Integrated Multi-omic Analysis of Esthesioneuroblastomas Identifies Two Subgroups Linked to Cell Ontogeny
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ABSTRACT:
PROVIDER: PRJNA487796 | ENA |
REPOSITORIES: ENA
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Project description:We report here the multi-omics analysis of esthesioneuroblastomas, which is a rare cancer arising from the olfactory bulb. We discovered ENB can be grouped in two subtypes, namely basal and neural ENBs. We also uncovered high rates of mutations (~35%) in basal ENB subgroup.
2018-09-30 | GSE118995 | GEO
Project description:A multi-omic approach in a clinical experimental study identified circulating biomarkers reflecting glucocorticoid exposure. Background: Endogenous glucocorticoids (GC) are mechanistically linked to common diseases and are important as drugs in the treatment of many disorders. There is no marker that can measure and quantify GC action. Our aim was to identify circulating biomarkers of GC action using a clinical experimental study. Methods: In a randomized, crossover, single-blind trial, subjects with primary adrenal insufficiency received intravenous hydrocortisone infusion in a circadian pattern (physiological GC exposure) or isotonic saline (GC withdrawal) over 22 hours. Samples were collected at 7 AM (end of infusion). Integrated multi-omic analysis was used because of the complexity in GC action and the low number of subjects. The transcriptome in peripheral blood mononuclear cells (PBMCs) and adipose tissue, plasma miRNAomic, and serum metabolomics were compared between the interventions. Replication of the plasma miRNA findings was performed in three independent studies. Results: During GC withdrawal, overnight urinary cortisol and cortisone excretion were undetectable. Correlation and hypernetwork analyses identified a transcriptomic profile derived from PBMCs and adipose tissue predictive of GC exposure, and a multi-omic cluster predictive of GC exposure. From the circulating ‘omic data, decreased expression of plasma miR-122-5p was associated with increased GC exposure. This finding was reproduced in three independent studies. Conclusion: We developed a human experimental model for physiological GC exposure and withdrawal. The integrated multi-omic data identified circulating miRNAs and metabolites associated with GC-responsive genes. In independent studies, miR-122-5p was shown to be associated with GC exposure. Background: Endogenous glucocorticoids (GC) are mechanistically linked to common diseases and are important as drugs in the treatment of many disorders. There is no marker that can measure and quantify GC action. Our aim was to identify circulating biomarkers of GC action using a clinical experimental study.
2021-04-02 | GSE148640 | GEO
Project description:A multi-omic approach in a clinical experimental study identified circulating biomarkers reflecting glucocorticoid exposure. Background: Endogenous glucocorticoids (GC) are mechanistically linked to common diseases and are important as drugs in the treatment of many disorders. There is no marker that can measure and quantify GC action. Our aim was to identify circulating biomarkers of GC action using a clinical experimental study. Methods: In a randomized, crossover, single-blind trial, subjects with primary adrenal insufficiency received intravenous hydrocortisone infusion in a circadian pattern (physiological GC exposure) or isotonic saline (GC withdrawal) over 22 hours. Samples were collected at 7 AM (end of infusion). Integrated multi-omic analysis was used because of the complexity in GC action and the low number of subjects. The transcriptome in peripheral blood mononuclear cells (PBMCs) and adipose tissue, plasma miRNAomic, and serum metabolomics were compared between the interventions. Replication of the plasma miRNA findings was performed in three independent studies. Results: During GC withdrawal, overnight urinary cortisol and cortisone excretion were undetectable. Correlation and hypernetwork analyses identified a transcriptomic profile derived from PBMCs and adipose tissue predictive of GC exposure, and a multi-omic cluster predictive of GC exposure. From the circulating ‘omic data, decreased expression of plasma miR-122-5p was associated with increased GC exposure. This finding was reproduced in three independent studies. Conclusion: We developed a human experimental model for physiological GC exposure and withdrawal. The integrated multi-omic data identified circulating miRNAs and metabolites associated with GC-responsive genes. In independent studies, miR-122-5p was shown to be associated with GC exposure. Background: Endogenous glucocorticoids (GC) are mechanistically linked to common diseases and are important as drugs in the treatment of many disorders. There is no marker that can measure and quantify GC action. Our aim was to identify circulating biomarkers of GC action using a clinical experimental study.
2021-04-02 | GSE148639 | GEO
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.
2020-10-29 | E-MTAB-9357 | biostudies-arrayexpress
Project description:Identification of human glucocorticoid response markers using integrated multi-omic analysis
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Project description:BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution
2021-02-16 | GSE166797 | GEO
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.
2012-08-02 | E-GEOD-39785 | biostudies-arrayexpress
Project description:This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, Sample Preparation for multi-Omics Technologies (SPOT), provides equivalent performance to typical individual omic preparation methods, but it greatly enhances throughput and minimizes the resources required for multi-omic experiments. SPOT was applied to a multi-omics time course experiment for zinc-treated HL60 cells.
2018-05-08 | ST001029 | MetabolomicsWorkbench
Project description:Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the interplay between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic model of gene expression that extends the popular RNA velocity framework by incorporating epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of gene regulation, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Fitting MultiVelo on single-cell multi-omic datasets revealed two distinct mechanisms of regulation by chromatin accessibility, quantified the degree of concordance or discordance between transcriptomic and epigenomic states within each cell, and inferred the lengths of time lags between transcriptomic and epigenomic changes.
2022-08-01 | GSE209878 | GEO