Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening.
Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening.
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DOI：
10.1021/acs.analchem.1c01616
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发表时间：
2021-07-06
影响因子：
7.4
通讯作者：
Viant MR
中科院分区：
文献类型：
--
作者：
Smith MJ;Ivanov DP;Weber RJM;Wingfield J;Viant MR
在药物发现管道的早期验证安全性数据是减少代价高昂的先导候选药物失败的关键。对于单个药物开发项目，我们估计每天需要筛选数千个样本（每次采集<10 s）。虽然基于色谱的代谢组学已被证明在从代谢生物标志物谱预测毒性方面具有价值，但它缺乏足够高的样品通量。声雾电离质谱（AMI-MS）是一种大气压电离方法，可以以无与伦比的速度直接从384孔板测量代谢物。我们试图实施信号处理和数据分析工作流程，以产生高质量的AMI-MS代谢组学数据，并展示其在药物安全性筛选中的应用。利用从代谢物标准品和HepG 2细胞裂解物的技术和生物学复制品以及毒性研究中获得的三个AMI-MS数据集，对现有的直接输注质谱工作流程进行了调整、扩展、优化和测试。在最小化方差和最大化特征计数的标准驱动下，设计了提取脉冲扫描数据的算法;优化了信噪比、重复过滤器、样品过滤器、缺失值过滤器和RSD过滤器的参数;调整了归一化和批次校正策略;实施了细胞表型过滤以排除高细胞毒性样品。使用高度重复的HepG 2毒性数据集证明了该工作流程，该数据集包括来自9种浓度的16种药物暴露的2772个样本，并在5小时内生成，揭示了表征特定作用模式的代谢表型和单个代谢物变化。这种AMI-MS工作流程为超高通量代谢组学筛选打开了大门，将样品分析速率提高了约2个数量级。
Incorporating safety data early in the drug discovery pipeline is key to reducing costly lead candidate failures. For a single drug development project, we estimate that several thousand samples per day require screening (<10 s per acquisition). While chromatography-based metabolomics has proven value at predicting toxicity from metabolic biomarker profiles, it lacks sufficiently high sample throughput. Acoustic mist ionization mass spectrometry (AMI–MS) is an atmospheric pressure ionization approach that can measure metabolites directly from 384-well plates with unparalleled speed. We sought to implement a signal processing and data analysis workflow to produce high-quality AMI–MS metabolomics data and to demonstrate its application to drug safety screening. An existing direct infusion mass spectrometry workflow was adapted, extended, optimized, and tested, utilizing three AMI–MS data sets acquired from technical and biological replicates of metabolite standards and HepG2 cell lysates and a toxicity study. Driven by criteria to minimize variance and maximize feature counts, an algorithm to extract the pulsed scan data was designed; parameters for signal-to-noise-ratio, replicate filter, sample filter, missing value filter, and RSD filter were all optimized; normalization and batch correction strategies were adapted; and cell phenotype filtering was implemented to exclude high cytotoxicity samples. The workflow was demonstrated using a highly replicated HepG2 toxicity data set, comprising 2772 samples from exposures to 16 drugs across 9 concentrations and generated in under 5 h, revealing metabolic phenotypes and individual metabolite changes that characterize specific modes of action. This AMI–MS workflow opens the door to ultrahigh-throughput metabolomics screening, increasing the rate of sample analysis by approximately 2 orders of magnitude.
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影响因子：
7.4
作者：
Guillaume-Gentil, Orane;Rey, Timo;Vorholt, Julia A.
通讯作者：
Vorholt, Julia A.
影响因子：
2.1
作者：
Zhang, Xing;Romm, Michelle;Baker, Erin S.
通讯作者：
Baker, Erin S.
影响因子：
6.1
作者：
Ramirez T;Strigun A;Verlohner A;Huener HA;Peter E;Herold M;Bordag N;Mellert W;Walk T;Spitzer M;Jiang X;Sperber S;Hofmann T;Hartung T;Kamp H;van Ravenzwaay B
通讯作者：
van Ravenzwaay B
DOI：
10.2174/138620709789104924
发表时间：
2009-09-01
影响因子：
1.8
作者：
Jonas, Maxine;LaMarr, William A.;Ozbal, Can
通讯作者：
Ozbal, Can
影响因子：
2
作者：
Donegan, M;Tomlinson, AJ;Juhasz, P
通讯作者：
Juhasz, P