High-performance liquid chromatography of amino acids, peptides and proteins. CXXXIII. Peak tracking of peptides in reversed-phase high-performance liquid chromatography

Exploring the utility of complementary separations in liquid chromatography

An alternative strategy is explored for the separation of samples by liquid chromatography (LC). Unlike traditional approaches that aim to resolve all components in a given sample within a single LC separation, the proposed strategy uses two or more distinct separations carried out with a different gradient program and/or using different separation chemistries i.e., a different set of mobile and stationary phase. This set of complementary incomplete separations (CIS) is selected such that each component is at least fully resolved once, meaning the most critical pairs of each individual separation can be left unseparated. This allows for a significant time saving per separation. To investigate whether such an approach can lead to overall shorter analysis times than is possible with the fastest single-run gradient separation, a comprehensive in silico study covering a statistically significant number of samples is undertaken. The investigation shows that, for the presently considered sample sets and chemistries, CIS has a substantially higher probability, about two times greater for the simplest samples considered in this work and as much as 30 times greater for more complex samples, to fully resolve an unknown sample compared to a single gradient separation. Comparing separation speeds, the CIS approach can achieve complete sample resolution on average approximately four times faster than a single separation. Our findings thus demonstrate the potential of CIS in enhancing separation efficiency and offer insights regarding their use for solving analytical challenges.

Automated method development in high-pressure liquid chromatography

Method development in liquid chromatography is a crucial step in the optimization of analytical separations for various applications. However, it is often a challenging endeavour due to its time-consuming, resource intensive and costly nature, which is further hampered by its complexity requiring highly skilled and experienced scientists. This review presents an examination of the methods that are required for a completely automated method development procedure in liquid chromatography, aimed at taking the human out of the decision loop. Some of the presented approaches have recently witnessed an important increase in interest as they offer the promise to facilitate, streamline and speed up the method development process. The review first discusses the mathematical description of the separation problem by means of multi-criteria optimization functions. Two different strategies to resolve this optimization are then presented; an experimental and a model-based approach. Additionally, methods for automated peak detection and peak tracking are reviewed, which, upon integration in an instrument, allow for a completely closed-loop method development process. For each of these approaches, various currently applied methods are presented, recent trends and approaches discussed, short-comings pointed out, and future prospects highlighted.

Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography

Method development in comprehensive two-dimensional liquid chromatography (LC × LC) is a complicated endeavor. The dependency between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, renders method development by "trial-and-error" time-consuming and highly dependent on user experience. In this work, an open-source algorithm for the automated and interpretive method development of complex gradients in LC × LC-mass spectrometry (MS) was developed. A workflow was designed to operate within a closed-loop that allowed direct interaction between the LC × LC-MS system and a data-processing computer which ran in an unsupervised and automated fashion. Obtaining accurate retention models in LC × LC is difficult due to the challenges associated with the exact determination of retention times, curve fitting because of the use of gradient elution, and gradient deformation. Thus, retention models were compared in terms of repeatability of determination. Additionally, the design of shifting gradients in the second dimension and the prediction of peak widths were investigated. The algorithm was tested on separations of a tryptic digest of a monoclonal antibody using an objective function that included the sum of resolutions and analysis time as quality descriptors. The algorithm was able to improve the separation relative to a generic starting method using these complex gradient profiles after only four method-development iterations (i.e., sets of chromatographic conditions). Further iterations improved retention time and peak width predictions and thus the accuracy in the separations predicted by the algorithm.

Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation

Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.

Peak-tracking algorithm for use in comprehensive two-dimensional liquid chromatography - Application to monoclonal-antibody peptides

A peak-tracking algorithm was developed for use in comprehensive two-dimensional liquid chromatography coupled to mass spectrometry. Chromatographic peaks were tracked across two different chromatograms, utilizing the available spectral information, the statistical moments of the peaks and the relative retention times in both dimensions. The algorithm consists of three branches. In the pre-processing branch, system peaks are removed based on mass spectra compared to low intensity regions and search windows are applied, relative to the retention times in each dimension, to reduce the required computational power by elimination unlikely pairs. In the comparison branch, similarity between the spectral information and statistical moments of peaks within the search windows is calculated. Lastly, in the evaluation branch extracted-ion-current chromatograms are utilized to assess the validity of the pairing results. The algorithm was applied to peptide retention data recorded under varying chromatographic conditions for use in retention modelling as part of method optimization tools. Moreover, the algorithm was applied to complex peptide mixtures obtained from enzymatic digestion of monoclonal antibodies. The algorithm yielded no false positives. However, due to limitations in the peak-detection algorithm, cross-pairing within the same peaks occurred and six trace compounds remained falsely unpaired.

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.

Identification and Discrimination of Brands of Fuels by Gas Chromatography and Neural Networks Algorithm in Forensic Research

The detection of adulteration of fuels and its use in criminal scenes like arson has a high interest in forensic investigations. In this work, a method based on gas chromatography (GC) and neural networks (NN) has been developed and applied to the identification and discrimination of brands of fuels such as gasoline and diesel without the necessity to determine the composition of the samples. The study included five main brands of fuels from Spain, collected from fifteen different local petrol stations. The methodology allowed the identification of the gasoline and diesel brands with a high accuracy close to 100%, without any false positives or false negatives. A success rate of three blind samples was obtained as 73.3%, 80%, and 100%, respectively. The results obtained demonstrate the potential of this methodology to help in resolving criminal situations.

Phenotypic taxonomy and metabolite profiling in microbial drug discovery

Microorganisms and in particular actinomycetes and microfungi are known to produce a vast number of bioactive secondary metabolites. For industrially important fungal genera such as Penicillium and Aspergillus the production of these compounds has been demonstrated to be very consistent at the species level. This means that direct metabolite profiling techniques such as direct injection mass spectrometry or NMR can easily be used for chemotyping/metabolomics of strains from both culture collections and natural samples using modern informatics tools. In this review we discuss chemotyping/metabolomics as part of intelligent screening and highlight how it can be used for identification and classification of filamentous fungi and for the discovery of novel compounds when used in combination with modern methods for dereplication. In our opinion such approaches will be important for future effective drug discovery strategies, especially for dereplication of culture collections in order to avoid redundancy in the selection of species. This will maximize the chemical diversity of the microbial natural product libraries that can be generated from fungal collections.

Peptide mapping by reversed-phase high-performance liquid chromatography employing silica rod monoliths

In this paper, a general procedure is described for the generation of peptide maps of proteins with monolithic silica-based columns. The peptide fragments were obtained by tryptic digestion of various cytochrome c species with purification of the tryptic fragments achieved by reversed-phase high-performance liquid chromatographic methods. Peak assignment of the various peptides was based on evaluation of the biophysical properties of the individual peptides and via mass spectrometric identification. The performance of several different monolithic sorbents prepared as columns of identical cross-sectional dimensions were investigated as part of these peptide mapping studies and the data evaluated by applying solvent strength theory. These studies revealed curvilinear dependencies in the corresponding relative resolution maps. These findings directly impact on the selection of specific sorbent types or column configurations for peptide separations with silica rod monoliths. Moreover, the influence of variations in the amino acid sequence of the cytochrome cs were evaluated with respect to their effect on intrinsic hydrophobicity, the number of experimental observed tryptic cleavage sites, detection limits of the derived fragments in relation to their molecular size, and the chromatographic selectivity and resolution of the various peptides obtained following enzymatic fragmentation of the parent protein. Finally, the scope of these approaches in method development was examined in terms of robustness and efficiency.

High-Throughput Analysis of Total RNA Expression Profiles by Capillary Gel Electrophoresis

We studied the expression profiles of total RNA in three types of human tissues. Reverse transcription was performed on total RNA samples extracted from kidney, normal breast, and breast tumor. Then, fluorophor-labeled cDNAs were synthesized by using random hexamers as primers for analysis by capillary gel electrophoresis. By overlaying the electropherograms and by performing multivariate analysis, we found that the RNA patterns were significantly different among the tissue samples. The protocol utilizes only submicroliter volumes of PCR products and is compatible with multiple capillary DNA sequencing instruments. These CE-based RNA patterns provide an alternative to RNA microarrays for expression profiling for understanding the molecular basis of disease.

HPLC of peptides and proteins: standard operating conditions

The standard operating conditions for the eight basic modes of HPLC are presented in this unit. They include: size-exclusion chromatography (HP-SEC), ion-exchange chromatography (HP-IEX), normal phase chromatography (HP-NPC), hydrophobic interaction chromatography (HP-HIC), reversed-phase chromatography (RP-HPLC), hydrophilic interaction chromatography (HP-HILIC), immobilized metal ion affinity chromatography (HP-IMAC), and biospecific/biomimetic affinity chromatography (HP-BAC). In addition, some subsets of these chromatographic modes, e.g., mixed mode chromatography (HP-MMC), charge transfer chromatography (HP-CTC), or ligand-exchange chromatography (HP-LEC) are described. Procedures for multimodal column switching are also included, as are guidelines for a systematic approach to method development. Example separations help illustrate the procedures. The standard operating conditions for the eight basic modes of HPLC are presented in this unit.