Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening

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Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening. / Smith, Matthew J; Ivanov, Delyan P; Weber, Ralf J M; Wingfield, Jonathan; Viant, Mark R.

In: Analytical Chemistry, Vol. 93, No. 26, 06.07.2021, p. 9258-9266.

Research output: Contribution to journalArticlepeer-review

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Smith, Matthew J ; Ivanov, Delyan P ; Weber, Ralf J M ; Wingfield, Jonathan ; Viant, Mark R. / Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening. In: Analytical Chemistry. 2021 ; Vol. 93, No. 26. pp. 9258-9266.

Bibtex

@article{389151d44ce8404e9ac83803351655d2,
title = "Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening",
abstract = "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.",
author = "Smith, {Matthew J} and Ivanov, {Delyan P} and Weber, {Ralf J M} and Jonathan Wingfield and Viant, {Mark R}",
year = "2021",
month = jul,
day = "6",
doi = "10.1021/acs.analchem.1c01616",
language = "English",
volume = "93",
pages = "9258--9266",
journal = "Analytical Chemistry",
issn = "0003-2700",
publisher = "American Chemical Society",
number = "26",

}

RIS

TY - JOUR

T1 - Acoustic Mist Ionization Mass Spectrometry for Ultrahigh-Throughput Metabolomics Screening

AU - Smith, Matthew J

AU - Ivanov, Delyan P

AU - Weber, Ralf J M

AU - Wingfield, Jonathan

AU - Viant, Mark R

PY - 2021/7/6

Y1 - 2021/7/6

N2 - 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.

AB - 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.

U2 - 10.1021/acs.analchem.1c01616

DO - 10.1021/acs.analchem.1c01616

M3 - Article

C2 - 34156839

VL - 93

SP - 9258

EP - 9266

JO - Analytical Chemistry

JF - Analytical Chemistry

SN - 0003-2700

IS - 26

ER -