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一种用于多组学系统水平分析的集成、高通量策略。

An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis.

出版信息

J Proteome Res. 2018 Oct 5;17(10):3396-3408. doi: 10.1021/acs.jproteome.8b00302. Epub 2018 Aug 30.

Abstract

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.

摘要

蛋白质组学、代谢组学和转录组学生成全面的数据集,而当前的生物计算能力允许对其进行有效的整合,以进行系统生物学分析。已发表的多组学研究涵盖了方法学的进展以及对生物学问题的应用。然而,很少有研究专注于开发高通量、统一的样品制备方法来补充高通量组学分析。本报告详细介绍了一种从共同样本中进行转录组、蛋白质组和代谢组分析的自动化、基准测试和应用策略。该方法称为多组学技术的样品制备(SPOT),它提供了与典型的单个组学制备方法相当的性能,但大大提高了通量并最小化了多组学实验所需的资源。SPOT 被应用于锌处理 HL-60 细胞的多组学时间过程实验。数据揭示了 Zn 对 NRF2 抗氧化和 NFkappaB 信号的影响。像这样的高通量方法对于获取具有时间分辨率、多条件、大型多组学数据集至关重要,这些数据集对于评估复杂的临床和生物学问题是必要的。最终,这种方法将提供对许多领域具有挑战性的科学问题的更深入理解。

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