Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry

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 (Ω_N2) 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 (Ω_N2) 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

Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry

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 (Ω_N2) 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 (Ω_N2) 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

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/)

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/)

MetDIA: Targeted Metabolite Extraction of Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition

With recent advances in mass spectrometry, there is an increased interest in data-independent acquisition (DIA) techniques for metabolomics. With DIA technique, all metabolite ions are sequentially selected and isolated using a wide window to generate multiplexed MS/MS spectra. Therefore, DIA strategy enables a continuous and unbiased acquisition of all metabolites and increases the data dimensionality, but presents a challenge to data analysis due to the loss of the direct link between precursor ion and fragment ions. However, very few DIA data processing methods are developed for metabolomics application. Here, we developed a new DIA data analysis approach, namely, MetDIA, for targeted extraction of metabolites from multiplexed MS/MS spectra generated using DIA technique. MetDIA approach considers each metabolite in the spectral library as an analysis target. Ion chromatograms for each metabolite (both precursor ion and fragment ions) and MS² spectra are readily detected, extracted, and scored for metabolite identification, referred as metabolite-centric identification. A minimum metabolite-centric identification score responsible for 1% false positive rate of identification is determined as 0.8 using fully ¹³C labeled biological extracts. Finally, the comparisons of our MetDIA method with data-dependent acquisition (DDA) method demonstrated that MetDIA could significantly detect more metabolites in biological samples, and is more accurate and sensitive for metabolite identifications. The MetDIA program and the metabolite spectral library is freely available on the Internet

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)

Effect of Surface Charge on the Uptake and Distribution of Gold Nanoparticles in Four Plant Species

Small (6–10 nm) functionalized gold nanoparticles (AuNPs) featuring different, well-defined surface charges were used to probe the uptake and distribution of nanomaterials in terrestrial plants, including rice, radish, pumpkin, and perennial ryegrass. Exposure of the AuNPs to plant seedlings under hydroponic conditions for a 5-day period was investigated. Results from these studies indicate that AuNP uptake and distribution depend on both nanoparticle surface charge and plant species. The experiments show that positively charged AuNPs are most readily taken up by plant roots, while negatively charged AuNPs are most efficiently translocated into plant shoots (including stems and leaves) from the roots. Radish and ryegrass roots generally accumulated higher amounts of the AuNPs (14–900 ng/mg) than rice and pumpkin roots (7–59 ng/mg). Each of the AuNPs used in this study were found to accumulate to statistically significant extents in rice shoots (1.1–2.9 ng/mg), while none of the AuNPs accumulated in the shoots of radishes and pumpkins

Determination of the Intracellular Stability of Gold Nanoparticle Monolayers Using Mass Spectrometry

Monolayer stability of core–shell nanoparticles is a key determinant of their utility in biological studies such as imaging and drug delivery. Intracellular thiols (e.g., cysteine, cysteamine, and glutathione) can trigger the release of thiolate-bound monolayers from nanoparticles, a favorable outcome for controllable drug release applications but an unfavorable outcome for imaging agents. Here, we describe a method to quantify the monolayer release of gold nanoparticles (AuNPs) in living cells using parallel measurements by laser desorption/ionization (LDI) and inductively coupled plasma (ICP) mass spectrometry. This combination of methods is tested using AuNPs with structural features known to influence monolayer stability and on cells types with varying concentrations of glutathione. On the basis of our results, we predict that this approach should help efforts to engineer nanoparticle surface monolayers with tunable stability, providing stable platforms for imaging agents and controlled release of therapeutic monolayer payloads

Four-Dimensional Untargeted Profiling of <i>N</i>‑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

Four-Dimensional Untargeted Profiling of <i>N</i>‑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