An important role for triglyceride in regulating spermatogenesis.

Drosophila is a powerful model to study how lipids affect spermatogenesis. Yet, the contribution of neutral lipids, a major lipid group which resides in organelles called lipid droplets (LD), to sperm development is largely unknown. Emerging evidence suggests LD are present in the testis and that loss of neutral lipid- and LD-associated genes causes subfertility; however, key regulators of testis neutral lipids and LD remain unclear. Here, we show LD are present in early-stage somatic and germline cells within the Drosophila testis. We identified a role for triglyceride lipase brummer (bmm) in regulating testis LD, and found that whole-body loss of bmm leads to defects in sperm development. Importantly, these represent cell-autonomous roles for bmm in regulating testis LD and spermatogenesis. Because lipidomic analysis of bmm mutants revealed excess triglyceride accumulation, and spermatogenic defects in bmm mutants were rescued by genetically blocking triglyceride synthesis, our data suggest that bmm-mediated regulation of triglyceride influences sperm development. This identifies triglyceride as an important neutral lipid that contributes to Drosophila sperm development, and reveals a key role for bmm in regulating testis triglyceride levels during spermatogenesis.

Capture of endogenous lipids in peptidiscs and effect on protein stability and activity.

Compared to protein-protein and protein-nucleic acid interactions, our knowledge of protein-lipid interactions remains limited. This is primarily due to the inherent insolubility of membrane proteins (MPs) in aqueous solution. The traditional use of detergents to overcome the solubility barrier destabilizes MPs and strips away certain lipids that are increasingly recognized as crucial for protein function. Recently, membrane mimetics have been developed to circumvent the limitations. In this study, using the peptidisc, we find that MPs in different lipid states can be isolated based on protein purification and reconstitution methods, leading to observable effects on MP activity and stability. Peptidisc also enables re-incorporating specific lipids to fine-tune the protein microenvironment and assess the impact on downstream protein associations. This study offers a first look at the illusive protein-lipid interaction specificity, laying the path for a systematic evaluation of lipid identity and contributions to membrane protein function.

Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis.

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Lymphatic endothelial-like cells promote glioblastoma stem cell growth through cytokine-driven cholesterol metabolism.

Glioblastoma is the most lethal primary brain tumor with glioblastoma stem cells (GSCs) atop a cellular hierarchy. GSCs often reside in a perivascular niche, where they receive maintenance cues from endothelial cells, but the role of heterogeneous endothelial cell populations remains unresolved. Here, we show that lymphatic endothelial-like cells (LECs), while previously unrecognized in brain parenchyma, are present in glioblastomas and promote growth of CCR7-positive GSCs through CCL21 secretion. Disruption of CCL21-CCR7 paracrine communication between LECs and GSCs inhibited GSC proliferation and growth. LEC-derived CCL21 induced KAT5-mediated acetylation of HMGCS1 on K273 in GSCs to enhance HMGCS1 protein stability. HMGCS1 promoted cholesterol synthesis in GSCs, favorable for tumor growth. Expression of the CCL21-CCR7 axis correlated with KAT5 expression and HMGCS1K273 acetylation in glioblastoma specimens, informing patient outcome. Collectively, glioblastomas contain previously unrecognized LECs that promote the molecular crosstalk between endothelial and tumor cells, offering potentially alternative therapeutic strategies.

Knockdown of PTEN promotes colon cancer progression and induces M2 macrophage polarization in the colon cancer cell environment.

Objective: This article aims to study the effect of phosphate and tension homolog deleted on chromosome ten (PTEN) knockdown on colon cancer progression and macrophage polarization in the cancer environment. Materials and Methods and Results: The expression of PTEN in colon cancer tissues and colon cancer cells was significantly lower than in precancerous tissues or CCD-18Co cells, and the decrease was most evident in SW620 cells. The expressions of phosphate (p)-p38, c-Jun N-terminal kinase (JNK), activator protein 1 (AP-1), B-cell lymphoma-2 (Bcl-2) protein in colon cancer tissues and cells were significantly higher than in precancerous tissues or CCD-18Co cells (P-values < 0.05). Bcl-2-associated X (Bax) and Caspase-3 expressions in colon cancer tissues and cells were significantly lower than in precancerous tissues or CCD-18Co cells (P-values < 0.05). 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was applied to measure cell viability. Transwell evaluated the cell migration and invasion ability. Si-PTEN improved the proliferation, migration, and invasion of SW620 cells (P-values < 0.05). The expression levels of arginase-1 (Arg-1), CD163, CD206 in colon cancer tissues were significantly higher than in precancerous tissues (P-values < 0.05). The cell cycle, the number of M1 and M2 double-positive cells were assessed by flow cytometry. Si-PTEN reduced the expression of tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1ß), and inducible nitric oxide synthase (iNOS), which upregulated the expression of Arg-1, CD206, CD163, p-p38, JNK, and AP-1 (P-values < 0.05). Conclusion: Si-PTEN promoted colon cancer progression and induced the polarization of M2 tumor-associated macrophages in the colon cancer cell environment.

De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics.

The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a de novo approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo MS2Purifier), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.

Addressing big data challenges in mass spectrometry-based metabolomics.

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.

Adaptive Box-Cox Transformation: A Highly Flexible Feature-Specific Data Transformation to Improve Metabolomic Data Normality for Better Statistical Analysis.

Metabolomic data normality is vital for many statistical analyses to identify significantly different metabolic features. However, despite the thousands of metabolomic publications every year, the study of metabolomic data distribution is rare. Using large-scale metabolomic data sets, we performed a comprehensive study of metabolomic data distributions. We showcased that metabolic features have diverse data distribution types, and the majority of them cannot be normalized correctly using conventional data transformation algorithms, including log and square root transformations. To understand the various non-normal data distributions, we proposed fitting metabolomic data into nine beta distributions, each representing a unique data distribution. The results of three large-scale data sets consistently show that two low normality types are very common. Next, we created the adaptive Box-Cox (ABC) transformation, a novel feature-specific data transformation approach for improving data normality. By tuning a power parameter based on a normality test result, ABC transformation was made to work for various data distribution types, and it showed great performance in normalizing skewed metabolomic data. Tested on a series of simulated data in Monte Carlo simulations, ABC transformation outperformed conventional data transformation approaches for both positively and negatively skewed data distributions. ABC transformation was further demonstrated in a real metabolomic study composed of three pairwise comparisons. Additional 84, 44, and 57 significant metabolites were newly confirmed after ABC transformation, corresponding to respective increases of 70.6, 13.4, and 22.9% in significant metabolites compared to the conventional metabolomic workflow. Some of these newly discovered metabolites showed promising biological meanings. ABC transformation was implemented in the R package ABCstats and is freely available on GitHub (https://github.com/HuanLab/ABCstats).

MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities.

MOTIVATION: Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms. RESULTS: MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics datasets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (i) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (ii) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis and more confirmed significantly altered metabolites. AVAILABILITY AND IMPLEMENTATION: The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction and demo data are available at https://github.com/HuanLab/MAFFIN. Other data in this work are available on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive assessment of the diminished statistical power caused by nonlinear electrospray ionization responses in mass spectrometry-based metabolomics.

It seems to be well received that nonlinear electrospray ionization (ESI) distorts the signal distribution in mass spectrometry (MS) analysis, thus leading to diminished statistical power for t-test. However, the exact consequence and possible solutions to this quantitative issue have not been systematically explored. In this work, using a serial diluted urine metabolomics dataset, we demonstrated that over 80% of the metabolic features present nonlinear ESI response patterns, causing either left-skewed or right-skewed MS signal distributions. Among them, right-skewed MS distributions cannot be rescued using conventional data transformation (e.g., log transformation, power transformation). Furthermore, using a Monte Carlo simulation, we quantitatively assessed the reduced statistical power for t-test calculated using MS signal data in various sample sizes and effect sizes. In all these comparisons, t-test using MS signal data has consistently lower statistical power than t-test using metabolic concentration data. To address this statistical issue, we proposed a bioinformatic workflow, termed PowerU, to minimize the diminished statistical power caused by both the nonlinear ESI response and the intrinsic non-normal distribution of metabolic concentrations. The PowerU workflow is composed of two steps. The first step is to convert MS signals to quality control (QC) sample injection amounts to solve the skewed MS signal distributions. The second step is to perform a Shapiro-Wilk test to determine data normality and then use the normality results to guide the application of t-test and Mann-Whitney U test for the best statistical outcome. PowerU was tested using a metabolomics study of mouse cecum samples. Results demonstrate that the PowerU workflow can significantly boost statistical power for t-test and facilitate the discovery of significantly altered metabolites for downstream biological interpretation.

JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(-) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

Rufinamide (RUF) suppresses inflammation and maintains the integrity of the blood-brain barrier during kainic acid-induced brain damage.

Rufinamide (RUF) is a structurally unique anti-epileptic drug, but its protective mechanism against brain injury remains unclear. In the present study, we validated how the RUF protected mice with kainic acid (KA)-induced neuronal damage. To achieve that, a mouse epilepsy model was established by KA intraperitoneal injection. After Nissl staining, although there was a significant reduction in Nissl bodies in mice treated with KA, 40, 80, and 120 mg/kg, RUF significantly reduced KA-induced neuronal damage, in a dose-dependent manner. Among them, 120 mg/kg RUF was most pronounced. Immunohistochemistry (IHC) and western blot analysis showed that RUF inhibited the IBA-1 overexpression caused by KA to block microglia cell overactivation. Further, RUF treatment partially reversed neuroinflammatory cytokine (IL-1ß, TNFα, HMGB1, and NLRP3) overexpression in mRNA and protein levels in KA mice. Moreover, although KA stimulation inhibited the expression of tight junctions, RUF treatment significantly upregulated expression of tight junction proteins (occludin and claudin 5) in both mRNA and protein levels in the brain tissues of KA mice. RUF inhibited the overactivation of microglia, suppressed the neuroinflammatory response, and reduced the destruction of blood-brain barrier, thereby alleviating the excitatory nerve damage of the KA-mice.

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms.

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a significant drawback of generating a problematic number of false positives. In this work, we take advantage of deep learning technology to develop a convolutional neural network (CNN)-based program that can automatically recognize metabolic features with poor EIC shapes, which are of low feature fidelity and more likely to be false. Our CNN model was trained using 25095 EIC plots collected from 22 LC-MS-based metabolomics projects of various sample types, LC and MS conditions. Notably, we manually inspected all the EIC plots to assign good or poor EIC quality for accurate model training. The trained CNN model is embedded into a C#-based program, named EVA (short for evaluation). The EVA Windows Application is a versatile platform that can process metabolic features generated by LC-MS systems of various vendors and processed using various data processing software. Our comprehensive evaluation of EVA indicates that it achieves over 90% classification accuracy. EVA can be readily used in LC-MS-based metabolomics projects and is freely available on the Microsoft Store by searching "EVA Metabolomics".

ISFrag: De Novo Recognition of In-Source Fragments for Liquid Chromatography-Mass Spectrometry Data.

In-source fragmentation (ISF) is a naturally occurring phenomenon during electrospray ionization (ESI) in liquid chromatography-mass spectrometry (LC-MS) analysis. ISF leads to false metabolite annotation in untargeted metabolomics, prompting misinterpretation of the underlying biological mechanisms. Conventional metabolomic data cleaning mainly focuses on the annotation of adducts and isotopes, and the recognition of ISF features is mainly based on common neutral losses and the LC coelution pattern. In this work, we recognized three increasingly important patterns of ISF features, including (1) coeluting with their precursor ions, (2) being in the tandem MS (MS2) spectra of their precursor ions, and (3) sharing similar MS2 fragmentation patterns with their precursor ions. Based on these patterns, we developed an R package, ISFrag, to comprehensively recognize all possible ISF features from LC-MS data generated from full-scan, data-dependent acquisition, and data-independent acquisition modes without the assistance of common neutral loss information or MS2 spectral library. Tested using metabolite standards, we achieved a 100% correct recognition of level 1 ISF features and over 80% correct recognition for level 2 ISF features. Further application of ISFrag on untargeted metabolomics data allows us to identify ISF features that can potentially cause false metabolite annotation at an omics-scale. With the help of ISFrag, we performed a systematic investigation of how ISF features are influenced by different MS parameters, including capillary voltage, end plate offset, ion energy, and "collision energy". Our results show that while increasing energies can increase the number of real metabolic features and ISF features, the percentage of ISF features might not necessarily increase. Finally, using ISFrag, we created an ISF pathway to visualize the relationships between multiple ISF features that belong to the same precursor ion. ISFrag is freely available on GitHub (https://github.com/HuanLab/ISFrag).

Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics.

Computational tools are commonly used in untargeted metabolomics to automatically extract metabolic features from liquid chromatography-mass spectrometry (LC-MS) raw data. However, due to the incapability of software to accurately determine chromatographic peak heights/areas for features with poor chromatographic peak shape, automated data processing in untargeted metabolomics faces additional quantitative variation (i.e., computational variation) besides the well-recognized analytical and biological variations. In this work, using multiple biological samples, we investigated how experimental factors, including sample concentrations, LC separation columns, and data processing programs, contribute to computational variation. For example, we found that the peak height (PH)-based quantification is more precise when MS-DIAL was used for data processing. We further systematically compared the different patterns of computational variation between PH- and peak area (PA)-based quantitative measurements. Our results suggest that the magnitude of computational variation is highly consistent at a given concentration. Hence, we proposed a quality control (QC) sample-based correction workflow to minimize computational variation by automatically selecting PH or PA-based measurement for each intensity value. This bioinformatic solution was demonstrated in a metabolomic comparison of leukemia patients before and after chemotherapy. Our novel workflow can be effectively applied on 652 out of 915 metabolic features, and over 31% (206 out of 652) of corrected features showed distinctly changed statistical significance. Overall, this work highlights computational variation, a considerable but underinvestigated quantitative variability in omics-scale quantitative analyses. In addition, the proposed bioinformatic solution can minimize computational variation, thus providing a more confident statistical comparison among biological groups in quantitative metabolomics.

Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome.

Hair is a unique biological matrix that adsorbs short-term exposures (e. g., environmental contaminants and personal care products) on its surface and also embeds endogenous metabolites and long-term exposures in its matrix. In this work, we developed an untargeted metabolomics workflow to profile both temporal exposure chemicals and endogenous metabolites in the same hair sample. This analytical workflow begins with the extraction of short-term exposures from hair surfaces through washing. Further development of mechanical homogenization extracts endogenous metabolites and long-term exposures from the cleaned hair. Both solutions of hair wash and hair extract were analyzed using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics for global-scale metabolic profiling. After analysis, raw data were processed using bioinformatic programs recently developed specifically for exposome research. Using optimized experimental conditions, we detected a total of 10,005 and 9,584 metabolic features from hair wash and extraction samples, respectively. Among them, 274 and 276 features can be definitively confirmed by MS2 spectral matching against spectral library, and an additional 3,356 and 3,079 features were tentatively confirmed as biotransformation metabolites. To demonstrate the performance of our hair metabolomics, we collected hair samples from three female volunteers and tested their hair metabolic changes before and after a 2-day exposure exercise. Our results show that 645 features from wash and 89 features from extract were significantly changed from the 2-day exposure. Altogether, this work provides a novel analytical approach to study the hair metabolome and exposome at a global scale, which can be implemented in a wide range of biological applications for a deeper understanding of the impact of environmental and genetic factors on human health.

Recognizing Contamination Fragment Ions in Liquid Chromatography-Tandem Mass Spectrometry Data.

Tandem mass spectral (MS/MS) data in liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis are often contaminated as the selection of precursor ions is based on a low-resolution quadrupole mass filter. In this work, we developed a strategy to differentiate contamination fragment ions (CFIs) from true fragment ions (TFIs) in an MS/MS spectrum. The rationale is that TFIs should coelute with their parent ions, but CFIs should not. To assess coelution, we performed a parallel LC-MS/MS analysis in data-independent acquisition (DIA) with all-ion-fragmentation (AIF) mode. Using the DIA (AIF) data, peak-peak correlation (PPC) score is calculated between the extracted ion chromatogram (EIC) of the fragment ion using the MS/MS scans and the EIC of the precursor ion using the MS1 scans. A high PPC score is an indication of TFIs, and a low PPC score is an indication of CFIs. Tested using metabolomics data generated by high resolution QTOF and Orbitrap MS from various vendors in different LC-MS configurations, we found that more than 70% of the fragment ions have PPC scores < 0.8 and identified three common sources of CFIs, including (1) solvent contamination, (2) adjacent chemical contamination, and (3) undetermined signals from artifacts and noise. Combining PPC scores with other precursor and fragment ion information, we further developed a machine learning model that can robustly and conservatively predict CFIs. Incorporating the machine learning model, we created an R program, MS2Purifier, to automatically recognize CFIs and clean MS/MS spectra of metabolic features in LC-MS/MS data with high sensitivity and specificity.

Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics.

Despite the well-known nonlinear response of electrospray ionization (ESI) in mass spectrometry (MS)-based analysis, its complicated response patterns and negative impact on quantitative comparison are still understudied. We showcase in this work that the patterns of nonlinear ESI response are feature-dependent and can cause significant compression or inflation to signal ratios. In particular, our metabolomics study of serial diluted human urine samples showed that over 72% and 16% metabolic features suffered ratio compression and inflation, respectively, whereas only 12% of the signal ratios represent real metabolic concentration ratios. More importantly, these ratio compression and inflation largely exist in the linear response ranges, suggesting that it cannot be resolved by simply diluting the sample solutions to the linear ESI response ranges. Furthermore, we demonstrated that a polynomial regression model that converts MS signals to sample injection amounts can correct the biased ratios and, surprisingly, outperform the linear regression model in both data fitting and data prediction. Therefore, we proposed a metabolic ratio correction (MRC) strategy to minimize signal ratio bias in untargeted metabolomics for accurate quantitative comparison. In brief, by using the data of serial diluted quality control (QC) samples, we applied a cross-validation strategy to determine the best regression model, between linear and polynomial, for each metabolic feature and to convert the measured MS intensities to QC injection amounts for accurate metabolic ratio calculation. Both the studies of human urine samples and a metabolomics application supported that our MRC approach is very efficient in correcting the biased signal ratios. This novel insight of patterned ESI nonlinear response and MRC workflow can significantly benefit the downstream statistical comparison and biological interpretation for untargeted metabolomics.

Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns.

Global profiling of the metabolome and lipidome of specific brain regions is essential to understanding the cellular and molecular mechanisms regulating brain activity. Given the limited amount of starting material, conventional mouse studies comparing brain regions have mainly targeted a set of known metabolites in large brain regions (e.g., cerebrum, cortex). In this work, we developed a multimodal analytical pipeline enabling parallel analyses of metabolomic and lipidomic profiles from anatomically distinct mouse brain regions starting with less than 0.2 mg of protein content. This analytical pipeline is composed of (1) sonication-based tissue homogenization, (2) parallel metabolite and lipid extraction, (3) BCA-based sample normalization, (4) ultrahigh performance liquid chromatography-mass spectrometry-based multimodal metabolome and lipidome profiling, (5) streamlined data processing, and (6) chord plot-based data visualization. We applied this pipeline to the study of four brain regions in males including the amygdala, dorsal hippocampus, nucleus accumbens and ventral tegmental area. With this novel approach, we detected over 5000 metabolic and 6000 lipid features, among which 134 metabolites and 479 lipids were directly confirmed via automated MS2 spectral matching. Interestingly, our analysis identified unique metabolic and lipid profiles in each brain regions. Furthermore, we identified functional relationships amongst metabolic and lipid subclasses, potentially underlying cellular and functional differences across all four brain regions. Overall, our novel workflow generates comprehensive region-specific metabolomic and lipidomic profiles using very low amount of brain sub-regional tissue sample, which could be readily integrated with region-specific genomic, transcriptomic, and proteomic data to reveal novel insights into the molecular mechanisms underlying the activity of distinct brain regions.