Advances in Food Analysis

Biopharmaceutical Analysis: The State Of The Art 

Analytically Speaking Ep. 6

Avoiding Problems That Can Occur Before Data Acquisition Begins

Pitfalls in Proteomics

The Electron Capture Detector (ECD), Its Unique Inventor James Lovelock (1919–2022), and GAIA

 Selectivity and Sensitivity

Sample Preparation by Electric Field–Assisted Extraction

More Questions on Your Extraction Knowledge

Class Is Back in Session

issue back in 1987 followed a major milestone in biology, the FDA approval of the first therapeutic monoclonal antibody (mAb). This event, together with the commercialization of recombinant insulin a few years earlier, marked the birth of a new class of medicines that have drastically reshaped the pharmaceutical landscape. Protein biopharmaceuticals are currently being developed and approved at an explosive rate and have attracted great interest from both biotech and big pharma. Their success is driven by their efficacy in disease areas with a high unmet medical need, such as oncology, autoimmune, and infectious diseases. Antibodies reign supreme but protein biopharmaceuticals come in many flavours, all sharing one common denominator, an enormous therapeutic or prophylactic potential with a layer of immense structural complexity highly demanding towards analytics. 

witnessed how the analytical community rose to the challenge and sharpened the analytical toolbox. An inspiring creativity amongst peers throughout these three decades resulted in many innovative chromatographic and mass spectrometric tools for the in-depth study of these ever-more complex molecules.

Today these fascinating study objects are chromatographically resolved from top to bottom (protein, peptide, glycan, amino acid, monosaccharide) using a diverse set of separation principles, stand‑alone or in conjunction, tackling different physicochemical properties (size, charge, hydrophobicity, hydrophilicity, affinity). To cope with the slow chromatographic diffusion, sub-2-μm fully porous or sub‑3‑μm superficially porous particles are widely employed. Ultrahigh-performance liquid chromatography (UHPLC) instrumentation has been made available to successfully operate the latter particles. Biocompatible or bioinert systems have been developed preventing corrosion at high salt concentrations and analyte loss. Stationary phases have been coated with hydrophilic layers to block nonspecific interactions with hydrophobic proteins, such as antibody–drug conjugates, in size‑exclusion chromatography (SEC), or they have embedded charged functionalities to boost resolution in reversed-phase LC-based peptide mapping while using mass spectrometry (MS)-friendly formic acid (FA) instead of the ion-suppressant trifluoroacetic acid (TFA). Difluoroacetic acid (DFA) has been proposed as a hybrid between FA and TFA, offering decent chromatographic resolution and MS sensitivity. Wide-pore reversed‑phase LC particles capable of withstanding temperatures up to 90 °C allow separations of even the most challenging proteins, and the on/off elution mechanism enables fast measurements, with ultra-short columns largely circumventing the negative impact of temperature on protein integrity. The introduction of wide-pore particles in hydrophilic interaction liquid chromatography (HILIC) has extended the use of this technology beyond mapping of fluorescently labelled glycans and allows glycoforms to be resolved at the protein level. Novel antibody degrading enzymes, such as IdeS, have been released to facilitate the latter. Glycans themselves are nowadays enzymatically liberated and tagged in record times with fluorophores with high proton affinity that boost MS sensitivity by at least 50-fold over the golden standard 2-aminobenzamide (2-AB). Column hardware (frits, inner wall) has been prepared stainless-steel-free, allowing recovery of challenging analytes, such as phosphorylated glycans in HILIC and antibody dimers in SEC, and preventing on-column oxidation during peptide mapping. Multidimensional liquid chromatography has successfully been adopted by the biopharma industry to drastically increase resolution, to make first dimension separations compatible with MS and to obtain orthogonal information. The technology has even 

been stretched from two to five dimensions by incorporating (electro)chemical and enzymatic reactors and thereby maximizing information gathering in a fully automated manner. The quest for ever more dimensions has recently reopened a discussion within the chromatographic community on terminology.

Electrospray ionization (ESI)—invented a couple of months after the inauguration of 

—has given wings to these molecular elephants, enabling successful mass analysis using high resolution time‑of‑flight (TOF) or orbital trap MS instruments equipped with various complementary fragmentation modes employed at top, middle, and bottom level. The coupling of historically incompatible chromatographic methods, such as SEC, ion exchange chromatography (IEC), hydrophobic interaction chromatography (HIC), or affinity chromatography, with MS has been made possible by two‑dimensional liquid chromatography (2D-LC), in which the second dimension enables desalting, or by direct MS hyphenation using volatile mobile phases. The latter sprays proteins under native conditions, contrasting with reversed-phase LC or HILIC–MS that operate under denaturing conditions, opening up perspectives towards studying protein-protein interactions and measuring previously unmeasurable proteins, as charged envelopes are shifted to higher 

values where spatial resolution is at its maximum. Beyond primary structural characterization, native MS, ion mobility (IM), hydrogen-deuterium exchange (HDX), and cross-linking (XL) MS have been shown appealing for higher order structural analysis, epitope mapping, and to assess structure/function relationships. LC–MS has furthermore been applied to study protein biopharmaceuticals in complex matrices in support of pharmacokinetic/pharmacodynamic (PK/PD) programs and to identify and quantify host cell proteins (HCPs) at low ppm levels—measurements historically performed by ligand binding assays.

Throughout the years we have seen products become increasingly complex and witnessed analytics become more and more performant and robust. With that impressive analytical arsenal on the battlefield, we can rest assured that well-characterized and safe innovator and biosimilar products reach the patient. Yet there remains so much to be explored, and with all these opportunities on the shelf, it is highly rewarding as a scientist to be involved in biopharmaceutical analysis today. What will the next 35 years bring? Will nucleic acid-based medicines deliver on their promises? Will we separate our solutes on pillars instead of particles? Will we assess all structural attributes at once or will we keep on using dozens of methods and the most expensive instrumentation out there? Will we continue to handle these enormous amounts of data in a semi-automated manner or will we finally get all the information through one push of the– button? Curious to see how the field will be moulded in the next 35 years. Stay tuned…for a little longer.

is CEO at RIC group (Kortrijk, Belgium) and visiting professor at Ghent University (Ghent, Belgium).

is founder and advisor of the RIC group and emeritus professor of Ghent University.

Articles

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, key opinion leaders from the separation science community explore contemporary trends in separation science and identify possible future developments.

Separation Science: The State of the Art: On the Contemporary Analysis of Protein Biopharmaceuticals

In all areas of analytical chemistry there are obvious advantages to be gained in being able to undertake the analysis at the point of interest or need, and this is particularly so for environmental analysis. Field-portable and long-term “deployable” chromatography systems can provide for robust at-site analysis (monitoring), allow for rapid on-site decision making, and reduce issues arising from sample degradation and/ or contamination during transportation back to the laboratory, or during long‑term sample storage. Further to this, there are significant financial savings to be had, as laboratory analyses are for the most part clearly more expensive.

The significance of liquid chromatography (LC) as a gold standard analytical technique within the analytical laboratory cannot be under-estimated, with this advanced and well-established analytical technology being the go-to technique for all sorts of investigative and confirmatory analytical science, across a hugely diverse range of disciplines and applications. However, translation of the standard liquid chromatograph to a mobile, portable, and even deployable instrument has been curiously slow, that is until now. Today, there are a number of small‑footprint LC-based systems commercially available, which are designed for portability, and interest in these systems has been growing rapidly (1–7).

Milton Lee and co-workers led this most recent charge in and around 2014, reporting on portable nano-flow LC systems, which were shown to deliver impressive performance (8–10). Their first prototype was based on an isocratic piston pump, with stop-flow injection, capillary column, and on‑capillary UV absorbance detector. In 2015, Sharma et al. reported upon a more advanced version of their previous system, which now included gradient-nanoflow dual‑pumps and an on-capillary deep-UV-LED-based detector (9). The pumps were able to operate up to an impressive 550 bar, covering flow rates of 0.5 to 10 μL/min. The dimensions of this system were 31 × 18 × 14 cm and it weighed a mere 8.0 kg—it could also be battery operated. It was later applied to several “in-field” applications, including illicit drug analysis (10).

Running parallel to the above developments is an ongoing project focused on a more modular approach to portable liquid chromatography, initiating 

from early work by Li et al., and involving a prototype open-platform LC with micro‑syringe pumps capable of simple gradient programs (11). The first micro‑LC was built using micro-syringe pumps (20 μL volume), mini-switching valves, and a manual injector all assembled on a breadboard, and used a commercial reversed-phase monolithic capillary column, with on-capillary deep-UV-LED absorbance detection. The dimensions were just 25 × 25 cm, weighing in at just 1.3 kg. This system was further developed by Lam et al., with modifications including a Z-cell-based LED detector, column heater, and an automated injector. This chromatograph weighed ~ 2.7 kg, and in this version all components were contained within a compact 3D-printed portable housing. This upgraded version was lightweight, small-size, and was demonstrated for the in-field analysis of plant extracts (12). A fully modular and re-engineered descendant of these earlier platforms was produced in 2020 (13). This full gradient capillary system (dimensions now of 30 × 45 × 12 cm and 7.2 kg weight) consisted of two syringe solvent pumps capable of handling up to 330 bar across flow rates of 1 to 100 μL/min, with retention time precision of < 1% (RSD). This instrument now included custom-built software and came with an open architecture design and a flexible configuration to fit a variety of intended on-site applications.

In the area of inorganic ion analysis, the standard laboratory (chromatographic) approach would be to use ion chromatography (IC), most commonly with suppressed conductivity detection. This now long-established methodology is the gold standard analytical technique for inorganic anions and cations, and is likely to remain so for the considerable future—particularly for the analysis of natural and potable waters. Interestingly, so-called 

were commercially available as far back as the early 1980s, although they did not generate a great deal of attention or application at that time. However, following a hiatus of three or so decades, there are today at least three commercial suppliers of truly field-portable ion chromatographs, engineered from the ground up to be applied out of the laboratory and operate effectively at the point of need (14–16).

One area where field-deployable ion chromatography has a great deal of potential is in the field of nutrient monitoring. Inorganic nutrients— including those most monitored species, namely ammonia, nitrite/nitrate, and phosphate—are of course the lead culprits in freshwater eutrophication. Today, almost every environmental monitoring organization around the globe routinely check for these species, predominantly via costly and slow grab‑sampling and lab-based analysis. For us, portable, low-cost, and low-pressure ion chromatography, including the push for “at-site” and long-term deployable systems, has been a focus of our research efforts dating back over two decades (17), but the last few years has seen this idea really take-off, and nowadays affordable commercial systems are available for precisely these applications (16). Recently, to test the robustness and reliability of one such field-deployable ion chromatograph under extreme environmental conditions, we set up a simple monitoring experiment within a greenhouse, in the Australian (Tasmanian) springtime, where ambient conditions can vary from between 4 to 40 °C. The experiment involved a field-portable ion chromatography-based system, which employs direct UV absorbance detection for nitrite and nitrate quantification, and which is a fully automated and wirelessly monitored system, capable of unmanned deployment for several months at a time (16). With a model sample system in place consisting of a large, filled water tank, containing a bottom layer of sheep‑manure (ammonia source), covered by a layer of soil and a top layer of sand, the system was set to monitor the production and equilibrium of nitrite and nitrate concentrations over a period of several weeks. Figure 1(a–b) shows the system in place within the greenhouse and the analytical data generated from this controlled field demonstration. The temperature plot demonstrates the daily extremes of temperature, whilst the rapid growth of nitrate and slower continual rise of nitrite levels in the water tank can be continually monitored. Given the number of sample chromatograms run during this deployment, it’s not difficult to see the savings in lab time and costs, when compared with taking grab‑samples run on an ion chromatography system within a centralized laboratory.

Of course, the ultimate test of an analytical system’s portability is taking it on a sampling journey! Figure 1(c) 

really demonstrates this new philosophy and capability. With a fully portable and battery-powered ion chromatograph, we are able to undertake river cruises and map nutrient levels as we go (and simultaneously enjoy some of our state’s most scenic and evidently unpolluted waterways), which for a chromatography‑based analyzer is really quite something. The benefit here is improved spatial coverage and mapping of dynamic systems, particularly inputs and temporally variable point sources, which can lead to better understanding and management of river catchments in the future.

In summary, it would seem clear that a number of instrument manufacturers and innovators have begun to see liquid chromatography as a platform technology that is indeed amenable to portable and “at-site” or deployable applications. This has awoken end-users to new possibilities and potential cost savings and process improvements. We certainly foresee a continuation in this direction and more systems coming to the market—hopefully with inherent flexibility and modularity to allow greater application to really interesting “out-of-lab” challenges.

Grant funding from the Australian Research Council gratefully acknowledged (Linkage Grant - LP200200394).

1) F. Rahimi, S. Chatzimichail, A. Saifuddin, A.J. Surman, S.D. Taylor-Robinson, and A. Salehi-Reyhani, 

2) E. Vasconcelos Soares Maciel, A.L. de Toffoli, E. Sobieski, C.E. Domingues Nazário, and F.M. Lanças, 

3) K. Mejía-Carmona, J. Soares da Silva Burato, J.V.B. Borsatto, A.L. de Toffoli, and F.M. Lanças, 

4) D.A. Vargas Medina, E.V.S. Maciel, and F.M. Lanças, 

5) D.A. Vargas Medina, E.V.S. Maciel, A.L. de Toffoli, and F.M. Lanças, 

6) S. Sharma, A. Plistil, R.S. Simpson, K. Liu, P.B. Farnsworth, S.D. Stearns, and M.L. Lee, 

7) E. Murray, P. Roche, M. Briet, B. Moore, A. Morrin, D. Diamond, and B. Paull, 

8) S. Sharma, L.T. Tolley, H.D. Tolley, A. Plistil, S.D. Stearns, and M.L. Lee, 

9) X. Zhao, X. Xie, S. Sharma, L.T. Tolley, A. Plistil, H.E. Barnett, M.P. Brisbin, A.C. Swensen, J.C. Price, P.B. Farnsworth, H.D. Tolley, S.D. Stearns, and M.L. Lee, 

10) S.W. Foster, X. Xie, M. Pham, P.A. Peaden, L.M. Patil, L.T. Tolley, P.B. Farnsworth, H.D. Tolley, M.L. Lee, and J.P. Grinias, 

11) Y. Li, M. Dvořák, P.N. Nesterenko, R. Stanley, N. Nuchtavorn, L.K. Krčmová, J. Aufartová, and M. Macka, 

12) S.C. Lam, L.J. Coates, M. Hemida, V. Gupta, P.R. Haddad, M. Macka, and B. Paull, 

13) L.J. Coates, S.C. Lam, A.A. Gooley, P.R. Haddad, B. Paull, and H.-J. Wirth, 

www. labtron.com/portable-ion-chromatograph/ lpic-a10

15) Shine – Portable ion chromatograph: 

www.dksh.com/global-en/products/ ins/shine-portable-ion-chromatograph

16) Aquamonitrix real-time nitrate and nitrite analyser: 

17) D. Victory, P. Nesterenko, and B. Paull, 

is a second year PhD student at the University of Tasmania

is a first year PhD student at the University of Tasmania.

is a research engineer at Sense-T within the University of Tasmania.

is the director of Sense-T at the University of Tasmania.

is an adjunct senior lecturer within the School of Natural Sciences – Chemistry at the University of Tasmania.

is R&D manager for Aquamonitrix Ltd., Carlow Ireland.

is professor of analytical chemistry at the University of Tasmania, and director of the ARC Training Centre for Hyphenated Analytical Separation Technologies (HyTECH).

Separation Science: The State of the Art: Portable and Field-Deployable Liquid Chromatography for Environmental Studies

In recent years, multidimensional liquid chromatography (MD–LC) has become a very powerful analytical method for the successful analysis of nonvolatile analytes in complex real-world samples. MD–LC is a powerful alternative technique to conventional one-dimensional (1D)-LC and involves the use of two “orthogonal” liquid separation systems (1). The main advantage of such a technique is linked to the augmented separation power, as a consequence of the increased selectivity and sensitivity of the two systems. In MD–LC, the fractions eluting from the first dimension (1D) are fractionated and re-injected into the second dimension (2D). When only a few selected parts eluting from the 1D are analyzed in the 2D, the approach is regarded as “single” heart‑cutting (LC–LC; Figure 1[a]); when two or more selected fractions are diverted into the 2D, the technique is called “multiple heart-cutting” (mLC–LC; Figure 1[b]). In addition, if the whole sample is transferred to the 2D, a full “comprehensive” (LC×LC) approach is accomplished (Figure 1[d]). Last but not least, if a specific region of the 1D separation is considered for the analysis, a selective LC×LC (sLC×LC) is carried out (Figure 1[c]) (2). Clear advantages of the LC–LC and the LC×LC mode over the 1D-LC counterpart are increased orthogonality (A0) and peak capacity (nc), the possibility to test different retention mechanisms, and better compound organization due to the formation of structured 2D plots; the main disadvantages can be traced back to the instrumental complexity, reduced sensitivity, and need for fast and high flow to employ in 2D separations. Fraction transfer from the 1D to the 2D can be performed either in offline or online mode, in the latter case by the use of a modulator or an interface, which is considered the “heart” of the multidimensional approach. In offline mode, the fractions are manually collected, evaporated, and re-dissolved in a solvent compatible with the 2D mobile phase prior to being re-injected into the 2D. As a consequence, a series of 2D separations, related to the number of fractions from the 1D, are obtained. The simplicity of such an approach avoids the use of any sophisticated modulators or dedicated interfaces; alternatively, this method can bring sample loss or contamination and certainly tedious, time-consuming procedures (3–6). The online mode involves the use of switching valves equipped with either identical internal volume loops or trapping columns, with augmented reproducibility, reduced sample contamination, and less time‑consuming sample treatment steps.

As far as separation modes are concerned, in MD–LC, different retention mechanisms may be selected, for example, reversed-phase LC, normal‑phase LC, hydrophilic interaction LC (HILIC), ion-pairing chromatography (IPC), ion exchange chromatography (IEC), hydrophobic interaction chromatography (HIC). To achieve the best system “orthogonality”, the two dimensions must have a different and independent separation mechanism, which leads to enhanced peak capacity (7). Also, when the LC×LC technique is employed for high-resolution separations, a remarkable advantage over the 1D-LC is the rapid peak‑production rate, which is roughly 1 peak per second vs. 1 peak per minute for a conventional 1D-LC (7).

The capability of the MD–LC technique may be augmented if an MS system is connected to the 2D separations. In fact, this in turn permits unambiguous 

compound identification in the investigated sample and beneficially allows matrix effects that hamper both quali-quantitative results to be reduced. Enhanced separations and a decrease of sample complexity prior to MS detection has the advantage of minimizing ion suppression and improving ionization efficiency (8).

However, MD–LC does present some pitfalls, for example, length of analysis time, which is normally higher than 1D-LC, and complex equipment with specific data treatment software. Notably, potential immiscibility or incompatibility of the mobile phases in both dimensions have to be considered; in this context, newly developed approaches, for example, fixed solvent modulation (FSM) or active solvent modulation (ASM) have been proposed (9). In this configuration, the implementation of any valve. for example, a 10-port/2- position or 8-port/2-position, normally used for LC×LC, is augmented with a “bypass connector” that establishes a fluidic connection between the 2D pump and 2D column in parallel with the path through the valve. One significant limitation of the FSM approach is that the bypass connection is fixed, and flow will go through both the valve and the bypass connector throughout the analysis. Since the total flow from the 2D pump is split into two paths, the “actual” flow rate through the sample loop is lower than the total flow and dependent on the split ratio. The ASM approach focuses on a new valve-based approach to apply fraction transfer in LC×LC that enables effective focusing of analyte bands at the inlet of the 2D column without the need for any additional instrument hardware. Such an approach offers important advantages over FSM for effective analyte focusing at the 2D column inlet and can be summarized as: 1) More rapid flushing of high solvent strength eluent from the sampling loops at the end of each 2D separation cycle 2) Less complex mobile phase profiles delivered to the 2D column during gradient elution, which results in less baseline disturbances and a larger practical elution window in each 2D separation.

On the other hand, a practical limitation to the ASM approach is the time it takes to displace the fractions of effluent collected from the 1D column from the sampling loop into the 2D column. Apart from this, it is reasonable to believe the development of ASM represents a significant step forwards in terms of improving the ease-of-use of LC×LC and the overall detection sensitivity of the technique.

On the basis of the above considerations, it is predictable that MD–LC techniques unquestionably present fundamental advancements in terms of increased 

orthogonality, separation power, and sensitivity—even more enhanced if powerful MS detectors are hyphenated— thus leading to an ever increasing number of applications in several research fields.

2) D.R. Stoll, K. Zhang, G.O. Staples, and A. Beck, 

(CRC Press, Boca Raton, Florida, USA, 2018, Vol. 56).

3) F. Cacciola, P. Donato, D. Sciarrone, P. Dugo, and L. Mondello, 

4) F. Cacciola, F. Rigano, P. Dugo, and L. Mondello, 

5) K. Arena, F. Mandolfino, F. Cacciola, P. Dugo, and L. Mondello, 

6) F. Cacciola, K. Arena, F. Mandolfino, D. Donnarumma, P. Dugo, and L. Mondello, 

7) B.W.J. Pirok, D.R. Stoll, and P.J. Schoenmakers, 

8) K. Arena, F. Cacciola, F. Rigano, P. Dugo, and L. Mondello, 

9) D.R. Stoll, K. Shoykhet, P. Petersson, and S. Buckenmaier, 

is associate professor of food chemistry at the Dipartimento di “Scienze Biomediche, Odontoiatriche e delle Immagini Morfologiche e Funzionali” of the University Messina, Italy.

is assistant professor of food chemistry at the Dipartimento di “Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali” of the University Messina, Italy

is full professor of food chemistry at the Dipartimento di “Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali” of the University Messina, Italy.

is full professor of analytical chemistry at the Dipartimento di “Scienze Chimiche, Biologiche, Farmaceutiche ed Ambientali” of the University Messina, Italy.

Separation Science: The State of the Art: New Frontiers in Multidimensional Liquid Chromatography

The first use of the term “lipidomic(s)” was in 2002, and up until now, almost 10,000 papers on lipidomics have been published (Figure 1), which clearly illustrates that lipidomics is probably the fastest growing omics discipline at present. However, this fast growth is also accompanied by some negative issues. Many newcomers and beginners are entering the lipidomic field, often without adequate knowledge of basic analytical methodology, nomenclature, and data reporting, which can cause confusion in data interpretation and reporting (1). Recently, the International Lipidomics Society (ILS) has been established with the goal of harmonizing the field, unifying the nomenclature by updating shorthand annotations in cooperation with the LIPID MAPS consortium (2), and introducing a checklist system for transparent reporting of lipidomic data (3). The first Gordon Research Conference on Lipidomics was organized in August 2022 by John Bowden and Kim Ekroos. A smart example of the recognition of lipidomic research is the fact that three distinguished lipidomic researchers (Erin Baker, Shane Ellis, and Livia Eberlin) were awarded prestigious medals at the last IMSC 2022 conference in Maastricht, Netherlands. This is just a brief illustration—and certainly not complete—that the lipidomic community has started to be active in the organization of various events and activities to promote further growth of the field.

The driving force of any research field must be scientific progress in the methodology, which may bring new applications. The lipidomic community pays considerable attention to proper quantitation using appropriate exogenous internal standards (4), including the validation of analytical methods and the use of quality controls (5). The broad consensus is the use of at least one internal standard for each lipid class to be quantified in case of lipid class separation (hydrophilic interaction liquid chromatography [HILIC] and ultrahigh-performance supercritical fluid chromatography [UHPSFC]) or shotgun approaches, but the lipid species separation techniques (reversed-phase ultrahigh-pressure liquid chromatography [UHPLC]) require more. The second important aspect is the lipidomic coverage. Biologists estimate the presence of about 100,000 lipid species in Nature, but the best analytical methods typically report less than 1000, so there is considerable room for improvement in the analytical methodology. However, one method cannot cover this complexity; therefore, we need several complementary analytical methods and sample preparation protocols. Lipidomic analysis is strongly dependent on mass spectrometry (MS) and chromatographic techniques. The main advantage of chromatography is the separation of various isomeric and isobaric lipids, which is sometimes easier than with MS-based approaches. The prevailing technique is UHPLC, but more groups realize the potential of UHPSFC (6), which is perfectly suited for molecules with large hydrophobic parts, such as lipids. From an MS point of view, many researchers appreciate the potential of ultrahigh‑resolving power mass spectrometers, typically orbital trap analyzers, and MS imaging for the visualization of tissues. The increasing resolving power in ion mobility has shown its full potential, which may be easily coupled with liquid separation techniques and MS (7). One trend that cannot be overlooked in lipidomic analysis is the shift towards more structural details, such as data on the fatty acyl level or even the double bond level, which could reveal new biological information previously unknown (8).

Multiomics integration of lipidomics with other omics approaches, such as metabolomics, proteomics, transcriptomics, and genomics, is the research direction of the future. Concentrations of lipid species are influenced by the activity of the corresponding enzymes responsible for the particular metabolic pathways involved in the biosynthesis and subsequent degradation of lipids. RNA—both coding and noncoding—shows a correlation with lipidome. The typical example is an alteration of miRNA in cancer. Known genetic mutations have connections to serious human diseases, which is also reflected in the dysregulation of particular metabolic pathways. The integration of multiple omics data is needed for the full understanding of the biological mechanisms of the diseases studied, but such research requires experts for each omics technique and a strong bioinformatic background, which is still in its infancy. I believe that the substantial effort invested in this area will result in fast progress and a better understanding of the biological roles of lipids in disease and health, with possible consequences in biomarker discovery and drug development.

The driving force for progress in lipidomic analysis is biomedical applications and clinical analyses because the translation of key discoveries from academic research laboratories into the real clinical environment could bring substantial benefits to clinicians. Many research groups around the world are working hard to investigate the potential of lipidomics in biomarker discovery applicable to early disease detection, the monitoring of therapeutic response, and helping to select the best possible therapy for a given phenotype, which is known as 

. The expectations are enormous, but unfortunately, successful clinical translation is rather rare. First, we must harmonize the molar concentrations reported by individual laboratories with their individual workflows, which is the main goal of a running ring trial on human plasma lipidome organized by the Clinical lipidomics Interest Group (CLIG) within ILS. I know of only one example of completed translation from the research laboratory into real clinical use, which is the ceramide test for cardiovascular risk (9) implemented by the Mayo Clinic. At present, the spin-off company created by my university and the FONS company is working on the clinical validation of our lipidomic test for early detection of pancreatic cancer based on blood analysis (10). The methodology is ready to use, with a sample throughput of 20,000 samples/year, but it will still take several years to collect a sufficient number of samples from high-risk subjects to prove the real benefit for the clinical management of cancer patients. It is interesting to note that we observe similar dysregulation patterns for other types of cancer (11). However, the biological mechanism of the observed lipidomic dysregulation has not yet been discovered and will require suitable biological models and a multiomics approach. Another exciting discovery was published by the Fendt group (12) that demonstrated that cancer cells exhibit enormous flexibility, which may result in the desaturation of fatty acyls in unusual positions not common for normal cells. I list here only examples of lipidomic research for cancer and cardiovascular diseases, but there are many examples in the literature about changed metabolic pathways for various lipids in other diseases, such as liver diseases and brain disorders.

4) M. Holčapek, G. Liebisch, and K. Ekroos, 

is a professor at the University of Pardubice, Faculty of Chemical Technology, Department of Analytical Chemistry, in Pardubice, Czech Republic.

Separation Science: The State of the Art: New Frontiers in Lipidomics

From Theoretical Concepts to an Industry Standard

Capillary gas chromatography (CGC) was introduced about 60 years ago and has evolved through many developmental milestones into an indispensable tool in many analytical laboratories. The authors had the privilege to witness and to actively participate in the growth of CGC technology up to its current state-of-the-art. It has been an exciting and very rewarding journey to see how CGC-based analytical methods have contributed to the better understanding of industrial processes and products, to quality control in foods, pharmaceuticals, and consumer products, to environmental and health protection, and to the discovery of chemicals that are key 

markers for aroma or diseases, to name just a few applications of CGC.

Although the fundamentals of CGC have been described by Marcel Golay in the late 1950s, GC using initially metal and later glass capillary columns was not sufficiently robust to be applied in an industrial environment, and GC using packed columns remained the standard until the introduction of fused silica columns at the end of the 1970s. At the University of Ghent in Belgium, we have contributed to capillary GC column technology by the development and optimization of stationary phase coatings, of stationary phase cross‑linking and immobilization, through the synthesis of new stationary phases and to the development and evaluation of GC instrumentation, including dedicated CGC inlets (split/splitless, cool on-column and programmed temperature vaporization) and CGC detectors. Together with many academic and industrial research groups, this has led to high performance GC instrumentation and columns. In our opinion, important milestones were the development and introduction of dedicated instrumentation, for example, by Carlo Erba in Europe and by Hewlett Packard in the US. In the mid-1990s, CGC instrumentation became available with state-of-the-art inlets and detectors, with electronic pressure/ flow control, on-board diagnostics, and options such as retention time locking and method translation calculators, as could be found in the HP 6890. At RIC, we immediately could benefit from such outstanding performance, as illustrated by detailed hydrocarbon analysis that could be used for fuel control in Formula 1 racing (“the 1995 Schumacher case”), and by high-throughput polychlorinated biphenyl (PCB) analysis, applied at RIC during the Belgian dioxin (PCB) crisis in 1999.

Since the new millennium, the revolution in GC technology has changed into an evolution. New GC instruments have further improved in terms of user‑friendliness and robustness. Tools are incorporated that increase productivity and reduce downtime. For instance, capillary flow technology facilitates the coupling of retention gaps and columns, allows column back-flushing, enabling effluent splitting to multiple detectors and making multidimensional GC applicable in industry and routine laboratories.

Today, CGC is often considered a “mature” technique. In our opinion, this is correct, but it does not mean that the technique is in decline or that no further progress can be made. Mature stands for high performance, robustness, and wide applicability in a routine and industrial environment.

Parallel with the evolution of CGC equipment, we have encountered a spectacular revolution in mass spectrometers that can be hyphenated to CGC. Note that due to the nature of the gaseous mobile phase, the commercialization of CGC–mass spectrometry (MS) systems was much faster compared with other chromatographic techniques! Next to extremely robust and sensitive single quadrupole systems, laboratories have access to triple quadrupole MS, time‑of‑flight (TOF-) MS, quadrupole TOF‑MS, and orbital trap MS instrumentation, depending on their applications. This has resulted in a trend away from mostly target compound analysis, as known from classical regulatory methods, for example, EPA, towards untargeted screening, as applied in the various “omics” approaches (metabolomics, foodomics, volatolomics).

An important consequence of this revolution is that one might put all trust in the mass spectrometer and “downgrade” the GC system to some kind of “MS‑inlet”. The power of high‑end mass spectrometers can, in our opinion, only be fully exploited when combined with a high‑performance CGC separation (inert flow path, correct sample introduction, correct CGC parameters). Although not the topic of this article, we would like to mention that this is also valid for the selection of the sample preparation procedure: the better the sample preparation is, the better the MS data will be. A great mass spectrometer will not work properly if the ion source is overloaded with dirt!

The (r)evolution in sensitivities that can be obtained with state-of-the art CGC–MS equipment has also had an important influence on the development of capillary columns. In the 1980–90s, a wide range of stationary phases was developed for CGC , with the focus on selectivity. Dedicated columns became available for specific applications, such as volatile organic compounds (“624”, “VRX”- type columns), PCBs, and polycyclic aromatic hydrocarbons (PAHs). In combination with the increasing sensitivity of the detector, other column criteria become more important. New developments in CGC column technology should therefore keep this as the focus. If the MS detector is capable of “deconvoluting” solutes, chromatographic resolution becomes less important, while inertness and column bleeding are essential for trace analysis. This also means that column testing should be done in a more critical way—not only by injecting a classical test mixture at 1000 ng/μL using a 1:100 split injection. Remember that the most critical test is your sample.

Obviously, in case of isomer speciation, MS cannot provide a solution and the CGC separation is crucial. To this, there is still room for development of “special” columns that have complementary selectivities, for instance, based on shape 

selectivity, chirality, or specific interaction with functional groups. Research on new stationary phases such as (polymeric) ionic liquids or calix-arenes looks very promising in this respect.

Comprehensive GC (or GC×GC) is often considered as the most important milestone in the evolution of gas chromatography since the 1990s. The technique was developed and the first concepts were demonstrated more than 25 years ago. Despite hundreds of publications, books, and dedicated symposia, the implementation of GC×GC instrumentation and methods in industrial laboratories is rather limited, with the exception of the petrochemical industry. We see two reasons here. First, further improvements in hardware and (mainly) software are required to bring the equipment to a more user-friendly level, to offer a reliable and transparent workflow from injection to report. In addition, the technique should be correctly positioned by protagonists of the technique and not applied to analyses that qualitatively and quantitatively can be performed perfectly well using classical one-dimensional GC. GC×GC is not at all a replacement for 1D-GC or GC–MS; it should complement it and be applied to those applications that require sample imaging, group type separation, and significantly higher peak capacity. We encountered a similar situation when (capillary) SFC was introduced. Once it was believed that cSFC would replace both GC and liquid chromatography (LC). Research in GC×GC should therefore not make the same mistake. The technique has a clear potential, can deliver great results in several applications, but it should only be applied when needed.

We are convinced that CGC technology will further evolve. This evolution should focus on further improvement of productivity, usability, energy, gas selection, and consumption. New column heating technologies have been applied in low thermal mass (LTM, resistive heated column) and the recent CGC platforms. These approaches drastically reduce energy consumption, but users need to adapt to the limitations, such as the fact that column trimming is no longer possible and columns are more expensive.

In the past decades, academic research has explored many ways to miniaturize GC instruments, ultimately to chip-GC formats. Micro-GC systems are commercially available and successfully applied in gas analysis (separation of permanent gases), but for applications in other fields major problems are encountered. These often relate to underestimation of the complexity of sample introduction in GC (called the 

), the role of solute capacity, and the required peak capacity. Demonstration of a micro-GC separation using a 

split injection of a standard mixture of test solutes, at both high and equal concentration level, for instance, does not really reflect a real-life situation whereby solutes are present in a sample at concentration levels that span several orders of magnitude. A splitless injection of 1 μL organic solvent can already result in a gas volume beyond the volume of the entire micro-GC column. In this respect, it is crucial that R&D in miniaturized GC systems keeps the requirements of real applications in mind.

A Mature Technique for a Broader Range of Application

To us, CGC, CGC–MS, and GC×GC all have a bright future. Technology will evolve, catalyzed by the need for higher productivity, lower costs, miniaturization, and trends towards greener technologies. CGC is currently being applied in more and more industries, beyond classical “home-grounds” such as petrochemical, environmental, and food safety application areas. There is still a wide range of potential applications to be discovered in life sciences, product recycling, new energy supplies (characterization of batteries), and more.

is principal scientist at RIC group, Kortrijk, Belgium.

is founder and advisor of RIC group and emeritus professor, Ghent University, Belgium.

Separation Science: The State of the Art: How Mature is Gas Chromatography? An Industry Perspective

It has become increasingly clear in recent years that climate change has accelerated rapidly and poses an imminent threat to the global ecosystem. At the heart of this issue is the dramatic increases in greenhouse gases—carbon dioxide being the most prominent of these. While it may feel like a long stretch to suggest separation science is a significant contributor to this issue, some simple modifications to the way these measurements are approached can enable significant reductions in the waste generated and carbon dioxide produced during analyses.

As a starting point in discussing sustainability, Anastas and Warner’s seminal 12 rules for green chemistry can be considered as a substrate for analytical chemistry (1). Written in 1998, these rules provide a framework on how to improve the sustainability of various aspects of synthetic chemistry. While this cannot be discussed at length in this article, the concepts detailed in the chapter on reducing volumes (both organic solvents and water), using safer and more sustainable solvents, and increasing instrument efficiency are all within the scope of this discussion (see also Figure 1).

Let us start by considering how we know if our separation science method is “green”. A key focus area for chromatographic methods and sustainability is the organic solvents used in the mobile phases, as these can be a source of carbon dioxide in their synthesis and also in their destruction (typically incineration) after use as waste. There have been several approaches proposed for objective assessment of method greenness, for example, references 2 and 3. A recent example of this is the “analytical method greenness score” (AMGS) produced by the American Chemical Society Green Chemistry Institute (ACS GCI) (4). The AMGS tool provides a “traffic-light” scoring approach for liquid chromatography (LC) and supercritical fluid chromatography (SFC) methods based on (i) the energy an instrument uses (the analysis time of method and number of samples), (ii) the volume of solvent used (in the chromatographic method and sample preparation), and (iii) safety considerations for the solvent. The tool also highlights the aspects that are detrimentally impacting this score in these three areas. A free to access web interface has been produced to generate these metrics (

), and future updates are planned to include gas chromatography (GC) methods.

When discussing organic solvent volumes, four areas should be considered:

1. Reduction: Can a separation be scaled to smaller volumetric flow dimensions without impacting the separation? This will reduce the amount of organic solvent and high purity water used (access to the latter can be limited in some geographic locations).

2. Replacement: Can environmentally hazardous chemicals be replaced with more sustainable or safer options without impacting the measurement? For example, the replacement of acetonitrile with ethanol or acetone.

3. Removal: Can organic solvents be completely removed from the chromatographic method? For example, could techniques such as capillary electrophoresis or ion chromatography (IC) be used instead of reversed-phase chromatography?

4. Recycle: Mobile phases can also be recycled while running isocratic chromatographic methods. While difficult, is it possible to recover certain solvents from the sample diluent or unused/waste mobile phase, for example, ethanol or dimethyl sulfoxide (DMSO)?

In terms of point 1, the move to smaller chromatographic particle sizes in reduced volume column dimensions is an easily accessible and impactful approach. The use of ultrahigh-pressure liquid chormatography (UHPLC) instead of high performance liquid chromatography (HPLC) separations is a common example of this. Preserving the 

p (column length/particle size) ratio to maintain efficiency (ensuring a scalable stationary phase is used with identical ligand and bonding density to preserve relative retention and selectivity) and volumetric flow scaling (many online tools are available to assist with this) can significantly reduce solvent use when moving to smaller column and stationary phase dimensions. It is not uncommon when moving from HPLC- to UHPLC-scale conditions to realize mobile phase savings in excess of 60%. Detrimentally, it is energetically easier to incinerate mobile phases rich in organic solvents than it is to incinerate those rich in water, but it is difficult to quantify this aspect of the mobile phase life cycle.

From a sustainability perspective, it is also important to minimize the number of experiments conducted during the development of a method. The use of in-silico modelling and prediction of analyte retention is very important to enable this. Once the column and mobile phase conditions are established (often through screening, or more recently, prediction of initial conditions based on analyte structure [5]), methods can often be optimized quickly in as few as two to four experiments (6).

The replacement of mobile phase solvents (point 2) can be challenging if a chromatographic method is already established and selectivity needs to be maintained. However, if developing a new method, then the use of methanol, ethanol, or even acetone (not all methods require UV detection!) could be utilized rather than acetonitrile (with ethanol, slightly increased column temperatures may be required to reduce mobile phase viscosity, which will impact system pressure and the usable flow rates). More sustainable and/or safer organic solvents can be chosen from the literature published by process chemists, for example, references 7 and 8.

The use of normal phase chromatography should be avoided whenever possible due to the high volumes of toxic or 

environmentally impactful organic solvents used. Normal phase chromatography was historically used for preparative-scale isolations, chiral separations, or retention of polar molecules. For the latter, hydrophilic interaction liquid chromatography (HILIC) has established itself as a key technique for the analysis of small and biomolecule (for example, glycan) separations. However, the main component of mobile phases used in this technique is typically acetonitrile, often in the order of 95% v/v, which produces significant volumes of organic waste. A greener technique that addresses these three areas is SFC. This techniques uses carbon dioxide as the primary component in the mobile phase and typically methanol as co-solvent (in volumes <50% v/v). While carbon dioxide is environmentally detrimental, if the gas is produced from a sustainable source, for example, recycled from the atmosphere, or possibly as a by‑product from chemical reactions, then the technique is unquestionably “greener” than many other chromatographic approaches.

While not fully addressing point 3, SFC provides reduced volumes of liquid waste. Alternatively, the use of capillary zone electrophoresis (CZE), or one of its numerous modes, all produce dramatically less waste, while providing a potentially beneficial and complementary separation methodology to chromatographic approaches. However, CE does have limitations, most notably detection sensitivity. Alongside this, the use of IC, which utilizes predominantly aqueous mobile phases, could provide a greener approach to analyzing certain classes of compounds.

Another area for consideration is sample preparation. As more efforts are made to reduce solvent volumes in liquid separations, then sample diluent volumes move further into focus. It is important that analysts have a fundamental understanding of key physicochemical parameters such as analyte p

to optimize analyte solubility, alongside understanding of efficient instrumental processes to minimize the proportions of organic solvent used and the absolute volumes of the samples prepared (9). The use of automation to provide reproducible small volume sample preparation will be a key focus area in the future. Equally, the ability to analyze samples without any sample preparation using online analysis approaches (for example, online LC or vibrational spectroscopy such as near infra‑red) is another important future direction.

Ultimately, the use of LC as a measurement approach is broadly not green, and, arguably, spectroscopic or spectrometric approaches can provide more sustainable measurements. However, as chromatography often provides the optimal approach to generate measurement data—for example, due to reduced sample matrix interferences—the approaches outlined above can all be considered to reduce the environmental impact of these techniques. While the impact of reducing the environmental burden of separation science is modest compared to the aviation industry for example, I am reminded of the concept of “aggregation of marginal gains” (10). If multiple industries using separation science in any form can reduce their environmental footprint, then these small numbers can become significant and create a positive and sustainable impact.

(Oxford University Press, New York, USA, 1998).

2) L.H. Keith, L.U. Gron, and J.L. Young, 

3) M. Cinelli, S.R.Coles, and K. Kirwan, 

5) P.R. Haddad, M. Taraji, and R. Szücs, 

9) R.E. Majors, Sample preparation fundamentals for chromatography (Agilent Technologies, 2014).

https://www.choosefi.com/the-aggregation-of-marginal-gains/

is an associate principal scientist in New Modalities & Parenteral Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK.

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, key opinion leaders from the separation science community
explore contemporary trends in separation science and identify possible future developments.

Separation Science: The State of the Art: The Paradox of Sustainability in Separation Science

To speed up the optimization of chromatographic separations—a process often referred to as 

—models that aim to predict the retention time or retention factor of a compound are frequently used. These models can be empirical or mechanistic in nature, and predict the retention of a compound based on one or more chromatographic parameters, such as the fraction of organic modifier in the mobile phase in isocratic separations, the gradient profile in gradient separations, or the pH or temperature of the mobile phase (1). Such models are typically established based on a number of scouting runs or exploratory runs, wherein the parameter under investigation, for example, the fraction

of organic modifier in the mobile phase, is varied a number of times—a number that is typically equal to or higher than the number of parameters in the retention model. Thereafter, a retention model is built by relating the experimentally obtained retention factors to the chromatographic parameter. Well-known examples of such retention models are the linear solvent strength model (2) and the Neue-Kuss model (3). An advantage of this approach is that (structural) information about the compound under consideration is not required. A disadvantage is that multiple informative scouting runs for each compound need to be executed before the model can be built and used, which takes time.

Alternatively, when structural information about the compound under consideration is available, quantitative structure retention relations (QSSR) can be used for retention time or retention factor predictions (4). QSSR are mathematical relations between the retention time of a compound and its structural features. Typically, QSSR are built for a specific chromatographic setup, that is, a fixed combination of stationary phase and mobile phase conditions for a large number of compounds with known structures. The obtained retention times are then correlated with the compounds’ structural features or descriptors to build a retention model. Once the model is available, the retention times of new compounds can be predicted without having to run any new experiments, significantly decreasing the time required to optimize separations. However, before the model becomes available, the retention times of typically at least 50–100 compounds on the particular chromatographic setup are required, while another prerequisite is that the structure of the compound should be known.

To build adequate QSRR, structural features or descriptors that adequately represent the interactions between the compounds under consideration and the chromatographic conditions need to be selected. These descriptors can be physicochemical, quantum mechanical, or topological in nature. Examples include molecular mass, carbon number, polarizability, and (calculated) partition coefficient, although thousands of possible candidates exist (5). Once suitable features have been selected, they are mapped against the experimental retention times or retention factors using regression models or machine learning algorithms, such as multi-layer perceptrons (MLP), random 

forests (RF), or support vector machines (SVM), to obtain a retention model (6).

More recent additions to these approaches are graph neural networks (GNNs). As opposed to the more traditional machine learning algorithms, GNNs use information about the atoms themselves and their bonds to adjacent atoms, to obtain optimized and meaningful molecular representations for retention time predictions. This is done by first encoding the atoms numerically. These atom encodings subsequently undergo a transformation and aggregation step to incorporate information about their neighbouring atoms via a set of learnable weight matrices. This is repeated a number of times (layers) for increasing distances (radii). This allows the GNN to learn complex relations between atoms or molecular sub-structures over both short and longer distances, leading to powerful and meaningful molecular representations. The obtained atom encodings are finally reduced into one-dimensional vector representations of the molecules, which are then inputted to a regression model (such as MLP) to produce retention time predictions. A simplified illustration of a GNN is shown in Figure 1(a).

We recently demonstrated the potential of GNNs for retention time predictions in different separation modes (reversed-phase and hydrophilic interaction liquid chromatography) (7). When compared with more traditional approaches, such as MLP, RF, and SVM, GNNs generally performed better than the traditional approaches, with mean absolute errors (MAEs) between the experimental and predicted retention times that were typically 5–25% lower than those obtained with the traditional models, depending on the dataset. This was attributed to the fact that GNNs result in a more expressive and discriminative molecular representation for retention time predictions, due to the learnable molecular representation based on the large number of atom and bond features. It was, however, also observed that GNNs in some cases suffer from some unusually high prediction errors. This was attributed to a possible overfitting of the GNNs to (some of) the training data, which could be explained by the large number of weights that need to be trained at each layer of the GNN. In future research, this could be tackled by adjusting the number of learnable weights or increasing the size of the datasets.

To better understand what molecular substructures contribute to the retention time predictions, so-called 

(GAMs) can be computed from the GNNs (8). Visualizing these GAMs can help understand to what part of the molecule the model is “looking” when it makes its retention time prediction and as such help understand what substructures contribute to retention under particular chromatographic conditions. For an example, see Figure 1(b).

In conclusion, GNNs show great promise as new machine learning tools for retention time predictions using QSRR and certainly deserve further exploration. Researchers who are interested in creating their own GNNs for retention time predictions of small molecules are welcomed to use the recently developed open source Python package MolGraph, which can be installed from: https://github.com/akensert/molgraph

1) M.C. García-Alvarez-Coque, G. Ramis-Ramos, J.R. Torres-Lapasió, and C. Ortiz-Bolsico, 

2) L.R. Snyder, J.W. Dolan, and J.R. Gant, 

4) R.I.J. Amos, P.R. Haddad, R. Szucs, J.W. Dolan, and C.A. Pohl, 

6) R. Bouwmeester, L. Martens, and S. Degroeve, 

7) A. Kensert, R. Bouwmeester, K. Efthymiadis, P. Van Broeck, G. Desmet, and D. Cabooter, 

8) P.E. Pope, S. Kolouri, M. Rostami, C.E. Martin, and H. Hoffmann, 

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

is a post-doc in the Department of Pharmaceutical and Pharmacological Sciences, University of Leuven (KU Leuven), Belgium.

is a full professor in the Department of Chemical Engineering, Vrije Universiteit Brussel, Belgium.

is a professor in the Department of Pharmaceutical and Pharmacological Sciences, University of Leuven.

In this extended special feature to celebrate the 35th anniversary edition of LCGC Europe, leading figures from the separation science community explore contemporary trends in separation science and identify possible future developments.

Separation Science: The State of the Art: Graph Neural Networks for Improved Retention Time Predictions

The analysis of peptides using liquid chromatography–mass spectrometry (LC–MS) for proteomics applications is powerful and can yield tremendously rich data sets. However, the exquisite sensitivity and global applicability of MS detection also makes it prone to contaminants that can easily and seriously compromise the quality of a data set. A short list of tips and tricks can increase the likelihood of consistently producing good LC–MS data in this context and streamline the troubleshooting process when problems do eventually occur.

Some applications of liquid chromatography (LC) involve highly specific “tips and tricks” that are acquired by analysts over time, and they can make the difference between producing data that is either terrible or great. The analysis of peptides by LC coupled with mass spectrometry (LC–MS) is one such area where it can appear that highly skilled analysts possess superpowers or “golden hands” that enable them to acquire better data than anyone else in a laboratory. In the end though, careful attention to detail and a thorough understanding of the conditions and chemistry involved in these analyses can help avoid problems before they occur or accelerate troubleshooting problems when they do appear. For this month’s instalment of LC Troubleshooting, I invited Daniel Meston to share his short list of knowledge, tips, tricks, and advice related to analyzing peptides by LC–MS. There are many details that are important to consider in the preparation of samples and in the instrument prior to LC–MS data acquisition that can increase the likelihood of acquiring high quality data.

LC–MS has become the gold standard methodology used for proteomics, which is the identification and quantification of protein abundances in biological samples. This is because of the ability of LC–MS to provide sensitive detection for untargeted peptides and identify unknown peptides through tandem MS (MS/MS) fragmentation and comparison of the experimental spectra to theoretical spectra prepared in silico. Subsequently, LC–MS-based proteomics tools have become invaluable in diverse disciplines ranging from immunology to microbiology and food chemistry. However, analysis of biomolecules is generally susceptible to a number of unique and perilous sample preparation pitfalls that can severely degrade the quality of data acquired, even when the most sophisticated LC–MS systems are used, which is because of the unique chemistry and size of large biological macromolecules, such as proteins, peptides, and oligonucleotides to name a few. In this instalment, we provide our views on some of the most common pitfalls in sample and instrument preparation relevant to proteomic analyses by LC–MS. We hope that increasing awareness of these potential problems can help users mitigate them and produce higher quality data in general.

All mass spectrometric methods require the ionization of the analytes of interest before they can be separated and ultimately detected. For the peptides encountered in proteomic workflows, the ionization step is generally straightforward because conventional tryptic digestion of proteins often results in amino acid chains with terminal amino groups. Indeed, the protonation of peptides is so favourable that they are often observed as multiply charged positive ions. Given the ease of ionization of peptides, MS can almost be regarded as a “universal detector” for peptide analysis. However, this sensitivity can also be a double-edged sword in the sense that the detector will also respond to very low concentrations of easy-to-ionize contaminants in 

our samples, thus degrading the overall quality of the acquired data.

Polymers are perhaps the most frequently encountered type of contamination in proteomic samples, probably because they are present in most laboratories at levels high enough to matter to LC–MS work. A few examples of polymer sources include skin creams and moisturizing products, pipette tips, chemical wipes that contain an abundance of different polyethylene glycols (PEGs), and siliconized surfaces that contain polysiloxanes (PSs). The presence of these contaminants can be readily recognized in MS spectra by their characteristically large numbers of regularly spaced peaks in the spectra (44 Da spacing for PEG, 77 Da spacing for PS).

Another source of PEG contaminants is the use of surfactant-based cell lysis methods that are routinely employed in most molecular biology laboratories and involve surfactants such as Tween, Nonident P-40, and Triton X-100. Residual surfactant in samples that are produced using these chemical lysis methods have the potential to produce MS signals that can largely obscure the MS signal of the target peptides, thus rendering the data useless. A dramatic example of such a result is shown in Figure 1. When such protocols are used, extreme care should be taken to selectively remove the surfactants from the sample prior to analysis. Although it is certainly possible to remove PEG contaminants using solid-phase extraction (SPE), it is far easier in the long run to avoid the problem altogether and not use the surfactants for cell lysis in the first place.

In addition to problems with surfactants, urea used in cell lysis buffers can also cause problems. It is known that urea can decompose to isocyanic acid, which can in turn covalently modify free amine groups in peptides through carbamylation reactions (1). This chemical modification of the peptide can be accounted for in peptide identification software, but only if the software is instructed to look for this.

In general, residual salts can negatively impact chromatographic performance as well, and they can cause physical damage to fluidics and the MS‑interface instrumentation by scratching the surfaces and clogging the emitter. As a result, residual salts should be removed from the sample prior to injection. Both urea and salts are most commonly removed using a reversed‑phase clean-up step, such as SPE.

Not all high quality laboratory water is created equally, and there are many ways problems can arise with the water used for sample and mobile phase preparation. For example, in-line filters that are used to filter out deoxyribonucleic acid (DNA) can inadvertently lead to contamination of the water with PEG. Even the highest quality water produced on-site in the laboratory can begin to accumulate contaminants within a few days of production. Generally, we should avoid using water that has been sitting around for more than a few days after opening the bottle (if purchased) or production by a polishing system on-site. It is good practice to dedicate a specific subset of mobile phase bottles in the laboratory for LC–MS use only and to avoid washing them with any kind of detergent (2,3).

The most abundant protein contamination found in proteomic samples are the keratin proteins that make up our skin, hair, and fingernails. It is not uncommon to observe more than 25% of the peptide content of a proteomics sample to originate from keratin-derived peptides. Any steps that can be taken to reduce this type of contamination will improve the ability to detect low-abundant proteins of interest. In general, natural fibre clothing, such as wool, should not be worn in proteomics laboratories. Ideally, all sample preparation steps should be performed in a laminar flow hood to prevent dust and skin from the analyst and laboratory air from entering samples. Additionally, gloves should be worn at all times and replaced after touching a contaminated surface, such as stopwatches, pens, and 

laboratory notebooks. However, once the proteins have been digested, one should consider not wearing gloves because these can be a further source of polymer contamination.

Peptide adsorption to the LC columns has been addressed in previous instalments of “LC Troubleshooting” (4). A lesser appreciated pitfall in proteomics sample preparation is adsorption of the peptides of interest to the vessel used for sample pretreatment (for example, digestion with trypsin), as well as LC sample vials. Peptides are multifunctional molecules with tremendous diversity in properties, including hydrophobicity and the number of acidic and basic functional groups. These properties make them prone to adsorption onto materials with very different properties, such as glass and plastics. This observation is also true for proteins but to an even greater degree (5). Adsorption of peptides from analytical samples onto such surfaces has been observed within timeframes as low as an hour after being placed in the LC vial, and they can result in a significant decrease in the apparent concentration of low-abundant peptides. A number of strategies to minimize sample adsorption to glass LC vials have been demonstrated, including the use of surfactants (which is not recommended for LC–MS, as discussed above). “Priming” vessels with a sacrificial protein, such as bovine serum albumin (BSA), is also done, similar to what is done with LC columns (4). The idea here is that the vessel is rinsed with a solution of a protein that is unrelated to the target proteins being analyzed (or synthetic peptides not found in biological samples); adsorption sites on the material are subsequently saturated and therefore unable to further adsorb peptides or proteins of interest from the analytical sample. Many vendors also offer “high-recovery” LC vials and other products that are engineered to minimize such undesirable analyte adsorption. An additional point to be aware of in this area is related to the removal of the sample solvent during the sample preparation process (for example, vacuum centrifugation to remove organic solvents). Completely removing all of the solvent promotes strong analyte adsorption onto surfaces; thus, it is helpful to avoid complete drying and to leave a small amount of liquid in the vial to increase analyte recovery.

Plastic micropipette tips present another opportunity to lose peptides to adsorption. With this in mind, it is important to limit the number of sample transfers during sample preparation, which has spurred the development of a number of so-called “one-pot” sample preparation methods that minimize contact between the sample and vessel, including nanoPOTS (6), SP3 (7), and FASP (8). From this research, it is becoming increasingly clear that single reactor vessel sample preparation protocols are superior to conventional protocols in the proteomics area.

Finally, when transferring peptide samples, it is best to avoid contact with metals. For example, most glass syringes are fitted with stainless steel needles by default. If a peptide sample is drawn up through a metal needle, peptides can be lost to adsorption on the metal surface. One way to avoid this scenario when using glass syringes is to remove the plunger, fill the sample into the glass syringe barrel using a glass pipette, and then push the sample out of the syringe using the plunger, but through a PEEK capillary fitted to the syringe via a Luer lock fitting, rather than pushing it through the metal needle. Doing so is particularly important in the case of MS calibrations where many vendors utilize a peptide calibrant that can be depleted by metal syringe needles.

Other aspects of the sample matrix are worthy of careful consideration. Although it is attractive to consider using trifluoroacetic acid (TFA) as a mobile-phase additive to improve chromatographic performance for peptides (that is, better peak shape, and increased retention, especially for hydrophilic peptides), this practice is generally frowned upon in the proteomics community because TFA can dramatically suppress peptide ionization, leading to lower overall detection sensitivity compared with using formic acid. An alternative to using TFA in the mobile phase is to add it to the sample and use formic acid to acidify the mobile phase. In this case, the TFA can enhance the retention of hydrophilic peptides on the precolumn, preventing them from eluting into the waste line. Meanwhile, the TFA exiting the precolumn is diverted to waste rather than entering the analytical column and MS interface.

Proteomics applications, which are outside conventional peptide sequencing, may also require specific buffers that preserve the structure or stability of proteins during analysis. It is not uncommon for these buffers to lead to salt precipitation on the electrospray emitter and the source cone of a MS system. In this way, one must optimize the conditions to use volatile buffers, such as ammonium acetate, or to physically clean the surfaces with water to prevent significant sensitivity loss. Recently, it has been demonstrated that postcolumn suppressor technology provides more flexibility in terms of the types of buffers that can be used, some of which are normally considered MS-incompatible (9).

Finally, the age of prepared mobile phases can be particularly problematic in proteomics applications where nano-flow systems are prevalent and mobile phase 

consumption is so low that a 100-mL batch of the mobile phase can last several weeks. Specifically, when buffer components with differing volatilities are mixed together, there can be selective evaporation of the more volatile component that will change the elution or ionic strength of the mobile phase, which can lead to changes in both absolute and relative peptide retention times. Primarily, this occurrence comes from incorrect viscosity values that change the mixing behaviour of modern nanoLC instruments. Additionally, changes in ionic strength will have a direct effect on the retention factor of the analytes, which will lead to variation in retention time. As a rule of thumb, mobile phases should be replaced every 1–2 weeks to ensure reproducible results. Additionally, in the case of pumping systems that rely on known mobile phase viscosities for accurate flow rates, the mobile phase viscosity should be determined when changing solvents. Instruments that require this feature build in software routines to facilitate these measurements. An additional point is to consider not using conventional membrane degassers when using nano-flow rate because the flow rate is so slow during this process. It has been noted in our laboratory that volatile buffer components, such as formic acid, are preferentially removed from the mobile phase during degassing. In these situations, it is better to utilize alternative degassing measures, such as sonication, that have a low risk for additional contamination of the mobile phase.

Optimizing the Electrospray Source Voltages in nanoLC–ESI–MS

Finally, as we reach the last step prior to the actual acquisition of LC–MS data, we must think about settings for the MS detector itself. Arguably, one of the most consequential missed opportunities in data acquisition for proteomics workflows is related to the optimization of electrospray source voltages. Once analytes are ionized, they are steered through the MS instrument in the direction of the detector using carefully manipulated electric fields. Therefore, analyte detection is highly dependent on the voltage settings for the ion optics that guide the motion of the ionized analytes. Failure to optimize these voltages can result in peptides over large regions of the mass range not making it to the detector, or significantly reduced numbers of detected ions.

Before analyzing any experimental samples, it is worth taking the time to incrementally change the ion source parameters step-wise (for a representative ion infused at the flow rate that will be used in the LC–MS method, we recommend the doubly charged ion of Glu-Fib or angiotensin II), while monitoring the signal change and the spectrum quality. For most modern MS instruments, this tuning step can be done automatically within the MS data acquisition software, but it can be beneficial to further manually fine-tune parameters to maximize signal for the ions of interest. Finally, once the detector settings have been adjusted, it is a good idea to analyze a well-characterized quality control (QC) sample (for example, a cytochrome C digest or HeLa cell digest) to enable a final check on the data quality before analyzing any novel samples.

Because of the size and varied chemistry of peptides found in proteomics samples, it is important to pay attention to details of each step leading to the acquisition of LC–MS data. This includes sample preparation chemistries, sample handling, optimization of instrument parameters, and control of mobile phases. Paying attention to these details from the start can help avoid data quality problems entirely and simplify troubleshooting when problems do arise. We hope that the tips and tricks discussed here are food for thought for analysts of all levels of experience, and ultimately improve data quality and minimize loss of precious samples and instrument time because of unanticipated problems.

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https://doi.org/10.1038/s41467-021-26514-2

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https://doi.org/10.1038/s41596-018-0082-x

8) J.R. Wiśniewski, A. Zougman, N. Nagaraj, and M. Mann, 

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https://doi.org/10.3389/frans.2022.1002935

is a postdoctoral researcher at the Free University of Brussels in Belgium. He also serves as the vice-president of the British Chromatographic Society.

is the editor of “LC Troubleshooting”. Stoll is a professor and the co-chair of chemistry at Gustavus Adolphus College in St. Peter, Minnesota, USA. Direct correspondence to: amatheson@mjhlifesciences.com

Knowing the tips and tricks of producing quality LC–MS data for peptide analysis can help streamline troubleshooting when problems occur.

Pitfalls in Proteomics: Avoiding Problems That Can Occur Before Data Acquisition Begins (EU)

This review article summarizes the results obtained from the combined efforts of a joint academic and industrial initiative to solve the real-life challenge of determining low levels of peptide-related impurities (typically 0.05–1% of the drug substance) in the presence of the related biologically active peptide at a high concentration. A rational screening strategy for pharmaceutically important peptides has been developed that uses combinations of reversed

This review article summarizes the results obtained from the combined efforts of a joint academic and industrial initiative to solve the real-life challenge of determining low levels of peptide-related impurities in the presence of the related biologically-active peptide at a high concentration.

Method Development for Reversed-Phase Separations of Peptides: A Rational Screening Strategy for Column and Mobile Phase Combinations with Complementary Selectivity