Accurate Retention Time Research Articles

Towards a chromatographic similarity index to establish localised quantitative structure-retention relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called “federation of local models” is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k=a – blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Qext(F2)2>0.8 and Mean Absolute Error<0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2min.

Prediction of gradient retention data for hydrophilic interaction liquid chromatographic separation of native and fluorescently labeled oligosaccharides

In this work, we have investigated the predictive properties of mixed-mode retention model and oligomeric mixed-mode model, taking into account the contribution of monomeric units to the retention, in hydrophilic interaction liquid chromatography. The gradient retention times of native maltooligosaccharides and their fluorescent derivatives were predicted in the oligomeric series with number of monomeric glucose units in the range from two to seven. The maltooligosaccharides were separated on a packed column with carbamoyl-bonded silica stationary phase and 15 gradient profiles with different initial and final mobile phase composition were used with the gradient times 5; 7.5 and 10min. The predicted gradient retention times were compared for calculations based on isocratic retention data and gradient retention data, which provided better accuracy of the results. By comparing two different mobile phase additives, the more accurate retention times were predicted in mobile phases containing ammonium acetate. The acidic derivatives, prepared by reaction of an oligosaccharide with 2-aminobenzoic acid or 8-aminonaphthalene-1,3,6-trisulfonic acid, provided more accurate predictions of the retention data in comparison to native oligosaccharides or their neutral derivatives. The oligomeric mixed-mode model allowed prediction of gradient retention times using only one gradient profile, which significantly speeded-up the method development.

Indirect quantitative structure-retention relationship for steroid identification: A chemometric challenge at “Chimiométrie 2016”

A chemometric challenge was proposed during the "Chimiométrie" congress 2016, held in Namur, Belgium, on 17–20 January. The aim of this contest was to challenge the ability of congress participants to build indirect Quantitative Structure-Retention Relationship models (QSRR) using the linear solvent strength (LSS) theory of reversed-phase liquid chromatography. QSRR is a very helpful method for the identification of unknown analytes, including the prediction of chromatographic retention time. Because of the potential presence of various isomeric compounds, accurate retention time prediction is particularly important in the context of steroid identification. In addition, the indirect prediction of retention time using the linear solvent strength (LSS) parameters S and log kW provides a great advantage for use in any gradient conditions. In the proposed dataset, the experimental values of S and log kW were estimated using Ultra High Pressure Liquid Chromatography separation with two linear gradients (5–95% ACN+0.1% FA) of 15 and 60min, respectively. The aim of the challenge was the accurate estimation of retention time for a 45min gradient by applying the LSS theory based on the predicted S and log kW values. Molecular descriptors were calculated from a series of reference steroid compounds using the VolSurf+ software. By these means, a collection of 128 variables related to molecular shape, volume, polarisability, polar surface area, hydrophobic surface area, lipophilicity, molecular diffusion, and solubility was generated automatically. The dataset (n=95) included 76 steroid compounds for calibration and 19 for validation. Experimental log kW, S and retention time values were provided for the calibration set only. The results were evaluated according to the smallest RMSEC obtained for the retention time predictions of the validation set with the 45min gradient using the LSS parameters. Moreover, each individual relative error should not exceed 5% of the experimental retention time for both the calibration and validation sets. This paper summarises the approaches proposed by the best three participants and the challenge organiser.

Identification of metabolites of kurarinone from &lt;i&gt;Sophora flavescens&lt;/i&gt; Ait in rat urine by ultra-performance liquid chromatography with linear ion trap orbitrap mass spectrometry

Purpose : To study the in vivo metabolism of kurarinone, a lavandulyl flavanone which is a major constituent of Kushen and a marker compound with many biological activities, using ultra-performance liquid chromatography coupled with linear ion trap Orbitrap mass spectrometry (UPLC-LTQ-Orbitrap-MS). Methods : Six male Sprague-Dawley rats were randomly divided into two groups. First, kurarinone was suspended in 0.5 % carboxymethylcellulose sodium (CMC-Na) aqueous solution, and was given to rats (n = 3, 2 mL for each rat) orally at 50 mg/kg. A 2 mL aliquot of 0.5 % CMC-Na aqueous solution was administered to the rats in the control group. Next, urine samples were collected over 0-24 h after the oral administrations and all urine samples were pretreated by a solid phase extraction (SPE) method. Finally, all samples were analyzed by a UPLC-LTQ-Orbitrap mass spectrometry coupled with an electrospray ionization source (ESI) that was operated in the negative ionization mode. Results : A total of 11 metabolites, including the parent drug and 10 phase II metabolites in rat urine, were first detected and interpreted based on accurate mass measurement, fragment ions, and chromatographic retention times. The results were based on the assumption that kurarinone glucuronidation was the dominant metabolite that was excreted in rat urine. Conclusion : The results from this work indicate that kurarinone in vivo is typically transformed to nontoxic glucuronidation metabolites, and these findings may help to characterize the metabolic profile of kurarinone. Keywords : Kurarinone, Metabolites, Sophora flavescens Ait., Glucuronidation metabolites

Proteomics Quality Control: Quality Control Software for MaxQuant Results.

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC .

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis

The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concentration level rely on analytical reference standards for chromatographic retention time (tR) and mass spectral comparisons. Chromatographic tR prediction can play a role in increasing confidence in suspect screening efforts for new compounds in the environment, especially when standards are not available, but reliable methods are lacking. The current work focuses on the development of artificial neural networks (ANNs) for tR prediction in gradient reversed-phase liquid chromatography and applied along with HRMS data to suspect screening of wastewater and environmental surface water samples. Based on a compound tR dataset of >500 compounds, an optimized 4-layer back-propagation multi-layer perceptron model enabled predictions for 85% of all compounds to within 2min of their measured tR for training (n=344) and verification (n=100) datasets. To evaluate the ANN ability for generalization to new data, the model was further tested using 100 randomly selected compounds and revealed 95% prediction accuracy within the 2-minute elution interval. Given the increasing concern on the presence of drug metabolites and other transformation products (TPs) in the aquatic environment, the model was applied along with HRMS data for preliminary identification of pharmaceutically-related compounds in real samples. Examples of compounds where reference standards were subsequently acquired and later confirmed are also presented. To our knowledge, this work presents for the first time, the successful application of an accurate retention time predictor and HRMS data-mining using the largest number of compounds to preliminarily identify new or emerging contaminants in wastewater and surface waters.

Use of individual retention modeling for gradient optimization in hydrophilic interaction chromatography: Separation of nucleobases and nucleosides

In this study, the separation of twelve nucleobases and nucleosides was optimized via chromatogram simulation (i.e., prediction of individual retention times and estimation of the peak widths) with the use of an empirical (reversed-phase) non-linear model proposed by Neue and Kuss. Retention time prediction errors of less than 2% were observed for all compounds on different stationary phases. As a single HILIC column could not resolve all peaks, the modeling was extended to coupled-column systems (with different stationary phase chemistries) to increase the separation efficiency and selectivity. The analytical expressions for the gradient retention factor on a coupled column system were derived and accurate retention time predictions were obtained (<2% prediction errors in general). The optimized gradient (predicted by the optimization software) included coupling of an amide and an pentahydroxy functionalized silica stationary phases with a gradient profile from 95 to 85%ACN in 6min and resulted in almost baseline separation of the twelve nucleobases and nucleosides in less than 7min. The final separation was obtained in less than 4h of instrument time (including equilibration times) and was fully obtained via computer-based optimization. As such, this study provides an example of a case where individual retention modeling can be used as a way to optimize the gradient conditions in the HILIC mode using a non-linear model such as the Neue and Kuss model.

IRT – A dimensionless peptidespecific retention time for accurate scheduling in multiplexed MRM experiments

Introduction Mulitple reaction monitoring (MRM) is a powerful method for peptide quantification. Multiplexing of measurements can be greatly improved by scheduling of the acquisition. The more accurate the prediction of the retention time (RT), the more peptides can be measured in a single LC-MRM analysis. We present iRT, a peptide-specific dimensionless retention time that allows for highly accurate retention time prediction.

Gradient-elution parameters in capillary liquid chromatography for high-speed separations of peptides and intact proteins

This contribution relates to the assessment of gradient-elution parameters in capillary liquid chromatography affecting the peak widths in the reversed-phase separation of peptides and intact proteins. Gradient separations were performed using both a poly(sytrene-co-divinylbenzene) monolithic column and a microparticulate fused-core column (silica C18, 2.7μm). The applicability of the conventional linear (LSS) and non-linear solvent-strength model (Neue–Kuss) were investigated to describe the retention behaviour of the compounds as a function of the mobile-phase composition. This was performed by using a wide range of gradient conditions, including different gradient slopes (β, ranging from 0.05 to 0.65min−1) and mobile-phase compositions (Δϕ, i.e. gradient span). Although the LSS-model provided accurate retention time predictions (<1.3% deviation) of scouting runs with more conventional gradient slopes, the prediction of high-speed separations with a high degree of accuracy (<2%) could only be obtained with the non-linear model. The solvent-strength parameters resulting from the use of both models, as well as the retention factors at the moment of elution (ke), further served as input parameters to assess the influence of the gradient slope on the expected peak-compression effects in gradient mode, with a focus on high-speed separations. The importance of the correct model choice was emphasized in terms of compression; while the LSS-model lead to the conclusion of peak broadening rather than peak sharpening, the use of a more accurate non-linear model showed the existence of peak compression effect. The results presented in this manuscript show the occurrence of gradient-related focusing effects, which appear to be more prevalent for extremely fast separations.

Accurate retention time determination of co-eluting proteins in analytical chromatography by means of spectral data

Chromatography is the method of choice for the separation of proteins, at both analytical and preparative scale. Orthogonal purification strategies for industrial use can easily be implemented by combining different modes of adsorption. Nevertheless, with flexibility comes the freedom of choice and optimal conditions for consecutive steps need to be identified in a robust and reproducible fashion. One way to address this issue is the use of mathematical models that allow for an in silico process optimization. Although this has been shown to work, model parameter estimation for complex feedstocks becomes the bottleneck in process development. An integral part of parameter assessment is the accurate measurement of retention times in a series of isocratic or gradient elution experiments. As high-resolution analytics that can differentiate between proteins are often not readily available, pure protein is mandatory for parameter determination. In this work, we present an approach that has the potential to solve this problem. Based on the uniqueness of UV absorption spectra of proteins, we were able to accurately measure retention times in systems of up to four co-eluting compounds. The presented approach is calibration-free, meaning that prior knowledge of pure component absorption spectra is not required. Actually, pure protein spectra can be determined from co-eluting proteins as part of the methodology. The approach was tested for size-exclusion chromatograms of 38 mixtures of co-eluting proteins. Retention times were determined with an average error of 0.6 s (1.6% of average peak width), approximated and measured pure component spectra showed an average coefficient of correlation of 0.992.

Using iRT, a normalized retention time for more targeted measurement of peptides.

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.

Chromatographic retention time prediction for posttranslationally modified peptides

Retention time prediction of peptides in liquid chromatography has proven to be a valuable tool for mass spectrometry-based proteomics, especially in designing more efficient procedures for state-of-the-art targeted workflows. Additionally, accurate retention time predictions can also be used to increase confidence in identifications in shotgun experiments. Despite these obvious benefits, the use of such methods has so far not been extended to (posttranslationally) modified peptides due to the absence of efficient predictors for such peptides. We here therefore describe a new retention time predictor for modified peptides, built on the foundations of our existing Elude algorithm. We evaluated our software by applying it on five types of commonly encountered modifications. Our results show that Elude now yields equally good prediction performances for modified and unmodified peptides, with correlation coefficients between predicted and observed retention times ranging from 0.93 to 0.98 for all the investigated datasets. Furthermore, we show that our predictor handles peptides carrying multiple modifications as well. This latest version of Elude is fully portable to new chromatographic conditions and can readily be applied to other types of posttranslational modifications. Elude is available under the permissive Apache2 open source License at http://per-colator.com or can be run via a web-interface at http://elude.sbc.su.se.

Application of LC–TOF MS to analysis of hemoglobin acetaldehyde adducts in alcohol detoxification patients

Acetaldehyde adducts of hemoglobin have been regarded as potential biochemical markers of alcohol exposure. In this study a novel sensitive method using liquid chromatography coupled to time-of-flight mass spectrometry (LC-TOF MS) has been used to investigate changes in adduct levels in alcohol detoxification patients. Hemoglobin and authentic blood samples from 66 adults with an alcohol-dependence syndrome and from 12 children were analyzed for acetaldehyde modifications with and without trypsin digestion using LC-TOF MS. After in-vitro incubation of hemoglobin with increasing concentrations of acetaldehyde, followed by tryptic digestion, 21 modified peptide fragments could be identified from their accurate mass and retention time shift. Eight of these could also be detected in authentic human blood samples. Trace amounts in children's blood were indicative of an endogenous source. Modified peptide levels in patients' samples with and without ethanol were significantly different, as also were levels in samples from admission and from five days later. Samples obtained 5, 10, or 15 days after admission did not differ in adduct levels. The LC-TOF MS method was sensitive enough to detect acetaldehyde-modified hemoglobin peptides in blood samples from patients with an alcohol-dependence syndrome. However, elevated levels were only observed after recent ethanol consumption and decreased during five days of abstinence, suggesting that acetaldehyde-modified tryptic peptides of hemoglobin are potential biomarkers only for short-term ethanol ingestion.

Application of peptide retention time in proteome research

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has been one of the most popular approaches in proteome analysis. As an independent parameter to mass spectrometry information, peptide retention time has been utilized to facilitate protein identification and quantification. In the field of peptide identification, the prediction of the retention time combined with routine tandem mass spectrometry database searching methods could help improve the confidence of identification. The sensitivity of identification could also be improved by matching peaks with both the accurate mass and retention time in multiple aligned LC-MS runs. Meanwhile, because small changes of liquid chromatography conditions lead to variability in retention times unavoidably, retention time alignment is crucial to label-free quantification. Additionally, post-translational modifications (PTM) could be identified by combining retention time shifts and mass deviation information.

HPLC‐chip‐mass spectrometry for protein signature identifications

This work investigates the use of an HPLC-chip microfluidic device interfaced to an IT mass spectrometer to search for biomarker signatures. To that end, the identification of autoantigens is chosen as a model. It not only constitutes a proof of concept model but also the growing interest in autoantibodies and autoantigens as new markers of diseases provides a practical application at the same time. The peptides are separated by the HPLC-chip system allowing suitable resolution and reproducibility. The determination of two parameters that characterize a peptide sequence during LC-MS/MS analyses, retention time (RT) and m/z ratio, improves the identification of a number of peptides derived from protein digests. These findings illustrate that accurate RT measurement obtained in a microfluidic device is useful to obtain mass/retention time (MRT) pairs for a given peptide, which can contribute to the definition of biomarker signatures.

High-throughput purification of combinatorial libraries I: a high-throughput purification system using an accelerated retention window approach.

We have developed a high-throughput purification system to purify combinatorial libraries at a 50-100-mg scale with a throughput of 250 samples/instrument/day. We applied an accelerated retention window method to shorten the purification time and targeted one fraction per injection to simplify data tracking, lower QC workload, and simplify the postpurification processing. First, we determined the accurate retention time and peak height for all compounds using an eight-channel parallel LC/UV/MS system, and calculated the specific preparative HPLC conditions for individual compounds. The preparative HPLC conditions include the compound-specific gradient segment for individual compounds with a fixed gradient slope and the compound-specific UV or ELSD threshold for triggering a fraction collection device. A unique solvent composition or solvent strength was programmed for each compound in the preparative HPLC in order to elute all compounds at the same target time. Considering the possible deviation of the predicted retention time, a 1-min window around the target time was set to collect peaks above a threshold based on UV or ELSD detection. Dual column preparative instruments were used to maximize throughput. We have purified more than 500 000 druglike compounds using this system in the past 3 years. We report various components of this high-throughput purification system and some of our purification results.

Trilinear chemometric analysis of two-dimensional comprehensive gas chromatography–time-of-flight mass spectrometry data

Two-dimensional comprehensive gas chromatography (GC×GC) is a powerful instrumental tool in its own right that can be used to analyze complex mixtures, generating selective data that is applicable to multivariate quantitative analysis and pattern recognition. It has been recently demonstrated that by coupling GC×GC to time-of-flight mass spectrometry (TOFMS), a highly selective technique is produced. One separation on a GC×GC/TOFMS provides retention times on two chromatographic columns and a complete mass spectrum for each component within the mixture. In this manuscript, we demonstrate how the selectivity of GC×GC/TOFMS combined with trilinear chemometric techniques such as trilinear decomposition (TLD) and parallel factor analysis (PARAFAC) results in a powerful analytical methodology. Using TLD and PARAFAC, partially resolved components in complex mixtures can be deconvoluted and identified using only one data set without requiring either signal shape assumptions or fully selective mass signals. Specifically, a region of overlapped peaks in a complex environmental sample was mathematically resolved with TLD and PARAFAC to demonstrate the utility of these techniques as applied to GC×GC/TOFMS data of a complex mixture. For this data, it was determined that PARAFAC initiated by TLD performed a better deconvolution than TLD alone. After deconvolution, mass spectral profiles were then matched to library spectra for identification. A standard addition analysis was performed on one of the deconvoluted analytes to demonstrate the utility of TLD-initiated PARAFAC for quantification without the need for accurate retention time alignment between sample and standard data sets.

Comprehensive two-dimensional gas chromatography and chemometrics for the high-speed quantitative analysis of aromatic isomers in a jet fuel using the standard addition method and an objective retention time alignment algorithm

A high-speed quantitative analysis of aromatic isomers in a jet fuel sample is performed using comprehensive two-dimensional gas chromatography (GC x GC) and chemometrics. A GC x GC separation time of 2.8 min is achieved for three aromatic isomers in jet fuel, which is 5 times faster than a reference method in which a singlecolumn separation resolves two of the three isomers of interest. The high-speed GC x GC separation is more than 10 times faster than a recent GC x GC separation that fully resolves the three components of interest in gasoline. The high-speed GC x GC analysis of jet fuel is accomplished through short GC columns, high gas velocities, and partial chromatographic peak resolution followed by chemometric resolution of overlapped peaks. The standard addition method and an objective retention time alignment algorithm are used to correct for retention time variations prior to the chemometric data analysis. The standard addition method corrects for chemical matrix effects that cause analytes in complex samples to have peak shapes, widths, and retention times that differ considerably from those of calibration standards in pure solvents. The retention time alignment algorithm corrects for the relatively small retention time variations caused by fluctuating instrumental parameters such as flow rate and temperature. The use of data point interpolation in the retention time alignment algorithm results in a more accurate retention time correction then previously achieved. The generalized rank annihilation method (GRAM) is the chemometric technique used to resolve the overlapped GC x GC peaks. The correction of retention time variations allows for successful GRAM signal deconvolution. Using the retention time alignment algorithm, GRAM quantification accuracy and precision are improved by a factor of 4. The methodology used in this paper should be applicable to other comprehensive separation methods, such as two-dimensional liquid chromatography, liquid chromatography coupled with capillary electrophoresis, and liquid chromatography coupled with gas chromatography.

Computer-assisted development of a high-performance liquid chromatographic method for fractionating selected nitro derivatives of polyromatic hydrocarbons

The process of developing a high-performance liquid chromatographic (HPLC) method for fractionating nitro derivatives of polyromatic hydrocarbons using the HPLC simulation program DryLab G is presented. A comparison of the computer-simulated results and experimental data revealed retention time prediction errors during preliminary stages of the method development. These errors resulted from the simulation of mobile phase conditions that required the program, based on the input data, to extrapolate beyond the point at which accurate retention times could be reasonably predicted. We discuss the cause and solution of these retention time prediction errors. After entering new input data, the final “optimum” HPLC method developed using Dry Lab G simulations correlated well with the experimentally observed chromatogram.