Zhiwei Zhou

All Publications

• Indole-3-acetic acid production is rare among gut bacteria and reflects OFOR-driven amino acid oxidation in acetogens. Gut microbes DeFeo, M. E., Liu, Y., Zhou, Z., Higginbottom, S. K., Dodd, D. 2026; 18 (1): 2689610

  Abstract

  Indole-3-acetic acid (IAA) is a tryptophan-derived gut microbial metabolite with reported anti-inflammatory activities, but the organisms and anaerobic pathways that support robust production remain unclear. Screening 206 human gut bacterial isolates by LC-MS revealed that IAA production is rare: only five strains exceeded the limit of quantitation, and high-capacity production was confined to the acetogens Blautia hydrogenotrophica and Intestinibacter bartlettii. Across growth conditions, IAA was a minor product that rose alongside carbohydrate-sensitive, OFOR-linked catabolism of multiple amino acids, generating abundant branched-chain and aromatic organic acids. In gnotobiotic mice mono-colonized with I. bartlettii, these metabolites were produced in vivo but showed distinct host handling, with branched-chain fatty acids largely extracted between portal and peripheral plasma, whereas aromatic acids and their glycine conjugates appeared in plasma and urine. Genomic analyzes and heterologous enzyme assays identified expanded repertoires of 2-oxoacid:ferredoxin oxidoreductases (OFORs) with activities spanning pyruvate/oxaloacetate, branched-chain, and aromatic 2-oxoacids, including indolepyruvate conversion to indoleacetyl-CoA, a putative intermediate en route to IAA. Finally, position-specific 13C tracing showed that CO2 released during amino acid oxidation is reassimilated into acetate via reductive acetogenesis, indicating that gut acetogens can maintain redox balance without fermenting partner strains. Together, these findings show that high IAA output is restricted to select gut acetogens and linked to a broader OFOR-driven anaerobic metabolism that generates additional metabolites that are absorbed by the host.

  View details for DOI 10.1080/19490976.2026.2689610

  View details for PubMedID 42324626

  View details for PubMedCentralID PMC13290082

• Indole-3-acetic acid production is rare among gut bacteria and reflects OFOR-driven amino acid oxidation in acetogens. bioRxiv : the preprint server for biology DeFeo, M. E., Liu, Y., Zhou, Z., Higginbottom, S. K., Dodd, D. 2026

  Abstract

  Indole-3-acetic acid (IAA) is a tryptophan-derived gut microbial metabolite with reported anti-inflammatory activities, but the organisms and anaerobic pathways that support robust production remain unclear. Screening 206 human gut bacterial isolates by LC-MS revealed that IAA production is rare: only five strains exceeded the limit of quantitation, and high-capacity production was confined to the acetogens Blautia hydrogenotrophica and Intestinibacter bartlettii. Across growth conditions, IAA was a minor product that rose alongside carbohydrate-sensitive, OFOR-linked catabolism of multiple amino acids, generating abundant branched-chain and aromatic organic acids. In gnotobiotic mice mono-colonized with I. bartlettii, these metabolites were produced in vivo but showed distinct host handling, with branched-chain fatty acids largely extracted between portal and peripheral plasma, whereas aromatic acids and their glycine conjugates appeared in plasma and urine. Genomic analyses and heterologous enzyme assays identified expanded repertoires of 2-oxoacid:ferredoxin oxidoreductases (OFORs) with activities spanning pyruvate/oxaloacetate, branched-chain, and aromatic 2-oxoacids, including indolepyruvate conversion to indoleacetyl-CoA, a putative intermediate en route to IAA. Finally, position-specific 13C tracing showed that CO2 released during amino acid oxidation is reassimilated into acetate via reductive acetogenesis, indicating that gut acetogens can maintain redox balance without fermenting partner strains. Together, these findings show that high IAA output is restricted to select gut acetogens and linked to a broader OFOR-driven anaerobic metabolism that generates additional metabolites that are absorbed by the host.

  View details for DOI 10.64898/2026.01.29.702580

  View details for PubMedID 41659482

  View details for PubMedCentralID PMC12873857

• IsoPairFinder: A tool for biochemical pathway discovery using stable isotope tracing metabolomics. bioRxiv : the preprint server for biology Zhou, Z., Liu, Y., Wang, M., Dodd, D. 2025

  Abstract

  The functional annotation of microbial genes lags far behind genome sequencing, leaving critical gaps in our knowledge of metabolic pathways. While integrating genetic manipulation with stable isotope tracing (SIT) metabolomics holds promise for pathway discovery, existing tools lack specialized capabilities for gene perturbation experiments. To address this need, we developed IsoPairFinder, a computational tool that identifies pathway intermediates by analyzing paired unlabeled (12C) and isotope-labeled (13C) metabolomics data from gene-edited microbes. By prioritizing substrate-specific feature pairs, IsoPairFinder efficiently prioritizes biologically relevant intermediates. Implemented as an open-source R package and integrated into the GNPS2 ecosystem, IsoPairFinder provides an accessible platform for the research community to accelerate novel pathway discovery and validation.

  View details for DOI 10.1101/2025.08.18.670916

  View details for PubMedID 40894616

  View details for PubMedCentralID PMC12393393

• Gut bacteria degrade purines via the 2,8-dioxopurine pathway. Nature microbiology Liu, Y., Zhou, Z., Jarman, J. B., Chen, H., Miranda-Velez, M., Terkeltaub, R., Dodd, D. 2025

  Abstract

  Approximately one-third of urate, which at elevated levels contributes to hyperuricaemia and gout, is excreted into the intestinal tract of healthy individuals where bacteria aid its elimination. However, the molecular details of purine metabolism in the gut microbiome are unclear. Here we uncovered the 2,8-dioxopurine pathway, an anaerobic route for purine degradation in the gut bacteria, Clostridium sporogenes and Escherichia coli. Reconstitution with purified enzymes and mutational analysis combined with isotope tracking and mass spectrometry identified a selenium-dependent enzyme, 2,8-dioxopurine dehydrogenase (DOPDH), and seven additional enzymes that connect purine metabolism to short-chain fatty acid synthesis and ATP generation (measured via luciferase assay). Competition experiments in gnotobiotic mice showed that bacteria harbouring this pathway exhibit a fitness advantage, with wild-type bacteria rapidly outcompeting a DOPDH-deficient strain. Widespread presence of these genes across host-associated microbiomes suggests a host-microbe symbiosis, where host-secreted urate fosters a metabolic niche for bacteria that break it down. These findings could have therapeutic implications for the modification and enhancement of intestinal elimination of urate.

  View details for DOI 10.1038/s41564-025-02079-4

  View details for PubMedID 40770490

  View details for PubMedCentralID 3956182

• Charting unknown metabolic reactions by mass spectrometry-resolved stable-isotope tracing metabolomics NATURE COMMUNICATIONS Gao, Y., Luo, M., Wang, H., Zhou, Z., Yin, Y., Wang, R., Xing, B., Yang, X., Cai, Y., Zhu, Z. 2025; 16 (1): 5059

  Abstract

  Metabolic reactions play important roles in organisms such as providing energy, transmitting signals, and synthesizing biomacromolecules. Charting unknown metabolic reactions in cells is hindered by limited technologies, restricting the holistic understanding of cellular metabolism. Using mass spectrometry-resolved stable-isotope tracing metabolomics, we develop an isotopologue similarity networking strategy, namely IsoNet, to effectively deduce previously unknown metabolic reactions. The strategy uncovers ~300 previously unknown metabolic reactions in living cells and mice. Specifically, we elaborately chart the metabolic reaction network related to glutathione, unveiling three previously unreported reactions nestled within glutathione metabolism. Among these, a transsulfuration reaction, synthesizing γ-glutamyl-seryl-glycine directly from glutathione, underscores the role of glutathione as a sulfur donor. Functional metabolomics studies systematically characterize biochemical effects of previously unknown reactions in glutathione metabolism, showcasing their diverse functions in regulating cellular metabolism. Overall, these newly uncovered metabolic reactions fill gaps in the metabolic network maps, facilitating exploration of uncharted territories in cellular biochemistry.

  View details for DOI 10.1038/s41467-025-60258-7

  View details for Web of Science ID 001499653300025

  View details for PubMedID 40450004

  View details for PubMedCentralID PMC12126588

• A LARGE-SCALE METABOLOMICS SCREEN TO DEFINE PATHWAYS FOR MICROBIOME-DERIVED SMALL MOLECULES Sekiba, K., Hou, B., Chen, H., Liu, Y., Zhou, Z., Crawl-Bey, A., Dodd, D. LIPPINCOTT WILLIAMS & WILKINS. 2024: S762

  View details for Web of Science ID 001366004002052

• A widely distributed gene cluster compensates for uricase loss in hominids. Cell Liu, Y., Jarman, J. B., Low, Y. S., Augustijn, H. E., Huang, S., Chen, H., DeFeo, M. E., Sekiba, K., Hou, B. H., Meng, X., Weakley, A. M., Cabrera, A. V., Zhou, Z., van Wezel, G., Medema, M. H., Ganesan, C., Pao, A. C., Gombar, S., Dodd, D. 2023; 186 (16): 3400-3413.e20

  Abstract

  Approximately 15% of US adults have circulating levels of uric acid above its solubility limit, which is causally linked to the disease gout. In most mammals, uric acid elimination is facilitated by the enzyme uricase. However, human uricase is a pseudogene, having been inactivated early in hominid evolution. Though it has long been known that uric acid is eliminated in the gut, the role of the gut microbiota in hyperuricemia has not been studied. Here, we identify a widely distributed bacterial gene cluster that encodes a pathway for uric acid degradation. Stable isotope tracing demonstrates that gut bacteria metabolize uric acid to xanthine or short chain fatty acids. Ablation of the microbiota in uricase-deficient mice causes severe hyperuricemia, and anaerobe-targeted antibiotics increase the risk of gout in humans. These data reveal a role for the gut microbiota in uric acid excretion and highlight the potential for microbiome-targeted therapeutics in hyperuricemia.

  View details for DOI 10.1016/j.cell.2023.06.010

  View details for PubMedID 37541197

• A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics NATURE COMMUNICATIONS Luo, M., Yin, Y., Zhou, Z., Zhang, H., Chen, X., Wang, H., Zhu, Z. 2023; 14 (1): 1813

  Abstract

  Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

  View details for DOI 10.1038/s41467-023-37539-0

  View details for Web of Science ID 000980769900015

  View details for PubMedID 37002244

  View details for PubMedCentralID PMC10066191

• Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics TRAC-TRENDS IN ANALYTICAL CHEMISTRY Cai, Y., Zhou, Z., Zhu, Z. 2023; 158

  View details for DOI 10.1016/j.trac.2022.116903

  View details for Web of Science ID 000913934900001

• Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking. Nature communications Zhou, Z., Luo, M., Zhang, H., Yin, Y., Cai, Y., Zhu, Z. 2022; 13 (1): 6656

  Abstract

  Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an invitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with insilico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.

  View details for DOI 10.1038/s41467-022-34537-6

  View details for PubMedID 36333358

• Four-Dimensional Untargeted Profiling of <i>N</i>-Acylethanolamine Lipids in the Mouse Brain Using Ion Mobility-Mass Spectrometry ANALYTICAL CHEMISTRY Liu, W., Zhang, W., Li, T., Zhou, Z., Luo, M., Chen, X., Cai, Y., Zhu, Z. 2022; 94 (36): 12472-12480

  Abstract

  N-Acylethanolamines (NAE) are a class of essential signaling lipids that are involved in a variety of physiological processes, such as energy homeostasis, anti-inflammatory responses, and neurological functions. NAE lipids are functionally different yet structurally similar and often have low concentrations in biological systems. Therefore, the comprehensive analysis of NAE lipids in complex biological matrices is very challenging. In this work, we developed an ion mobility-mass spectrometry (IM-MS) based four-dimensional (4D) untargeted technology for comprehensive analysis of NAE lipids. First, we employed the picolinyl derivatization to significantly improve ionization sensitivity of NAE lipids by 2-9-fold. Next, we developed a two-step quantitative structure-retention relationship (QSRR) strategy and used the AllCCS software to curate a 4D library for 170 NAE lipids with information on m/z, retention time, collision cross-section, and MS/MS spectra. Then, we developed a 4D untargeted technology empowered by the 4D library to support unambiguous identifications of NAE lipids. Using this technology, we readily identified a total of 68 NAE lipids across different biological samples. Finally, we used the 4D untargeted technology to comprehensively quantify 47 NAE lipids in 10 functional regions in the mouse brain and revealed a broad spectrum of the age-associated changes in NAE lipids across brain regions. We envision that the comprehensive analysis of NAE lipids will strengthen our understanding of their functions in regulating distinct physiological activities.

  View details for DOI 10.1021/acs.analchem.2c02650

  View details for Web of Science ID 000851367300001

  View details for PubMedID 36044263

• Global stable-isotope tracing metabolomics reveals system-wide metabolic alternations in aging Drosophila NATURE COMMUNICATIONS Wang, R., Yin, Y., Li, J., Wang, H., Lv, W., Gao, Y., Wang, T., Zhong, Y., Zhou, Z., Cai, Y., Su, X., Liu, N., Zhu, Z. 2022; 13 (1): 3518

  Abstract

  System-wide metabolic homeostasis is crucial for maintaining physiological functions of living organisms. Stable-isotope tracing metabolomics allows to unravel metabolic activity quantitatively by measuring the isotopically labeled metabolites, but has been largely restricted by coverage. Delineating system-wide metabolic homeostasis at the whole-organism level remains challenging. Here, we develop a global isotope tracing metabolomics technology to measure labeled metabolites with a metabolome-wide coverage. Using Drosophila as an aging model organism, we probe the in vivo tracing kinetics with quantitative information on labeling patterns, extents and rates on a metabolome-wide scale. We curate a system-wide metabolic network to characterize metabolic homeostasis and disclose a system-wide loss of metabolic coordinations that impacts both intra- and inter-tissue metabolic homeostasis significantly during Drosophila aging. Importantly, we reveal an unappreciated metabolic diversion from glycolysis to serine metabolism and purine metabolism as Drosophila aging. The developed technology facilitates a system-level understanding of metabolic regulation in living organisms.

  View details for DOI 10.1038/s41467-022-31268-6

  View details for Web of Science ID 000813768100022

  View details for PubMedID 35725845

  View details for PubMedCentralID PMC9209425

• Trapped ion mobility spectrometry-mass spectrometry improves the coverage and accuracy of four-dimensional untargeted lipidomics ANALYTICA CHIMICA ACTA Chen, X., Yin, Y., Luo, M., Zhou, Z., Cai, Y., Zhu, Z. 2022; 1210: 339886

  Abstract

  Lipids play vital roles in many physiological and pathological processes in living organisms. Due to the high structural diversity and the numerous isomers and isobars of lipids, high-coverage and high-accuracy lipidomic analysis of complex biological samples remain the bottleneck to investigate lipid metabolism. Here, we developed the trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) based four-dimensional untargeted lipidomics to support accurate lipid identification and quantification in biological samples. We first demonstrated that the TIMS based multi-dimensional separation improved the differentiations of isomeric and isobaric lipids, and increased the purity of precursor ion isolation and the quality of MS/MS spectra. Hyphenation of TIMS and PASEF technologies significantly improved the coverages of MS/MS spectra. These technological advantages jointly improved the coverage and accuracy of lipid identification in untargeted lipidomics. We further demonstrated that the CCS values of lipids acquired using TIMS were highly consistent with those from drift tube ion mobility spectrometry (DTIMS). Lipid identification and quantification results of NIST human plasma samples were also verified with inter-laboratory reports. Finally, we applied the TIMS-MS based untargeted lipidomics to characterize the spatial distributions of 1393 distinctive lipids in the mouse brain, and demonstrated that diverse lipid distributions and compositions among brain regions contributed to different functions of brain regions. Altogether, TIMS-MS based four-dimensional untargeted lipidomics significantly improved the coverage and accuracy of untargeted metabolomics, thereby facilitating a system-level understanding of lipid metabolism in biological organisms.

  View details for DOI 10.1016/j.aca.2022.339886

  View details for Web of Science ID 000805838700005

  View details for PubMedID 35595363

• Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain NATURE COMMUNICATIONS Li, T., Yin, Y., Zhou, Z., Qiu, J., Liu, W., Zhang, X., He, K., Cai, Y., Zhu, Z. 2021; 12 (1): 4343

  Abstract

  Aberrant sterol lipid metabolism is associated with physiological dysfunctions in the aging brain and aging-dependent disorders such as neurodegenerative diseases. There is an unmet demand to comprehensively profile sterol lipids spatially and temporally in different brain regions during aging. Here, we develop an ion mobility-mass spectrometry based four-dimensional sterolomics technology leveraged by a machine learning-empowered high-coverage library (>2000 sterol lipids) for accurate identification. We apply this four-dimensional technology to profile the spatially resolved landscapes of sterol lipids in ten functional regions of the mouse brain, and quantitatively uncover ~200 sterol lipids uniquely distributed in specific regions with concentrations spanning up to 8 orders of magnitude. Further spatial analysis pinpoints age-associated differences in region-specific sterol lipid metabolism, revealing changes in the numbers of altered sterol lipids, concentration variations, and age-dependent coregulation networks. These findings will contribute to our understanding of abnormal sterol lipid metabolism and its role in brain diseases.

  View details for DOI 10.1038/s41467-021-24672-x

  View details for Web of Science ID 000675913500002

  View details for PubMedID 34267224

  View details for PubMedCentralID PMC8282640

• Development of a combined strategy for accurate lipid structural identification and quantification in ion-mobility mass spectrometry based untargeted lipidomics ANALYTICA CHIMICA ACTA Chen, X., Yin, Y., Zhou, Z., Li, T., Zhu, Z. 2020; 1136: 115-124

  Abstract

  Lipids are an important class of biomolecules, and play many essential functions in biology. Ion mobility-mass spectrometry (IM-MS) has emerged as a promising technology for lipidomics by providing a holistic and multi-dimensional characterization of lipid structures. However, the lipid identification using the multi-dimensional match (i.e., MS1, retention time, collision cross section, and MS/MS spectra) gives multiple lipid candidates, and often over-reports the structural information. Here, we developed a lipid identification strategy that integrated library-based match and rule-based refinement for accurate lipid structural elucidation in IM-MS based lipidomics. The new strategy took the advantage of multi-dimensional information for high-coverage identification, while it also utilized the fragmentation rules to determine the accurate structural information. We demonstrated that the combined strategy accurately determined the lipid structures as lipid species level, fatty acyl level, or fatty acyl position level for different lipid classes in the lipid standard mixture and various biological samples. The combined strategy efficiently reduced the redundancy and improved the accuracy for different lipid classes, and identified a total of 440-960 lipid species in various biological samples. Finally, we performed quantitative lipidomics analysis of NIST SRM 1950 human plasma using IM-MS technology. The measured concentrations of most quantified lipids (>80%) were highly consistent with values reported from other independent laboratories. In summary, the developed lipid identification strategy allowed for the accurate identification of lipid structures, and facilitated accurate lipid quantification in IM-MS based untargeted lipidomics.

  View details for DOI 10.1016/j.aca.2020.08.048

  View details for Web of Science ID 000579369600014

  View details for PubMedID 33081935

• Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics NATURE COMMUNICATIONS Zhou, Z., Luo, M., Chen, X., Yin, Y., Xiong, X., Wang, R., Zhu, Z. 2020; 11 (1): 4334

  Abstract

  The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.

  View details for DOI 10.1038/s41467-020-18171-8

  View details for Web of Science ID 000607079100019

  View details for PubMedID 32859911

  View details for PubMedCentralID PMC7455731

• A lipidome atlas in MS-DIAL 4 NATURE BIOTECHNOLOGY Tsugawa, H., Ikeda, K., Takahashi, M., Satoh, A., Mori, Y., Uchino, H., Okahashi, N., Yamada, Y., Tada, I., Bonini, P., Higashi, Y., Okazaki, Y., Zhou, Z., Zhu, Z., Koelmel, J., Cajka, T., Fiehn, O., Saito, K., Arita, M., Arita, M. 2020; 38 (10): 1159-+

  Abstract

  We present Mass Spectrometry-Data Independent Analysis software version 4 (MS-DIAL 4), a comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry information. We formulated mass spectral fragmentations of lipids across 117 lipid subclasses and included ion mobility tandem mass spectrometry. Using human, murine, algal and plant biological samples, we annotated and semiquantified 8,051 lipids using MS-DIAL 4 with a 1-2% estimated false discovery rate. MS-DIAL 4 helps standardize lipidomics data and discover lipid pathways.

  View details for DOI 10.1038/s41587-020-0531-2

  View details for Web of Science ID 000540408500002

  View details for PubMedID 32541957

  View details for PubMedCentralID 6660005

• The Application of Ion Mobility-Mass Spectrometry in Untargeted Metabolomics: from Separation to Identification JOURNAL OF ANALYSIS AND TESTING Luo, M., Zhou, Z., Zhu, Z. 2020; 4 (3): 163-174

  View details for DOI 10.1007/s41664-020-00133-0

  View details for Web of Science ID 000535850100001

• The Use of LipidIMMS Analyzer for Lipid Identification in Ion Mobility-Mass Spectrometry-Based Untargeted Lipidomics ION MOBILITY-MASS SPECTROMETRY: METHODS AND PROTOCOLS Chen, X., Zhou, Z., Zhu, Z. edited by Paglia, G., Astarita, G. 2020; 2084: 269-282

  Abstract

  Untargeted lipidomics aims to comprehensively measure and characterize all lipid species in biological systems. Ion mobility-mass spectrometry (IM-MS) has showed a great potential for untargeted lipidomic analysis. Coupling with liquid chromatography and data-independent tandem MS techniques, acquired IM-MS data set contains four-dimensional information for lipid identification, including m/z of MS1 ion, retention time (RT), collision cross section (CCS), and MS/MS spectra. In this protocol, we introduced a data processing workflow using an integrative web server, namely, LipidIMMS Analyzer, to support accurate lipid identification. The protocol demonstrated the integration of all four dimensional information to achieve unambiguous identifications of lipids in complex biological samples.

  View details for DOI 10.1007/978-1-0716-0030-6_17

  View details for Web of Science ID 000631643900018

  View details for PubMedID 31729667

• The emerging role of ion mobility-mass spectrometry in lipidomics to facilitate lipid separation and identification TRAC-TRENDS IN ANALYTICAL CHEMISTRY Tu, J., Zhou, Z., Li, T., Zhu, Z. 2019; 116: 332-339

  View details for DOI 10.1016/j.trac.2019.03.017

  View details for Web of Science ID 000470827600031

• LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics BIOINFORMATICS Zhou, Z., Shen, X., Chen, X., Tu, J., Xiong, X., Zhu, Z. 2019; 35 (4): 698–700

  Abstract

  Ion mobility-mass spectrometry (IM-MS) has showed great application potential for lipidomics. However, IM-MS based lipidomics is significantly restricted by the available software for lipid structural identification. Here, we developed a software tool, namely, LipidIMMS Analyzer, to support the accurate identification of lipids in IM-MS. For the first time, the software incorporates a large-scale database covering over 260 000 lipids and four-dimensional structural information for each lipid [i.e. m/z, retention time (RT), collision cross-section (CCS) and MS/MS spectra]. Therefore, multi-dimensional information can be readily integrated to support lipid identifications, and significantly improve the coverage and confidence of identification. Currently, the software supports different IM-MS instruments and data acquisition approaches.The software is freely available at: http://imms.zhulab.cn/LipidIMMS/.Supplementary data are available at Bioinformatics online.

  View details for DOI 10.1093/bioinformatics/bty661

  View details for Web of Science ID 000459316300028

  View details for PubMedID 30052780

• Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era CURRENT OPINION IN CHEMICAL BIOLOGY Zhou, Z., Tu, J., Zhu, Z. 2018; 42: 34-41

  Abstract

  Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics.

  View details for DOI 10.1016/j.cbpa.2017.10.033

  View details for Web of Science ID 000427343000006

  View details for PubMedID 29136580

• LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics ANALYTICAL CHEMISTRY Zhou, Z., Tu, J., Xiong, X., Shen, X., Zhu, Z. 2017; 89 (17): 9559–66

  Abstract

  The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values. In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids. The use of optimized molecular descriptors together with a large set of standard CCS values for lipids (458 in total) to build the prediction model significantly improved the precision. The prediction precision of LipidCCS was externally validated with median relative errors (MRE) of ∼1% using independent data sets across different instruments (Agilent DTIM-MS and Waters TWIM-MS) and laboratories. We also demonstrated that the improved precision in the predicted LipidCCS database (15 646 lipids and 63 434 CCS values in total) could effectively reduce false-positive identifications of lipids. Common users can freely access our LipidCCS web server for the following: (1) the prediction of lipid CCS values directly from SMILES structure; (2) database search; and (3) lipid match and identification. We believe LipidCCS will be a valuable tool to support IM-MS-based lipidomics. The web server is freely available on the Internet ( http://www.metabolomics-shanghai.org/LipidCCS/ ).

  View details for DOI 10.1021/acs.analchem.7b02625

  View details for Web of Science ID 000410014900133

  View details for PubMedID 28764323

• MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics BIOINFORMATICS Zhou, Z., Xiong, X., Zhu, Z. 2017; 33 (14): 2235-2237

  Abstract

  In metabolomics, rigorous structural identification of metabolites presents a challenge for bioinformatics. The use of collision cross-section (CCS) values of metabolites derived from ion mobility-mass spectrometry effectively increases the confidence of metabolite identification, but this technique suffers from the limit number of available CCS values. Currently, there is no software available for rapidly generating the metabolites' CCS values. Here, we developed the first web server, namely, MetCCS Predictor, for predicting CCS values. It can predict the CCS values of metabolites using molecular descriptors within a few seconds. Common users with limited background on bioinformatics can benefit from this software and effectively improve the metabolite identification in metabolomics.The web server is freely available at: http://www.metabolomics-shanghai.org/MetCCS/ .jiangzhu@sioc.ac.cn.Supplementary data are available at Bioinformatics online.

  View details for DOI 10.1093/bioinformatics/btx140

  View details for Web of Science ID 000405289100080

  View details for PubMedID 28334295

• Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry ANALYTICAL CHEMISTRY Zhou, Z., Shen, X., Tu, J., Zhu, Z. 2016; 88 (22): 11084–91

  Abstract

  The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility-mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites. In this work, we first experimentally measured CCS values (ΩN2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. The high prediction precision of this method was externally validated using an independent set of metabolites with a median relative error (MRE) of ∼3%, better than conventional theoretical calculation. Using the SVR based prediction method, a large-scale predicted CCS database was generated for 35 203 metabolites in the Human Metabolome Database (HMDB). For each metabolite, five different ion adducts in positive and negative modes were predicted, accounting for 176 015 CCS values in total. Finally, improved metabolite identification accuracy was demonstrated using real biological samples. Conclusively, our results proved that the SVR based prediction method can accurately predict nitrogen CCS values (ΩN2) of metabolites from molecular descriptors and effectively improve identification accuracy and efficiency in untargeted metabolomics. The predicted CCS database, namely, MetCCS, is freely available on the Internet.

  View details for DOI 10.1021/acs.analchem.6b03091

  View details for Web of Science ID 000388154700045

  View details for PubMedID 27768289