Collision energies on QTof and Orbitrap instruments: How to make proteomics measurements comparable?

Advancing the Prediction of MS/MS Spectra Using Machine Learning

Tandem mass spectrometry (MS/MS) is an important tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. While popular, this strategy is limited by the contents of existing reference libraries. In response to this limitation, various methods are being developed for the in silico generation of spectra to augment existing libraries. Recently, machine learning and deep learning techniques have been applied to predict spectra with greater speed and accuracy. Here, we investigate the challenges these algorithms face in achieving fast and accurate predictions on a wide range of small molecules. The challenges are often amplified by the use of generic machine learning benchmarking tactics, which lead to misleading accuracy scores. Curating data sets, only predicting spectra for sufficiently high collision energies, and working more closely with experimental mass spectrometrists are recommended strategies to improve overall prediction accuracy in this nuanced field.

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

Comparative metabolite analysis of Piper sarmentosum organs approached by LC-MS-based metabolic profiling

Piper sarmentosum Roxb. (Piperaceae) is a traditional medicinal and food plant widely distributed in the tropical and subtropical regions of Asia, offering both health and culinary benefits. In this study the secondary metabolites in different organs of P. sarmentosum were identified and their relative abundances were characterized. The metabolic profiles of leaves, roots, stems and fruits were comprehensively investigated by liquid chromatography high-resolution mass spectrometry (LC-HR-MS) and the data subsequently analyzed using multivariate statistical methods. Manual interpretation of the tandem mass spectrometric (MS/MS) fragmentation patterns revealed the presence of 154 tentatively identified metabolites, mostly represented by alkaloids and flavonoids. Principle component analysis and hierarchical clustering indicated the predominant occurrence of flavonoids, lignans and phenyl propanoids in leaves, aporphines in stems, piperamides in fruits and lignan-amides in roots. Overall, this study provides extensive data on the metabolite composition of P. sarmentosum, supplying useful information for bioactive compounds discovery and patterns of their preferential biosynthesis or storage in specific organs. This can be used to optimize production and harvesting as well as to maximize the plant's economic value as herbal medicine or in food applications.

Energy Resolved Mass Spectrometry for Interoperable Non-resonant Collisional Spectra in Metabolomics

In untargeted metabolomics, the unambiguous identification of metabolites remains a major challenge. This requires high-quality spectral libraries for reliable metabolite identification, which is essential for translating metabolomics data into meaningful biological information. Several attempts have been made to generate reproducible product ion spectra (PIS) under a low collision energy (ELab) regime and nonresonant collisional conditions but have not fully succeeded. We examined the ERMS (energy-resolved mass spectrometry) breakdown curves of two lipo-amino acids and showed the possibility to highlight "singular points", called descriptors hereafter (linked to respective ELab depending on the instrument), for each of the monomodal product ion profiles. Using several instruments based on different technologies, the PIS recorded at these specific ELab sites shows remarkable similarities. The descriptors appeared as being independent of the fragmentation mechanisms and can be used to overcome the main instrumental effects that limit the interoperability of spectral libraries. This proof-of-concept study, performed on two particular lipo-amino acids, demonstrates the high potential of ERMS-derived information to determine the instrument-specific ELab at which PIS recorded in nonresonant conditions become highly similar and instrument-independent, thus comparable across platforms. This innovative but straightforward approach could help remove some of the obstacles to metabolite identification in nontargeted metabolomics, putting an end to a challenging chimera.

Tandem Mass Spectrometry across Platforms

PubChem serves as a comprehensive repository, housing over 100 million unique chemical structures representing the breadth of our chemical knowledge across numerous fields including metabolism, pharmaceuticals, toxicology, cosmetics, agriculture, and many more. Rapid identification of these small molecules increasingly relies on electrospray ionization (ESI) paired with tandem mass spectrometry (MS/MS), particularly by comparison to genuine standard MS/MS data sets. Despite its widespread application, achieving consistency in MS/MS data across various analytical platforms remains an unaddressed concern. This study evaluated MS/MS data derived from one hundred molecular standards utilizing instruments from five manufacturers, inclusive of quadrupole time-of-flight (QTOF) and quadrupole orbitrap "exactive" (QE) mass spectrometers by Agilent (QTOF), Bruker (QTOF), SCIEX (QTOF), Waters (QTOF), and Thermo QE. We assessed fragment ion variations at multiple collisional energies (0, 10, 20, and 40 eV) using the cosine scoring algorithm for comparisons and the number of fragments observed. A parallel visual analysis of the MS/MS spectra across instruments was conducted, consistent with a standard procedure that is used to circumvent the still prevalent issue of mischaracterizations as shown for dimethyl sphingosine and C20 sphingosine. Our analysis revealed a notable consistency in MS/MS data and identifications, with fragment ions' m/z values exhibiting the highest concordance between instrument platforms at 20 eV, the other collisional energies (0, 10, and 40 eV) were significantly lower. While moving toward a standardized ESI MS/MS protocol is required for dependable molecular characterization, our results also underscore the continued importance of corroborating MS/MS data against standards to ensure accurate identifications. Our findings suggest that ESI MS/MS manufacturers, akin to the established norms for gas chromatography mass spectrometry instruments, should standardize the collision energy at 20 eV across different instrument platforms.

Can We Boost N-Glycopeptide Identification Confidence? Smart Collision Energy Choice Taking into Account Structure and Search Engine

High confidence and reproducibility are still challenges in bottom-up mass spectrometric N-glycopeptide identification. The collision energy used in the MS/MS measurements and the database search engine used to identify the species are perhaps the two most decisive factors. We investigated how the structural features of N-glycopeptides and the choice of the search engine influence the optimal collision energy, delivering the highest identification confidence. We carried out LC-MS/MS measurements using a series of collision energies on a large set of N-glycopeptides with both the glycan and peptide part varied and studied the behavior of Byonic, pGlyco, and GlycoQuest scores. We found that search engines show a range of behavior between peptide-centric and glycan-centric, which manifests itself already in the dependence of optimal collision energy on m/z. Using classical statistical and machine learning methods, we revealed that peptide hydrophobicity, glycan and peptide masses, and the number of mobile protons also have significant and search-engine-dependent influence, as opposed to a series of other parameters we probed. We envisioned an MS/MS workflow making a smart collision energy choice based on online available features such as the hydrophobicity (described by retention time) and glycan mass (potentially available from a scout MS/MS). Our assessment suggests that this workflow can lead to a significant gain (up to 100%) in the identification confidence, particularly for low-scoring hits close to the filtering limit, which has the potential to enhance reproducibility of N-glycopeptide analyses. Data are available via MassIVE (MSV000093110).

In silico MS/MS prediction for peptidoglycan profiling uncovers novel anti-inflammatory peptidoglycan fragments of the gut microbiota

Peptidoglycan is an essential exoskeletal polymer across all bacteria. Gut microbiota-derived peptidoglycan fragments (PGNs) are increasingly recognized as key effector molecules that impact host biology. However, the current peptidoglycan analysis workflow relies on laborious manual identification from tandem mass spectrometry (MS/MS) data, impeding the discovery of novel bioactive PGNs in the gut microbiota. In this work, we built a computational tool PGN_MS2 that reliably simulates MS/MS spectra of PGNs and integrated it into the user-defined MS library of in silico PGN search space, facilitating automated PGN identification. Empowered by PGN_MS2, we comprehensively profiled gut bacterial peptidoglycan composition. Strikingly, the probiotic Bifidobacterium spp. manifests an abundant amount of the 1,6-anhydro-MurNAc moiety that is distinct from Gram-positive bacteria. In addition to biochemical characterization of three putative lytic transglycosylases (LTs) that are responsible for anhydro-PGN production in Bifidobacterium, we established that these 1,6-anhydro-PGNs exhibit potent anti-inflammatory activity in vitro, offering novel insights into Bifidobacterium-derived PGNs as molecular signals in gut microbiota-host crosstalk.

Pentafluorobenzylpyridinium: new thermometer ion for characterizing the ions produced by collisional activation during tandem mass spectrometry

In this study, pentafluorobenzylpyridinium (F5-BnPy+), which has the highest dissociation energy among the reported benzylpyridinium thermometer ion, is proposed to characterize the internal energy distributions of ions activated by higher energy collisional dissociation (HCD) and ion-trap collision-induced dissociation (CID) during tandem mass spectrometry. The dissociation threshold energies of F5-BnPy+ was determined using quantum chemistry calculations at the CCSD(T)/6-311++G(d,p)//M06-2X-D3/6-311++G(d,p) level of theory, and the appearance energies for ion dissociation in HCD and ion-trap CID were estimated using Rice-Ramsperger-Kassel-Marcus theory. The main differences between HCD and ion-trap CID are the collision energies used and the timescales of collisional activation. For both HCD and ion-trap CID, the average internal energy of the ions increased with increasing collision energy. In contrast, the average value for the internal energy of the ions activated by ion-trap CID was lower than that of ions activated by HCD, probably because of the smaller collisional energy and longer activation time of the ion-trap CID experiments. The reported method will aid in the determination of the optimum tandem mass spectrometry parameters for the analysis of small molecules such as metabolites.

All-in-One Pseudo-MS3 Method for the Analysis of Gas-Phase Cleavable Protein Crosslinking Reactions

Crosslinking mass spectrometry (XL-MS) supports structure analysis of individual proteins and highly complex whole-cell interactomes. The identification of crosslinked peptides from enzymatic digests remains challenging, especially at the cell level. Empirical methods that use gas-phase cleavable crosslinkers can simplify the identification process by enabling an MS3-based strategy that turns crosslink identification into a simpler problem of detecting two separable peptides. However, the method is limited to select instrument platforms and is challenged by duty cycle constraints. Here, we revisit a pseudo-MS3 concept that incorporates in-source fragmentation, where a fast switch between gentle high-transmission source conditions and harsher in-source fragmentation settings liberates peptides for standard MS2-based peptide identification. We present an all-in-one method where retention time matches between the crosslink precursor and the liberated peptides establish linkage, and MS2 sequencing identifies the source-liberated peptides. We demonstrate that DC4, a very labile cleavable crosslinker, generates high-intensity peptides in-source. Crosslinks can be identified from these liberated peptides, as they are chromatographically well-resolved from monolinks. Using bovine serum albumin (BSA) as a crosslinking test case, we detect 27% more crosslinks with pseudo-MS3 over a best-in-class MS3 method. While performance is slightly lower for whole-cell lysates (generating two-thirds of the identifications of a standard method), we find that 60% of these hits are unique, highlighting the complementarity of the method.

Optimum collision energies for proteomics: The impact of ion mobility separation

Ion mobility spectrometry (IMS) is a widespread separation technique used in various research fields. It can be coupled to liquid chromatography-mass spectrometry (LC-MS/MS) methods providing an additional separation dimension. During IMS, ions are subjected to multiple collisions with buffer gas, which may cause significant ion heating. The present project addresses this phenomenon from the bottom-up proteomics point of view. We performed LC-MS/MS measurements on a cyclic ion mobility mass spectrometer with varied collision energy (CE) settings both with and without IMS. We investigated the CE dependence of identification score, using Byonic search engine, for more than 1000 tryptic peptides from HeLa digest standard. We determined the optimal CE values-giving the highest identification score-for both setups (i.e., with and without IMS). Results show that lower CE is advantageous when IMS separation is applied, by 6.3 V on average. This value belongs to the one-cycle separation configuration, and multiple cycles may supposedly have even larger impact. The effect of IMS is also reflected in the trends of optimal CE values versus m/z functions. The parameters suggested by the manufacturer were found to be almost optimal for the setup without IMS; on the other hand, they are obviously too high with IMS. Practical consideration on setting up a mass spectrometric platform hyphenated to IMS is also presented. Furthermore, the two CID (collision induced dissociation) fragmentation cells of the instrument-located before and after the IMS cell-were also compared, and we found that CE adjustment is needed when the trap cell is used for activation instead of the transfer cell. Data have been deposited in the MassIVE repository (MSV000090944).

Charge-State-Dependent Collision-Induced Dissociation Behaviors of RNA Oligonucleotides via High-Resolution Mass Spectrometry

Mass spectrometry (MS)-based analysis of RNA oligonucleotides (oligos) plays an increasingly important role in the development of RNA therapeutics and epitranscriptomics research. However, MS fragmentation behaviors of RNA oligomers are understood insufficiently. Herein, we characterized the negative-ion-mode fragmentation behaviors of 26 synthetic RNA oligos containing four to eight nucleotides using collision-induced dissociation (CID) on a high-resolution, accurate-mass instrument. We found that in CID spectra acquired under the normalized collision energy (NCE) of 35%, approximately 70% of the total peak intensity was attributed to sequencing ions (a-B, a, b, c, d, w, x, y, z), around 25% of the peak intensity came from precursor ions that experienced complete or partial loss of a nucleobase in the form of either a neutral or an anion, and the remainder were internal ions and anionic nucleobases. The top five sequencing ions were the y, c, w, a-B, and a ions. Furthermore, we observed that CID fragmentation behaviors of RNA oligos were significantly impacted by their precursor charge. Specifically, when the precursors had a charge from 1^- to 5^-, the fractional intensity of sequencing ions decreased, while that of precursors that underwent either neutral or charged losses of a nucleobase increased. Additionally, we found that RNA oligos containing 3'-U tended to produce precursors with HNCO and/or NCO^- losses, which presumably corresponded to isocyanic acid and cyanate anion, respectively. These findings provide valuable insights for better comprehending the mechanism behind RNA fragmentation by MS/MS, thereby facilitating the future automated identification of RNA oligos based on their CID spectra in a more efficient manner.

Collision energies: Optimization strategies for bottom-up proteomics

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

Phenyl Sulfate Derivatives: New Thermometer Ions for Characterization of Internal Energy of Negative Ions Produced by Electrospray Ionization

Although positive thermometer ions are widely used for evaluating the internal energy distribution of gas-phase ions, negative thermometer ions have not yet been proposed. In this study, phenyl sulfate derivatives were tested as thermometer ions to characterize the internal energy distribution of ions produced by electrospray ionization (ESI) in the negative mode because the activation of phenyl sulfate preferentially undergoes SO₃ loss, providing a phenolate anion. The dissociation threshold energies for the phenyl sulfate derivatives were determined using quantum chemistry calculations at the CCSD(T)/6-311++G(2df,p)//M06-2X-D3/6-311++G(d,p) level of theory. The values for the appearance energies of the fragment ions of the phenyl sulfate derivatives depend on the dissociation time scale in the experiment; therefore, the dissociation rate constants of the corresponding ions were estimated using the Rice-Ramsperger-Kassel-Marcus theory. The phenyl sulfate derivatives were used as thermometer ions to determine the internal energy distribution of negative ions activated by the in-source collision-induced dissociation (CID) and higher-energy collisional dissociation. Both mean and full width at half-maximum values increased with increasing ion collision energy. In the in-source CID experiments, the internal energy distributions obtained by phenyl sulfate derivatives are similar to that when all voltages are mirrored, and the traditional benzylpyridinium thermometer ions are used. The reported method will aid in determining the optimum voltage for ESI mass spectrometry and the subsequent tandem mass spectrometry of acidic analyte molecules.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

Recent advances in analytical techniques provide the opportunity to quantify even low-abundance glycopeptides derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Herein, we discuss the sample preparation procedures and the mass spectrometry (MS) strategies that have facilitated glycopeptide quantification, as well as the standards used for glycopeptide quantification. For sample preparation, various glycopeptide enrichment methods are summarized including the columns used for glycopeptide separation in liquid chromatography separation. For MS analysis strategies, MS1 level-based quantification and MS2 level-based quantification are described, either with or without labeling, where we have covered isotope labeling, TMT/iTRAQ labeling, data dependent acquisition, data independent acquisition, multiple reaction monitoring, and parallel reaction monitoring. The strengths and weaknesses of these methods are compared, particularly those associated with the figures of merit that are important for clinical biomarker studies and the pathological and functional studies of glycoproteins in various diseases. Possible future developments for glycopeptide quantification are discussed.

Diversity Matters: Optimal Collision Energies for Tandem Mass Spectrometric Analysis of a Large Set of N-Glycopeptides

Identification and characterization of N-glycopeptides from complex samples are usually based on tandem mass spectrometric measurements. Experimental settings, especially the collision energy selection method, fundamentally influence the obtained fragmentation pattern and hence the confidence of the database search results ("score"). Using standards of naturally occurring glycoproteins, we mapped the Byonic and pGlyco search engine scores of almost 200 individual N-glycopeptides as a function of collision energy settings on a quadrupole time of flight instrument. The resulting unprecedented amount of peptide-level information on such a large and diverse set of N-glycopeptides revealed that the peptide sequence heavily influences the energy for the highest score on top of an expected general linear trend with m/z. Search engine dependence may also be noteworthy. Based on the trends, we designed an experimental method and tested it on HeLa, blood plasma, and monoclonal antibody samples. As compared to the literature, these notably lower collision energies in our workflow led to 10-50% more identified N-glycopeptides, with higher scores. We recommend a simple approach based on a small set of reference N-glycopeptides easily accessible from glycoprotein standards to ease the precise determination of optimal methods on other instruments. Data sets can be accessed via the MassIVE repository (MSV000089657 and MSV000090218).

Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification

Tandem mass spectrometry (MS/MS) is a primary tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra data sets with statistically interpolated spectra at unreported collision energies. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. Applying this method yields significant improvements (∼10% more spectra correctly identified) on large data sets (2000–10 000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50–90 eV), and with smaller data sets available through MassBank.

The Key Role of Metal Adducts in the Differentiation of Phosphopeptide from Sulfopeptide Sequences by High-Resolution Mass Spectrometry

Site localization of protein sulfation by high-throughput proteomics remains challenging despite the technological improvements. In this study, sequence analysis and site localization of sulfation in tryptic peptides were determined under a conventional nano-liquid chromatography-mass spectrometry configuration. Tryptic sulfopeptide standards were used to study different fragmentation strategies, including collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), electron-transfer dissociation (ETD), electron-transfer/higher-energy collision dissociation (EThcD), and electron-transfer/collision-induced dissociation (ETciD), in the positive ionization mode. Sulfopeptides displayed only neutral loss of SO3 under CID, while the sequence could be determined for all other tested fragmentation techniques. Results were compared to the same sequences with phosphotyrosine, indicating important differences, as the sequence and modification localization could be studied by all fragmentation strategies. However, the use of metal adducts, especially potassium, provided valuable information for sulfopeptide localization in ETD and ETD-hybrid strategies by stabilizing the modification and increasing the charge state of sulfopeptides. In these conditions, both the sequence and localization could be obtained. In-source neutral loss of SO3 under EThcD provided diagnostic peaks suitable to distinguish the sulfopeptides from the nearly isobaric phosphopeptides. Further confirmation on the modification type was found in the negative ionization mode, where phosphopeptides always had the typical phosphate product ion corresponding to PO3-.

Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning

Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The training data are composed of 180,833 peptides from the National Institute of Standards and Technology and Proteomics Identification database, which were fragmented by either quadrupole time-of-flight or triple-quadrupole collision-induced dissociation methods. Exploratory analysis of the peptide fragmentation pattern was focused on the highest intensity peaks that showed proline, peptide length, and a sliding window of four amino acid combination that can be exploited as key features. The amino acid sequence of each peptide and each of the key features were allocated to different layers of the model, where recurrent neural network, convolutional neural network, and fully connected neural network were used. The trained model, PrAI-frag, accurately predicts the fragmentation spectra compared to previous machine learning-based prediction algorithms. The model excels at high-intensity peak prediction, which is advantageous to selective/multiple reaction monitoring application. PrAI-frag is provided via a Web server which can be used for peptides of length 6-15.

CFM-ID 4.0 - a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ∼22% better and a compound identification accuracy that is ∼35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

Shotgun Lipidomic Analysis for Differentiation of Niche Cold Pressed Oils

The fast-growing food industry is bringing significant number of new products to the market. To protect consumers’ health and rights, it is crucial that food control laboratories are able to ensure reliable quality testing, including product authentication and detection of adulterations. In our study, we applied a fast and eco-friendly method based on shotgun-lipidomic mass spectrometry for the authentication of niche edible oils. Comprehensive lipid profiles of camelina (CA), flax (FL) and hemp (HP) seed oils were obtained. With the aid of principal component analysis (PCA), it was possible to detect and distinguish each of them based on their lipid profiles. Lipidomic markers characteristic ofthe oils were also identified, which can be used as targets and expedite development of new multiplexed testing methods.

Mass Spectrometric Evaluation of β-Cyclodextrins as Potential Hosts for Titanocene Dichloride

Bent metallocene dichlorides (Cp₂MCl₂, M = Ti, Mo, Nb, …) have found interest as anti-cancer drugs in order to overcome the drawbacks associated with platinum-based therapeutics. However, they suffer from poor hydrolytic stability at physiological pH. A promising approach to improve their hydrolytic stability is the formation of host-guest complexes with macrocyclic structures, such as cyclodextrins. In this work, we utilized nanoelectrospray ionization tandem mass spectrometry to probe the interaction of titanocene dichloride with β-cyclodextrin. Unlike the non-covalent binding of phenylalanine and oxaliplatin to β-cyclodextrin, the mixture of titanocene and β-cyclodextrin led to signals assigned as [βCD + Cp₂Ti–H]⁺, indicating a covalent character of the interaction. This finding is supported by titanated cyclodextrin fragment ions occurring from collisional activation. Employing di- and trimethylated β-cyclodextrins as hosts enabled the elucidation of the influence of the cyclodextrin hydroxy groups on the interaction with guest structures. Masking of the hydroxy groups was found to impair the covalent interaction and enabling the encapsulation of the guest structure within the hydrophobic cavity of the cyclodextrin. Findings are further supported by breakdown curves obtained by gas-phase dissociation of the various complexes.

Deep learning neural network tools for proteomics

Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategies using artificial deep neural networks (or "deep learning") that have started to break barriers and accelerate progress in the field of shotgun proteomics. Deep learning now accurately predicts physicochemical properties of peptides from their sequence, including tandem mass spectra and retention time. Furthermore, deep learning methods exist for nearly every aspect of the modern proteomics workflow, enabling improved feature selection, peptide identification, and protein inference.

Fragmentation mechanisms of protonated cyclodextrins in tandem mass spectrometry

Tandem mass spectrometry has found widespread application as a powerful tool for the characterization of linear and branched oligosaccharides. Though the technique has been applied to the analysis of cyclic oligosaccharides as well, the underlying fragmentation mechanisms have hardly been investigated. This study focuses on the mechanistic aspects of the gas-phase dissociation of protonated β-cyclodextrins. Elucidation of the dissociation mechanisms is supported by tandem mass spectrometric experiments and by experiments on di- and trimethylated cyclodextrin derivatives. The fragmentation pathway comprises the linearization of the macrocyclic structure as the initial step of the decomposition, followed by the elimination of glucose subunits and the subsequent release of water and formaldehyde moieties from the glucose monomer and dimer fragment ions. Linearization of the macrocycle occurs due to proton-driven scission of the glycosidic bond adjacent to carbon atom C1 in conjunction with the formation of a new hydroxy group. The resulting ring-opened structure further decomposes in charge-independent processes forming either zwitterionic fragments, a 1,4-anhydroglucose moiety, or a new macrocyclic structure, that is lost as a neutral, and an oxonium ion. Since the hydroxy group formed at the ring-opening site can be regarded as the non-reducing end of the linearized structure, the fragment ion nomenclature commonly used for linear and branched oligosaccharides, which relies on the designation of a reducing and a non-reducing end, can also be applied to the description of fragment ions derived from cyclic structures.

Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices

Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.

Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide–spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography–tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10–40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).