Project description:Tumors show substantial amounts of cellular heterogeneity by forming complex ecosystems of malignant and non-malignant cells. Herein, we present a comprehensive multi-omic cell atlas of matched single-cell transcriptome and single-cell chromatin accessibility profiles spanning over 150,000 cells from 11 human gynecologic tumors. By jointly analyzing these transcriptomic and chromatin accessibility profiles at single-cell resolution, we identify 115,734 total peak-to-gene links representing putative regulatory interactions. We find some of these regulatory interactions explain cell type-specific expression patterns of hallmark cancer pathway regulators such as the mTOR activator RHEB. We also leverage these data to infer differential transcription factor activity, such as ZEB1, across cell type-specific enhancers between two different fractions of the same patient tumor. Our work highlights the importance of precision medicine in the treatment of gynecologic cancers and we show that this resource will deepen our understanding of non-coding genomic regions in the context of tumor biology.
Project description:This study utilizes multi-omic biological data to perform deep immunophenotyping on the major immune cell classes in COVID-19 patients. 10X Genomics Chromium Single Cell Kits were used with Biolegend TotalSeq-C human antibodies to gather single-cell transcriptomic, surface protein, and TCR/BCR sequence information from 254 COVID-19 blood draws (a draw near diagnosis (-BL) and a draw a few days later (-AC)) and 16 healthy donors.
Project description:We used genome-scale modeling and multi-omics (transcriptomics, proteomics, and metabolomics) analysis to assess metabolic features that are critical for macrophage activation. We constructed a genome-scale metabolic network for the RAW 264.7 cell line to determine metabolic modulators of activation. Metabolites well-known to be associated with immunoactivation (glucose and arginine) and immunosuppression (tryptophan and vitamin D3) were among the most critical effectors. Intracellular metabolic mechanisms were assessed, identifying a suppressive role for de-novo nucleotide synthesis. Finally, underlying metabolic mechanisms of macrophage activation are identified by analyzing multi-omic data obtained from LPS-stimulated RAW cells in the context of our flux-based predictions. Two condition (flagellin and LPS) time course exposure of RAW 264.7 cell line at 1, 2, 4, and 24 hours. Two replicates for each condition and time point. All conditions compared to a pool of untreated cells at a 0 hour time point.