Project description:The liver plays a crucial role in essential physiological processes, and its impaired function due to liver fibrosis from various causes is an increasingly significant health issue. The liver's functionality relies on the precise arrangement of its cellular structures, yet the molecular architecture of these units remains only partially understood. We created a comprehensive molecular atlas detailing all the major cell types present in the adult mouse liver through deep single-cell RNA sequencing. Our analysis offers new insights into hepatic endothelial and mesenchymal cells, specifically highlighting the differences between the cells of the periportal microvasculature, the sinusoids, and the portal vein, the latter exhibiting a mixed arterio-venous phenotype. We identified distinct subpopulations of hepatic stellate cells, fibroblasts, and vascular mural cells located in different anatomical regions. Comparisons with transcriptomic data from disease models indicate that a previously unrecognized capsular population of hepatic stellate cells expands in response to fibrotic disease. Our findings reveal that various fibroblast subpopulations respond differently to pathological insults. This data resource will be invaluable for advancing therapeutic interventions targeting hepatic diseases.
Project description:Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Here we present one of the largest comprehensive and high-resolution methylation atlas based on Reduced Representative Bisulfite Sequencing (RRBS) data of 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively alidated the methylation signature atlas in independent methylation datasets, orthogonal epigenomic markers, and transcription regulatory elements. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA.
Project description:Whole-genome transcriptional profiles of rice cell types, isolated by laser-capture microdissection. The first comprehensive multi-organ cell type transcriptome atlas from a plant, with 40 distinct cell types from several developmental stages of seeds, shoots and roots, reveals cell-specific promoter motifs, interaction partner candidates, hormone response centers, and other previously unrecognized cellular properties and patterns. Keywords: Cell type comparison