays showed reduced association with the nucleolus and decreased interchromosomal interactions, consistent with the model that nucleolar organizer function depends on transcriptional activity. Removing rDNA methylation activated silent arrays. Array activity status remained stable through induced pluripotent stem cell reprogramming and differentiation into cerebral and intestinal organoids. Haplotype tracing in two unrelated family trios showed paternal transmission of silent arrays. We propose that the epigenetic state buffers rRNA gene dosage, specifies nucleolar organizer function, and can propagate transgenerationally.

Most genetic polymorphisms associated with complex traits are found in non-coding regions of the genome. Characterizing their effect presents a formidable challenge, and expression quantitative trait locus (eQTLs) mapping has been a key approach to do so. As comprehensive eQTL maps are available only for a few species, here we developed the Drosophila outbred synthetic population (Dros-OSP) and used it to characterize the landscape of transcriptional regulation in Drosophila melanogaster. We collected head and body transcriptomes and genomes from 1,286 outbred flies and mapped local and distant eQTLs for 98% of the genes. We characterized the network organization of the transcriptome across tissues and described the properties of local and distal eQTLs in terms of genetic diversity, heritability, connectivity, and pleiotropy. These results provide new insights into the genetic basis of transcriptional regulation in the fruit fly and offer a new mapping resource that will expand the possibilities currently available for the Drosophila community.

Though there has been substantial progress in the development of anti-human epidermal growth factor receptor 2 (HER2) therapies to treat HER2-positive metastatic breast cancer (MBC) within the past two decades, most patients still experience disease progression and cancer-related death. HER2-directed tyrosine kinase inhibitors can be highly effective therapies for patients with HER2-positive MBC; however, an understanding of resistance mechanisms is needed to better inform treatment approaches. We performed whole-exome sequencing on 111 patients with 73 tumor biopsies and 120 cell-free DNA samples to assess mechanisms of resistance. In 11 of 26 patients with acquired resistance, we identified alterations in previously characterized genes, such as PIK3CA and ERBB2, that could explain treatment resistance. Mutations in growing subclones identified potential mechanisms of resistance in 5 of 26 patients and included alterations in ESR1, FGFR2, and FGFR4. Additional studies are needed to assess the functional role and clinical utility of these alterations in driving resistance.

Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.