Intestinal epithelial dopamine receptor signaling drives sex-specific disease exacerbation in a mouse model of multiple sclerosis.

Although the gut microbiota can influence central nervous system (CNS) autoimmune diseases, the contribution of the intestinal epithelium to CNS autoimmunity is less clear. Here, we showed that intestinal epithelial dopamine D2 receptors (IEC DRD2) promoted sex-specific disease progression in an animal model of multiple sclerosis. Female mice lacking Drd2 selectively in intestinal epithelial cells showed a blunted inflammatory response in the CNS and reduced disease progression. In contrast, overexpression or activation of IEC DRD2 by phenylethylamine administration exacerbated disease severity. This was accompanied by altered lysozyme expression and gut microbiota composition, including reduced abundance of Lactobacillus species. Furthermore, treatment with N2-acetyl-L-lysine, a metabolite derived from Lactobacillus, suppressed microglial activation and neurodegeneration. Taken together, our study indicates that IEC DRD2 hyperactivity impacts gut microbial abundances and increases susceptibility to CNS autoimmune diseases in a female-biased manner, opening up future avenues for sex-specific interventions of CNS autoimmune diseases.

Neutral lysophosphatidylcholine mediates α-synuclein-induced synaptic vesicle clustering.

α-synuclein (α-Syn) is a presynaptic protein that is involved in Parkinson's and other neurodegenerative diseases and binds to negatively charged phospholipids. Previously, we reported that α-Syn clusters synthetic proteoliposomes that mimic synaptic vesicles. This vesicle-clustering activity depends on a specific interaction of α-Syn with anionic phospholipids. Here, we report that α-Syn surprisingly also interacts with the neutral phospholipid lysophosphatidylcholine (lysoPC). Even in the absence of anionic lipids, lysoPC facilitates α-Syn-induced vesicle clustering but has no effect on Ca2+-triggered fusion in a single vesicle-vesicle fusion assay. The A30P mutant of α-Syn that causes familial Parkinson disease has a reduced affinity to lysoPC and does not induce vesicle clustering. Taken together, the α-Syn-lysoPC interaction may play a role in α-Syn function.

A vitamin-C-derived DNA modification catalysed by an algal TET homologue.

Methylation of cytosine to 5-methylcytosine (5mC) is a prevalent DNA modification found in many organisms. Sequential oxidation of 5mC by ten-eleven translocation (TET) dioxygenases results in a cascade of additional epigenetic marks and promotes demethylation of DNA in mammals1,2. However, the enzymatic activity and function of TET homologues in other eukaryotes remains largely unexplored. Here we show that the green alga Chlamydomonas reinhardtii contains a 5mC-modifying enzyme (CMD1) that is a TET homologue and catalyses the conjugation of a glyceryl moiety to the methyl group of 5mC through a carbon-carbon bond, resulting in two stereoisomeric nucleobase products. The catalytic activity of CMD1 requires Fe(II) and the integrity of its binding motif His-X-Asp, which is conserved in Fe-dependent dioxygenases3. However, unlike previously described TET enzymes, which use 2-oxoglutarate as a co-substrate4, CMD1 uses L-ascorbic acid (vitamin C) as an essential co-substrate. Vitamin C donates the glyceryl moiety to 5mC with concurrent formation of glyoxylic acid and CO2. The vitamin-C-derived DNA modification is present in the genome of wild-type C. reinhardtii but at a substantially lower level in a CMD1 mutant strain. The fitness of CMD1 mutant cells during exposure to high light levels is reduced. LHCSR3, a gene that is critical for the protection of C. reinhardtii from photo-oxidative damage under high light conditions, is hypermethylated and downregulated in CMD1 mutant cells compared to wild-type cells, causing a reduced capacity for photoprotective non-photochemical quenching. Our study thus identifies a eukaryotic DNA base modification that is catalysed by a divergent TET homologue and unexpectedly derived from vitamin C, and describes its role as a potential epigenetic mark that may counteract DNA methylation in the regulation of photosynthesis.

CLOCK Acetylates ASS1 to Drive Circadian Rhythm of Ureagenesis.

In addition to responding to environmental entrainment with diurnal variation, metabolism is also tightly controlled by cell-autonomous circadian clock. Extensive studies have revealed key roles of transcription in circadian control. Post-transcriptional regulation for the rhythmic gating of metabolic enzymes remains elusive. Here, we show that arginine biosynthesis and subsequent ureagenesis are collectively regulated by CLOCK (circadian locomotor output cycles kaput) in circadian rhythms. Facilitated by BMAL1 (brain and muscle Arnt-like protein), CLOCK directly acetylates K165 and K176 of argininosuccinate synthase (ASS1) to inactivate ASS1, which catalyzes the rate-limiting step of arginine biosynthesis. ASS1 acetylation by CLOCK exhibits circadian oscillation in human cells and mouse liver, possibly caused by rhythmic interaction between CLOCK and ASS1, leading to the circadian regulation of ASS1 and ureagenesis. Furthermore, we also identified NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 9 (NDUFA9) and inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) as acetylation substrates of CLOCK. Taken together, CLOCK modulates metabolic rhythmicity by acting as a rhythmic acetyl-transferase for metabolic enzymes.

ONE-seq: epitranscriptome and gene-specific profiling of NAD-capped RNA.

The hub metabolite, nicotinamide adenine dinucleotide (NAD), can be used as an initiating nucleotide in RNA synthesis to result in NAD-capped RNAs (NAD-RNA). Since NAD has been heightened as one of the most essential modulators in aging and various age-related diseases, its attachment to RNA might indicate a yet-to-be discovered mechanism that impacts adult life-course. However, the unknown identity of NAD-linked RNAs in adult and aging tissues has hindered functional studies. Here, we introduce ONE-seq method to identify the RNA transcripts that contain NAD cap. ONE-seq has been optimized to use only one-step chemo-enzymatic biotinylation, followed by streptavidin capture and the nudix phosphohydrolase NudC-catalyzed elution, to specifically recover NAD-capped RNAs for epitranscriptome and gene-specific analyses. Using ONE-seq, we discover more than a thousand of previously unknown NAD-RNAs in the mouse liver and reveal epitranscriptome-wide dynamics of NAD-RNAs with age. ONE-seq empowers the identification of NAD-capped RNAs that are responsive to distinct physiological states, facilitating functional investigation into this modification.

Encoding LC-MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides comprehensive and quantitative profiling of metabolites in clinical investigations. The use of whole metabolome profiles is a promising strategy for disease diagnosis but technically challenging. Here, we developed an approach, namely MetImage, to encode LC-MS-based untargeted metabolomics data into multi-channel digital images. Then, the images that represent the comprehensive metabolome profiles can be employed for developing deep learning-based AI models toward clinical diagnosis. In this work, we demonstrated the application of MetImage for clinical screening of esophageal squamous cell carcinoma (ESCC) in a clinical cohort with 1104 participants. A convolutional neuronal network-based AI model was trained to distinguish ESCC screening positive and negative subjects using their serum metabolomics data. Superior performances such as sensitivity (85%), specificity (92%), and area under curve (0.95) were validated in an independent testing cohort (N = 442). Importantly, we demonstrated that our AI-based ESCC screening model is not a "black box". The encoded images reserved the characteristics of mass spectra from the raw LC-MS data; therefore, metabolite identifications in key image features were readily achieved. Altogether, MetImage is a unique approach that encodes raw LC-MS-based untargeted metabolomics data into images and facilitates the utilization of whole metabolome profiles for AI-based clinical applications with improved interpretability.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations.

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

metID: an R package for automatable compound annotation for LC-MS-based data.

SUMMARY: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g. untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials. AVAILABILITY AND IMPLEMENTATION: https://jaspershen.github.io/metID. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Four-Dimensional Untargeted Profiling of N-Acylethanolamine Lipids in the Mouse Brain Using Ion Mobility-Mass Spectrometry.

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.

WaveICA 2.0: a novel batch effect removal method for untargeted metabolomics data without using batch information.

INTRODUCTION: Untargeted metabolomics based on liquid chromatography-mass spectrometry is inevitably affected by batch effects that are caused by non-biological systematic bias. Previously, we developed a novel method called WaveICA to remove batch effects for untargeted metabolomics data. To detect batch effect information, the method relies on a batch label. However, it cannot be used in the scenario in which there is only one batch of data or the batch label is unknown. OBJECTIVES: We aim to improve the WaveICA method to remove batch effects for untargeted metabolomics data without using batch information. METHODS: We improved the WaveICA method by developing WaveICA 2.0 to remove batch effects for metabolomics data, and provided an R package WaveICA_2.0 to implement this method. RESULTS: The performance of the WaveICA 2.0 method was evaluated on real metabolomics data. For metabolomics data with three batches, the performance of the WaveICA 2.0 method was similar to that of the WaveICA method in terms of gathering quality control samples (QCSs) and subject samples together in principle component analysis score plots, increasing the similarity of QCSs, increasing differential peaks, and improving classification accuracy. For metabolomics data with only one batch, the WaveICA 2.0 method had a strong ability to remove intensity drift and reveal more biological information and outperformed the QC-RLSC and QC-SVRC methods in our study using our metabolomics data. CONCLUSION: Our results demonstrated that the WaveICA 2.0 method can be used in practice to remove batch effects for untargeted metabolomics data without batch information.

NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.

Untargeted metabolomics based on liquid chromatography-mass spectrometry is affected by nonlinear batch effects, which cover up biological effects, result in nonreproducibility, and are difficult to be calibrate. In this study, we propose a novel deep learning model, called Normalization Autoencoder (NormAE), which is based on nonlinear autoencoders (AEs) and adversarial learning. An additional classifier and ranker are trained to provide adversarial regularization during the training of the AE model, latent representations are extracted by the encoder, and then the decoder reconstructs the data without batch effects. The NormAE method was tested on two real metabolomics data sets. After calibration by NormAE, the quality control samples (QCs) for both data sets gathered most closely in a PCA score plot (average distances decreased from 56.550 and 52.476 to 7.383 and 14.075, respectively) and obtained the highest average correlation coefficients (from 0.873 and 0.907 to 0.997 for both). Additionally, NormAE significantly improved biomarker discovery (median number of differential peaks increased from 322 and 466 to 1140 and 1622, respectively). NormAE was compared with four commonly used batch effect removal methods. The results demonstrated that using NormAE produces the best calibration results.

MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery.

SUMMARY: Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery. AVAILABILITY AND IMPLEMENTATION: The MetFlow is freely available on http://metflow.zhulab.cn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LipidIMMS Analyzer: integrating multi-dimensional information to support lipid identification in ion mobility-mass spectrometry based lipidomics.

SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The software is freely available at: http://imms.zhulab.cn/LipidIMMS/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted Metabolomics.

SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technology has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quantitative accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatographic peaks of fragment ions in multiplexed spectra and decompose them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biological samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the experimental MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biological samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA .

Development of a Correlative Strategy To Discover Colorectal Tumor Tissue Derived Metabolite Biomarkers in Plasma Using Untargeted Metabolomics.

The metabolic profiling of biofluids using untargeted metabolomics provides a promising choice to discover metabolite biomarkers for clinical cancer diagnosis. However, metabolite biomarkers discovered in biofluids may not necessarily reflect the pathological status of tumor tissue, which makes these biomarkers difficult to reproduce. In this study, we developed a new analysis strategy by integrating the univariate and multivariate correlation analysis approach to discover tumor tissue derived (TTD) metabolites in plasma samples. Specifically, untargeted metabolomics was first used to profile a set of paired tissue and plasma samples from 34 colorectal cancer (CRC) patients. Next, univariate correlation analysis was used to select correlative metabolite pairs between tissue and plasma, and a random forest regression model was utilized to define 243 TTD metabolites in plasma samples. The TTD metabolites in CRC plasma were demonstrated to accurately reflect the pathological status of tumor tissue and have great potential for metabolite biomarker discovery. Accordingly, we conducted a clinical study using a set of 146 plasma samples from CRC patients and gender-matched polyp controls to discover metabolite biomarkers from TTD metabolites. As a result, eight metabolites were selected as potential biomarkers for CRC diagnosis with high sensitivity and specificity. For CRC patients after surgery, the survival risk score defined by metabolite biomarkers also performed well in predicting overall survival time ( p = 0.022) and progression-free survival time ( p = 0.002). In conclusion, we developed a new analysis strategy which effectively discovers tumor tissue related metabolite biomarkers in plasma for cancer diagnosis and prognosis.

Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology.

Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MSE, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics.

SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at: http://www.metabolomics-shanghai.org/MetCCS/ . CONTACT: jiangzhu@sioc.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer.

INTRODUCTION: Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT). OBJECTIVES: An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes. METHODS: In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n = 30) and response (n = 27) patients to NACT were studied using UHPLC-quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods. RESULTS: The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199). CONCLUSION: These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

LipidCCS: Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility-Mass Spectrometry-Based Lipidomics.

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/ ).