Tag: 2D-LC

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Parallel Gradients 2DLC-HRMS of complex protein digest

2D-LC System
2D-LC System

Investigating the proteins in biological samples can help us understand and identify diseases and improve the effectiveness of medication. To study proteins in these samples, they are typically digested into peptides and subsequently analyzed by liquid chromatography (LC) hyphenated with high-resolution mass spectrometry (HRMS).

Comprehensive two-dimensional LC (LC×LC) offers increased separation power over traditional LC methods. However, most common gradient designs require re-equilibration of every second-dimension run, resulting in high flow rate operations to limit the empty separation space. This also limits MS sensitivity as flow splitting is required to handle such flow rates.

In this work, we developed an LC×LC method using a so-called parallel-gradient design, which omits the need for column re-equilibration and enables the use of the entire separation space. Moreover, this allows for lower flow rates and maintains the sensitivity for low-abundant analytes. The parallel-gradient design achieved higher surface coverages and sensitivity at lower effective peak capacities. Most importantly, both methods were applied to analyze a Human IMR90 lung fibroblast cell line digest to assess its applicability to real complex samples. The parallel-gradient method was able to identify significantly more proteins than the current state-of-the-art methods while using the same analysis time and at a lower solvent consumption. The applicability of the parallel-gradient design could be improved even further by shortening the modulation times, as it was not limited by column re-equilibration.

The study is a collaborative work done thanks for the contribution of many colleagues and students. The link to the publication is reported below.

https://doi.org/10.1021/acs.analchem.4c02172

 

 

 

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Optimisation of 2D-LC separations by AutoLC

Optimisation of 2D-LC separation technology
Optimisation of 2D-LC separation technology

In their latest work, the AutoLC team of the CAST group of Pirok at the University of Amsterdam extended their method-development workflow to facilitate optimisation of 2D-LC separations [1].

The demonstrated workflow was capable of unsupervised gradient optimization for comprehensive 2D-LC-MS methods without needing to specify sample information. The required algorithms were designed by CAST researchers Stef Molenaar, Tijmen Bos and Jim Boelrijk, under the supervision of Bob Pirok.

The workflow was inspired by the original theoretical paper by Pirok in 2016 [2], in which retention models are constructed to computationally simulate methods and select the optimal parameters. In a nutshell, the system first utilizes very generic methods to measure the sample. This data is then used to construct retention models that describe the retention behavior of the analytes as a function of the mobile-phase composition. A large number of methods are then simulated computationally, and a computed optimum is submitted to the LC system. The LC system then carries out the proposed method and submits its data back to the system afterwards. Consequently, unsupervised optimisation of 2D-LC separations may be attained.

Unsupervised optimization of comprehensive 2D-LC separations
Figure 1. The AutoLC algorithm was demonstrated on a peptide digest separation using RPLC in both dimensions. Panels A, B, D and E show scanning conditions, with Panels C and F showing a computed method that was proposed unsupervised by the algorithm. Reproduced with permission from [1].

The Pirok group earlier had demonstrated their AutoLC platform on 1D separations [3], but now extended this to the more complicated 2D separation methods [1]. To accomplish this, algorithms were designed to compute and optimize complex second-dimension shifted-gradient assemblies.

The researchers also investigated the robustness of the retention models and the influence of the errors in peak-width predictions.

The study was conducted in a collaboration with the group of Dwight Stoll at Gustavus Adolphus College where the measurements were conducted. The developed tool for optimisation of 2D-LC separations was demonstrated on a complex peptide digest sample with RPLC in both of the dimensions.

The work was a product of Pirok’s UPSTAIRS (Unleashing the Potential of Separation Technology to Achieve Innovation in Research and Society) project, which is financed by the Dutch Research Council (NWO), as well as the synergetic UNMATCHED project, which is supported by BASF, DSM and Nouryon, and also receives funding from NWO.

The work was published open-access in Journal of Chromatography A and can be accessed free of charge here.

References

  1. Computer-driven optimization of complex gradients in comprehensive two-dimensional liquid chromatography, S.R.A. Molenaar, T.S. Bos, J. Boelrijk, T.A. Dahlseid, D.R. Stoll, B.W.J. Pirok, Chromatogr. A, 2023, 1707, 464396, DOI: 10.1016/j.chroma.2023.464306.
  2. Program for the interpretive optimization of two-dimensional resolution, B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061.
  3. Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography S. Bos, J. Boelrijk, S.R.A. Molenaar, B. van ‘t Veer, L.E. Niezen, D. van Herwerden, S. Samanipour, D.R. Stoll, P. Forré, B. Ensing, G.W. Somsen, B.W.J. Pirok, Anal. Chem. 2022, 94(46), 16060–16068, DOI: 10.1021/acs.analchem.2c03160.
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2D-LC in industry: technological innovations reviewed

2D-LC in industry: technological innovations reviewed
2D-LC in industry: technological innovations reviewed

CAST scientist Rick van den Hurk wrote a review on recent developments in 2D-LC and the use of 2D-LC in industry. He did this under the supervision of Bob Pirok and in collaboration with Matthias Pursh (Dow) and Dwight Stoll (Gustavus Adolphus College).

Two-dimensional liquid chromatography (2D-LC) greatly advances the separation powered of analytical separation sciences through a better peak capacity as well as offering more-tailored selectivity combinations.

However, the field of 2D-LC is, in particular in contrast to 2D-GC, still very immature and under significant development. In 2019, Bob Pirok, Dwight Stoll and Peter Schoenmakers published a review in which they examined the latest trends from 2015 until 2019 [1].

In this recent installment [2], Rick van den Hurk reviewed the recent innovations between 2019 and 2023. In addition the review also devotes significant focus to the implementation of the technique in industry. The review was co-written by Bob Pirok, Dwight Stoll (Gustavus Adolphus College) and Matthias Pursch (Dow).

The authors examined over 200 articles and also compared these with the articles published prior to 2019. In their review, the authors concluded that mobile-phase mismatch continues to be an important focus area for the field, and several modulation strategies and new variants were discussed.

Van den Hurk and co-workers also noticed that a third of the publications had at least one author affiliated with industry. Application fields that particularly demonstrated involvement were the polymer characterization, metabolomics, and pharmaceutical and biopharmaceutical analysis. Furthermore, industrial applications favored the use of heart-cut 2D-LC and largely employed on-line hyphenation. The authors did note that the database was likely to be missing out on a number of industrial works that are not published for confidentiality reasons.

Other important developments were the increased popularity of computer-aided strategies, alternative gradient-elution methods to facilitate modulation, as well as multi-stage, multi-dimensional separations, the latter of which were applied to the characterization of protein therapeutics.

2D-LC in industry: technological innovations reviewed
Figure. Number of applications per application area distributed by non-comprehensive (light blue) and comprehensive (dark blue) applications between 2019 and 2023. Reproduced with permission from [2].

Two-dimensional liquid chromatography is of paramount importance to the PARADISE project of CAST scientist Bob Pirok in which multi-dimensional separation technology is used to achieve separation of highly complex samples. In addition, the project aims to characterize the correlation of different sample properties within a single analytical solution. The outcomes of this recent review are thus of value to the ongoing progress in the PARADISE project. Rick van den Hurk is a PhD candidate in the PARADISE project, which stands for Propelling Analysts by Removing Analytical-, Data-, Instrument- and Sample-related Encumbrances and receives funding from the Dutch Research Council (NWO), as well as a number of public and private organisations. Read more about the PARADISE project here.

In addition, the review was part of Pirok’s UPSTAIRS project, which aims to improve the accessibility of advanced separation technology by developing computational methods to leverage chromatographic theory in an unsupervised workflow. This project also receives funding from NWO.

The work was published in TrAC Trends in Analytical Chemistry as open access, and can thus be accessed for free here.

References

  1. Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications, B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841
  2. Recent trends in two-dimensional liquid chromatography, R.S. van den Hurk, M. Pursch, D.R. Stoll, B.W.J. Pirok, TrAC Trends in Analytical Chemistry, 2023, 166, 117166, DOI: 1016/j.trac.2023.117166
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Packed loops may reduce dispersion in 2D-LC

Packed particles may reduce dispersion in 2D-LC
Packed particles may reduce dispersion in 2D-LC

Modulation interfaces employing sample loops are applied in many hyphenated separations such as two-dimensional liquid chromatography (2D-LC). When the first-dimension effluent in 2D-LC is eluted from the modulation loop, dispersion effects occur due to differences in the laminar flow velocity of the filling and emptying flow. These effects were recently studied by Moussa et al. whom recommended the use of coiled loops to promote radial diffusion and reduce this effect. In the 1980s, Coq et al. investigated the use of packed loops, which also promote radial diffusion, in large volume injection 1D-LC. Unfortunately, this concept was never investigated in the context of 2D-LC modulation.

In their study CAST scientist Wouter Knol evaluated use of packed loops in 2D-LC modulation and compares them to unpacked coiled and uncoiled modulation loops [1]. The work was conducted under the supervision of Prof. Ron Peters and Dr. Bob Pirok. The effect of the solvents, loop volume, differences in filling and emptying rates, and loop elution direction on the elution profile were investigated.

Packed loops may reduce dispersion in 2D-LC
Figure 1. Schematic representation of the experiential setup in the loop filling and emptying valve position. Along with the packing of loops, the effect of coiling was also investigated. Reproduced with permission from [1].

To pragmatically quantify elution profile characteristics, Knol employed statistical moments. Decreased dispersion was observed in all cases for the packed loops compared to unpacked loops and unpacked coiled loops. In particular for larger loop volumes the dispersion was reduced significantly. Furthermore, countercurrent elution resulted in narrower elution profiles in all cases compared to concurrent elution.

Knol found that packed modulation loops are of high interested when analytes are not refocussed in the second-dimension separation (e.g. for size-exclusion chromatography). One additional observation was that the work suggested that the use of packed loops may aid in prevention of loop overfilling.

Packed loops may reduce dispersion in 2D-LC
Figure 2. Schematic representation of the experiential setup in the loop filling and emptying valve position. Along with the packing of loops, the effect of coiling was also investigated. Reproduced with permission from [1].

This study was conducted in the context of the UNMATCHED project which received funding from BASF, DSM and Nouryon, as well as the Dutch Research Council (NWO). The work was published in Journal of Chromatography A and can be accessed for free here.

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Characterisation of Smokeless Powders in Explosives by 2D-LC

Analysis of Smokeless Powders by 2D-LC

Smokeless powders are explosives that are typically used as propellant for ammunition but are also encountered in improvised explosive devices such as pipe bomb. This is particularly true for the United States where smokeless powders are readily available [1]. While residue analysis is possible post-explosion, forensic experts often also encounter explosive devices pre-explosion. In such an explosion, investigators must establish a chemical profile of the propellant and compare it to the energetic material found elsewhere such as a suspect’s home.

Scientist Rick van den Hurk from the Chemometrics and Advanced Separations Team (CAST) at the van ‘t Hoff Institute for Molecular Sciences (HIMS) of the University of Amsterdam developed a method to simultaneously characterize smokeless powders both on molecular weight distribution and additive profile. For the method, Van den Hurk employed two-dimensional liquid chromatography (Figure 1). Combining organic size-exclusion chromatography and aqueous reversed-phase liquid chromatography, Van den Hurk designed a two-stage trapping system to improve compatibility and enhance sensitivity.

Figure 1. Schematic overview of the heart-cut SEC-RPLC setup used for characterizing smokeless powders. Reproduced with permission from [2].

The size-exclusion chromatography (SEC) is conducted as first-dimension separation and resolves the smokeless powders based on size. Larger fragments will elute earlier than smaller fragments. An example is shown in Figure 2 on the left-hand side, with a comparison of two samples and their confidence intervals for the entire range.

Next, the additives, which are among the smallest molecules relative to the very large nitrocellulose molecules, are gathered by the trapping interface and injected into a second-dimension reversed-phase LC (RPLC) separation. An example of the additive package for three different smokeless powders is shown in Figure 2.

Figure 2. Examples of feature analysis of smokeless powders. Left: Molecular weight distribution by size-exclusion chromatography of two forensic samples labelled as 315 and 395. Right: Additive analysis of smokeless powders by reversed-phase LC. The different colors indicate different forensic samples. It can be seen that for the additives there is a significant difference between the fingerprint of each sample. Reproduced with permission from [2].

The project included contributions of students Anouk van Beurden and Mabel Dekker who co-developed the method within their internship project. Dekker examined the discrimination power of the SEC method, whereas van Beurden focused on the RPLC separation. Van Beurden also extensively investigated alternative methods to characterize the nitrocellulose in detail including the use of enzymatic degradation.    

The project was conducted in collaboration with Annemieke Hulsbergen-van den Berg from the Dutch Forensics Institute (NFI), and investigated under the lead of Prof. Arian van Asten and Dr. Bob Pirok. Van Asten and Pirok together lead the PARADISE project along with Prof. Govert Somsen at the Vrije Universiteit of Amsterdam. The PARADISE project is a public-private funded collaboration between a consortium of industrial and academic partners. The project was funded by consortium members Covestro, DSM, Genentech, Shell, the Dutch Forensics Institute and the Dutch Science Council (NWO) and aims to advance the analysis of complex mixtures encountered in industry and society.

The work was published open-access in Journal of Chromatography A and can be freely downloaded here.

References

[1] K.D. Smith, B.R. McCord, W.A. MacCrehan, K. Mount, W.F. Rowe, J. Forensic Sci. 44, 1999, 14554J, DOI: 10.1520/jfs14554j

[2] R.S. van den Hurk, N. Abdulhussain, A.S.A. van Beurden, B.E. Dekker, A. Hulsbergen, R.A.H. Peters, B.W.J. Pirok, A.C. van Asten, J. Chromatogr. A, 1672, 2022, 463072, DOI: 10.1016/j.chroma.2022.463072

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Simulated Impact of Machine Learning on 2D-LC Optimization

The chromatographic community has for long pondered the promise of machine learning. CAST member Jim Boelrijk simulated the feasibility of Bayesian Optimization for automating method development in comprehensive 2D-LC.

The prospect of simplified method development for 1D and 2D-LC separations has long been sought for. Indeed, past CAST publications, but also those by many other groups, have investigated the classical approach used also extensively for 1D separations using empirical retention models. Meanwhile, machine-learning tools have emerged as an alternative across STEM fields. It is thus not surprising that its application has been of interest to several groups in the chromatographic community.

Together with dr. Patrick Forré from the Institute of Informatics at the University of Amsterdam, as well as researchers from the Van ‘t Hoff Institute for Molecular Sciences dr. Bernd Ensing (Computational Chemistry) and CAST member dr. Bob Pirok (Analytical Chemistry), PhD candidate Jim Boelrijk (Institute of Informatics) studied the feasibility of using Bayesian Optimization for the optimization of method development in 2D-LC separations.

For any machine learning tool to operate effectively within the context of method optimization, the use of a chromatographic response function or objective function is of paramount importance. Such functions quantify a particular quality descriptor that represent the performance of the separation method. Known examples in the field of 2D separations are peak capacity and orthogonality. However, maximised peak capacity or orthogonality does not necessarily translate into a high information yield. Resolution has also been investigated but its use is impaired by scaling issues. Consequently, the present study employed the concept of connected components (Figure 1). 

Figure 1. Example of labelling of a chromatogram by the chromatographic response function. Blue dots denote components separated with resolutions higher than 1 from all other peaks; red dots denote peaks that are within proximity to neighbors and are clustered together, illustrated by the red lines.

The simulated method development cycles yielded a larger number of separated peaks clusters (connected components) relative to the random and grid search algorithms (Figure 2).

Figure 2. Comparison of the random search, grid search and Bayesian optimization algorithm for sample A (top-left), B (top-right), C (bottom-left) and D (bottom-right) for 100 trials. The vertical black dashed line shows the maximum observed in the grid search (out of 11,664 experiments), while the blue and orange bars denote the best score out 104 iterations for the random search and Bayesian optimization algorithm, respectively.

The study by Boelrijk demonstrated that Bayesian optimization is a viable method for optimization of chromatographic experiments with many method parameters, and therefore also for direct experimental optimization of simple to moderate separation problems. This study was conducted under a simplified chromatographic reality (Gaussian peaks and equal concentration of analytes, generated compounds). Boelrijk thus remains interested to continue this research by working towards actual direct experimental optimization.

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Parallel gradients as alternative to shifted gradients in 2D-LC

In pursuit of full usage of the two-dimensional separation space as prescribed by Giddings [1], the LC×LC chromatographic community is continuously scouting for new methods that yield fully orthogonal separations. With the rich and diverse LC toolkit of available retention mechanisms, chromatographers mainly focus on improving the compatibility of orthogonal – yet incompatible – separation methods. This has spurred the development of active-modulation techniques such as stationary-phase-assisted modulation [2] and active-solvent modulation (ASM) [3]. Ultimately, this angle of innovation is mainly driven by the selection of stationary -and mobile phases, as well as their underlying retention mechanisms.

Rather than fine-tuning selectivity, another branch focuses on tweaking retention factors. For LC×LC separations, this has led to the introduction of shifted gradients. Here, the second-dimension gradient is altered and adapted as a function of the first-dimension gradient program [4]. While extremely effective, the optimization of shifted-gradient assemblies introduces additional complexity to the already more-complex method development process for comprehensive 2D-LC.

The situation of LC×LC contrasts heavily with that of GC×GC. For GC×GC, orthogonal separations are extremely difficult if not impossible due to analyte volatility. As a consequence, wrap-around effects are frequently generated, yet this is rather seen as advantage than disadvantage.

Prof. Tadeusz Gorecki (University of Waterloo, Canada) thus set to investigate what would happen if the same approach would be applied in LC×LC [5]. The project was an international collaboration with Alshymaa Aly (Minia University, Egypt, and University of Waterloo, Canada), Prof. Andre de Villiers and Magriet Muller from Stellenbosch University in South Africa, and Bob Pirok from the CAST team at the University of Amsterdam in the Netherlands.

RPLC×RPLC separations were simulated based on experimental data using the MOREPEAKS framework (formerly PIOTR [6]). Predicted separations using shifted gradients and parallel gradients were compared. The results suggested that parallel gradients indeed may advantageous. To verify this assessment, optimized experimental methods were executed and the resulting separations compared.

Supported by both experimental data and theoretical simulations, the authors concluded that non-orthogonal separation mechanisms could still yield good separation methods in LC×LC.

References

[1] Two-dimensional separations: concept and promise. J.C. Giddings, Anal. Chem. 1984, 56(12), 1258A–1270A, DOI: 10.1021/ac00276a003

[2] Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications. B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841

[3] Active Solvent Modulation: A Valve-Based Approach To Improve Separation Compatibility in Two-Dimensional Liquid Chromatography. D.R. Stoll, K. Shoykhet, P. Petersson, and S. Buckenmaier, Anal. Chem. 2017, 89(17), 9260–9267, DOI: 10.1021/acs.analchem.7b02046

[4] Optimizing separations in online comprehensive two-dimensional liquid chromatography
B.W.J. Pirok, A.F.G. Gargano and P.J. Schoenmakers, J. Sep. Sci., 2018, 41(1), 68–98, DOI: 10.1002/jssc.201700863

[5] Parallel gradients in comprehensive multidimensional liquid chromatography enhance utilization of the separation space and the degree of orthogonality when the separation mechanisms are correlated. A.A. Aly, M. Muller, A. de Villiers, B.W.J. Pirok, T. Górecki, J. Chromatogr. A, 1628, 2020, 461452, DOI: 10.1016/j.chroma.2020.461452

[6] Program for the interpretive optimization of two-dimensional resolution. B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061