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EasySci, a scalable single-cell profiling technique, uncovered over 300 mammalian brain cell states, revealing molecular features and dynamics of rare cell states linked to aging and Alzheimer’s disease. This work offers insights into cell states that expand (rare astrocytes and vascular leptomeningeal cells in the olfactory bulb, reactive microglia, and oligodendrocytes) or are depleted (neuronal progenitors, neuroblasts and committed oligodendrocyte precursors) during normal and pathological aging.
Combined analysis of genome-wide association studies and epigenetic data has identified certain immune cell types as drivers of autoimmune disease, but current methods have not been able to pinpoint key effector immune cell states. Using single-cell data from inflammatory tissues, we identified effector cell states embedded within inflammatory tissues — including T peripheral helper cells and tissue regulatory T cells — that capture disproportionate disease heritability.
Genome-wide analyses of blood cell phenotypes derived from perturbations coupled with flow cytometry-based functional readouts identify loci associated with latent cellular traits, yielding insights into biological mechanisms underlying common diseases.
Whole-genome analysis of paired follicular lymphoma and double-hit lymphoma shows that lymphoma progression is accompanied by enhanced somatic mutations targeting super-enhancer-embedded promoters.
Analyses of in vivo models, cell lines and patient-derived samples show that apolipoprotein B mRNA-editing catalytic subunit 3B (APOBEC3B) not only restrains lung tumor initiation but also that its upregulation is associated with resistance to targeted therapies. This study highlights the complex and context-dependent role of APOBEC3B in lung cancer.
We present a model to predict the chance of each possible de novo mutation in the human genome informed by recent insights into determinants of mutagenesis. Predictions were applied to refine demographic models, identify constrained genes, and uncover mutagenic effects of polymerase III transcription and transcription factor binding in testis.
Deep learning shows promise for predicting gene expression levels from DNA sequences. However, recent studies show that current state-of-the-art models struggle to accurately characterize expression variation from personal genomes, limiting their usefulness in personalized medicine.
Variants in the HLA region on chromosome 6 are strongly associated with many immune-related diseases. A method to construct personalized HLA genomes from single-cell RNA sequencing data, coupled with single-cell HLA expression quantitative trait loci modeling, identifies how genetic variants influence HLA gene expression across cell states.
Single-cell transcriptomes and single-cell chromatin accessibility profiles generated using EasySci provide a global view of aging and Alzheimer’s pathogenesis-associated cell population dynamics in human and mouse brains.
Analysis of single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin with sequencing data derived from synovium of patients with rheumatoid arthritis identifies regions with dynamic accessibility that correlate with cell states. Dynamic peaks are more strongly enriched for autoimmune disease heritability.
A downsampling approach to assess causal variant fine-mapping, replication failure rate, finds that commonly used methods may be miscalibrated. Simulations suggest this is probably due to a nonsparse genetic architecture model misspecification. Incorporating infinitesimal effects in the SuSiE and FINEMAP frameworks improves performance.
This Perspective article explores complex synthetic lethal relationships in cancer, which can involve several partners. Understanding this complexity presents challenges and opportunities for the development of therapeutics that target these interactions.
Cross-ancestry genome-wide association meta-analyses identify new risk loci for peptic ulcer diseases and provide evidence that gastrointestinal cell differentiation and hormone regulation contribute to their etiology.
Neural networks are a common machine learning architecture for predicting phenotype from genomic sequence. This analysis finds that they err in calling the variant direction of effect, with important implications for personalized predictions.
Population analysis of 516 wild and domesticated broomcorn millet genomes and a graph-based pangenome based on de novo assemblies of 32 representative accessions identify genomic variations associated with domestication traits.
Roulette enables the estimation of germline mutation rates at basepair resolution from humans. Genes encoding small nuclear RNA showed significant deviations from the mutation rate predicted by Roulette, highlighting RNA polymerase III (Pol III)-dependent transcription as a potent source of mutations in the human genome.
A test of four genomic sequence-to-expression deep learning models (Enformer, Basenji2, ExPecto, Xpresso) finds that they often fail to predict the correct direction of effect of cis-regulatory genetic variation on gene expression.
