FAIMS Research Articles

Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques

The polyclonal repertoire of circulating antibodies potentially holds valuable information about an individual's humoral immune state. While bottom-up proteomics is well suited for serum proteomics, the vast number of antibodies and dynamic range of serum challenge this analysis. To acquire the serum proteome more comprehensively, we incorporated high-field asymmetric waveform ion-mobility spectrometry (FAIMS) or two-dimensional chromatography into standard trypsin-based bottom-up proteomics. Thereby, the number of variable region (VR)-related spectra increased 1.7-fold with FAIMS and 10-fold with chromatography fractionation. To match antibody VRs to spectra, we combined de novo searching and BLAST alignment. Validation of this approach showed that, as peptide length increased, the de novo accuracy decreased and BLAST performance increased. Through in silico calculations on antibody repository sequences, we determined the uniqueness of tryptic VR peptides and their suitability as antibody surrogate. Approximately one-third of these peptides were unique, and about one-third of all antibodies contained at least one unique peptide.

Application and integration of deep learning in FAIMS for identifying acetone concentration

In practical applications, analytical instruments are used for both qualitative and quantitative analysis. However, for high-field asymmetric-waveform ion mobility spectrometry (FAIMS), most studies to date have been focused on the qualitative analysis of substances, with limited research on quantitative analysis. Explored here is the feasibility of using deep learning in FAIMS for quantitative analysis, aided by redesigning the FAIMS upper computer. Integrating spectrum creation and deep learning analysis into the FAIMS upper computer boosts the processing and analysis of FAIMS data, laying a foundation for applying FAIMS practically. For analysis using image processing, multiple FAIMS spectral lines obtained under different conditions are converted into a three-dimensional thermodynamic map known as a FAIMS spectrum, and multiple FAIMS spectrum are preprocessed to obtain the data set of this experiment. The principles of partial-least-squares regression and the XGBoost and ResNeXt models are introduced in detail, and the data are analyzed using these models, while exploring the effects of different model parameters and determining their optimal values. The experimental results show that the pre-trained ResNeXt deep learning model performs the best on the test set, with a root mean square error of 0.86 mg/mL, indicating the potential of deep learning in realizing quantitative analysis of substances in FAIMS.

Digital Microfluidics Supported Microproteomics for Quantitative Proteome Analysis of Single Caenorhabditis elegans Nematodes.

Miniaturization of sample preparation, including omissible manual sample handling steps, is key for reproducible nanoproteomics, as material is often restricted to only hundreds of cells or single model organisms. Here, we demonstrate a highly sensitive digital microfluidics (DMF)-based sample preparation workflow making use of single-pot solid-phase enhanced sample preparation (SP3) in combination with high-field asymmetric-waveform ion mobility spectrometry (FAIMS), and fast and sensitive ion trap detection on an Orbitrap tribrid MS system. Compared to a manual in-tube SP3-supported sample preparation, the numbers of identified peptides and proteins were markedly increased, while lower standard deviations between replicates were observed. We repeatedly identified up to 5000 proteins from single nematodes. Moreover, label-free quantification of protein changes in single Caenorhabditis elegans treated with a heat stimulus yielded 45 differentially abundant proteins when compared to the untreated control, highlighting the potential of this technology for low-input proteomics studies. LC-MS data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD033143.

Evaluation of Disposable Trap Column nanoLC-FAIMS-MS/MS for the Proteomic Analysis of FFPE Tissue.

Proteomic biomarker discovery using formalin-fixed paraffin-embedded (FFPE) tissue requires robust workflows to support the analysis of large cohorts of patient samples. It also requires finding a reasonable balance between achieving a high proteomic depth and limiting the overall analysis time. To this end, we evaluated the merits of online coupling of single-use disposable trap column nanoflow liquid chromatography, high-field asymmetric-waveform ion-mobility spectrometry (FAIMS), and tandem mass spectrometry (nLC-FAIMS-MS/MS). The data show that ≤600 ng of peptide digest should be loaded onto the chromatographic part of the system. Careful characterization of the FAIMS settings enabled the choice of optimal combinations of compensation voltages (CVs) as a function of the employed LC gradient time. We found nLC-FAIMS-MS/MS to be on par with StageTip-based off-line basic pH reversed-phase fractionation in terms of proteomic depth and reproducibility of protein quantification (coefficient of variation ≤15% for 90% of all proteins) but requiring 50% less sample and substantially reducing sample handling. Using FFPE materials from the lymph node, lung, and prostate tissue as examples, we show that nLC-FAIMS-MS/MS can identify 5000-6000 proteins from the respective tissue within a total of 3 h of analysis time.

Evaluation and Optimization of High-Field Asymmetric Waveform Ion-Mobility Spectrometry for Multiplexed Quantitative Site-Specific N-Glycoproteomics.

The heterogeneity and complexity of glycosylation hinder the depth of site-specific glycoproteomics analysis. High-field asymmetric-waveform ion-mobility spectrometry (FAIMS) has been shown to improve the scope of bottom-up proteomics. The benefits of FAIMS for quantitative N-glycoproteomics have not been investigated yet. In this work, we optimized FAIMS settings for N-glycopeptide identification, with or without the tandem mass tag (TMT) label. The optimized FAIMS approach significantly increased the identification of site-specific N-glycopeptides derived from the purified immunoglobulin M (IgM) protein or human lymphoma cells. We explored in detail the changes in FAIMS mobility caused by N-glycopeptides with different characteristics, including TMT labeling, charge state, glycan type, peptide sequence, glycan size, and precursor m/z. Importantly, FAIMS also improved multiplexed N-glycopeptide quantification, both with the standard MS2 acquisition method and with our recently developed Glyco-SPS-MS3 method. The combination of FAIMS and Glyco-SPS-MS3 methods provided the highest quantitative accuracy and precision. Our results demonstrate the advantages of FAIMS for improved mass spectrometry-based qualitative and quantitative N-glycoproteomics.

Separation of Structurally Similar Anabolic Steroids as Cation Adducts in FAIMS-MS

Novel synthetic anabolic androgenic steroids have been developed not only to dodge current antidoping tests at the professional sports level, but also for consumption by noncompetitive bodybuilders. These novel anabolic steroids are commonly referred to as "designer steroids" and pose a significant risk to users because of the lack of testing for toxicity and safety in animals or humans. Manufacturers of designer steroids dodge regulation by distributing them as nutritional or dietary supplements. Improving the throughput and accuracy of screening tests would help regulators to stay on top of illicit anabolic steroids. High-field asymmetric-waveform ion mobility spectrometry (FAIMS) utilizes an alternating asymmetric electric field to separate ions by their different mobilities at high- and low-fields as they travel through the separation space. When coupled to mass spectrometry (MS), FAIMS enhances the separation of analytes from other interfering compounds with little to no increase in analysis time. Here we investigate the effects of adding various cation species to sample solutions for the separation of structurally similar or isomeric anabolic androgenic steroids. FAIMS-MS spectra for these cation-modified samples show an increased number of compensation field (CF) peaks, some of which are confirmed to be unique for one steroid isomer over another. The CF peaks observed upon addition of cation species correspond to both monomer steroid-cation adduct ions and larger multimer ion complexes. Notably, the number of CF peaks and their CF shifts do not appear to have a straightforward relationship with cation size or electronegativity. Future directions aim at investigating the structures for these analyte-cation adduct ions for building a predictive model for their FAIMS separations.

Effects of Solvent Vapor Modifiers for the Separation of Opioid Isomers in Micromachined FAIMS-MS.

Opioid addiction is an escalating problem that is compounded by the introduction of synthetic opiate analogues such as fentanyl. Screening methods for these compound classes are challenged by the availability of synthetically manufactured analogues, including isomers of existing substances. High-field asymmetric-waveform ion mobility spectrometry (FAIMS) utilizes an alternating asymmetric electric field to separate ions by their different mobilities at high and low fields as they travel through the separation space. When coupled to mass spectrometry (MS), FAIMS enhances the separation of analytes from other interfering compounds with little to no increase in analysis time. Addition of solvent vapor into the FAIMS carrier gas has been demonstrated to enable and improve the separation of isomers. Here we investigate the effects of several solvents for the separation of four opioids. FAIMS-MS spectra with added solvent vapors show dramatic compensation field (CF) shifts for opioid [M+H]+ ions when compared to spectra acquired using dry nitrogen. Addition of vapor from aprotic solvents, such as acetonitrile and acetone, produces significantly improved resolution between the tested opioids, with baseline resolution achieved between certain opioid isomers. For protic solvents, notable CF shift differences were observed in FAIMS separations between addition of water vapor and vapors from small alcohols. Graphical Abstract.

Ion Mobility Spectrometry a Versatile Analytical Tool for Metabolomics Applications in Food Science

Linear Ion Mobility Spectrometry (IMS) is a vanguard analytical technique for the determination of volatile and semi-volatile compounds, thanks to the gas-phase separation of the different ions under a weak electric field at atmospheric pressure. The ions migrate through a drift tube with defined length. Monitoring of the ion current reveals the ion drift time. The drift time correlates to the specific ion-mobility, a function of the ion mass, charge and collision cross section with the ambient gas. In this article, IMS, a technology initially developed for detecting traces of explosive and narcotic residues, as well as chemical warfare agents will be presented to meet the special requirements of metabolomics analysis. With the right sampling approach, IMS enables the detection of a wide number of compounds from different chemistry families, including alcohols, aldehydes, aromatics, amines, esters and ketones, among others, even from most complicated and problematic matrices. The selectivity of IMS can be improved by coupling to Gas Chromatography or Mass Spectrometry in order to improve separation or detection of analytes respectively. Other types of IMS such as, Travelling Wave IMS (TWIMS) and High Field Asymmetric Waveform IMS (FAIMS) have also enhanced the analytical possibilities of this technique.

The solution of nonlinear function of ion mobility based on FAIMS spectrum peak position

FAIMS's ion separation mechanism is based on analyte's characteristic nonlinear relationship between its ion mobility and applied electric field strength. Present characterization methods for this nonlinear relationship are based on precarious assumptions which incur substantial errors under many circumstances. A rigorous method for solving the second and fourth taylor series coefficient of this relationship based on dispersion voltage value (assuming half-sinusoidal waveform) and associated compensation voltage value of spectrum peak is presented, alongside with rigorous analytical functions. FAIMS spectrums were obtained for ethanol, metaxylene and n-butanol using custom-built FAIMS spectrometer, and corresponding second and fourth taylor series coefficients were obtained with the proposed method. Evaluation shows that this method substantially reduces the RMS error between interpolated and measured peak compensation voltage values under different dispersion voltages, confirming its superiority over present methods. This rigorous method would help improve spectral resolutions of FAIMS spectrometer, facilitating high precision FAIMS spectrum database construction and accurate analyte discrimination.

Design and evaluation of a novel hemispherical FAIMS cell

High-field asymmetric-waveform ion mobility spectrometry (FAIMS) is a rapidly growing gas-phase separation technique with a wide variety of applications. The geometry of the FAIMS cell influences ion transmission and resolution. Commercial FAIMS systems employ planar or cylindrical cell geometries. A spherical or hemispherical FAIMS cell would allow ions to travel equal path lengths through the FAIMS device while minimizing diffusional losses, improving ion resolution without sacrificing ion transmission. Here we detail the development of a novel hemispherical FAIMS cell and demonstrate the separation of a mixture of trinitrotoluene (TNT) and 3,4-dinitrotoluene (3,4-DNT). The resolution between TNT and 3,4-DNT using the hemispherical FAIMS cell is improved over that achieved with a commercial cylindrical cell while maintaining equivalent ion transmission. The novel hemispherical FAIMS cell provides improved resolution and resolving powers when compared to a commercially available cell.