Characterization of branched and linear polyethyleneimine by trapped ion mobility-time of flight-mass spectrometry.

Determination of ion mobility collision cross sections for unresolved isomeric mixtures using tandem mass spectrometry and chemometric deconvolution

Collision cross section (CCS) values generated from ion mobility mass spectrometry (IM-MS) have commonly been employed to facilitate lipid identification. However, this is hindered by the limited available lipid standards. Recently, CCS values were predicted by means of computational calculations, though the prediction precision was generally not good and the predicted CCS values of the lipid isomers were almost identical. To address this challenge, a least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids' CCS values in this study. In this method, an array of molecular descriptors were screened and optimized to reflect the subtle differences in structures among the different lipid isomers. The use of molecular descriptors together with a wealth of standard CCS values for the lipids (365 in total) significantly improved the accuracy and precision of the LASSO model. Its accuracy was externally validated with median relative errors (MREs) of <1.1% using an independent data set. This approach was demonstrated to allow differentiation of cis/trans and sn-positional isomers. The results also indicated that the LASSO-based prediction method could practically reduce false-positive identifications in IM-MS-based lipidomics.

Polyoxometalates (POMs) are remarkable oxo-clusters forming compact highly charged anions. We measured their collision cross sections (CCS) in N2 with drift tube ion mobility spectrometry (DTIMS). These values were then used to calibrate a traveling wave ion mobility spectrometry (TWIMS) device and the accuracy of the calibration was tested. Six POM standards were analyzed by DTIM-MS (Tofwerk, Thun, Switzerland) at different voltages to determine absolute DT CCS (N2 ) values. Five POM compounds (Lindqvist TBA2 Mo6 O19; decatungstate TBA4 W10 O32; Keggin TBA3 PMo12 O40 ; TBA3 PW12 O40 and Dawson TBA6 P2 W18 O62 ) were used for the calibration of the TWIM-MS instrument (Synapt G2 HDMS, Waters, Manchester, UK) and a sixth Dawson POM, TBA9 P2 Nb3 W15 O62 , was used to compare the accuracy of the calibrations with POM or with polyalanine and dextran reference ions. We determined 45 DT CCS (N2 ) values at 30°C or 60°C. Fourteen DT CCS (N2 ) values at 30°C were used to perform calibration of the TWIMS instrument. Better correlations were observed than when DT CCS values in helium from the literature were used. The accuracy tests on six ions of Dawson POM TBA9 P2 Nb3 W15 O62 led to relative errors below 3.1% while relative errors of 3.6% to 10.1% were observed when calibration was performed with polyalanine and dextran reference ions. Our novel calibration strategy for determination of CCS values of multiply negatively charged ions on TWIM-MS devices based on DT CCS (N2 ) of standard POM structures covered a wider range of CCS and improved the accuracy to 2.1% relative error on average compared with 6.9% using polyalanine and dextran calibration.

We determined collision cross section (CCS) values for singly and doubly charged cucurbit[n]uril (n = 5-7), decamethylcucurbit[5]uril, and cyclohexanocucurbit[5]uril complexes of alkali metal cations (Li+-Cs+). These hosts are relatively rigid. CCS values calculated using the projection approximation (PA) for computationally modeled structures of a given host are nearly identical for +1 and +2 complexes, with weak metal ion dependence, whereas trajectory method (TM) calculations of CCS for the same structures consistently yield values 7-10% larger for the +2 complexes than for the corresponding +1 complexes and little metal ion dependence. Experimentally, we measured relative CCS values in SF6 for pairs of +1 and +2 complexes of the cucurbituril hosts using the cross-sectional areas by Fourier transform ion cyclotron resonance ("CRAFTI") method. At center-of-mass collision energies <∼30 eV, CRAFTI CCS values are sensitive to the relative binding energies in the +1 and +2 complexes, but at collision energies >∼40 eV (sufficient that ion decoherence occurs on essentially every collision) that dependence is not evident. Consistent with the PA calculations, these experiments found that the +2 complex ions have CCS values ranging between 94 and 105% of those of their +1 counterparts (increasing with metal ion size). In contrast, but consistent with the TM CCS calculations, ion mobility measurements of the same complexes at close to thermal energies in much less polarizable N2 find the CCS of +2 complexes to be in all cases 9-12% larger than those of the corresponding +1 complexes, with little metal ion dependence.

Collision cross section (CCS) values are descriptors of the 3D structure of ions which can be determined by ion mobility spectrometry (IMS). Currently, most lipidomic studies involving CCS value determination concern eukaryote samples (e.g. human, bovine) and to a lower extent prokaryote samples (e.g. bacteria). Here, we report CCS values obtained from traveling wave ion mobility spectrometry (TWCCSN2) measurements from the bacterial membrane of Pseudomonas aeruginosa-a bacterium ranked as priority 1 for the R&D of new antibiotics by the World Health Organization. In order to cover the lack of reference compounds which could cover the m/z and CCS ranges of the membrane lipids of P. aeruginosa, three calibrants (polyalanine, dextran and phospholipids) were used for the TWCCSN2 calibration. A shift from the published lipid CCS values was systematically observed (ΔCCS% up to 9%); thus, we proposed a CCS correction strategy. This correction strategy allowed a reduction in the shift (ΔCCS%) between our measurements and published values to less than 2%. This correction was then applied to determine the CCS values of Pseudomonas aeruginosa lipids which have not been published yet. As a result, 32 TWCCSN2 values for [M+H]+ ions and 24 TWCCSN2 values for [M-H]- ions were obtained for four classes of phospholipids (phosphatidylethanolamines (PE), phosphatidylcholines (PC), phosphatidylglycerols (PG) and diphosphatidylglycerols-known as cardiolipins (CL)). Graphical abstract.

Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.

Collision cross section (CCS) values determined in ion mobility-mass spectrometry (IM-MS) are increasingly employed as additional descriptors in metabolomics studies. CCS values must therefore be reproducible and the causes of deviations must be carefully known and controlled. Here, we analyzed lipid standards by trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) to evaluate the effects of solvent and flow rate in flow injection analysis (FIA), as well as electrospray source parameters including nebulizer gas pressure, drying gas flow rate, and temperature, on the ion mobility and CCS values. The stability of ion mobility experiments was studied over 10 h, which established the need for a delay-time of 20 min to stabilize source parameters (mostly pressure and temperature). Modifications of electrospray source parameters induced shifts of ion mobility peaks and even the occurrence of an additional peak in the ion mobility spectra. This behavior could be essentially explained by ion-solvent cluster formation. Changes in source parameters were also found to impact CCS value measurements, resulting in deviations up to 0.8%. However, internal calibration with the Tune Mix calibrant reduced the CCS deviations to 0.1%. Thus, optimization of source parameters is essential to achieve a good desolvation of lipid ions and avoid misinterpretation of peaks in ion mobility spectra due to solvent effects. This work highlights the importance of internal calibration to ensure interoperable CCS values, usable in metabolomics annotation.

Ion mobility-mass spectrometry (IM-MS) is a powerful analytical tool for structural characterization. IM measurement provides collision cross section (CCS) values that facilitate analyte identification. While CCS values can be directly calculated from mobility measurements obtained using drift tube ion mobility spectrometry (DT-IMS), this method has limited mobility resolution due to the practical constraints on the length of the ion drift path. Consequently, DT-IMS cannot differentiate analytes with similar mobilities or resolve fine mobility features of individual ions. Cyclic IMS (cIMS) instruments leverage a cyclic path enabled by traveling wave ion mobility (TWIM) technology and offer increased mobility solution to address this challenge. While TWIM devices must first be calibrated to enable CCS measurements, current calibration strategies are primarily tailored for single-pass analyses. This preference is partly attributed to the challenges associated with multipass calibration methods, which require both calibrants and analytes to experience the same number of passes. Achieving this consistency can be complicated due to factors like peak splitting and diffusion, and may not be feasible for online IM-MS analyses. A recent report employed average ion velocities obtained from multiple measurements under different separation pathlengths as a path length-independent metric for CCS calibration. However, the ability to exploit this averaging approach is limited by observed variation in ion drift time/velocity in these measurements. In this study, we introduce a novel calibration strategy designed for multipass cIMS analyses, directly targeting the root cause for the path length- and mobility-dependent variations in ion drift time. With this method, we demonstrate that CCS values derived from multipass measurements closely align with those obtained from single-pass analyses, with an average deviation of 0.1%. We apply this method to characterize four isomeric trisaccharides. Our approach not only results in excellent agreement between our measured cIMSCCS values and the reported DTCCS values, with an average difference of only 0.5%, but also allows us to effectively identify subtle mobility characteristics of each compound and determine their respective CCS values. This level of detail and accuracy was previously unattainable using DT-IMS or single-pass cIMS measurements. We developed an algorithm for reconstructing arrival time distribution in cases where wrap-around has resulted in peak splitting. Collectively, the new calibration strategy and the reconstruction procedure maintain reproducibility and precision in CCS measurements while largely eliminating the need for meticulous selection of separation times. We expect that our method will empower researchers to harness the high mobility resolution offered by multipass cIMS analyses without compromising the accuracy of CCS measurement, making it appropriate for straightforward use across a wide range of applications.

A re-calibration procedure for interoperable lipid collision cross section values measured by traveling wave ion mobility spectrometry

The use of collision cross-section (CCS) values derived from ion mobility-mass spectrometry (IM-MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values. In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids. The use of optimized molecular descriptors together with a large set of standard CCS values for lipids (458 in total) to build the prediction model significantly improved the precision. The prediction precision of LipidCCS was externally validated with median relative errors (MRE) of ∼1% using independent data sets across different instruments (Agilent DTIM-MS and Waters TWIM-MS) and laboratories. We also demonstrated that the improved precision in the predicted LipidCCS database (15 646 lipids and 63 434 CCS values in total) could effectively reduce false-positive identifications of lipids. Common users can freely access our LipidCCS web server for the following: (1) the prediction of lipid CCS values directly from SMILES structure; (2) database search; and (3) lipid match and identification. We believe LipidCCS will be a valuable tool to support IM-MS-based lipidomics. The web server is freely available on the Internet ( http://www.metabolomics-shanghai.org/LipidCCS/ ).

Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.

Positive-ion laser desorption/ionization (LDI) of fullerenes contained in soot as produced by the Krätschmer-Huffman process delivers a wide range of fullerene molecular ions from C56+• to above C300+•. Here, the collision cross section (CCS) values of those fullerene molecular ions are determined using a trapped ion mobility-quadrupole-time-of-flight (TIMS-Q-TOF) instrument. While CCS values in the range from C60+• to C96+• are already known with high accuracy, those of ions from C98+• onward had yet to be determined. The fullerene molecular ions covered in this work have CCS values from about 200 to 440 Å2. The fullerene molecular ion series is evenly spaced at C2 differences in composition, and thus, small CCS differences of just 2.2–3.5 Å2 were determined across the entire range. Fullerene M+• ions may be employed as mobility calibrants, in particular, when very narrow 1/K0 ranges are being analyzed to achieve high TIMS resolving power. In addition, due to the simple elemental composition, M+• ions of fullerenes could also serve for mass calibration. This study describes the determination of CCS values of fullerene molecular ions from C56+• to C240+• and the application of ions from C56+• to C220+• to calibrate the ion mobility scale of a Bruker timsTOFflex instrument in any combination of LDI, matrix-assisted laser desorption/ionization (MALDI), and electrospray ionization (ESI) modes in the CCS range from about 200 to 420 Å2. This use was exemplified along with ions from Agilent Tune Mix, leucine-enkephalin, angiotensin I, angiotensin II, and substance P.Graphical

In the last years, ion mobility mass spectrometry (IMS-MS) has improved structural analysis and compound identification by giving access to the collision cross section (CCS). An increasingly wide and accurate database of CCS values is now available but often without assessment of the influence of different instrumental settings on CCS values. Here, we present 75 CCS values in helium (DTCCSHe) for phosphoric acid cluster anions [(H3PO4)n - zH]z- with charge state (z) up to 4-. The CCS values, noted DTCCSHe, were obtained with a commercial drift tube ion mobility mass spectrometer, in helium, by applying a classic multifield approach. Phosphoric acid clusters are fragile structures that allow to evaluate the effect of different experimental conditions on the retention of weak bonds and their effect on CCS values. We probed harsh and soft voltage gradients in the electrospray (ESI) source before the IMS and two different voltage gradients in the post-IMS region. The variations in the ion mobility and mass spectra consisted in a change in the distribution of the cluster anions aggregation numbers (n) and charge states (z), with a higher amount of multiply charged species for the soft pre-IMS voltage gradient and a lower proportion of cluster dissociation for soft post-IMS conditions. However, the CCS values did not change with experimental conditions for a given cluster, as long as it stays intact from the IMS to the mass analyzer. The DTCCSHe were found in good agreement among 3 to 10 replicated values, with a relative standard deviation between 0.1 and 1.7%.

The rapid development of metabolomics has significantly advanced health and disease related research. However, metabolite identification remains a major analytical challenge for untargeted metabolomics. While the use of collision cross-section (CCS) values obtained in ion mobility-mass spectrometry (IM-MS) effectively increases identification confidence of metabolites, it is restricted by the limited number of available CCS values for metabolites. Here, we demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites. In this work, we first experimentally measured CCS values (ΩN2) of ∼400 metabolites in nitrogen buffer gas and used these values as training data to optimize the prediction method. The high prediction precision of this method was externally validated using an independent set of metabolites with a median relative error (MRE) of ∼3%, better than conventional theoretical calculation. Using the SVR based prediction method, a large-scale predicted CCS database was generated for 35 203 metabolites in the Human Metabolome Database (HMDB). For each metabolite, five different ion adducts in positive and negative modes were predicted, accounting for 176 015 CCS values in total. Finally, improved metabolite identification accuracy was demonstrated using real biological samples. Conclusively, our results proved that the SVR based prediction method can accurately predict nitrogen CCS values (ΩN2) of metabolites from molecular descriptors and effectively improve identification accuracy and efficiency in untargeted metabolomics. The predicted CCS database, namely, MetCCS, is freely available on the Internet.

Ion mobility mass spectrometry (IM-MS) is a rapid, gas-phaseseparationtechnology that can resolve ions on the basis of their size-to-charge and mass-to-charge ratios. Since each class ofbiomolecule has a unique relationship between size and mass, IM-MSspectra of complex biological samples are organized into trendlinesthat each contain one type of biomolecule (i.e., lipid, peptide, metabolite).These trendlines can aid in the identification of unknown ions byproviding a general classification, while more specific identificationsrequire the conversion of IM arrival times to collision cross section(CCS) values to minimize instrument-to-instrument variability. However,the process of converting IM arrival times to CCS values varies betweenthe different IM devices. Arrival times from traveling wave ion mobility(TWIM) devices must undergo a calibration process to obtain CCS values,which can impart biases if the calibrants are not structurally similarto the analytes. For multiomic mixtures, several different types ofcalibrants must be used to obtain the most accurate CCS values fromTWIM platforms. Here we describe the development of a multiomic CCScalibration tool, MOCCal, to automate the assignment of unknown featuresto the power law calibration that provides the most accurate CCS value.MOCCal calibrates every experimental arrival time with up to threeclass-specific calibration curves and uses the difference (in Å2) between the calibrated TWCCSN2 valueand DTCCSN2 vs m/z regression lines to determine the best calibration curve. Usingreal and simulated multiomic samples, we demonstrate that MOCCal providesaccurately calibrated TWCCSN2 values for smallmolecules, lipids, and peptides.