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Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in
Ipputa Tada1, Romanas Chaleckis2,3, Hiroshi Tsugawa4,5
1Department of Genetics, The Graduate University for Advanced Studies, SOKENDAI, 1111 Yata, Mishima, Shizuoka 411-8540, Japan.
Analytical Chemistry
|July 11, 2020
Summary
We developed Correlation-based Deconvolution (CorrDec) to improve data-independent acquisition mass spectrometry (DIA-MS) spectral analysis. This method enhances compound identification in untargeted metabolomics by leveraging ion correlations across multiple samples.
Area of Science:
- Analytical Chemistry
- Metabolomics
- Mass Spectrometry
Background:
- Data-independent acquisition mass spectrometry (DIA-MS) is crucial for untargeted metabolomics.
- Complex MS2 spectra in DIA-MS present significant challenges for accurate compound annotation.
- Existing deconvolution methods struggle with coeluting low-abundance compounds.
Purpose of the Study:
- To introduce Correlation-based Deconvolution (CorrDec) as an advancement to the MS-DIAL software.
- To improve the deconvolution of complex DIA-MS spectra using ion abundance correlations.
- To enhance compound annotation and identification in multisample metabolomics studies.
Main Methods:
- Developed CorrDec, a novel deconvolution method for DIA-MS data.
- Utilized ion abundance correlations across multiple samples to deconvolute spectra.
- Integrated CorrDec into the MS-DIAL software package.
Main Results:
- CorrDec significantly improved deconvolution accuracy compared to the previous MS2Dec method.
- Demonstrated enhanced discrimination of coeluting low-abundance compounds.
- Validated CorrDec performance in chemical standard dilution series and a large-scale urinary metabolomics study (224 samples).
Conclusions:
- CorrDec effectively deconvolutes complex DIA-MS spectra by exploiting quantitative information across samples.
- The method requires multiple samples but shows utility with as few as 10 unique samples.
- CorrDec offers a valuable tool for improving compound identification in large cohort metabolomics studies.
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