Retention-time locked methods in gas chromatography

Accurate prediction of isothermal gas chromatographic Kováts retention indices

We describe a freely available web server called Retention Index Predictor (RIpred) (https://ripred.ca) that rapidly and accurately predicts Gas Chromatographic Kováts Retention Indices (RI) using SMILES strings as chemical structure input. RIpred performs RI prediction for three different stationary phases (semi-standard non-polar (SSNP), standard non-polar (SNP), and standard polar (SP)) for both derivatized (trimethylsilyl (TMS) and tert‑butyldimethylsilyl (TBDMS) derivatized) and underivatized (base compound) forms of GC-amenable structures. RIpred was developed to address the need for freely available, fast, highly accurate RI predictions for a wide range of derivatized and underivatized chemicals for all common GC stationary phases. RIpred was trained using a Graph Neural Network (GNN) that used compound structures, their extracted features (mostly atom-level features) and the GC-RI data from the National Institute of Standards and Technology databases (NIST 17 and NIST 20). We curated this NIST 17 and NIST 20 GC-RI data, which is available for all three stationary phases, to create appropriate inputs (molecular graphs in this case) needed to enhance our model performance. The performance of different RIpred predictive models was evaluated using 10-fold cross validation (CV). The best performing RIpred models were identified and when tested on hold-out test sets from all stationary phases, achieved a Mean Absolute Error (MAE) of <73 RI units (SSNP: 16.5-29.5, SNP: 38.5-45.9, SP: 46.52-72.53). The Mean Absolute Percentage Error (MAPE) of these models were typically within 3% (SSNP: 0.78-1.62%, SNP: 1.87-2.88%, SP: 2.34-4.05%). When compared to the best performing model by Qu et al., 2021, RIpred performed similarly (MAE of 16.57 RI units [RIpred] vs. 16.84 RI units [Qu et al., 2021 predictor] for derivatized compounds). RIpred also includes ∼5 million predicted RI values for all GC-amenable compounds (∼57,000) in the Human Metabolome Database HMDB 5.0 (Wishart et al., 2022).

Optimizing column-to-column retention time alignment in high-speed gas chromatography by combining retention time locking and correlation optimized warping

We examine and then optimize alignment of chromatograms collected on nominally identical columns using retention time locking (RTL), an instrumental alignment tool, and software-based alignment using correlation optimized warping (COW). For this purpose, three samples are constructed by spiking two sets of analytes into a base test mixture. The three samples are analyzed by high-speed gas chromatography with four nominally identical columns and identical separation conditions. The data is first analyzed without alignment, then using COW alone, then RTL alone, and finally with RTL followed by COW to correct the severe column-to-column misalignment. Principal component analysis (PCA) is used to investigate how well each alignment method clustered the chromatograms into the three sample classes via a scores plot without being compromised by the specific column(s) used. The degree-of-class separation (DCS) is used as a classification metric, measured as the Euclidian distance between the centroids of two clusters in PC space in the scores plot, normalized by their pooled variance. With no alignment, the average DCS between sample classes (DCS_sam) was 3.0, while the average DCS between the four nominally identical columns, i.e., column classes (DCS_col) was 76.1 (ideally the DCS_col should be 0), indicating the chromatograms were initially classified by the columns used. Using either COW or RTL alone also produced unsatisfactory results, with COW alone incorrectly aligning many peaks, leading to a DCS_sam of only 1.9 and DCS_col of 1.7, while RTL alone provided a DCS_sam of 4.7 and DCS_col of 4.2. Finally, using RTL followed by COW alignment, DCS_sam increased to 32.5, indicating successful classification by chemical differences between sample classes, while the DCS_col decreased to 0.4, indicating virtually no classification due to column-to-column differences, as desired. Thus, RTL provided a "first-order" correction of the initial retention mismatch observed for the nominally identical columns, while additional alignment via COW was required to optimize sample classification by PCA.

An automated workflow on data processing (AutoDP) for semiquantitative analysis of urine organic acids with GC-MS to facilitate diagnosis of inborn errors of metabolism

Determination of urine organic acids (UOAs) is essential to understand the disease progress of inborn errors of metabolism (IEM) and often relies on GC-MS analysis. However, the efficiency of analytical reports is sometimes restricted by data processing due to labor-intensive work if no proper tool is employed. Herein, we present a simple and rapid workflow with an R-based script for automated data processing (AutoDP) of GC-MS raw files to quantitatively analyze essential UOAs. AutoDP features automatic quality checks, compound identification and confirmation with specific fragment ions, retention time correction from analytical batches, and visualization of abnormal UOAs with age-matched references on chromatograms. Compared with manual processing, AutoDP greatly reduces analytical time and increases the number of identifications. Speeding up data processing is expected to shorten the waiting time for clinical diagnosis, which could greatly benefit clinicians and patients with IEM. In addition, with quantitative results obtained from AutoDP, it would be more feasible to perform retrospective analysis of specific UOAs in IEM and could provide new perspectives for studying IEM.

Endogenous Honeybee Gut Microbiota Metabolize the Pesticide Clothianidin

Including probiotics in honeybee nutrition represents a promising solution for mitigating diseases, and recent evidence suggests that various microbes possess mechanisms that can bioremediate environmental pollutants. Thus, the use of probiotics capable of degrading pesticides used in modern agriculture would help to both reduce colony losses due to the exposure of foragers to these toxic molecules and improve honeybee health and wellbeing globally. We conducted in vitro experiments to isolate and identify probiotic candidates from bacterial isolates of the honeybee gut (i.e., endogenous strains) according to their ability to (i) grow in contact with three sublethal concentrations of the pesticide clothianidin (0.15, 1 and 10 ppb) and (ii) degrade clothianidin at 0.15 ppb. The isolated bacterial strains were indeed able to grow in contact with the three sublethal concentrations of clothianidin. Bacterial growth rate differed significantly depending on the probiotic candidate and the clothianidin concentration used. Clothianidin was degraded by seven endogenous honeybee gut bacteria, namely Edwardsiella sp., two Serratia sp., Rahnella sp., Pantoea sp., Hafnia sp. and Enterobacter sp., measured within 72 h under in vitro conditions. Our findings highlight that endogenous bacterial strains may constitute the base material from which to develop a promising probiotic strategy to mitigate the toxic effects of clothianidin exposure on honeybee colony health.

Comparative evaluation of multi-residue methods for analysis of pesticide residues in black pepper by gas chromatography tandem mass spectrometry: critical evaluation of matrix co-extractives and method validation

A gas chromatography tandem mass spectrometry method was developed for simultaneous determination of 133 pesticides in Black pepper (Piper nigrum). QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) sample preparation method is preferred over multistep methods. Extraction was done by using acetonitrile followed by clean up using graphitized carbon, primary secondary amine and octadecyl silica (C₁₈). Even after the cleanup, non-volatile co-extractives stick to the liner and column which results in affecting the performance of the instrument and volatile co-extractives impact the analysis by enhancing the analyte concentration. So we evaluated a dilution procedure to overcome the drawbacks. The limit of quantification of 0.01 mg kg^-1 was achieved for fifty times diluted sample extract with S/N ≥ 10. The recovery was between 70 and 120% for 0.01, 0.025 and 0.05 mg kg^-1 for fortified samples and corresponding precision was between 3 and 16% RSD. The seven-level calibration curve shows a regression co-efficient of 0.99.

Standardization of chromatographic signals - Part I: Towards obtaining instrument-agnostic fingerprints in gas chromatography

One of the main causes for the sparse use of multivariate analytical methods in routine laboratory work is the dependency on the measuring instrument from which the analytical signal is acquired. This issue is especially critical in chromatographic equipment and results in limitations of their applicability. The solution to this problem is to obtain a standardized instrument-independent signal -or instrument-agnostic signal- regardless of the measuring instrument or of the state of the same instrument from which it has been acquired. The combined use of both internal and external standard series, allows us to have external and transferable references for the normalization of both the intensity and the position of each element of the data vector being arranged from the raw signal. From this information, a simple mathematical data treatment process is applied and instrument-agnostic signals can be secured. This paper describes and applies the proposed methodology to be followed for obtaining standardized instrumental fingerprints from two significant fractions of virgin olive oil (volatile organic compounds and triacylglycerols), obtained by gas chromatography coupled to mass spectrometry (GC-MS) and analysed with two temperature conditions (conventional and high-temperature, respectively). The results of both case studies show how the instrument-agnostic fingerprints obtained are coincidental, regardless of the state of the chromatographic system or the time of acquisition.

Untargeted and Targeted Fingerprinting of Extra Virgin Olive Oil Volatiles by Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Challenges in Long-Term Studies

Comprehensive two-dimensional gas chromatography coupled with mass spectrometric detection (GC × GC-MS) offers an information-rich basis for effective chemical fingerprinting of food. However, GC × GC-MS yields 2D-peak patterns (i.e., sample 2D fingerprints) whose consistency may be affected by variables related to either the analytical platform or to the experimental parameters adopted for the analysis. This study focuses on the complex volatile fraction of extra-virgin olive oil and addresses 2D-peak patterns variations, including MS signal fluctuations, as they may occur in long-term studies where pedo-climatic, harvest year, or shelf life changes are studied. The 2D-pattern misalignments are forced by changing chromatographic settings and MS acquisition. All procedural steps, preceding pattern recognition by template matching, are analyzed and a rational workflow defined to accurately realign patterns and analytes metadata. Signal-to-noise ratio (SNR) detection threshold, reference spectra extraction, and similarity match factor threshold are critical to avoid false-negative matches. Distance thresholds and polynomial transform parameters are key for effective template matching. In targeted analysis (supervised workflow) with optimized parameters, method accuracy reaches 92.5% (i.e., % of true-positive matches) while for combined untargeted and targeted ( UT) fingerprinting (unsupervised workflow), accuracy reaches 97.9%. Response normalization also is examined, evidencing good performance of multiple internal standard normalization that effectively compensates for discriminations occurring during injection of highly volatile compounds. The resulting workflow is simple, effective, and time efficient.

High-throughput metabolomics screen coupled with multivariate statistical analysis identifies therapeutic targets in alcoholic liver disease rats using liquid chromatography-mass spectrometry

Identification of metabolic alterations is useful for elucidating the pathophysiology and mass spectrometry-aided metabolomic, which provides small molecule metabolites to understand underlying mechanisms of complex diseases. In this work, we developed a novel liquid chromatography coupled with tandem mass spectrometry-based metabolomics screen coupled with multivariate statistical analysis workflow to obtaining the metabolite annotation, visualization and rapid characterization of small molecular metabolites, then revealed the altered metabolic pathway in a rat model of alcohol-induced liver damage rats as case study. The ions were detected in both positive and negative mode, and the key biomarkers were selected through multivariate statistical screening analysis. We employed the enhanced mass spectrometry coverage method to comprehensive analysis of the metabolite data. Importantly, we further find these small molecule metabolites were associated with multiple metabolic pathways. The elucidation of these altered metabolic pathways should help identify new and specific therapeutic targets. In addition, this metabolomics workflow coupled to LC-MS/MS has been successfully applied to the identification of small molecule metabolites in this complex disease. We strongly believe that metabolomics studies will benefit from incorporating liquid chromatography coupled with tandem mass spectrometry strategies.

The heartworm (Dirofilaria immitis) seems to be able to metabolize organochlorine pesticides and polychlorinated biphenyls: A case-control study in dogs

It has been described that the co-existence of parasite infection and chemical exposure has various effects on the accumulation of persistent organochlorine pollutants (POPs) in the host. Certain parasites are not only able to accumulate POPs but also seem to have the ability to metabolize certain compounds. We have designed a case-control study aimed to disclose the role of Dirofilaria immitis in the bioavailability of POPs in dogs trying to know whether these parasites store or metabolize the POPs. A total of 40 common POPs (18 polychlorinated biphenyls congeners (PCBs) and 22 organochlorine pesticides were quantified in dog serum. The study included three groups of dogs prospectively recruited in the island of Gran Canaria (Canary Islands, Spain): a) control animals, non-parasitized (serologically tested negative, n=24); b) a group constituted by dogs tested positive for heartworm disease (D. immitis) and negative for other parasites (n=25); and c) the same group of parasitized dogs after the treatment against the parasite (n=25). The presence of D. immitis was strongly associated with lower serum levels of a wide range of pollutant in their hosts (PCB congeners 28, 105, 118, 123, 138, 153, 167 and 180; hexachlorobenzene, lindane, aldrin, dieldrin, and methoxychlor). The serum levels of these substances remained at very low levels after the treatment against the parasite, suggesting that D. immitis do not simply store such compounds, but they probably have some ability to metabolize these pollutants. We encourage the use of the parasite infestation status as a cofactor that needs to be taken into account in studies aimed to evaluate the serum levels of POPs.

Influence of parasitism in dogs on their serum levels of persistent organochlorine compounds and polycyclic aromatic hydrocarbons

Persistent organochlorine pollutants (POPs) are toxic chemicals, which accumulate in humans and animals, as only few species have the capability of eliminating them. However, some authors have pointed to the possibility that certain species of invertebrates (i.e. nematodes) could metabolize this type of compounds. As certain species of nematodes act as parasites of vertebrates, this research was designed to explore the influence of some of the most common parasites of the dogs in their serum levels of 56 common POPs. The study included three groups of dogs (n=64), which were prospectively recruited in the island of Gran Canaria (Canary Islands, Spain): a) control animals, non-parasitized (serologically tested negative, n=24); b) dogs tested positive for intestinal parasites and negative for other parasites (n=24); and c) dogs tested positive for heartworm disease (Dirofilaria immitis) and negative for other parasites (n=16). The presence of Dirofilaria immitis was strongly associated with lower serum levels of a wide range of pollutant in their hosts (PCB congeners 28, 52, 118, 138, 153, and 180; hexachlorobenzene, lindane, aldrin, dieldrin, anthracene and pyrene). We also found an inverse association between the hosts' serum levels of PCBs and intestinal parasites. We did not find any association with DDT or its metabolites, but this might be explained by the recently suggested ability of dogs for the efficient metabolization of these compounds. According to the results of this study certain forms of parasitism would reduce the bioavailability of the major classes of POPs in dogs. However, further studies are needed to elucidate whether this phenomenon is due to a competence between parasites and hosts or could respond to a possible capability of parasitic nematodes for the metabolization of these POPs.

Comparative study of the alignment method on experimental and simulated chromatographic data

One of the major problems in the signal comparison of chromatographic data is the variability of response caused by instrumental drifts and others instabilities. Measures of quality control and evaluation of conformity are inherently sensitive to shift. It is essential to be able to compare test samples to reference samples in an evolutionary analytical environment by offsetting the inevitable drift. Therefore, prior to any multivariate analysis, the alignment of analytical signals is a compulsory preprocessing step. During recent years, many researchers have taken a greater interest in the study of the alignment. The present paper is an updated review on the alignment algorithms, methods, and improvements used in chromatography. The study is dedicated to one-dimensional signals. Several of the exposed methods have common theoretical bases and can differ through their optimization methods. The main issue for the operator is to choose the appropriate method according to the type of signals to be processed.

Qualitative analysis of seized synthetic cannabinoids and synthetic cathinones by gas chromatography triple quadrupole tandem mass spectrometry

Designer drugs are analogues or derivatives of illicit drugs with a modification of their chemical structure in order to circumvent current legislation for controlled substances. Designer drugs of abuse have increased dramatically in popularity all over the world for the past couple of years. Currently, the qualitative seized-drug analysis is mainly performed by gas chromatography-electron ionization-mass spectrometry (GC-EI-MS) in which most of these emerging designer drug derivatives are extensively fragmented not presenting a molecular ion in their mass spectra. The absence of molecular ion and/or similar fragmentation pattern among these derivatives may cause the equivocal identification of unknown seized-substances. In this study, the qualitative identification of 34 designer drugs, mainly synthetic cannabinoids and synthetic cathinones, were performed by gas chromatography-triple quadrupole-tandem mass spectrometry with two different ionization techniques, including electron ionization (EI) and chemical ionization (CI) only focusing on qualitative seized-drug analysis, not from the toxicological point of view. The implementation of CI source facilitates the determination of molecular mass and the identification of seized designer drugs. Developed multiple reaction monitoring (MRM) mode may increase sensitivity and selectivity in the analysis of seized designer drugs. In addition, CI mass spectra and MRM mass spectra of these designer drug derivatives can be used as a potential supplemental database along with EI mass spectral database.

Highly sensitive and selective analysis of widely targeted metabolomics using gas chromatography/triple-quadrupole mass spectrometry

In metabolomics studies, gas chromatography coupled with time-of-flight or quadrupole mass spectrometry has frequently been used for the non-targeted analysis of hydrophilic metabolites. However, because the analytical platform employs the deconvolution method to extract single-metabolite information from co-eluted peaks and background noise, the extracted peak is artificial product depending on the mathematical parameters and is not completely compatible with the pure component obtained by analyzing a standard compound. Moreover, it has insufficient ability for quantitative metabolomics. Therefore, highly sensitive and selective methods capable of pure peak extraction without any complicated mathematical techniques are needed. For this purpose, we have developed a novel analytical method using gas chromatography coupled with triple-quadrupole mass spectrometry (GC-QqQ/MS). We developed a selected reaction monitoring (SRM) method to analyze the trimethylsilyl derivatives of 110 metabolites, using electron ionization. This methodology enables us to utilize two complementary techniques-non-targeted and widely targeted metabolomics in the same sample preparation protocol, which would facilitate the formulation or verification of novel hypotheses in biological sciences. The GC-QqQ/MS analysis can accurately identify a metabolite using multichannel SRM transitions and intensity ratios in the analysis of living organisms. In addition, our methodology offers a wide dynamic range, high sensitivity, and highly reproducible metabolite profiles, which will contribute to the biomarker discoveries and quality evaluations in biology, medicine, and food sciences.

A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ions monitoring

Non-targeted metabolic profiling is the most widely used method for metabolomics. In this paper, a novel approach was established to transform a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ion monitoring (RTL-GC/MS-SIM). To achieve this transformation, an algorithm based on the automated mass spectral deconvolution and identification system (AMDIS), GC/MS raw data and a bi-Gaussian chromatographic peak model was developed. The established GC/MS-SIM method was compared with GC/MS-full scan (the total ion current and extracted ion current, TIC and EIC) methods, it was found that for a typical tobacco leaf extract, 93% components had their relative standard deviations (RSDs) of relative peak areas less than 20% by the SIM method, while 88% by the EIC method and 81% by the TIC method. 47.3% components had their linear correlation coefficient higher than 0.99, compared with 5.0% by the EIC and 6.2% by TIC methods. Multivariate analysis showed the pooled quality control samples clustered more tightly using the developed method than using GC/MS-full scan methods, indicating a better data quality. With the analysis of the variance of the tobacco samples from three different planting regions, 167 differential components (p<0.05) were screened out using the RTL-GC/MS-SIM method, but 151 and 131 by the EIC and TIC methods, respectively. The results show that the developed method not only has a higher sensitivity, better linearity and data quality, but also does not need complicated peak alignment among different samples. It is especially suitable for the screening of differential components in the metabolic profiling investigation.

Evaluation of automated sample preparation, retention time locked gas chromatography-mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species

In this paper, automated sample preparation, retention time locked gas chromatography-mass spectrometry (GC-MS) and data analysis methods for the metabolomics study were evaluated. A miniaturized and automated derivatisation method using sequential oximation and silylation was applied to a polar extract of 4 types (2 types×2 ages) of Arabidopsis thaliana, a popular model organism often used in plant sciences and genetics. Automation of the derivatisation process offers excellent repeatability, and the time between sample preparation and analysis was short and constant, reducing artifact formation. Retention time locked (RTL) gas chromatography-mass spectrometry was used, resulting in reproducible retention times and GC-MS profiles. Two approaches were used for data analysis. XCMS followed by principal component analysis (approach 1) and AMDIS deconvolution combined with a commercially available program (Mass Profiler Professional) followed by principal component analysis (approach 2) were compared. Several features that were up- or down-regulated in the different types were detected.

Comparative evaluation of software for retention time alignment of gas chromatography/time-of-flight mass spectrometry-based metabonomic data

In chromatography-based metabonomic research, retention time (RT) alignment of chromatographic peaks poses a challenge for the accurate profiling of biomarkers. Although a number of RT alignment software has been reported, the performance of these software packages have not been comprehensively evaluated. This study aimed to evaluate the RT alignment accuracy of publicly available and commercial RT alignment software. Two gas chromatography/mass spectrometry (GC/MS) datasets acquired from a mixture of standard metabolites and human bladder cancer urine samples, were used to assess three publicly available software packages, MetAlign, MZmine and TagFinder, and two commercial applications comprising the Calibration feature and Statistical Compare of ChromaTOF software. The overall RT alignment accuracies in aligning standard compounds mixture were 93, 92, 74, 73 and 42% for Calibration feature, MZmine, MetAlign, Statistical Compare and TagFinder, respectively. Additionally, unique trends were observed for the individual software with regards to the different experimental conditions related to extent and direction of RT shifts. Conflicting performance was observed for human urine samples suggesting that RT misalignments still occurred despite the use of RT alignment software. While RT alignment remains an inevitable step in data preprocessing, metabonomic researchers are recommended to perform manual check on the RT alignment of important biomarkers as part of their validation process.