Comprehensive Two-Dimensional Liquid Chromatography-High-Resolution Mass Spectrometry for Complex Protein Digest Analysis Using Parallel Gradients.

Despite the high gain in peak capacity, online comprehensive two-dimensional liquid chromatography coupled with high-resolution mass spectrometry (LC × LC-HRMS) has not yet been widely applied to the analysis of complex protein digests. One reason is the method's reduced sensitivity which can be linked to the high flow rates of the second separation dimension (2D). This results in higher dilution factors and the need for flow splitters to couple to ESI-MS. This study reports proof-of-principle results of the development of an RPLC × RPLC-HRMS method using parallel gradients (2D flow rate of 0.7 mL min-1) and its comparison to shifted gradient methods (2D of 1.4 mL min-1) for the analysis of complex digests using HRMS (QExactive-Plus MS). Shifted and parallel gradients resulted in high surface coverage (SC) and effective peak capacity (SC of 0.6226 and 0.7439 and effective peak capacity of 779 and 757 in 60 min). When applied to a cell line digest sample, parallel gradients allowed higher sensitivity (e.g., average MS intensity increased by a factor of 3), allowing for a higher number of identifications (e.g., about 2600 vs 3900 peptides). In addition, reducing the modulation time to 10 s significantly increased the number of MS/MS events that could be performed. When compared to a 1D-RPLC method, parallel RPLC × RPLC-HRMS methods offered a higher separation performance (FHWH from 0.12 to 0.018 min) with limited sensitivity losses resulting in an increase of analyte identifications (e.g., about 6000 vs 7000 peptides and 1500 vs 1990 proteins).

Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.

Clean high-resolution mass spectra (HRMS) are essential to a successful structural elucidation of an unknown feature during nontarget analysis (NTA) workflows. This is a crucial step, particularly for the spectra generated during data-independent acquisition or during direct infusion experiments. The most commonly available tools only take advantage of the time domain for spectral cleanup. Here, we present an algorithm that combines the time domain and mass domain information to perform spectral deconvolution. The algorithm employs a probability-based cumulative neutral loss (CNL) model for fragment deconvolution. The optimized model, with a mass tolerance of 0.005 Da and a scoreCNL threshold of 0.00, was able to achieve a true positive rate (TPr) of 95.0%, a false discovery rate (FDr) of 20.6%, and a reduction rate of 35.4%. Additionally, the CNL model was extensively tested on real samples containing predominantly pesticides at different concentration levels and with matrix effects. Overall, the model was able to obtain a TPr above 88.8% with FD rates between 33 and 79% and reduction rates between 9 and 45%. Finally, the CNL model was compared with the retention time difference method and peak shape correlation analysis, showing that a combination of correlation analysis and the CNL model was the most effective for fragment deconvolution, obtaining a TPr of 84.7%, an FDr of 54.4%, and a reduction rate of 51.0%.

Characterization of Dye-Loaded Poly(lactic-co-glycolic acid) Nanoparticles by Comprehensive Two-Dimensional Liquid Chromatography Combining Hydrodynamic and Reversed-Phase Liquid Chromatography.

Analytical methods for the assessment of drug-delivery systems (DDSs) are commonly suitable for characterizing individual DDS properties, but do not allow determination of several properties simultaneously. A comprehensive online two-dimensional liquid chromatography (LC × LC) system was developed that is aimed to be capable of characterizing both nanoparticle size and encapsulated cargo over the particle size distribution of a DDS by using one integrated method. Polymeric nanoparticles (NPs) with encapsulated hydrophobic dyes were used as model DDSs. Hydrodynamic chromatography (HDC) was used in the first dimension to separate the intact NPs and to determine the particle size distribution. Fractions from the first dimension were taken comprehensively and disassembled online by the addition of an organic solvent, thereby releasing the encapsulated cargo. Reversed-phase liquid chromatography (RPLC) was used as a second dimension to separate the released dyes. Conditions were optimized to ensure the complete disassembly of the NPs and the dissolution of the dyes during the solvent modulation step. Subsequently, stationary-phase-assisted modulation (SPAM) was applied for trapping and preconcentration of the analytes, thereby minimizing the risk of analyte precipitation or breakthrough. The developed HDC × RPLC method allows for the characterization of encapsulated cargo as a function of intact nanoparticle size and shows potential for the analysis of API stability.

Comprehensive two-dimensional gas chromatography- A discussion on recent innovations.

Although comprehensive 2-D GC is an established and often applied analytical method, the field is still highly dynamic thanks to a remarkable number of innovations. In this review, we discuss a number of recent developments in comprehensive 2-D GC technology. A variety of modulation methods are still being actively investigated and many exciting improvements are discussed in this review. We also review interesting developments in detection methods, retention modeling, and data analysis.

Automated Feature Mining for Two-Dimensional Liquid Chromatography Applied to Polymers Enabled by Mass Remainder Analysis.

A fast algorithm for automated feature mining of synthetic (industrial) homopolymers or perfectly alternating copolymers was developed. Comprehensive two-dimensional liquid chromatography-mass spectrometry data (LC × LC-MS) was utilized, undergoing four distinct parts within the algorithm. Initially, the data is reduced by selecting regions of interest within the data. Then, all regions of interest are clustered on the time and mass-to-charge domain to obtain isotopic distributions. Afterward, single-value clusters and background signals are removed from the data structure. In the second part of the algorithm, the isotopic distributions are employed to define the charge state of the polymeric units and the charge-state reduced masses of the units are calculated. In the third part, the mass of the repeating unit (i.e., the monomer) is automatically selected by comparing all mass differences within the data structure. Using the mass of the repeating unit, mass remainder analysis can be performed on the data. This results in groups sharing the same end-group compositions. Lastly, combining information from the clustering step in the first part and the mass remainder analysis results in the creation of compositional series, which are mapped on the chromatogram. Series with similar chromatographic behavior are separated in the mass-remainder domain, whereas series with an overlapping mass remainder are separated in the chromatographic domain. These series were extracted within a calculation time of 3 min. The false positives were then assessed within a reasonable time. The algorithm is verified with LC × LC-MS data of an industrial hexahydrophthalic anhydride-derivatized propylene glycol-terephthalic acid copolyester. Afterward, a chemical structure proposal has been made for each compositional series found within the data.

Combining Photodegradation in a Liquid-Core-Waveguide Cell with Multiple-Heart-Cut Two-Dimensional Liquid Chromatography.

Photodegradation greatly affects everyday life. It poses challenges when food deteriorates or when objects of cultural heritage fade, but it can also create opportunities applied in advanced oxidation processes in water purification. Studying photodegradation, however, can be difficult because of the time needed for degradation, the inaccessibility of pure compounds, and the need to handle samples manually. A novel light-exposure cell, based on liquid-core-waveguide (LCW) technology, was embedded in a multiple-heart-cut two-dimensional liquid chromatography system by coupling the LCW cell to the multiple-heart-cut valve. The sample was flushed from the heart-cut loops into the cell by an isocratic pump. Samples were then irradiated using different time intervals and subsequently transferred by the same isocratic pump to a second-dimension sample loop. The mixture containing the transformation products was then subjected to the second-dimension separation. In the current setup, about 30-40% of the selected fraction was transferred. Multiple degradation products could be monitored. Degradation was found to be faster when a smaller sample amount was introduced (0.3 µg as compared to 1.5 µg). The system was tested with three applications, that is, fuchsin, a 19th-century synthetic organic colorant, annatto, a lipophilic food dye, and vitamin B complex.

Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography.

The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(-mass spectrometry) separations ("AutoLC"). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4-10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data.

Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.

Detection challenges in quantitative polymer analysis by liquid chromatography.

Accurate quantification of polymer distributions is one of the main challenges in polymer analysis by liquid chromatography. The response of contemporary detectors is typically influenced by compositional features such as molecular weight, chain composition, end groups, and branching. This renders the accurate quantification of complex polymers of which there are no standards available, extremely challenging. Moreover, any (programmed) change in mobile-phase composition may further limit the applicability of detection techniques. Current methods often rely on refractive index detection, which is not accurate when dealing with complex samples as the refractive-index increment is often unknown. We review current and emerging detection methods in liquid chromatography with the aim of identifying detectors, which can be applied to the quantitative analysis of complex polymers.

Recent applications of retention modelling in liquid chromatography.

Recent applications of retention modelling in liquid chromatography (2015-2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention-mechanism studies, for determining physical parameters, such as lipophilicity, and for various more-practical purposes, including method development and optimization, method transfer, and stationary-phase characterization and comparison. The review focusses on the effects of mobile-phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most-common models are addressed in detail, i.e. the log-linear (linear-solvent-strength) model, the quadratic model, the log-log (adsorption) model, the mixed-mode model, and the Neue-Kuss model. Isocratic and gradient-elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one- and two-dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.

Recent applications of chemometrics in one- and two-dimensional chromatography.

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Characterization of Dye Extracts from Historical Cultural-Heritage Objects Using State-of-the-Art Comprehensive Two-Dimensional Liquid Chromatography and Mass Spectrometry with Active Modulation and Optimized Shifting Gradients.

Unbiased characterization of dyes and their degradation products in cultural-heritage objects requires an analytical method which provides universal separation power regardless of dye classes. Dyes are small molecules that vary widely in chemical structure and properties, which renders their characterization by a single method challenging. We have developed a comprehensive two-dimensional liquid chromatography method hyphenated with mass spectrometry and UV-vis detection. We use stationary-phase-assisted modulation to enhance the method in terms of detection limits and solvent compatibility and to reduce the analysis time. The PIOTR program was used to optimize an assembly of shifting second-dimension gradients, which resulted in a high degree of orthogonality (80% in terms of the asterisk concept). The resulting method is universally applicable to all classes of dyes extracted from cultural-heritage objects. Thanks to the high peak capacity and orthogonality, dye components can be separated from chemically similar impurities and degradation products, providing a detailed fingerprint of the dyes mixture in a specific sample. The method was applied to a number of challenging dye extracts from 17th- and 19th-century cultural-heritage objects.

Perspectives on the future of multi-dimensional platforms.

Two-dimensional liquid chromatography (2D-LC) formats have emerged to help address separation problems that are too complex for conventional one-dimensional LC. There are a number of obstacles to the proliferation of 2D-LC that are gradually being removed. Reliable commercial instrumentation has become available and data analysis software is being improved. Detector-sensitivity and phase-system compatibility issues can largely be solved by using active-modulation strategies. The remaining challenge, developing good and fast 2D-LC methods within a reasonable time, may be solved with smart algorithms. The technology platform that has been developed for 2D-LC also creates a number of other possibilities. Between the two separation stages, all kinds of physical (e.g. dissolution) or chemical (e.g. enzymatic or light-induced degradation) processes can be made to take place, allowing a wide variety of experiments to be performed within a single, efficient and automated analysis. All these developments are discussed in this paper and a number of critical issues are identified. A practical example, the characterization of polysorbates by high-resolution comprehensive two-dimensional liquid chromatography in combination with high-resolution mass spectrometry, is described as a culmination of recent developments in 2D-LC and as an illustration of the current state of the art.

Peak-Tracking Algorithm for Use in Automated Interpretive Method-Development Tools in Liquid Chromatography.

A peak-tracking algorithm for chromatograms recorded using liquid chromatography and mass spectrometry was developed. Peaks are tracked across chromatograms using the spectrometric information, the statistical moments of the chromatographic peaks, and the relative retention. The algorithm can be applied to pair chromatographic peaks in two very different chromatograms, obtained for different samples using different methods. A fast version of the algorithm was specifically tailored to process chromatograms obtained during method development or optimization, where a few similar mobile-phase-composition gradients (same eluent components, but different ranges and programming rates) are applied to the same sample for the purpose of obtaining model parameters to describe the retention of sample components. Due to the relative similarity between chromatograms, time-saving preselection protocols can be used to locate a candidate peak in another chromatogram. The algorithm was applied to two different samples featuring isomers. The automatically tracked peaks and the resulting retention parameters generally yielded prediction errors of less than 1%.

Optimizing separations in online comprehensive two-dimensional liquid chromatography.

Online comprehensive two-dimensional liquid chromatography has become an attractive option for the analysis of complex nonvolatile samples found in various fields (e.g. environmental studies, food, life, and polymer sciences). Two-dimensional liquid chromatography complements the highly popular hyphenated systems that combine liquid chromatography with mass spectrometry. Two-dimensional liquid chromatography is also applied to the analysis of samples that are not compatible with mass spectrometry (e.g. high-molecular-weight polymers), providing important information on the distribution of the sample components along chemical dimensions (molecular weight, charge, lipophilicity, stereochemistry, etc.). Also, in comparison with conventional one-dimensional liquid chromatography, two-dimensional liquid chromatography provides a greater separation power (peak capacity). Because of the additional selectivity and higher peak capacity, the combination of two-dimensional liquid chromatography with mass spectrometry allows for simpler mixtures of compounds to be introduced in the ion source at any given time, improving quantitative analysis by reducing matrix effects. In this review, we summarize the rationale and principles of two-dimensional liquid chromatography experiments, describe advantages and disadvantages of combining different selectivities and discuss strategies to improve the quality of two-dimensional liquid chromatography separations.

Nanoparticle Analysis by Online Comprehensive Two-Dimensional Liquid Chromatography combining Hydrodynamic Chromatography and Size-Exclusion Chromatography with Intermediate Sample Transformation.

Polymeric nanoparticles have become indispensable in modern society with a wide array of applications ranging from waterborne coatings to drug-carrier-delivery systems. While a large range of techniques exist to determine a multitude of properties of these particles, relating physicochemical properties of the particle to the chemical structure of the intrinsic polymers is still challenging. A novel, highly orthogonal separation system based on comprehensive two-dimensional liquid chromatography (LC × LC) has been developed. The system combines hydrodynamic chromatography (HDC) in the first-dimension to separate the particles based on their size, with ultrahigh-performance size-exclusion chromatography (SEC) in the second dimension to separate the constituting polymer molecules according to their hydrodynamic radius for each of 80 to 100 separated fractions. A chip-based mixer is incorporated to transform the sample by dissolving the separated nanoparticles from the first-dimension online in tetrahydrofuran. The polymer bands are then focused using stationary-phase-assisted modulation to enhance sensitivity, and the water from the first-dimension eluent is largely eliminated to allow interaction-free SEC. Using the developed system, the combined two-dimensional distribution of the particle-size and the molecular-size of a mixture of various polystyrene (PS) and polyacrylate (PACR) nanoparticles has been obtained within 60 min.

Generating realistic data through modeling and parametric probability for the numerical evaluation of data processing algorithms in two-dimensional chromatography.

BACKGROUND: Comprehensive two-dimensional chromatography generates complex data sets, and numerous baseline correction and noise removal algorithms have been proposed in the past decade to address this challenge. However, evaluating their performance objectively is currently not possible due to a lack of objective data. RESULT: To tackle this issue, we introduce a versatile platform that models and reconstructs single-trace two-dimensional chromatography data, preserving peak parameters. This approach balances real experimental data with synthetic data for precise comparisons. We achieve this by employing a Skewed Lorentz-Normal model to represent each peak and creating probability distributions for relevant parameter sampling. The model's performance has been showcased through its application to two-dimensional gas chromatography data where it has created a data set with 458 peaks with an RMSE of 0.0048 or lower and minimal residuals compared to the original data. Additionally, the same process has been shown in liquid chromatography data. SIGNIFICANCE: Data analysis is an integral component of any analytical method. The development of new data processing strategies is of paramount importance to tackle the complex signals generated by state-of-the-art separation technology. Through the use of probability distributions, quantitative assessment of algorithm performance of new algorithms is now possible. Therefore, creating new opportunities for faster, more accurate, and simpler data analysis development.

Exploring the chemical subspace of RPLC: A data driven approach.

BACKGROUND: The chemical space is comprised of a vast number of possible structures, of which an unknown portion comprises the human and environmental exposome. Such samples are frequently analyzed using non-targeted analysis via liquid chromatography (LC) coupled to high-resolution mass spectrometry often employing a reversed phase (RP) column. However, prior to analysis, the contents of these samples are unknown and could be comprised of thousands of known and unknown chemical constituents. Moreover, it is unknown which part of the chemical space is sufficiently retained and eluted using RPLC. RESULTS: We present a generic framework that uses a data driven approach to predict whether molecules fall 'inside', 'maybe' inside, or 'outside' of the RPLC subspace. Firstly, three retention index random forest (RF) regression models were constructed that showed that molecular fingerprints are able to predict RPLC retention behavior. Secondly, these models were used to set up the dataset for building an RPLC RF classification model. The RPLC classification model was able to correctly predict whether a chemical belonged to the RPLC subspace with an accuracy of 92% for the testing set. Finally, applying this model to the 91 737 small molecules (i.e., ≤1 000 Da) in NORMAN SusDat showed that 19.1% fall 'outside' of the RPLC subspace. SIGNIFICANCE AND NOVELTY: The RPLC chemical space model provides a major step towards mapping the chemical space and is able to assess whether chemicals can potentially be measured with an RPLC method (i.e., not every RPLC method) or if a different selectivity should be considered. Moreover, knowing which chemicals are outside of the RPLC subspace can assist in reducing potential candidates for library searching and avoid screening for chemicals that will not be present in RPLC data.

Enhancing LC×LC separations through multi-task Bayesian optimization.

Method development in comprehensive two-dimensional liquid chromatography (LC×LC) is a challenging process. The interdependencies between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, make trial-and-error method development time-consuming and highly dependent on user experience. Retention modeling and Bayesian optimization (BO) have been proposed as solutions to mitigate these issues. However, both approaches have their strengths and weaknesses. On the one hand, retention modeling, which approximates true retention behavior, depends on effective peak tracking and accurate retention time and width predictions, which are increasingly challenging for complex samples and advanced gradient assemblies. On the other hand, Bayesian optimization may require many experiments when dealing with many adjustable parameters, as in LC×LC. Therefore, in this work, we investigate the use of multi-task Bayesian optimization (MTBO), a method that can combine information from both retention modeling and experimental measurements. The algorithm was first tested and compared with BO using a synthetic retention modeling test case, where it was shown that MTBO finds better optima with fewer method-development iterations than conventional BO. Next, the algorithm was tested on the optimization of a method for a pesticide sample and we found that the algorithm was able to improve upon the initial scanning experiments. Multi-task Bayesian optimization is a promising technique in situations where modeling retention is challenging, and the high number of adjustable parameters and/or limited optimization budget makes traditional Bayesian optimization impractical.