Retention Time Data Research Articles

Identification and screening of potential anti-pneumonia active ingredients and targets of Qing-Kai-Ling oral liquid via UHPLC-Q-Exactive Orbitrap mass spectrometry based on data post-processing

Qing-Kai-Ling oral liquid is commonly used clinically for the treatment of fever and upper respiratory tract infection. Moreover, studies have shown that Qing-Kai-Ling oral liquid has an anti-pneumonia effect. However, owing to its complex pharmacodynamic material basis, its pharmacological research and clinical application are limited. To address this problem, the chemical constituents of Qing-Kai-Ling oral liquid were identified by ultra-high performance liquid chromatography quadrupole-Exactive Orbitrap mass (UHPLC-Q-Exactive Orbitrap MS) and network pharmacology methods, which were used to predict its potential anti-pneumonia target and signalling pathway. A total of 150 compounds were identified and tentatively characterized, including 35 amino acids and their derivatives, 36 organic acids, 20 terpenoids, 20 alkaloids, 12 glycosides, 7 flavonoids, and 20 others. Among them, 14 compounds were accurately identified by comparing their retention time and mass spectrum data with those of reference substances. Additionally, we performed molecular simulation calculations via Density Function Theory to determine the plausibility of the compound cleavage reactions and further confirm compound structures. Furthermore, 90 key targets were screened through network pharmacology, with the particular focus on the PI3K-AKT, MAPK and TNF signalling pathways. This method achieved the first comprehensive identification of the chemical composition of Qing-Kai-Ling oral liquid and elucidated its potential mechanism of anti-pneumonia. The results provide valuable reference and data support for pharmacodynamic substance research and quality control.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

#1336 Serotonin levels in hemodialysis patients with Long COVID-associated cognitive impairment

Abstract Background and Aims Long COVID comprises a broad range of persistent symptoms that emerge after COVID-19 and can last for months or years and have a direct impact on daily life. Cognitive impairment and loss of short-term memory are examples of symptoms associated with long COVID. To date, there are neither generally agreed-upon diagnostic criteria nor effective treatments available. A recent study found an association of serotonin deficiency and long COVID in the general population [1], leading to vagal dysfunction and impaired cognition. In this study, we aimed to evaluate longitudinal serotonin levels in hemodialysis (HD) patients with or without cognitive symptoms associated with long COVID. Method We analyzed routinely collected serum samples (n = 102) from 16 HD patients collected between 60 days before and 60 days after a positive SARS-CoV-2 RT-PCR. Metabolites were extracted using cold methanol and subjected to reverse phase liquid chromatography/mass spectrometry (LC/MS). Serotonin was identified by matching retention time, accurate mass, and fragmentation data against an in-house LC/MS library. Log2 of serotonin signal intensity was used for quantification. The COVID-19 Yorkshire Rehabilitation Scale (C19-YRS) was administered shortly after positive RT-PCR test, as well as 1- and 6-months post-COVID. Patients were classified as having cognitive symptoms if they reported short-term memory loss or difficulties with concentration or communication problems as per C19-YRS. Unpaired t-test or Fisher's exact tests were performed to compare characteristics of patients with and without cognitive symptoms. We defined three study periods relative to the date of COVID-19 diagnosis: –60 to –15 days (baseline); –14 to +14 days (acute COVID); and +15 to +60 days (post-COVID). Differences in serotonin signal intensity (log2) between patients with and without cognitive symptoms were evaluated with unpaired t-tests. Linear mixed effects models with random intercepts were constructed to determine changes in serotonin levels over time with the baseline period as the comparator. This research was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK130067). Results Demographic data are presented in Table 1a. Of the 16 patients, 7 (44%) were classified with cognitive symptoms associated with long COVID. There were no differences in age, sex, race, ethnicity, vintage, diabetes, and hypertension between patients with and without cognitive symptoms. Serotonin levels in patients with vs. without cognitive symptoms did not significantly differ at baseline and during acute COVID (Fig. 1). Of note, in the post-COVID phase, serotonin levels were significantly lower in the group with impaired cognition (p = 0.012) compared to symptoms-free patients (Fig. 1). Within each group, serotonin levels remained unchanged at acute-COVID or post-COVID periods compared to baseline (Table 1b). Conclusion We show that HD patients with impaired cognition have lower serotonin in the post-COVID phase compared to patients without cognitive symptoms. These results corroborate findings in the general population. Further investigation in a larger cohort is warranted to evaluate the potential of serotonin as a target for the prevention or as a marker of neurocognitive symptoms associated with long COVID.

Identification of horn-derived animal medicines based on detection of keratin-derived characteristic peptides

Due to the highly stable structure of keratin, the extraction and dissolution steps of animal medicines rich in keratin are complex, which seriously restricts the detection efficiency and flux. Therefore, this study simplified the pre-treatment steps of horn samples and optimized the detection methods of characteristic peptides to improve the efficiency of identifying the specificity of horn-derived animal medicines. For detection of the characteristic peptides in horn-derived animal medicines treated with/without iodoace-tamide(IAA), the ion pair conditions of the characteristic peptides were optimized, and the retention time, intensity and other data of the specific peptides were compared between the samples treated with/without IAA. Two pre-treatment methods, direct enzymatic hydrolysis and total protein extraction followed by enzymatic hydrolysis, were used to prepare horn-derived animal medicine samples. The effects of different methods on the detection of specific peptides in the samples of Saiga antelope horn, water buffalo horn, goat horn, and yak horn were compared regarding the retention time of specific peptides and ion intensity. The results indicated that after direct enzymatic hydrolysis, the specific peptides in the samples without IAA treatment can be detected. Compared with the characteristic peptides in the samples treated with IAA, their retention time shifted back and the mass spectrometry response slightly decreased. The specific peptides of the samples without IAA treatment had good specificity and did not affect the specificity identification of horn-derived animal medicines. Overall, the process of direct enzymatic hydrolysis can be used to treat horn samples, omitting the steps of protein extraction and dithiothreitol and IAA treatment, significantly improving the pre-treatment efficiency without affecting the specificity identification of horn-derived animal medicines. This study provides ideas for quality research and standard improvement of horn-derived animal medicines.

Improved prediction of settling behavior of solid particles through machine learning analysis of experimental retention time data

The motion of particles through density-stratified interfaces is a common phenomenon in environmental and engineering applications. However, the mechanics of particle-stratification interactions in various combinations of particle and fluid properties are not well understood. This study presents a novel machine-learning (ML) approach to experimental data of inertial particles crossing a density-stratified interface. A simplified particle settling experiment was conducted to obtain a large number of particles and expand the parameter range. Using ML, the study explores new correlations that collapse the data gathered in this and in previous work by Verso et al. (2019). The “delay time”, which is the time between the particle exiting the interfacial layer and reaching a steady-state velocity, is found to strongly depend on six dimensionless parameters formulated by ML feature selection. The data shows a correlation between the Reynolds and Froude numbers within the range of the experiments, and the best symbolic regression is based on the Froude number only. This experiment provides valuable insights into the behavior of inertial particles in stratified layers and highlights opportunities for future improvement in predicting their motion.

A perspective on the use of deep deterministic policy gradient reinforcement learning for retention time modeling in reversed-phase liquid chromatography

Artificial intelligence and machine learning techniques are increasingly used for different tasks related to method development in liquid chromatography. In this study, the possibilities of a reinforcement learning algorithm, more specifically a deep deterministic policy gradient algorithm, are evaluated for the selection of scouting runs for retention time modeling. As a theoretical exercise, it is investigated whether such an algorithm can be trained to select scouting runs for any compound of interest allowing to retrieve its correct retention parameters for the three-parameter Neue-Kuss retention model. It is observed that three scouting runs are generally sufficient to retrieve the retention parameters with an accuracy (mean relative percentage error MRPE) of 1 % or less. When given the opportunity to select additional scouting runs, this does not lead to a significantly improved accuracy. It is also observed that the agent tends to give preference to isocratic scouting runs for retention time modeling, and is only motivated towards selecting gradient scouting runs when penalized (strongly) for large analysis/gradient times. This seems to reinforce the general power and usefulness of isocratic scouting runs for retention time modeling. Finally, the best results (lowest MRPE) are obtained when the agent manages to retrieve retention time data for % ACN at elution of the compound under consideration that spread the entire relevant range of ACN (5 % ACN to 95 % ACN) as well as possible, i.e., resulting in retention data at a low, intermediate and high % ACN. Based on the obtained results, we believe reinforcement learning holds great potential to automate and rationalize method development in liquid chromatography in the future.

DrugBank 6.0: the DrugBank Knowledgebase for 2024.

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time)and RI (retention index) data for 9464 of DrugBank's 11710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.

Characterization of chemical constituents in Huangqi Guizhi Wuwu decoction using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry.

Huangqi Guizhi Wuwu decoction (HGWWD) is a classic traditional Chinese medicine prescription for the treatment of ischemic stroke, etc. However, the material basis of its efficacy remains unclear, seriously affecting drug development and clinical applications. In the present study, an ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry method was developed to separate and identify the chemical components of HGWWD. A total of 81 compounds were identified and tentatively characterized. Eight compounds were accurately identified by comparing the retention time and mass spectrometry data with those of reference substances, the remaining compounds were characterized by comparing the mass spectrometry data and reference information. Based on the results of compound attribution, 35 compounds were from Astragali Radix, six compounds were from Cinnamomi Ramulus, 23 compounds were from Paeoniae Radix Alba, eight compounds were from Zingiberis Rhizoma Recens and nine compounds were from Jujubae Fructus. The results showed that monoterpenoids, flavonoids, organic acids, triterpenes, amino acids, gingerols, alkaloids, and glycosides were the main chemical components of HGWWD. This analytical method is suitable for characterizing the chemical constituents of HGWWD, and the results provide important information for elucidating its pharmacodynamic material basis and mechanism of action.

Predictive Quantitative Read-Across Structure-Property Relationship Modeling of the Retention Time (Log tR) of Pesticide Residues Present in Foods and Vegetables.

The retention time (log tR) of pesticidal compounds in a reverse-phase high-performance liquid chromatography (HPLC) analysis has a direct relationship with lipophilicity, which could be related to the ecotoxicity potential of the compounds. The novel quantitative read-across structure-property relationship (q-RASPR) modeling approach uses similarity-based descriptors for predictive model generation. These models have been shown to enhance external predictivity in previous studies for several end points. The current study describes the development of a q-RASPR model using experimental retention time data (log tR) in the HPLC experiments of 823 environmentally significant pesticide residues collected from a large compound database. To model the retention time (log tR) end point, 0D-2D descriptors have been used along with the read-across-derived similarity descriptors. The developed partial least squares (PLS) model was rigorously validated by various internal and external validation metrics as recommended by the Organization for Economic Co-operation and Development (OECD). The final q-RASPR model is proven to be a good fit, robust, and externally predictive (ntrain = 618, R2 = 0.82, Q2LOO = 0.81, ntest = 205, and Q2F1 = 0.84) that literally outperforms the external predictivity of the previously reported quantitative structure-property relationship (QSPR) model. From the insights of modeled descriptors, lipophilicity is found to be the most important chemical property, which positively correlates with the retention time (log tR). Various other characteristics, such as the number of multiple bonds (nBM), graph density (GD), etc., have a substantial and inversely proportionate relationship with the retention time end point. The software tools utilized in this study are user-friendly, and most of them are free, which makes our methodology quite cost-effective when compared to experimentation. In a nutshell, to obtain better external predictivity, interpretability, and transferability, q-RASPR is an efficient technique that has the potential to be employed as a good alternative approach for retention time prediction and ecotoxicity potential identification.

Novel Strategy to Rapidly Profile and Identify Oxidized Species of Polysorbate 80 Using Ultra-High-Performance Liquid Chromatography Coupled with High-Resolution Mass Spectrometry.

Polysorbate 80 (PS80) is widely used as an excipient in vaccines and biopharmaceuticals. The oxidized species of PS80 have raised concern because of their potential to compromise product stability and pose a clinical risk. Analytical methods to profile and identify the oxidized species are hard to develop owing to their complexity and low abundance. Herein, a novel strategy was demonstrated to comprehensively profile and identify the oxidized species of PS80 using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry. The characteristic fragmentation patterns of the oxidized species were obtained under the "all ions" scan mode. Then, 10 types of distinct fragments from oxidized oleates were identified and confirmed using two purified oxidized species (polyoxyethylene (POE) sorbitan mono-hydroxy oleate and POE mono-keto oleate) whose structures were elucidated via nuclear magnetic resonance. A total of 348 (32 types) oxidized species were profiled and identified in the oxidized PS80 samples, including 119 (10 types) species found for the first time to our knowledge. Mathematical models were established and validated based on the good logarithmic relation between the POE degree of polymerization and the relative retention time and used to rapidly discover and identify the oxidized species. A novel strategy was established to profile and identify the PS80 oxidized species based on their retention time, HRMS, and HRMS2 data of the detected peaks using an in-house dataset. Using this strategy, 104 (14 types) and 97 (13 types) oxidized species were identified for the first time in PS80 and its preparations, respectively.

Critical assessment of chromatographic metadata in publicly available metabolomics data repositories

IntroductionThe structural identification of metabolites represents one of the current bottlenecks in non-targeted liquid chromatography-mass spectrometry (LC–MS) based metabolomics. The Metabolomics Standard Initiative has developed a multilevel system to report confidence in metabolite identification, which involves the use of MS, MS/MS and orthogonal data. Limitations due to similar or same fragmentation pattern (e.g. isomeric compounds) can be overcome by the additional orthogonal information of the retention time (RT), since it is a system property that is different for each chromatographic setup.ObjectivesIn contrast to MS data, sharing of RT data is not as widespread. The quality of data and its (re-)useability depend very much on the quality of the metadata. We aimed to evaluate the coverage and quality of this metadata from public metabolomics repositories.MethodsWe acquired an overview on the current reporting of chromatographic separation conditions. For this purpose, we defined the following information as important details that have to be provided: column name and dimension, flow rate, temperature, composition of eluents and gradient.ResultsWe found that 70% of descriptions of the chromatographic setups are incomplete (according to our definition) and an additional 10% of the descriptions contained ambiguous and/or incorrect information. Accordingly, only about 20% of the descriptions allow further (re-)use of the data, e.g. for RT prediction. Therefore, we have started to develop a unified and standardized notation for chromatographic metadata with detailed and specific description of eluents, columns and gradients.ConclusionReporting of chromatographic metadata is currently not unified. Our recommended suggestions for metadata reporting will enable more standardization and automatization in future reporting.

Development of Liquid Chromatographic Retention Index Based on Cocamide Diethanolamine Homologous Series (C(n)-DEA).

There is a growing need for indexing and harmonizing retention time (tR) data in liquid chromatography derived under different conditions to aid in the identification of compounds in high resolution mass spectrometry (HRMS) based suspect and nontarget screening of environmental samples. In this study, a rigorously tested, inexpensive, and simple system-independent retention index (RI) approach is presented for liquid chromatography (LC), based on the cocamide diethanolamine homologous series (C(n = 0-23)-DEA). The validation of the CDEA based RI system was checked rigorously on eight different instrumentation and LC conditions. The RI values were modeled using molecular descriptor free technique based on structural barcoding and convolutional neural network deep learning. The effect of pH on the elution pattern of more than 402 emerging contaminants were studied under diverse LC settings. The uncertainty associated with the CDEA RI model and the pH effect were addressed and the first RI bank based on CDEA calibrants was developed. The proposed RI system was used to enhance identification confidence in suspect and nontarget screening while facilitating successful comparability of retention index data between various LC settings. The CDEA RI app can be accessed at https://github.com/raalizadeh/RIdea.

Prediction of Retention Time and Collision Cross Section (CCSH+, CCSH-, and CCSNa+) of Emerging Contaminants Using Multiple Adaptive Regression Splines.

Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.

Rapid Chemical Profiling of Compound Huanggen Granules and Absorbed Prototypes in Cynomolgus Monkey Plasma by Integrating UHPLC-Q-TOF-MSE Method and Data Post-Processing Strategy.

In this study, we aim to establish an integrated research strategy for the rapid chemical profiling of Compound Huanggen Granules (CHG) and absorbed prototypes in plasma by integrating the UHPLC-Q-TOF-MSE method and data post-processing strategy, to provide some valuable research basis for the further studies on the quality control, pharmacokinetics and pharmacodynamics of CHG. Compound Huanggen Granules (CHG), a traditional Chinese medicine (TCM) hospital preparation, has long been used in clinical practice for the prevention and treatment of liver fibrosis. However, due to the lack of in vitro chemical and in vivo metabolism studies, its pharmacodynamic material basis is still unrevealed. To simplify the mass data post-processing process and enhance the structural identification efficiency by reducing the possibility of false positive, and rapidly identify the absorbed prototypes in plasma after oral administration of CHG. An analytical strategy integrating ultra high-performance liquid chromatography coupled with quadrupletime- of-flight mass spectrometry (UHPLC-Q-TOF-MSE, E represents collision energy) method and data postprocessing strategy based on a self-built in-house components database was established and utilized for the rapid characterization of the multi-constituents of CHG and prototypes in cynomolgus monkey plasma after oral administration. As a result, a total of 81 compounds, including 14 phenolic acids, 6 coumarins, 25 flavonoids, 5 anthraquinones, 5 phenylpropanoids, 15 triterpenoid saponins, and 11 others, were plausibly or unambiguously identified based on their accurate masses, and MS/MS fragment pathways analysis, and also by comparison of retention time and MS data with reference standards. In the in vivo study, according to the extracted ion chromatograms (EICs) of identified components, 34 absorbed prototypical components were rapidly identified in cynomolgus monkey plasma after oral administration. It was demonstrated that the data post-processing strategy applied in this study could greatly simplify the data post-processing process and enhance the structural identification efficiency by reducing the possibility of false positives, and the results obtained might be helpful for further studies on the quality control, pharmacokinetics and pharmacodynamics of CHG.

CaO‐Based Heterogeneous Catalyst from Cow Bone for Direct Cracking of Triglycerides

A calcium oxide (CaO)-based heterogeneous catalyst derived from waste cow bone for the direct cracking of Moringa oleifera seed oil was investigated. Optimization was carried out using a five-level-four-factorial central composite design based on response surface methodology in 30 experimental runs. Under the optimal conditions of reaction temperature, catalyst concentration, reaction time, and agitation speed, 68 % biodiesel was achieved. A total of four major fatty acid methyl esters were identified in the synthesized biodiesel by the retention time and fragmentation pattern data of the gas chromatography-mass spectrometry analysis. The statistical model for predicting the biodiesel yield was developed for the effects and percentage contributions of the optimization variables.

Probabilistic metabolite annotation using retention time prediction and meta-learned projections

Retention time information is used for metabolite annotation in metabolomic experiments. But its usefulness is hindered by the availability of experimental retention time data in metabolomic databases, and by the lack of reproducibility between different chromatographic methods. Accurate prediction of retention time for a given chromatographic method would be a valuable support for metabolite annotation. We have trained state-of-the-art machine learning regressors using the 80, 038 experimental retention times from the METLIN Small Molecule Retention Tim (SMRT) dataset. The models included deep neural networks, deep kernel learning, several gradient boosting models, and a blending approach. 5, 666 molecular descriptors and 2, 214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software. The models were trained using only the descriptors, only the fingerprints, and both types of features simultaneously. Bayesian hyperparameter search was used for parameter tuning. To avoid data-leakage when reporting the performance metrics, nested cross-validation was employed. The best results were obtained by a heavily regularized deep neural network trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of 39.2 pm 1.2; s and 17.2 pm 0.9;s, respectively. To the best of our knowledge, these are the most accurate predictions published up to date over the SMRT dataset. To project retention times between chromatographic methods, a novel Bayesian meta-learning approach that can learn from just a few molecules is proposed. By applying this projection between the deep neural network retention time predictions and a given chromatographic method, our approach can be integrated into a metabolite annotation workflow to obtain z-scores for the candidate annotations. To this end, it is enough that just as few as 10 molecules of a given experiment have been identified (probably by using pure metabolite standards). The use of z-scores permits considering the uncertainty in the projection when ranking candidates, and not only the accuracy. In this scenario, our results show that in 68% of the cases the correct molecule was among the top three candidates filtered by mass and ranked according to z-scores. This shows the usefulness of this information to support metabolite annotation. Python code is available on GitHub at https://github.com/constantino-garcia/cmmrt.

Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites.

In gas chromatography–mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

UHPLC-Q-Exactive Orbitrap MS/MS-based rapid identification of chemical components in substance benchmark of Kaixin San

Ultra-performance liquid chromatography-quadrupole-electrostatic field Orbitrap mass spectrometry(UHPLC-Q-Exactive Orbitrap MS/MS) was used for rapid identification of the chemical components in Kaixin San substance benchmark. The gradient elution was performed through a Waters ACQUITY~(TM) BEH C_(18) column(2.1 mm×150 mm, 1.7 μm) with water-acetonitrile as mobile phase, a column temperature of 30 ℃, a flow rate of 0.3 mL·min~(-1), and a sample size of 1 μL. The scanning was performed in the negative ion mode. The complex component groups in Kaixin San substance benchmark were quickly and accurately identified and clearly assigned based on the comparison of the retention time and MS data with those of the reference substance as well as the relative molecular weight of the same or similar components in the mass spectrum database and literature. A total of 77 compounds were identified, including 26 saponins, 13 triterpenoid acids, 20 oligosaccharide esters, 5 xanthones, and 13 other compounds. The qualitative method established in this study can systematically, accurately, and quickly identify the chemical components in Kaixin San substance benchmark, which can provide a basis for the further analysis of its active components in vivo and the establishment of its quality control system.

Application of a Target-Guided Data Processing Approach in Saturated Peak Correction of GC×GC Analysis.

Detector and column saturations are problematic in comprehensive two-dimensional gas chromatography (GC×GC) data analysis. This limits the application of GC×GC in metabolomics research. To address the problems caused by detector and column saturations, we propose a two-stage data processing strategy that will incorporate a targeted data processing and cleaning approach upstream of the “standard” untargeted analysis. By using the retention time and mass spectrometry (MS) data stored in a library, the annotation and quantification of the targeted saturated peaks have been significantly improved. After subtracting the nonperfected signals caused by saturation, peaks of coelutes can be annotated more accurately. Our research shows that the target-guided method has broad application prospects in the data analysis of GC×GC chromatograms of complex samples.