Project description:To understand the diversity of expression states within melanoma tumors, we obtained freshly resected samples, dissagregated the samples, sorted into single cells and profiled them by single-cell RNA-seq. Tumors were disaggregated, sorted into single cells, and profiled by Smart-seq2. *Raw data files absent for samples GSM1851356 and GSM1851494.* **Submitter declares reads will be made available through dbGaP.**
Project description:Cis-regulatory elements (CREs) encode the genomic blueprints for coordinating the spatiotemporal regulation of gene transcription programs necessary for highly specialized cellular functions. To identify cis-regulatory elements underlying cell-type specification and developmental transitions, we implemented single-cell sequencing of Assay for Transposase Accessible Chromatin (scATAC-seq) in an atlas of Zea mays tissues and organs. We describe 92 distinct patterns of chromatin accessibility across more than 165,913 putative CREs, greater than 56,575 cells, and 52 known cell-types using a novel regularized quasibinomial logistic model for estimating single cell accessibility. Cell-type specification could be largely explained by combinatorial accessibility of transcription factors (TFs) and their associated binding. Analysis of cell type-specific co-accessible chromatin recapitulated higher-order chromatin interactions, providing novel insight into cell type-specific regulatory dynamics. Integration of genetic diversity data revealed cell-type specific CREs contributed to specific morphological and molecular phenotypic traits indicative of their cellular functions, expanding our understanding of the molecular influence of complex traits in a eukaryotic species.
Project description:<p>A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.</p><p><br></p><p>All MALDI-imaging MS data as well as metabolite and lipid annotations and images are publicly available through METASPACE (<a href='https://metaspace2020.eu/project/Rappez_2021_SpaceM' rel='noopener noreferrer' target='_blank'>https://metaspace2020.eu/project/Rappez_2021_SpaceM</a>)</p>
Project description:Tumor-infiltrating myeloid cells play important roles in tumor development. We used single cell RNA sequencing (scRNA-seq) to analyze the diversity of myeloid cells in tumor.
Project description:To characterize the cellular diversity in the human kidney cortical nephrogenic niche we dissociated cells from the cortex and performed 10X Genomics single-cell RNA sequencing.
Project description:To characterize the cellular diversity in the human kidney cortical nephrogenic niche we dissociated cells from the cortex and performed 10X Genomics single-cell RNA sequencing.
