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CellCharter is a flexible, platform-agnostic method for identifying cell niches in spatially resolved data. Analysis of lung cancers demonstrates the importance of considering spatial information, exemplified by a neutrophil-associated niche that correlates with an aggressive cancer cell state and patient prognosis.
A strategy for inferring phase for rare variant pairs is applied to exome sequencing data for 125,748 individuals from the Genome Aggregation Database (gnomAD). This resource will aid interpretation of rare co-occurring variants in the context of recessive disease.
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.
