Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics.

Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics.
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DOI:
10.3390/metabo12010052
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发表时间:
2022-01-08
期刊:
影响因子:
4.1
通讯作者:
Viant MR
Viant MR
中科院分区:
生物学3区
文献类型:
--
作者:
Malinowska JM;Palosaari T;Sund J;Carpi D;Lloyd GR;Weber RJM;Whelan M;Viant MR

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监管机构已经开始认识到体外筛选和代谢组学作为化学风险评估的两种新方法方法(NAMs)的价值,但很少有高通量体外毒性代谢组学研究的报道。一个重要的挑战是实现通常用于体外筛选的低生物量样品的自动化样品制备。在之前工作的基础上,我们开发、表征和演示了一种自动化的样品制备和分析工作流程,用于96孔微孔板中HepaRG细胞的体外代谢组学,使用Biomek i7混合工作站(Beckman Coulter)和Orbitrap Elite (Thermo Scientific)高分辨率纳米电喷雾直接输注质谱(nESI-DIMS),跨越极性代谢物和脂质。评估的实验条件包括代谢物提取的天数,样品在96孔微孔板上的提取顺序,96孔微孔板在仪器平台上的位置以及孔在微孔板内的位置。通过使用光谱特征的中位数相对标准偏差(mRSD(%)),我们证明了工作流程的良好重复性(最终mRSD < 30%),并且用于统计分析的阈值之外的特征百分比很低。为了进一步提高自动化工作流程的质量,对方法进行了小的修改,然后应用于大型队列研究(三次nESI-DIMS试验中4860个样品输注),证实了从细胞培养到代谢物测量的整个工作流程的高重复性,同时显著提高了样品吞吐量。预计自动化体外代谢组学工作流程将有助于推进代谢组学(作为NAMs的一部分)在化学品安全中的应用,主要是作为高通量筛选和优先排序的方法。
Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument’s deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.
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