Multi-omic measurements of heterogeneity in HeLa cells across laboratories
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PROVIDER: PRJNA437150 | ENA |
REPOSITORIES: ENA
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Project description:Reproducibility in research can be compromised by both biological and technical variation, but most of the focus is on removing the latter. Here we investigate the effects of biological variation in HeLa cell lines using a systems-wide approach. We determine the degree of molecular and phenotypic variability across 14 stock HeLa samples from 13 international laboratories. We cultured cells in uniform conditions and profiled genome-wide copy numbers, mRNAs, proteins and protein turnover rates in each cell line. We discovered substantial heterogeneity between HeLa variants, especially between lines of the CCL2 and Kyoto varieties, and observed progressive divergence within a specific cell line over 50 successive passages. Genomic variability has a complex, nonlinear effect on transcriptome, proteome and protein turnover profiles, and proteotype patterns explain the varying phenotypic response of different cell lines to Salmonella infection. These findings have implications for the interpretation and reproducibility of research results obtained from human cultured cells.
2019-02-01 | GSE111485 | GEO
Project description:We collected 14 stock Hela aliquots from 13 different laboratories across the globe and cultured them in the same conditions. We extensively profiled the genome-wide copy numbers, mRNAs, proteins, protein turnover rates by genomics techniques and the highly reproducible and accurate proteomic method, SWATH mass spectrometry. The cell lines were also phenotyped with respect to the ability of transfected Let7 mimics to modulate Salmonella infection. We discovered significant heterogeneity between Hela variants especially differences between the CCL2 and Kyoto lines collected from different sites. In addition, we observed progressive divergence within a specific cell line over 50 successive passages. By associating proteotype and phenotype we identified molecular patterns that varied between cell lines and explained varying responses to Salmonella infection across the cells. The results furthermore quantify how the cells respond to genomic variability across the transcriptome and proteome.
2019-02-11 | PXD009273 | Pride
Project description:Most existing single-cell techniques can only make one type of molecular measurements. Although computational approaches have been developed to integrate single-cell datasets, their efficacy still needs to be determined with reference to authentic single-cell multi-omic profiles. To address this challenge, we devised single-nucleus methylCytosine, Chromatin accessibility and Transcriptome sequencing (snmC2T-seq) and applied the approach to post-mortem human frontal cortex tissue. We developed a computational framework to evaluate the quality of finely defined cell types using multi-modal information and validated the efficacy of computational multi-omic integration methods. Correlation analysis in individual cells revealed gene groups showing distinct relations between methylation and expression. Integration of snmC2T-seq with other multi- and single- modal datasets enabled joint analyses of the methylome, chromatin accessibility, transcriptome, and chromatin architecture for 63 human cortical cell types. We reconstructed the regulatory lineage of these cortical cell types and found pronounced cell-type-specific enrichment of disease risks for neuropsychiatric traits, predicting causal cell types that can be targeted for treatment.
2019-12-12 | GSE140493 | 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:BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution
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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:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.
2020-08-29 | GSE157103 | 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