Cell genomics最新文献

The repair of a DNA double-strand break (DSB) by non-homologous end joining (NHEJ) generally leaves an intact or minimally modified sequence. Resection exposes single-stranded DNA and directs repair toward homology-dependent pathways and away from NHEJ. Here, we report that in Saccharomyces cerevisiae, the Cdc13/Stn1/Ten1 (CST) complex, characterized for its telomeric functions, acts after resection initiation to mediate a back-up NHEJ repair. We found a CST-specific mutation signature after repair characterized by deletions of 5-85 bp that were mostly dependent on NHEJ, with a subset dependent on microhomology-mediated end joining (MMEJ). The interaction between CST and Polα-primase is critical for these intermediate-size deletions, suggesting a role for fill-in synthesis, thus limiting extensive resection, which would otherwise lead to MMEJ-dependent deletions of several kilobases. Collectively, these results depict a complex picture of repair pathway choice where CST facilitates post-resection NHEJ repair, promoting local deletions but guarding against larger and potentially more deleterious deletions and rearrangements.

Circulating cell-free DNA (cfDNA) is a promising molecular biomarker, but its role in severe infection is unclear. Here, we profile cfDNA from sepsis patients and controls, demonstrating a 41-fold increase during disease. Methylation-based deconvolution revealed similar cfDNA compositions in the two groups, suggesting that cfDNA accumulation during disease is due not to excess cell death but to impaired hepatic clearance. Fragmentation and end-motif patterns both support this hypothesis, suggesting prolonged exposure of cfDNA to circulating nucleases. In addition, we show that cfDNA retains nucleosome footprints informative of gene activity. By developing a novel method to quantify these footprints and integrate them with single-cell data, we report an increase in cfDNA from Kupffer cells and liver parenchyma in patients with liver dysfunction. Finally, we show that cfDNA contains pathogen-derived material, highlighting its diagnostic potential. This high-throughput, multimodal study provides a reference for understanding cfDNA's role in sepsis and critical illness.

Inherited genetic variants contribute to Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC), but it is unknown which cell types are involved in this process. We performed single-cell RNA sequencing of BE, EAC, and paired normal tissues and integrated genome-wide association data to determine cell-type-specific genetic risk and cellular processes that contribute to BE and EAC. The analysis reveals that EAC development is driven to a greater extent by local cellular processes than BE development and suggests that one cell type of BE origin (intestinal metaplasia cells) and cellular processes that control the differentiation of columnar cells are of particular relevance for EAC development. Specific subtypes of fibroblasts and endothelial cells likely contribute to BE and EAC development, while dendritic cells and CD4⁺ memory T cells seem to contribute to BE development. The diagnostic value of markers characterizing the cell types and cellular processes should be explored for EAC prediction.

Enhancers are known to spatiotemporally regulate gene transcription, yet the identification of enhancers and their target genes is often indirect, low resolution, and/or assumptive. To identify and functionally perturb enhancers at their endogenous sites, we performed a pooled tiling CRISPR activation (CRISPRa) screen surrounding PHOX2B, a master regulator of neuronal cell fate and a key player in neuroblastoma, and found many CRISPRa-responsive elements (CaREs) that alter cellular growth. To determine CaRE target genes, we developed TESLA-seq (targeted single-cell activation), which combines CRISPRa screening with targeted single-cell RNA sequencing and enables the parallel readout of the effect of hundreds of enhancers on all genes in the locus. While most TESLA-revealed CaRE-gene relationships involved neuroblastoma-related regulatory elements, we found many CaREs and target connections normally active only in other tissues. This highlights the power of TESLA-seq to reveal gene regulatory networks, including edges active outside of a given experimental system.

Single-cell omics, named Method of the Year three times, have revolutionized biological research by enabling the high-resolution exploration of cellular heterogeneity and molecular processes. Initially centered on transcriptomics, this rapidly evolving field now ranges from multiomics to spatial analysis, with expanding customization options. The ubiquity of such analyses and the lack of a unified pipeline necessitate the development of scalable, flexible, and integrated tools and workflows. The Galaxy platform has responded to these technological advancements, extending its repertoire of freely accessible tools and workflows, backed by expert-reviewed and user-informed training resources to empower researchers to perform and interpret their own analyses. With more than 175 tools, 120 training resources, and 300,000 jobs running at the time of writing, this process has culminated in the development of Galaxy single-cell and spatial omics community (SPOC), designed to promote global collaboration in advancing usable, reproducible, accessible, and sustainable single-cell and spatial omics research.

Transposable elements can drive genetic innovation. In this issue of Cell Genomics, Adami et al.¹ investigate the impact of hominoid-specific L1 retrotransposons on early embryonic and brain development. Using induced pluripotent stem cells and cerebral organoid models, multi-omics, and CRISPRi-mediated silencing, they uncover a cis-regulatory role for these young retrotransposons in early human brain development.

The preview discusses the scalable platform for methylation-based trait mapping published in Cell Genomics by Goldberg et al.¹ This work represents not only a methodological advance but also marks a conceptual shift toward scalable and high-throughput functional, targeted, and context-sensitive epigenomic screening.

Historical expansions of wild boars (Sus scrofa) across Eurasia have shaped phenotypic variation, genetic diversity, and local adaptation of their populations. The study by Wang et al.¹ investigates the demographic history and genetic adaptation of Eurasian wild boars based on 96 whole-genome sequences, informing a critical role of Central Asian populations in their expansions and identifying key genes and variants associated with their local adaptation. Also, the adaptive variants are potentially useful for domestic pig breeding in future.

Genetic heterogeneity exists within all microbial populations, with sympatric cells of the same species often exhibiting single-nucleotide variations that influence phenotypic traits, including metabolic efficiency. However, the evolutionary dynamics of these strain-level differences in response to environmental stress remain poorly understood. Here, we present a first-of-its-kind study tracking the adaptive evolution of an anaerobic, carbon-fixing microbiota under a controlled engineered ecosystem focused on carbon dioxide bioconversion into methane. Leveraging strain-resolved metagenomics with an ad hoc variant calling and phasing approach, we mapped mutation trajectories and observed that the two dominant Methanothermobacter species maintained distinct sweeping haplotypes over time, most likely due to niche-specific metabolic roles. By combining population genetic statistics and peptide reconstruction, mer and mcrB genes emerged as potential drivers of archaeal strain-level competition. These findings pave the way for targeted engineering of microbial communities to enhance bioconversion efficiency, with significant implications for sustainable energy and carbon management in anaerobic systems.

Over the past 2 to 3 years, mass-spectrometry-based single-cell proteomics (SCP) has experienced transformative improvements in microfluidic and robotic sample preparation, innovative MS1- and MS2-based multiplexing strategies, and specialized hardware (e.g., timsTOF Ultra 2, Astral), which have dramatically boosted sensitivity, throughput, and proteome coverage from picogram-level protein inputs. Concurrently, tailored computational workflows that encompass normalization, imputation, and no-code platforms have addressed pervasive missing data challenges and standardized analyses, collectively enabling high-throughput, reproducible profiling of cellular heterogeneity. This minireview summarizes the latest progress in SCP technology and software solutions, highlighting how the closer integration of analytical, computational, and experimental strategies will facilitate a deeper and broader coverage of single-cell proteomes.