An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis
| Autor: | Stacy D. Sherrod, Richard M. Caprioli, John A. McLean, Ashley T Jordan, Randi L. Gant-Branum, Lauren D. Palmer, Melissa A. Farrow, Jeremy L. Norris, Danielle B. Gutierrez, Yuan-Wei Nei, Carrie E Romer, Simona G. Codreanu, Eric P. Skaar, Nikesh Dahal, Jamie L. Allen |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Proteomics Computer science NF-E2-Related Factor 2 Systems biology Sample (statistics) HL-60 Cells Biochemistry Article 03 medical and health sciences 0302 clinical medicine Metabolomics Humans Throughput (business) business.industry Gene Expression Profiling Systems Biology NF-kappa B General Chemistry Benchmarking Genomics Data science Automation Zinc 030104 developmental biology Analytics business 030217 neurology & neurosurgery Signal Transduction |
| Zdroj: | Journal of proteome research. 17(10) |
| ISSN: | 1535-3907 |
| Popis: | Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. 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 greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields. |
| Databáze: | OpenAIRE |
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