Cell genomics最新文献

Sustaining and expanding genomic surveillance capacity requires broader investment in genomics that target both novel and pandemic pathogens. Currently, there is no standardized methodology to evaluate the cost and benefit of a multi-pathogen surveillance system. We propose a framework for pathogen genomic surveillance that links public health and systems considerations to a stepwise approach.

Genome-wide CRISPR screening in the organism has tremendous potential to answer long-standing questions of mammalian physiology and disease. However, bringing this powerful technology in vivo presents unique challenges, including delivering a genome-wide sgRNA library to the appropriate cell type, achieving sufficient coverage of the library, and selecting for the phenotype of interest. In this review, we highlight recent advances in sgRNA delivery, library design, and phenotypic readout that can help overcome these technical challenges and thereby bring high-throughput genetic dissection to an increasing number of tissues and questions. We are excited about the potential for ongoing innovation in these areas to ultimately enable genome-wide CRISPR screening in any cell type of interest in the organism, allowing for unprecedented investigation into diverse questions of mammalian physiology and disease.

Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how concentrations of the dosage-sensitive TFs TWIST1 and SOX9 affect regulatory element (RE) chromatin accessibility in facial progenitor cells, achieving near-experimental accuracy. High-affinity motifs that allow for heterotypic TF co-binding and are concentrated at the center of REs buffer against quantitative changes in TF dosage and predict unperturbed accessibility. Conversely, low-affinity or homotypic binding motifs distributed throughout REs drive sensitive responses with minimal impact on unperturbed accessibility. Both buffering and sensitizing features display purifying selection signatures. We validated these sequence features through reporter assays and demonstrated that TF-nucleosome competition can explain low-affinity motifs' sensitizing effects. This combination of transfer learning and quantitative chromatin response measurements provides a novel approach for uncovering additional layers of the cis-regulatory code.

Because of urbanization, deforestation, pollution, climate change, and natural disasters, the loss of biodiversity is a pressing concern globally. As part of our efforts toward biodiversity conservation, we propose the establishment of a genomic garden, where the genome of each plant in the garden is elucidated. Combining science, horticulture, and a digital content hub accessible with any handheld device, the genomic garden serves multiple purposes, from enhancing urban landscapes, facilitating biomedical research, and improving population health to providing entertainment and education for visitors.

Cell-type-specific gene activation is regulated by enhancers, sometimes located at large genomic distances from target gene promoters. Whether distal enhancers require specific factors to orchestrate gene regulation remains unclear. Here, we used enhancer distance-controlled reporter screens to find candidate factors. We depleted them and employed activity-by-contact predictions to genome-wide classify genes based on enhancer distance. Predicted distal enhancers typically control tissue-restricted genes and often are strong enhancers. We find cohesin, but also mediator, most specifically required for long-range activation, with cohesin repressing short-range gene activation and prioritizing distal over proximal HBB genes competing for shared enhancers. Long-range controlled genes are also most sensitive to perturbations of other regulatory proteins and to BET inhibitor JQ1, this being more a consequence of their distinct enhancer features than distance. Our work predicts that lengthening of intervening sequences can help limit the expression of target genes to specialized cells with optimal trans-factor environments.

Neuromyelitis optica spectrum disorder (NMOSD) is a rare autoimmune disease characterized by optic neuritis and transverse myelitis, with an unclear genetic background. A genome-wide meta-analysis of NMOSD in Japanese individuals (240 patients and 50,578 controls) identified significant associations with the major histocompatibility complex region and a common variant close to CCR6 (rs12193698; p = 1.8 × 10^-8, odds ratio [OR] = 1.73). In single-cell RNA sequencing (scRNA-seq) analysis (25 patients and 101 controls), the CCR6 risk variant showed disease-specific expression quantitative trait loci effects in CD4⁺ T (CD4T) cell subsets. Furthermore, we detected somatic mosaic chromosomal alterations (mCAs) in various autoimmune diseases and found that mCAs increase the risk of NMOSD (OR = 3.37 for copy number alteration). In scRNA-seq data, CD4T cells with 21q loss, a recurrently observed somatic event in NMOSD, showed dysregulation of type I interferon-related genes. Our integrated study identified novel germline and somatic mutations associated with NMOSD pathogenesis.

Most GWAS loci are presumed to affect gene regulation; however, only ∼43% colocalize with expression quantitative trait loci (eQTLs). To address this colocalization gap, we map eQTLs, chromatin accessibility QTLs (caQTLs), and histone acetylation QTLs (haQTLs) using molecular samples from three early developmental-like tissues. Through colocalization, we annotate 10.4% (n = 540) of GWAS loci in 15 traits by QTL phenotype, temporal specificity, and complexity. We show that integration of chromatin QTLs results in a 2.3-fold higher annotation rate of GWAS loci because they capture distal GWAS loci missed by eQTLs, and that 5.4% (n = 13) of GWAS colocalizing eQTLs are early developmental specific. Finally, we utilize the iPSCORE multiomic QTLs to prioritize putative causal variants overlapping transcription factor motifs to elucidate the potential genetic underpinnings of 296 GWAS-QTL colocalizations.

DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophrenia-associated differentially methylated regions that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia. We anticipate that scMeFormer will be a valuable tool for advancing single-cell DNAm studies.

In this issue of Cell Genomics, Choi et al.¹ report a novel approach, privateQTL, which leverages secure multiparty computation (MPC) to enable federated expression quantitative trait loci (eQTL) mapping across institutions without compromising data privacy. Zhang et al. preview their approach and discuss its application in future genetic analyses.

Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spatial structures overlooked by spot-based analytic tools, facilitating novel biological discoveries. The core concept of directly analyzing overall gene expression patterns also allows for a broader application beyond spatial transcriptomics, positioning STMiner for continuous expansion as spatial omics technologies evolve.