Retention Time Prediction Model Research Articles

Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links.

In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.

High-throughput multitarget quantitative assay to profile the whole grain-specific phytochemicals alkylresorcinols, benzoxazinoids and avenanthramides in whole grain and grain-based foods.

Currently, there is a growing interest in using whole grain (WG)-specific phytochemicals to perform WG research, including research on dietary assessment, health mechanisms, and quality control. However, the current approaches used for WG-specific phytochemical analysis cannot simultaneously achieve coverage, specificity, and sensitivity. In the present study, a series of WG-specific phytochemicals (alkylresorcinols (ARs), benzoxazinoids (BXs) and avenanthramides (AVAs)) were identified, and their mass spectrometry (MS) fragmentation mechanism was studied by TOF MS. Based on diagnostic fragmentation ions and retention time prediction models, a LC-MS/MS method was developed. Through this method, 56 ARs, 13 BXs, and 19 AVAs in WGs and grain-based foods were quantified for the first time. This method was validated and yielded excellent specificity, high sensitivity and negligible matrix effects. Finally, we established WG-specific phytochemical fingerprints in a variety of WG and grain-based foods. This method can be used for WG quality control and WG precision nutrition research.

Global Analysis of 2-Hydroxy Fatty Acids by Gas Chromatography-Tandem Mass Spectrometry Reveals Species-Specific Enrichment in Echinoderms.

Abnormal levels of 2-hydroxy fatty acids (2-OH FAs) are characterized in multiple diseases, and their quantification in foodstuffs is critical to identify the sources of supplementation for potential treatment. However, due to the structural complexity and limited available standards, the comprehensive profiling of 2-OH FAs remains an ongoing challenge. Herein, an innovative approach based on gas chromatography-tandem mass spectrometry (GC-MS/MS) was developed to determine the full profile of these FA metabolites. MS and MS/MS spectra of the trimethylsilyl (TMS) derivatives of 2-OH fatty acid methyl esters (FAMEs) were collected for peak annotation by their signature fragmentation patterns. The structures were further confirmed by validated structure-dependent retention time (RT) prediction models, taking advantage of the correlation between the RT, carbon chain length, and double bond number from commercial standards and pseudostandards identified in the whole-brain samples from mice. An in-house database containing 50 saturated and monounsaturated 2-OH FAs was established, which is expandible when additional molecular species with different chain lengths and backbone structures are identified in the future. A quantitation method was then developed by scheduled multiple reaction monitoring (MRM) and applied to investigate the profiling of 2-OH FAs in echinoderms. Our results revealed that the levels of total 2-OH FAs in sea cucumber Apostichopus japonicas (8.40 ± 0.28 mg/g dry weight) and starfish Asterias amurensis (7.51 ± 0.18 mg/g dry weight) are much higher than that in sea urchin Mesocentrotus nudus (531 ± 108 μg/g dry weight). Moreover, 2-OH C24:1 is the predominant molecular species accounting for 67.9% of the total 2-OH FA in sea cucumber, while 2-OH C16:0 is the major molecular species in starfish. In conclusion, the current innovative GC-MS approach has successfully characterized distinct molecular species of 2-OH FAs that are highly present in sea cucumbers and starfish. Thus, these findings suggest the possibility of developing future feeding strategies for preventing and treating diseases associated with 2-OH FA deficiency.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry

The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.

A chemical derivatization-based pseudotargeted LC-MS/MS method for high coverage determination of dipeptides

Dipeptides (DPs) have attracted more and more attention in many research fields due to their important biological functions and promising roles as disease biomarkers. However, the determination of DPs in biological samples is very challenging owing to the limited availability of commercial standards, high structure diversity, distinct physical and chemical characteristics, wide concentration range, and the extensive existence of isomers. In this study, a pseudotargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method coupled with chemical derivatization for the simultaneous analysis of 400 DPs and their constructing amino acids (AAs) in biospecimens is established. Dansyl chloride (Dns-Cl) chemical derivatization was introduced to provide characteristic MS fragments for annotation and improve the chromatographic separation of DP isomers. A retention time (RT) prediction model was constructed using 83 standards (63 DPs and 20 AAs) based on their quantitative structural retention relationship (QSRR) after the Dns-Cl labeling, which largely facilitated the annotation of the DPs without standards. Finally, we applied this method to investigate the profile change of DPs in a cisplatin-induced acute kidney injury (AKI) rat model. The established workflow provides a platform to profile DPs and expand our understanding of these little-studied metabolites.

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC–MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC–MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC–RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC–RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC–RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.

Identification of new Omega-3 very long chain poly-unsaturated fatty acids in meibomian gland secretions

Three new very long chain polyunsaturated fatty acids (VLC PUFA) belonging to the omega-3 family have been identified in meibum samples collected by Schirmer strips. These VLC PUFA, namely FA (32:3), FA (34:3) and FA (36:3), were detected in O-acyl-ω-hydroxy fatty acids using a molecular network approach, and as free fatty acids. Identification was supported by retention time prediction model, exact mass determination and isotopic patterns. Double bond location was determined using cross metathesis reaction associated to tandem mass spectrometry. In meibum, synthesis of these VLC PUFA is likely to be mediated by elongation of very long chain fatty acids 4 enzyme. The biological role of these newly VLC PUFA and their occurrence in other tissues and biological fluids remains to be elucidated.

Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances

Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R2) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

The development of retention time prediction model using multilinear gradient profiles of seven pharmaceuticals

The ICH guidance on pharmaceutical development recommends a systematic development approach including robustness studies which assure performance of manufacturing and analytical method development of drug product. The retention model by T. Kawabe et al have an excellent correlation between observed and predicted retention time in various kinds of pharmaceutical compounds during isocratic elution by the multiple regression modeling of solvent strength parameters. However, it cannot be successfully applied to the predictability of the retention time during multilinear gradient elution and also it does not consider the instrument dependent parameters such as dwell volume. The current study demonstrated that the solution of the fundamental gradient elution equation was applied to T. Kawabe’s retention time prediction model to predict the retention time using a multilinear gradient profile with taking the delay volume of HPLC system into account. Seven pharmaceutical compounds were used for evaluation of prediction models for retention time. The predicted retention time was compared with the measured retention time obtained by several multilinear gradient using two HPLC systems with different dwell volume. The evaluated prediction error (%) was 1.10 % and 1.54 % with H-Class and Nexera XR HPLC systems, respectively. In order to evaluate the robustness of the analytical method and to set the system suitability test (SST) for proper method performance, the design space for the ACN/MeOH mixture ratio in the total organic solvent and the full width at half maximum (FWHM) relationship to the minimum resolution was simulated by the developed retention time prediction. The optimized condition of the ACN/MeOH mixture ratio, the acceptance criterion of the SST for achieving the robust separation was estimated based on the simulated design space. As a conclusion, the developed retention time prediction will be useful during analytical method transfer among different manufacturing/analytical sites of the pharmaceuticals with different HPLC systems.

Investigation of Biotransformation Products of p-Methoxymethylamphetamine and Dihydromephedrone in Wastewater by High-Resolution Mass Spectrometry.

There is a paucity of information on biotransformation and stability of new psychoactive substances (NPS) in wastewater. Moreover, the fate of NPS and their transformation products (TPs) in wastewater treatment plants is not well understood. In this study, batch reactors seeded with activated sludge were set up to evaluate biotic, abiotic, and sorption losses of p-methoxymethylamphetamine (PMMA) and dihydromephedrone (DHM) and identify TPs formed during these processes. Detection and identification of all compounds was performed with target and suspect screening approaches using liquid chromatography quadrupole-time-of-flight mass spectrometry. Influent and effluent 24 h composite wastewater samples were collected from Athens from 2014 to 2020. High elimination rates were found for PMMA (80%) and DHM (97%) after a seven-day experiment and degradation appeared to be related to biological activity in the active bioreactor. Ten TPs were identified and the main reactions were O- and N-demethylation, oxidation, and hydroxylation. Some TPs were reported for the first time and some were confirmed by reference standards. Identification of some TPs was enhanced by the use of an in-house retention time prediction model. Mephedrone and some of its previously reported human metabolites were formed from DHM incubation. Retrospective analysis showed that PMMA was the most frequently detected compound.

From Metabolomics to HRMS-Based Exposomics: Adapting Peak Picking and Developing Scoring for MS1 Suspect Screening.

The technological advances of cutting-edge high-resolution mass spectrometry (HRMS) have set the stage for a new paradigm for exposure assessment. However, some adjustments of the metabolomics workflow are needed before HRMS-based methods can detect the low-abundant exogenous chemicals in human matrixes. It is also essential to provide tools to speed up marker identifications. Here, we first show that metabolomics software packages developed for automated optimization of XCMS parameters can lead to a false negative rate of up to 80% for chemicals spiked at low levels in blood. We then demonstrate that manual selection criteria in open-source (XCMS, MZmine2) and vendor software (MarkerView, Progenesis QI) allow to decrease the rate of false negative up to 4% (MZmine2). We next report an MS1 automatized suspect screening workflow that allows for a rapid preannotation of HRMS data sets. The novelty of this suspect screening workflow is to combine several predictors based on m/z, retention time (Rt) prediction models, and isotope ratio to generate intermediate and global scorings. Several Rt prediction models were tested and hierarchized (PredRet, Retip, retention time indices, and a log P model), and a nonlinear scoring was developed to account for Rt variations observed within individual runs. We then tested the efficiency of this suspect screening tool to detect spiked and nonspiked chemicals in human blood. Compared to other existing annotation tools, its main advantages include the use of Rt predictors using different models, its speed, and the use of efficient scoring algorithms to prioritize preannotated markers and reduce false positives.

Towards compound identification of synthetic opioids in nontargeted screening using machine learning techniques.

The constant evolution of the illicit drug market makes the identification of unknown compounds problematic. Obtaining certified reference materials for a broad array of new analogues can be difficult and cost prohibitive. Machine learning provides a promising avenue to putatively identify a compound before confirmation against a standard. In this study, machine learning approaches were used to develop class prediction and retention time prediction models. The developed class prediction model used a naïve Bayes architecture to classify opioids as belonging to either the fentanyl analogues, AH series or U series, with an accuracy of 89.5%. The model was most accurate for the fentanyl analogues, most likely due to their greater number in the training data. This classification model can provide guidance to an analyst when determining a suspected structure. A retention time prediction model was also trained for a wide array of synthetic opioids. This model utilised Gaussian process regression to predict the retention time of analytes based on multiple generated molecular features with 79.7% of the samples predicted within ±0.1min of their experimental retention time. Once the suspected structure of an unknown compound is determined, molecular features can be generated and input for the prediction model to compare with experimental retention time. The incorporation of machine learning prediction models into a compound identification workflow can assist putative identifications with greater confidence and ultimately save time and money in the purchase and/or production of superfluous certified reference materials.

Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry

The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (tR) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R2) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure–ion intensity relationship (QSIIR) model.

Non-target screening method for VOCs analysis based on GC-QTOF

Volatile organic compounds (VOCs) are an important family of pollutants in the atmosphere, which will produce a series of ecological and environmental effects as well as human health effects. China has issued a series of documents to strengthen the control of VOCs. The monitoring and analysis of VOCs is the premise and foundation of its management and control. Although target analysis has the advantage of clear compound list, it also has the problem of pollutant missing because VOCs are a great variety in structure. To date, non-target screening has become a research hotspot in the field of environmental analysis. In this study, a non-target VOCs screening method was established based on qualitative screening of environmental air samples by GC-QTOF. To optimize mass spectrum signal, the effects of ion source temperature and electron ionization energy on molecular ion peaks were investigated. The results show that the ion source temperature in the range from 200 to 270°C has little influence on the abundance of molecular ion peaks. However, the response of molecular ion peaks for different VOCs varies significantly with the electron ionization energies from 15 to 70 eV, which results from the difference in its reaction ability with electrons because of diverse structure. According to the variation trend of molecular ion peaks, the tested VOCs can be divided into three categories: first, the molecular ion peaks show little change with the increase of electron ionization energy, such as benzene, chlorobenzene and phenol. Second, the molecular ion peak shows an upward trend with the increase of electron ionization energy, as dichloromethane, dibromodichloromethane, etc. Third, molecular ion peak decreases with the increase of electron ionization energy, and even molecular ion peak loss occurs such as phthalates, straight chain alkanes, and some haloalkanes under 70 eV. Therefore, ionization at 70 eV was convenient to match with the NIST mass spectrum library, and Ionization at 15 eV was helpful to obtain the molecular ion signal. Based on the boiling point, a prediction model of VOCs retention time was established. The final non-target screening process includes: (1) Firstly analyze VOCs in the sample under 70 eV, compare the obtained mass spectrogram with the spectrogram library of the instrument to screen out possible candidates; (2) the molecular ion peak was obtained under the condition of 15 eV, and the compound was identified according to the accuracy error of the candidate molecular ion 5 ppm as the mass threshold value; (3) isotopic pattern analysis if it involves; (4) further identification of compounds using the retention time prediction model; (5) standard samples can be used to confirm the identified substances finally. Preliminary testing of 108 standard substances as unknown VOCs in the non-target screening process demonstrated that 94 species could be correctly identified with 87% accuracy. The constructed non-target screening method was used to analyze VOCs in the environmental air samples collected from the workshop and boundary of one factory in Dalian, and 30 VOCs were screened out, among which 23 compounds were confirmed by standard samples. The sample analysis indicates that the non-target screening method constructed in this study can be used for the qualitative identification of VOCs in environment air samples.

QSRR Automator: A Tool for Automating Retention Time Prediction in Lipidomics and Metabolomics.

The use of retention time is often critical for the identification of compounds in metabolomic and lipidomic studies. Standards are frequently unavailable for the retention time measurement of many metabolites, thus the ability to predict retention time for these compounds is highly valuable. A number of studies have applied machine learning to predict retention times, but applying a published machine learning model to different lab conditions is difficult. This is due to variation between chromatographic equipment, methods, and columns used for analysis. Recreating a machine learning model is likewise difficult without a dedicated bioinformatician. Herein we present QSRR Automator, a software package to automate retention time prediction model creation and demonstrate its utility by testing data from multiple chromatography columns from previous publications and in-house work. Analysis of these data sets shows similar accuracy to published models, demonstrating the software’s utility in metabolomic and lipidomic studies.

Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics.

Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available data sets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography data set (HILIC) of 981 primary metabolites and biogenic amines,and the RIKEN plant specialized metabolome annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM), and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.retip.app). Keras outperformed other machine learning algorithms in the test set with minimum overfitting, verified by small error differences between training, test, and validation sets. Keras yielded a mean absolute error of 0.78 min for HILIC and 0.57 min for RPLC. Retip is integrated into the mass spectrometry software tools MS-DIAL and MS-FINDER, allowing a complete compound annotation workflow. In a test application on mouse blood plasma samples, we found a 68% reduction in the number of candidate structures when searching all isomers in MS-FINDER compound identification software. Retention time prediction increases the identification rate in liquid chromatography and subsequently leads to an improved biological interpretation of metabolomics data.

Strategy for Global Profiling and Identification of 2- and 3-Hydroxy Fatty Acids in Plasma by UPLC–MS/MS

2-Hydroxy fatty acids (2-OHFAs) and 3-hydroxy fatty acids (3-OHFAs) with the same carbon backbone are isomers, both of which are closely related to diseases involving fatty acid oxidation disorder. However, the comprehensive profiling of 2- and 3-OHFAs remains an ongoing challenge due to their high structure similarity, few structure-informative product ions, and limited availability of standards. Here, we developed a new strategy to profile and identify 2- and 3-OHFAs according to structure-dependent retention time prediction models using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Both accurate MS and MS/MS spectra were collected for peak annotation by comparison with an in-house database of theoretically possible 2- and 3-OHFAs. The structures were further confirmed by the validated structure-dependent retention time prediction models, taking advantage of the correlation between the retention time, carbon chain length and number of double bonds, as well as the hydroxyl position-induced isomeric retention time shift rule. With the use of this strategy, 18 2-OHFAs and 32 3-OHFAs were identified in the pooled plasma, of which 7 2-OHFAs and 20 3-OHFAs were identified for the first time in this work, furthering our understanding of OHFA metabolism. Subsequent quantitation method was developed by scheduled multiple reaction monitoring (MRM) and then applied to investigate the alteration of 2- and 3-OHFAs in esophageal squamous cell carcinoma (ESCC) patients. Finally, a potential biomarker panel consisting of six OHFAs with good diagnostic performance was achieved. Our study provides a new strategy for isomer identification and analysis, showing great potential for targeted metabolomics in clinical biomarker discovery.

Peptide retention time prediction in hydrophilic interaction liquid chromatography: Zwitter-ionic sulfoalkylbetaine and phosphorylcholine stationary phases

Peptide retention time prediction models have been developed for zwitter-ionic ZIC-HILIC and ZIC-cHILIC stationary phases (pH 4.5 eluents) using proteomics-derived retention datasets of ~30 thousand tryptic peptides each. Overall, hydrophilicity of these stationary phases was found to be similar to the previously studied Amide HILIC phase, but lower compared to bare silicas. Peptide retention is driven by interactions of all charged (hydrophilic) residues at pH 4.5 (Asp, Glu, Arg, Lys, His), but shows specificity according to orientation of functional groups in zwitter-ionic pair. Thus, ZIC-cHILIC exhibits an increased contribution of negatively charged Asp and Glu due to the distal positioning of positively charged quaternary amines on the stationary phase. These findings confirm that HILIC interactions are driven by both peptide distribution between water layer adsorbed on the stationary phase and by interactions specific to functional groups of the packing material. Sequence-Specific Retention Calculator HILIC models were optimized for these columns showing 0.967–0.976 R2-values between experimental and predicted retention values. ZIC-HILIC separations represent a good choice as a first dimension in 2D LC-MS of peptide mixtures with correlations between retention values of ZIC-HILIC against RPLC found at 0.197 (ZIC-HILIC) and 0.137 (ZIC-cHILIC) R2-values, confirming a good orthogonality.