Metabolomics is a rapidly developing field of "omics" research concerned with the high-throughput identification
and quantification of small molecule metabolites in the metabolome. The metabolome constitutes a wide array of
compound classes that are crucial for the normal functioning of a biological system. As a result, the
metabolomics approach promises to offer new insights in many areas of biological investigation. Exposomics
intends to characterize the totality of human environmental exposures called the exposome and study
the impact of environmental exposures on human health throughout the life course.
Both metabolomics and exposomics research benefit greatly from advances in mass spectrometry (MS), liquid
chromatography (LC), and gas chromatography (GC). These advances allow researchers to detect many metabolites
and compounds that could not be detected previously. On the other hand, the high complexity of LC- and GC-MS
data generated from biological and environmental samples makes data preprocessing and analysis non-trivial.
Figure 1 provides an overview of the necessary informatics
capabilities. In addition, big data in metabolomics, exposomics, and relevant areas is being produced at an
unprecedented pace. Maximizing the value of these data calls for specific informatics capabilities.
Figure 1. Components of an informatics pipeline for analyzing LC- and GC-MS
metabolomics and exposomics data.
Mass spectrometry has been and will continue to be essential in moving metabolomics and exposomics forward.
However, the overall cost
is high and the turnaround time is long. Portable nanomaterial-based biosensing devices addresses these two
issues by monitoring a small set of metabolites or compounds. Du-Lab has been developing the associated mobile
app and a cloud resource with the eventual goal to establish an integrated nanosensor-smartphone-cloud platform
for
large-scale biomonitoring
using a
network of biosensors.
(2) ADAP Informatics Ecosystem
To address the needs of mass spectrometry-based metabolomics and exposomics, Du-Lab has been developing
the
ADAP informatics ecosystem as shown in Figure 2.
Figure 2. ADAP informatics ecosystem for metabolomics and exposomics
.
Related Publications
Smirnov A, Liao Y, Du X*: Memory-Efficient Searching of Gas-Chromatography Mass Spectra
Accelerated by Prescreening. Metabolites 2022, 12(6).
Smirnov A, Liao Y, Fahy E, Subramaniam S, Du X*: ADAP-KDB: A Spectral Knowledgebase
for
Tracking and
Prioritizing Unknown GC-MS Spectra in the NIH's Metabolomics Data Repository. Anal Chem 2021,
93(36):12213-12220.
Smirnov A, Qiu Y, Jia W, Walker DI, Jones DP, Du X: ADAP-GC 4.0: Application of
Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass
Spectrometry Metabolomics Data. Anal Chem 2019, 91(14):9069-9077.
Aleksandr Smirnov, Wei Jia, Douglas I. Walker, Dean P. Jones, Xiuxia Du*: ADAP-GC 3.2: Graphical
Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High Resolution Mass Spectrometry Metabolomics Data. Journal of Proteome Research 2018, 17 (1):470-478.
Owen Myers, Susan Sumner, Shuzhao Li, Stephen Barnes, Xiuxia Du*: One step forward for reducing
false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Analytical Chemistry 2017, 89(17):8696-8703.
Owen Myers, Susan Sumner, Shuzhao Li, Stephen Barnes, Xiuxia Du*: A detailed investigation and
comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Analytical Chemistry 2017, 89(17):8689-8695.
Yan Ni, Mingming Su, Yunping Qiu, Wei Jia, Xiuxia Du*: ADAP-GC 3.0: Improved Peak Detection and
Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies, Analytical Chemistry 2016, 88 (17):8802-8811.
Xiuxia Du*,; Steve Zeisel: Spectral deconvolution for gas chromatography mass spectrometry-based
metabolomics: current status and future perspectives.Computational and structural biotechnology journal 2013, 4, e201301013.
Yan Ni, Yunping Qiu, Wenxin Jiang, Kyle Suttlemyre, Mingming Su, Wenchao Zhang, Wei
Jia, Xiuxia Du*: ADAP-GC 2.0: deconvolution of coeluting metabolites from GC/TOF-MS data for metabolomics studies.Analytical chemistry 2012, 84 (15):6619-6629.
Wenxin Jiang, Yunping Qiu, Yan Ni, Mingming Su, Wei Jia, Xiuxia Du*: An automated data
analysis pipeline for GC-TOF-MS metabonomics studies.Journal of proteome research 2010, 9 (11):5974-5981.
(3) Nanosensor-smartphone-cloud platform
Du-Lab has been developing a mobile app and a cloud resource for a nanosensing device for onsite
rapid and sensitive detection of exposures to organophosphate pesticides with a tiny drop of
finger-stick blood.
2. Computational Proteomics
Chemical cross-linking combined with mass spectrometry provides a powerful method for identifying protein-protein
interactions and probing the structure of protein complexes. A number of strategies have been reported that take advantage of the high sensitivity and high resolution of modern mass spectrometers. Approaches typically include synthesis of novel cross-linking compounds, and/or isotopic labeling of the cross-linking reagent and/or protein, and label-free methods.
Du-Lab has developed Xlink-Identifier, a comprehensive data analysis platform to support label-free analyses. It
can identify interpeptide, intrapeptide, and deadend cross-links as well as underivatized peptides. The software streamlines data preprocessing, peptide scoring, and visualization and provides an overall data analysis strategy for studying protein-protein interactions and protein structure using mass spectrometry.
Related Publications
Severine Clavier, Xiuxia Du, Sandrine Sagan, Gerard Bolbach, Emmanuelle Sachon: An integrated cross-linking-MS approach to investigate cell penetrating peptides interacting partners.EuPA OPEN PROTEOMICS 2014; 3:229-238.
Aleksandra A. Watson, Pravin Mahajan, Haydyn D. T. Mertens, Michael J. Deery, Wenchao Zhang, Peter
Pham, Xiuxia Du, Till Bartke, Wei Zhang, Christian Edlich, Georgina Berridge, Yun Chen, Nicola A. Burgess-Brown, Tony Kouzarides,
Nicola Wiechens, Tom Owen-Hughes, Dmitri I. Svergun, Opher Gileadi and Ernest D. Laue: The PHD and chromo domains regulate the ATPase activity of the human chromatin remodeler CHD4.Journal of molecular biology 2012, 422 (1), 3-17.
Xiuxia Du, Saiful M. Chowdhury, Nathan P. Manes, Si Wu, M. Uljana Mayer, Joshua N. Adkins, Gordon A. Anderson, and Richard D. Smith: Xlink-identifier: an automated data analysis platform for confident identifications of chemically cross-linked peptides using tandem mass spectrometry.Journal of proteome research 2011, 10 (3), 923-931.
Saiful M. Chowdhury, Xiuxia Du, Nikola Tolic, Si Wu, Ronald J. Moore, M. Uljana Mayer, Richard D. Smith, and Joshua N. Adkins: Identification of cross-linked peptides after click-based enrichment using sequential collision-induced dissociation and electron transfer dissociation tandem mass spectrometry.Analytical chemistry 2009, 81 (13), 5524-5532.