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Multidimensional separation systems offer several advantages over traditional one-dimensional separation systems, particularly their ability to separate molecules from complex mixtures. Two-dimensional liquid chromatography (2D-LC) significantly enhances the ability to analyze complex mixtures by providing greater separation power, sensitivity, and flexibility, making it an invaluable tool for metabolomics research. The 2D-LC is an exciting mode when pursuing untargeted analysis, as it allows for high-resolution separation and subsequent identification and quantification of more analytes. This chapter summarizes the current applications of 2D-LC in metabolomics and the setups of different separation modes that are being employed, presenting the most suitable combinations of chromatographic methods for different targeted and untargeted metabolomics applications.
Department of Biology, University of Mississippi, Oxford, MS, USA
Sixue Chen
Department of Biology, University of Mississippi, Oxford, MS, USA
*Address all correspondence to: ysing3@olemiss.edu, yatendra.saph@gmail.com
1. Introduction
Metabolomics is the comprehensive study of metabolites within biological systems. Metabolites are small molecules that are intermediates and products of metabolism. The metabolome is closest to the phenotype of a biological system (cells, tissues, organs, or biological fluids) and provides a snapshot of the metabolic state of a system at a specific time. Metabolomics involves identifying and quantifying metabolites to understand metabolic processes and their alterations under various conditions [1]. Comprehensive analysis of the metabolome is challenging due to its diverse physicochemical properties, from very hydrophilic to lipophilic and neutral to multiply charged, necessitating a combination of different analytical techniques to provide comprehensive coverage of the metabolites.
Analytical techniques like gas chromatography (GC), capillary electrophoresis (CE), and liquid chromatography (LC) are commonly used to separate metabolites. Among them, LC is superior in analyzing a wide range of metabolites. However, LC cannot easily separate many metabolites from complex mixtures due to co-elution and often cannot separate isomers like Leucine and isoleucine. Sometimes peaks may overlap with other relevant analytes or matrix compounds. When dealing with complex mixtures and the need for high-resolution separation, two-dimensional liquid chromatography (2D-LC) is an attractive approach. The 2D-LC coupled with mass spectrometry (MS) allows for high-throughput and high-resolution analysis of metabolites [2]. It ensures that metabolites enter MS with minimal interference, improving the accuracy of MS analysis. Recently, 2D-LC has gained popularity in metabolomics research and metabolomics has a tremendous application that covers a wide range of samples (e.g. plasma, cosmetics, fecal, urinary, and plant extracts) [3]. 2D-LC becomes especially powerful when handling highly complex samples. For this, different 2D-LC combinations of separation modes like HILIC × HILIC, HILIC × RPLC, RPLC × RPLC, NPLC × RPLC, RPLC × SFC, Achiral LC × Chiral LC, HILIC × SFC, and so forth (HILIC, hydrophilic interaction chromatography; RPLC, reversed-phase liquid chromatography; NPLC, normal-phase liquid chromatography; SFC, supercritical fluid chromatography) were introduced and hyphenated with MS. In 2D-LC, both separations are orthogonal, meaning that separation mechanisms in the first dimension (1D) and second dimension (2D) are significantly different. Currently, 2D-LC has predominantly been used in clinical research, pharmaceutical [4, 5], food [6, 7], and plant biology [8], toxicology [9], and environmental research [10] are widespread applications and are gaining popularity [11, 12].
Current trends in chromatographic prediction using artificial intelligence (AI) and machine learning (ML) algorithms enable fast and accurate predictions of chromatographic behavior [13]. AI-driven retention time (RT) prediction models can accurately estimate RTs, by analyzing molecular properties and experimental conditions, thereby aiding metabolomics research. The AI deep learning techniques like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can analyze chromatograms and identify peaks, patterns, and anomalies with high precision. They facilitate automated peak integration, deconvolution, and noise reduction, leading to improved quantification accuracy [14].
This chapter focuses on the recent application of 2D-LC in metabolomics research. LC × LC is the hyphenation of two chromatography systems, and it is used for isomer separation, screening, and profiling because it enables the separation of different classes of metabolites and their constituents. Moreover, the integration of AI and 2D-LC MS/MS hyphenation that permits fragmentation is a useful tool for achieving global metabolic profiling of various types of biological samples.
The 2D-LC can be broadly categorized into offline and online configurations. Offline 2D-LC involves completing 1D, collecting the fractions manually, followed by injecting these fractions into 2D for further separation. This configuration appears to be laborious and time-consuming, involves manual handling of the collected fractions, and carries a risk of sample loss. On the other hand, this configuration provides greater flexibility in method development, independent optimization, and separation efficiency. Offline setups also have the advantage of preconcentration, derivatization, and reconstitution into an appropriate mobile phase between the two separations [1]. Online 2D-LC involves the direct transfer of eluents through a high-pressure switching valve from the 1D dimension to the 2D without manual intervention. Online 2D-LC is preferred for high-throughput applications, automation and faster analysis, reduced sample handling, and decreased risk of contamination and degradation [15]. It has been successfully applied to separating and analyzing trace analytes and samples with complex matrices [16, 17]. Online setup requires complex instrumentation, simultaneous method optimization is challenging, and less flexibility in the independent adjustment of conditions and eluate amount.
The method development in 2D-LC is not straightforward. While selecting the 1D and 2D columns, multiple factors should be considered. The chromatographic parameters such as suitable column dimensions (length and diameters), particle sizes, flow rates, and second-dimension injection volumes (i.e., loop sizes) should be optimized in comprehensive 2D-LC [18]. Furusho et al. suggested that a shallow gradient should be used in the 1D [16]. The separation power of the 1D system should be high to separate as many metabolites as possible, while in the 2D, separation should be efficient, quick, and compatible with the detector attached to the system.
The distribution of various separation mechanisms employed in the 1D and 2D was discussed by Wei-Bin et al. [9]. RPLC remains the most popular mechanism, and it is utilized in 46 and 75% of 1D and 2D separations, respectively. Of all online 2D separations, approximately 33% use RPLC in both dimensions and about 33% of all online 2D [11]. Various types of mass spectrometry (72.1%) are the dominant detection techniques [8], for which volatile solvents (e.g., acetonitrile and methanol) and volatile salts (e.g., ammonium acetate) are often preferred for LC.
2D-LC allows the detection of low-abundance metabolites in complex mixtures that may be missed in 1D-LC. It can differentiate isomers by separating them based on different properties in each dimension, which is often challenging for 1D-LC. Moreover, it reduces matrix effects and interferences from other metabolites in the sample that might affect the metabolite identification and quantification. 2D-LC combines different separation systems like reverse phase chromatography, normal phase chromatography, volume exclusion chromatography, affinity chromatography, and ion exchange chromatography to resolve the chromatographic separation. It also contributes to the enrichment of target metabolites, and abundant sample information can be obtained efficiently. It has become a popular technique in various fields (Figure 1) owing to its unique advantages, but it also increases the cost of the instrument to a certain extent.
Figure 1.
Major 2D-LC applications utilized in metabolomics research.
Although excellent separation of complex mixtures was achieved by 2D-LC, detection sensitivity is another aspect of performance that requires serious attention in metabolomics research. The dilution problem arises in 2D-LC because analytes eluted from the 1D column may be diluted during the 2D separation before reaching the detector. This resulted in a lower concentration of each analyte reaching the detector. It directly affects the ability to detect and quantify trace amounts of metabolites. Insufficient sensitivity has long been seen as a major limitation of 2D methods, and this concern is justified in applications with limited sample amounts. To address the sensitivity issue, researchers often employ pre-concentration techniques like solid-phase extraction (SPE), or liquid-liquid extraction (LLE), and specific detection techniques like MS and modulation techniques. Temperature-responsive liquid chromatography (TRLC) in the 1D hyphenated with RPLC in 2D solves the dilution problem and enhances sensitivity via exploiting the solute refocusing effect [19, 20]. The solvent mismatch problem is another challenge associated with 2D-LC [5]. In recent years, various active modulation approaches have been developed to address the mismatch problem between 1D effluent and the 2D mobile phase. Since the introduction of stationary phase-assisted modulation (SPAM), it has become a widely used method [21]. Later, active solvent modulation (ASM), which is a valve-based approach, was introduced [22]. Schmitz et al. presented the concept of utilizing an auxiliary pump, known as a “transfer pump,” to force fractions out of sample loops to mix them with a 2D mobile phase that serves as a diluent [23]. It is much like the previously introduced at-column dilution (ACD), which requires an auxiliary pump, which allows continuous dilution factor variation [24]. To lessen the effects of 1D and 2D mobile phase mismatch, Tang et al. reported the utility of installing an inline mixer between the interface valve and the 2D column [25]. Vacuum-evaporation modulation [26] and thermal modulation [27] are other active modulation techniques. The reader can refer to cited articles for basic principles of various modulation techniques [2, 28, 29, 30].
The World Health Organization estimated that approximately 0.4 million people die yearly from ingestion of contaminated food. Analyzing all products where chromatography is applicable is the solution, not just for food safety prevention but also for introducing high-quality products into the supply chain. Current trends of AI and ML in chromatographic prediction can lead to faster and more accurate chromatographic analysis. Recently, Phytocontrol (a French company) has collaborated with Fujitsu (a Japanese company) to automate chromatographic techniques for AI-guided food contaminant analysis [31]. Den Uijl et al. have recently conducted a thorough investigation on retention modeling in conjunction with scanning gradients for LC optimization [32]. This work investigated the feasibility of using the RT data obtained from fast 2D separations to build retention models to guide the development of 1D elution conditions using the same stationary phase. The authors discovered that doing so produced erroneous predictions, most likely because of extrapolation to gradient slopes beyond the range of those involved in the initial data collection using 2D-LC conditions. Subsequently, it was demonstrated by Den Uijl et al. that isocratic retention of analytes can be predicted by scanning-gradient-based models on trap columns under the dilution-flow conditions used in SPAM [32]. The authors emphasized the careful selection of the dilution flow under specific conditions, and dilution with a weaker eluent was shown to be counterproductive [32]. In another study, Boelrijk et al. explained how to apply Bayesian optimization for 2D-LC optimization of various parameters derived from experiments [33]. Molenaar et al. developed a peak-tracking algorithm for LC × LC and applied it to peptide retention data obtained under different chromatographic conditions [34]. Retention modeling was used to expedite the development of 2D-LC methods for chiral and achiral pharmaceutical separations [35, 36]. In these studies, the retention data obtained from 1D-LC or 2D-LC were used to train 2D retention models. The resulting models were then used to develop resolution maps depending on various variables, such as column temperature and mobile phase composition. The generated resolution maps can be used to determine the circumstances that are most likely to produce key peak pairs in the 2D with appropriate resolution. Stoll and Pirok presented retention modeling for 2D-LC and concluded that it can decrease the amount of trial and error required [37]. Subsequently, they developed a 2D-LC-MS method using this model to discover impurities co-eluting with primary peaks in 1D-LC therapeutic peptide analysis [38, 39].
Artificial neural network (ANN) was reported to be the most effective and accurate for predicting LC chromatographic characteristics. The ANN combined with the quantitative structure retention relationship (QSRR) was used for RT prediction [13]. Therefore, AI algorithms are powerful tools that automate tasks and enhance the precision of analytical methods, especially in metabolomics. The 2D-LC is an effective tool for separating and identifying metabolites, but it generates massive and complicated datasets, which can be challenging. Therefore, AI algorithms are usually used to analyze these datasets and identify patterns and trends that would be difficult to find through manual inspection. Signal preprocessing is primarily concerned with individual chromatographic signals. It addresses problems such as modulation-phase adjustment [40, 41], peak identification [42], noise removal [43], baseline drift correction [44], and peak distortions [45]. Its main goal is to improve the raw 2D-LC data analysis. Additionally, in data processing, the ROIMCR (regions of interest multivariate curve resolution) approach [46] is a sensitive and adaptable technique for identifying and quantifying a broad spectrum of metabolites in complex samples [47]. Recently, software “Program for Interpretive Optimization of Two-dimensional Resolution” (PIOTR) developed by Pirok et al. [48] has shown to speed up LC × LC method development, based on only a few experiments with the consideration of retention behavior of the analytes under varying isocratic or gradient mobile-phase conditions. Therefore, it is anticipated that these algorithms are expected to be applied to ensure the accuracy of individual peaks. Several AI technologies have been developed to optimize signal preprocessing strategies, improve the quality of individual signals before data integration, and handle the complexities of each chromatographic trace [48, 49]. Furthermore, large amounts of data produced by metabolomics studies were analyzed using ML-based techniques to identify patterns and distinguish constituents within their classes by assessing features specific to each class [23]. These techniques include cluster analysis, principal component analysis, hierarchical clustering, uniform manifold approximation and projection, t-distributed stochastic neighbor embedding, k-nearest neighbors, fisher ratio, naïve Bayes, logistic regression, linear discriminant analysis, partial least squares, partial least squares-discriminant analysis, and quadratic discriminant analysis [23, 24, 25].
The integration of AI into the development and optimization of 2D-LC represents a significant technological advancement, offering enhanced capabilities, efficiency, and accuracy in the analysis of complex mixtures. Despite these advances, challenges remain. Data quality and quantity are critical for successful AI and ML applications. Hybrid models, integrating one or more AI or ML models, can be used to predict different chromatographic characteristics. An ongoing trend is the creation of large, well-curated chromatographic databases that facilitate model learning, training, and validation. As AI continues to evolve, its applications in 2D-LC are expected to expand, providing even greater benefits to analytical chemistry.
5. Experimental design, sample preparation, and separation technologies
An effective metabolite extraction procedure is the most critical step in obtaining high-quality metabolomics data. To achieve this, sample quenching was performed in liquid nitrogen, followed by homogenization (Figure 2). After homogenization, several selective metabolite extraction methods like microwave-assisted extraction, ultrasound-assisted extraction, high-voltage electric discharge extraction, supercritical fluid extraction, enzyme-assisted extraction, and SPE were utilized, which are extensively covered in cited references [50, 51, 52]. Combining two or more separation techniques in a single analysis can also reduce the co-elution of metabolites and improve separation efficiency. Some examples include 2D gas chromatography (2D-GC), supercritical fluid chromatography (2D-SFC), and two- and three-dimensional LC (2D-LC and 3D-LC) [53]. Among them, 2D-LC exhibits superior chromatographic capabilities, including enhanced resolution, peak capacity, and sensitivity, and covers a wide range of metabolites. It has been extensively utilized for untargeted metabolomics. Different 2D-LC combinations of chromatography like HILIC × HILIC, HILIC × RPLC, RPLC × RPLC, NPLC × RPLC, RPLC × SFC, Achiral LC × Chiral LC, HILIC × SFC, and so on were introduced and hyphenated with various types of MS detectors. Lv et al. used parallel column 2D-LC-MS for broad coverage of metabolome and lipidome [54]. Online 2D-LC offers high-throughput and automated analysis, although 2D chromatographic resolution can be reduced by short separation times.
Figure 2.
A general workflow for 2D-LC-based metabolomics research, including sample preparation, two-dimensional separation, MS data acquisition, and analysis. Please note that for online 2D-LC, the 1D detector and fractions are eliminated.
2D-LC is a powerful analytical technique increasingly used in metabolomics research due to its enhanced separation capabilities, sensitivity, and resolution. 2D-LC allows detailed profiling of polar to nonpolar metabolites (Figure 1). This section summarizes recent applications of 2D-LC in Table 1 and discusses them in the text. The following parameters, such as sample type, 1D and 2D, detection technique, and analytical purpose are included in each application (Table 1). The recent applications of 2D-LC are explored in clinical research, pharmaceutical research, food research, plant sciences, toxicology research, and environmental research (Figure 1).
Sample type
1D
2D
Detection technique
Analytical purpose of the application and its references
HILIC offers significant advantages when combined with RPLC for metabolomics by providing a valuable strategy to enhance metabolite coverage and separation efficiency. RPLC is widely used due to its reproducibility and reliability, whereas HILIC enhances metabolite coverage, especially for highly polar compounds. Combining HILIC × RPLC allows for simultaneous analysis of hydrophilic and hydrophobic metabolites in a single injection [88, 89, 90], yielding a broader metabolome coverage due to the complementary separation mechanisms. Additionally, HILIC × RPLC enhances separation efficiency, peak capacity, and overall analytical performance in untargeted metabolomics, providing a deep insight into the metabolic changes associated with health and disease states [91]. The HILIC × RPLC was tested using several biological samples, that is, plasma, serum, urine, fecal, seminal plasma, and liver. Guo et al. used this system to measure 417 metabolites in plasma, serum, urine, fecal, and seminal plasma, covering the polar to the nonpolar range [56]. Wu et al. reported that 331 metabolite features were identified using the offline HILIC × RPLC from Lilium lancifolium and L. brownii, which are commonly used in the production of Chinese medicine and vegetables [92]. Using HILIC × RPLC coupled with HR-MS, 302 metabolites were structurally identified or tentatively characterized from Cuscuta chinensis [57]. Zhang et al. devised a HILIC × RPLC approach based on dilution modulation that allows for the simultaneous detection of 126 prohibited substances in cosmetic products [59]. The HILIC × RPLC has also been used for targeted analysis of specific metabolites. For example, Helmer et al. recently utilized the HILIC × RPLC method to separate cardiolipins and their oxidation products [93]. Dang et al. conducted a target separation of flavonoids from Saxifraga tangutica [94]. The technology is also applied in the simultaneous analysis of highly polar and nonpolar pesticides in food [55]. Laan et al. developed a comprehensive identification workflow to identify taste-related RT and m/z features of unknown compounds in soy sauce [71].
The hyphenation of HILIC × RPLC with HR-MS is advantageous for lipidomics. Separation according to the head group can be achieved using HILIC, which allows the collection of individual classes. Then, the lipids can be further separated according to fatty acid chain properties using RPLC. Sorensen et al. implemented HILIC in the 1D to fractionate lipids in human plasma into nine fractions and subjected them to a 2D for further separation using a C18 column to resolve over 1000 lipids from human plasma [58]. To tackle the challenge of multiple lipids co-eluting from LC causing ion suppression in MS, Xu et al. developed Lipid Wizard software to analyze lipid profiles acquired by HILIC × RPLC-MS, leading to high confidence in lipid assignment and high accuracy of lipid quantification [95].
Recently, a synthesized monolithic column composed of a copolymer of styrene, divinyl benzene, and 1-vinyl-1,2,4-tria-zole has allowed for the separation of both polar and nonpolar metabolites [68]. A new mixed-mode stationary phase derived from [2-(3, 4-epoxycyclohexyl) ethyl] trimethoxysilane offers hydrophilic interaction, reversed-phase, and ion-exchange functionalities, which facilitate the simultaneous separation of polar, nonpolar, and ionic compounds [91]. These advancements have great potential in pushing biomolecular separation to the next level of frontiers.
Another popular 2D-LC method is RPLC × RPLC, which offers wide applicability of reverse-phase conditions to separate metabolites with high efficiency and great resolution. In addition to manipulating the functional group chemistry of the stational phase, high pH and low pH fractionation are often utilized to change the chemical properties of analytes and achieve high-resolution separation. With HILIC fractionation of metabolite extracts from three Astragalus species (A. membranaceus var. mongholicus, and A. membranaceus), an online RPLC × RPLC was used to separate and characterize 513 metabolites from the fractionated samples [53]. The RPLC × RPLC technique increases peak capacity and enables the resolution of potential co-eluting metabolites, leading to the detection of a higher number of metabolites than the conventional 1D-LC system. For example, Wong et al. report that 120 metabolites were detected using the RPLC × RPLC system from Glycyrrhiza glabra extract, and compared with 1D-LC, 2-time more metabolites were separated using 2D-LC [70]. Xu et al. used 1D-LC and RPLC × RPLC LC to identify 2357 metabolites in normal human urine. The 1D-LC-MS/MS technique identified lipid and amino acids, whereas the 2D-LC-MS/MS technique profiled additional metabolites to lipid and amino acids [74]. Liu et al. used ion exchange chromatography in 1D separation to fractionate between acidic and weakly acidic components. These subfractions were then separated by 2D and 3D chromatography using either online RPLC × RPLC or offline HILIC × RPLC. As a result, 1097 metabolites were identified from three Glycyrrhiza species [75].
NPLC × RPLC is another popular 2D-LC combination that offers high resolution for polar metabolites in 1D and nonpolar metabolites in 2D. NPLC separates compounds in a mixture based on their polarity using columns packed with polar stationary phases like silica gel or alumina and nonpolar or moderately polar mobile phases. It can be used for a wide range of applications, from small organic molecules to larger natural products (Figure 1). This technique is also useful for neutral metabolites, differing in hydrophobicity and polarity. Yang et al. demonstrated the promising potential of NPLC × RPLC 2D-LC-QToF-MS for identifying 13 lipid species that were proposed as potential lipid biomarkers for Lacunar infarction [80]. In addition, NPLC × RPLC 2D-LC-Orbitrap-MS coupled with mass defect filter (MDF) technology was used to characterize 1631 prenylated phenolics in a targeted manner [81].
The achiral derivatization of analytes can improve their molecular properties by considering chromatographic separation, ionization stability, and MS detection sensitivity. For example, several researchers performed pre-column derivatization of amino acids, to improve separation and detection sensitivity [96, 97, 98]. Generally, direct chiral metabolomics involves chiral stationary phases or chiral derivatizing reagents with polysaccharide derivatives, macrocyclic antibiotics, chiral crown ethers, chiral ion exchangers, and donor-acceptor phases as chiral selectors. For untargeted chiral metabolomics, a unique strategy was developed based on the simultaneous chiral derivatization of hydroxy/amine moiety-containing metabolites, including all hydroxy acids and amino acids, with the enantiomeric pair of diacetyl-tartaric anhydride. This method separated 214 chiral compounds, including 106 amino acids and 28 hydroxy acids [99]. To achieve better metabolite coverage, a 3D-LC system equipped with reversed-phase, anion-exchange, and enantioselective columns has been developed to measure trace levels of D-asparagine, D-serine, D-alanine, and D-proline, potential biomarkers of chronic kidney disease in human plasma [16]. Due to the structural similarities of cannabinoids, their purification remains a bottleneck. Offline 2D-semipreparative chromatography (macroporous resin column in 1D and C18 column in 2D) employed sequential processes for the scalable purification of cannabinoids from ethanolic extracts of cannabis inflorescence [100].
The LC × SFC or SFC × LC integration exhibits interesting orthogonality for separating ionizable and neutral molecules. Sarrut et al. demonstrated that RPLC × SFC can generate a slightly higher peak capacity than RPLC × RPLC [101]. It has been proven a desirable tool for low-to-moderate molecular weight and thermally labile metabolites, which cannot be separated by GC and CE. Specifically, 2D SFC employing supercritical CO2 as the primary mobile phase has polarity like hexane that can be adjusted using polar organic solvents (modifier) as the mobile phase. SFC is a green technology because of its low consumption of organic solvents [102]. The unique properties of supercritical CO2 include its high density, low viscosity, good diffusivity, and outstanding solvating power, enabling quick and high-resolution investigation using SFC [103]. It has been applied to several areas like pharmaceuticals, pesticides, foods, herbicides, and fossil fuels [104].
Lignin depolymerization produces numerous low-molecular-weight phenolic compounds via selective bond cleavage, which can be further used for valuable chemical production. Tammekivi et al. developed a novel online RPLC × SFC method with a trapping column interface for separating phenolic compounds in depolymerized lignin samples [105]. Moreover, an offline 2D RPLC × SFC coupled with Q-ToF-MS/MS was applied to the untargeted analysis of depolymerized lignin. The monomers, dimers, trimers, and tetramers of lignin were separated based on the number of hydroxyl groups and steric effects; as a result, 471 metabolites were detected [105]. Moreover, the SFC method presents special benefits for separating isomers of natural products due to the high degree of orthogonality and significant peak capacity offered by the RP × SFC. Qu et al. identified 324 sesquiterpene alkaloid isomers using RP × SFC, reflecting their separation ability for efficient isomers characterization [106].
Other applications of SFCs hyphenated with the RPLC-HILIC system include the hidden target screening of environmental water samples. This serial coupling of multidimensional chromatography provides an important benefit of extending the range of separable and detectable compounds from “non-polar” to “polar” and even “very polar.” This study validated 274 environmental compounds by comparing their RTs and masses with those of their reference standard compounds [3]. Wastewater from hydrothermal liquefaction contains many compounds belonging to several classes that are hazardous. Teboul et al. used RPLC × SFC to analyze hydrothermal liquefaction wastewater from algae conversion, illustrating the power of 2D separation [107]. Sarrut et al. employed the RPLC × SFC system to separate neutral compounds especially, aromatic compounds from an aqueous extract of bio-oil [101]. This integrated system is also useful for separating fatty acids from fish oils [108]. Moreover, online RPLC x SFC can significantly improve the characterization of bio-oils containing thousands of compounds covering a very wide range of molecular weights and polarities [109].
As a modern SFC, ultra-high performance SFC (UHPSFC) can dramatically improve repeatability, stability, and reliability compared to conventional SFCs. Recently, the RPLC × UHPSFC system has been applied to characterize bufadienolides in Venenum Bufonis and examine the lipidome differences among three different species of ginseng [103, 110]. Despite the apparent promise of coupling LC with SFC for neutral chemical analysis, some drawbacks have been noted. Because of the nature of the SFC mobile phase, online SFC × LC implementation requires a sophisticated link between the two dimensions. The opposite combination LC × SFC experiences severe injection effects, particularly when the LC dimension is operated in a reversed phase. Currently, inadequate commercial instrumentation limits its potential in online mode [111].
Both offline and online 2D LC-based methods were used to examine toxicants in household products. Both methods identified the same number of toxicants in the sample. Conventionally, the offline 2D LC-MS method identified ester unsaturated ester quaternary ammonium compounds (QACs) as causative agents of observed toxicity [73]. The online 2D-LC method exploits the combination of a mixed-mode weak anion exchange-reversed phase (RPWAX) with an octadecyl stationary phase (C18), separating 24 poly- and perfluorinated compounds according to ionic classes and chain length [86].
Conjugated fatty acids, produced by on oxidation of polyunsaturated fatty acids, are present as contaminants in pharmaceutical lipid formulations. For quality control and impurity profiling, 1D LC may not be the best method due to the structural complexity of the resulting multicomponent samples. Olfert et al. applied the 2D-LC chiral × RPLC method hyphenated with online DAD-UV and QTOF-MS/MS for the separation of conjugated polyunsaturated fatty acid isomers and structurally related (saturated, unconjugated, and oxidized) compounds [76]. Additionally, combining orthogonal LC techniques increases the peak capacity compared with 1D LC, enhances the number of identified lipids, and reduces interfering matrix components. Although processing heavy oil into useable products adds value, it necessitates sophisticated technology and thorough characterization required to maximize the production of the most profitable products. Separation of heavy oil has been achieved using 2D-LC and characterized de-asphalted maltenes [112].
Phosphorylated sugar isomer separation is a highly demanding and active field of study, and various unique and sensitive techniques have been employed. For instance, Su et al. evaluated the ability of HILIC to separate metabolites from the pentose phosphate and glycolysis pathways using a mixed-mode HILIC/strong anion exchange (SAX) approach [87]. Co-elute fructose 6-phosphate and glucose 1-phosphate were separated in 2D using a HILICpak VT50-2D column. It allows undisturbed determination of glycolytic phosphorylated carbohydrate metabolites via their chromatographic separation from hexose monophosphate metabolites [87].
The inherent complexity of samples containing thousands of metabolites has led to the development of 2D-LC. Recent technological advancements have enhanced throughput and detection sensitivity and have solved the problem of solvent mismatches. Synthesized monolithic and mixed mode stational phase columns have started to show utility in metabolomics. Comprehensive 2D-LC-MS/MS has enabled the profiling of a wide range of metabolites and has proven to be a vital tool in many fields of biology and medicine. It appears to be a powerful technique for both targeted and untargeted metabolomics. AI and ML are emerging frontiers in metabolomics that require more learning, training, and validation to predict different chromatographic and MS characteristics.
program for interpretive optimization of two-dimensional resolution
QSRR
quantitative structure retention relationship
RNNs
recurrent neural networks
ROIMCR
regions of interest multivariate curve resolution
RPLC
reverse-phase liquid chromatography
SFC
supercritical fluid chromatography
SPAM
stationary-phase-assisted modulation
TRLC
TEMPERATURE-responsive liquid chromatography
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Written By
Yatendra Singh and Sixue Chen
Submitted: 25 July 2024Reviewed: 01 August 2024Published: 02 October 2024
Open access peer-reviewed chapter - ONLINE FIRST
