High-field Asymmetric Ion Mobility Research Articles

FAIMS Shotgun Lipidomics for Enhanced Class- and Charge-State Separation Complemented by Automated Ganglioside Annotation.

The analysis of gangliosides is extremely challenging, given their structural complexity, lack of reference standards, databases, and software solutions. Here, we introduce a fast 6 min high field asymmetric ion mobility spectrometry (FAIMS) shotgun lipidomics workflow, along with a dedicated software solution for ganglioside detection. By ramping FAIMS compensation voltages, ideal ranges for different ganglioside classes were obtained. FAIMS revealed both class- and charge-state separation behavior based on the glycan headgroup moiety. The number of sialic acids attached to the glycan moiety correlates positively with their preferred charge states, i.e., trisialylated gangliosides were mainly present as [M - 3H]3- ions, whereas [M - 4H]4- and [M - 5H]5- ions were observed for GQ1 and GP1. For data evaluation, we developed a shotgun/FAIMS extension for the open-source Lipid Data Analyzer (LDA), enabling automated annotation of gangliosides up to the molecular lipid species level. This extension utilized combined orthogonal fragmentation spectra from CID, HCD, and 213 nm UVPD ion activation methods and covers 29 ganglioside classes, including acetylated and fucosylated modifications. With our new workflow and software extension 117 unique gangliosides species were identified in porcine brain extracts. While conventional shotgun lipidomics favored the observation of singly charged ganglioside species, the utilization of FAIMS made multiply charged lipid species accessible, resulting in an increased number of detected species, primarily due to an improved signal-to-noise ratio arising from FAIMS charge state filtering. Therefore, this FAIMS-driven workflow, complemented by new software capabilities, offers a promising strategy for complex ganglioside and glycosphingolipid characterization in shotgun lipidomics.

Application and performance enhancement of FAIMS spectral data for deep learning analysis using generative adversarial network reinforcement

When using High-field asymmetric ion mobility spectrometry (FAIMS) to process complex mixtures for deep learning analysis, there is a problem of poor recognition performance due to the lack of high-quality data and low sample diversity. In this paper, a Generative Adversarial Network (GAN) method is introduced to simulate and generate highly realistic and diverse spectral for expanding the dataset using real mixture spectral data of 15 classes collected by FAIMS. The mixed datasets were put into VGG and ResNeXt for testing respectively, and the experimental results proved that the best recognition effect was achieved when the ratio of real data to generated data was 1:4: where accuracy improved by 24.19% and 6.43%; precision improved by 23.71% and 6.97%; recall improved by 21.08% and 7.09%; and F1-score improved by 24.50% and 8.23%. The above results strongly demonstrate that GAN can effectively expand the data volume and increase the sample diversity without increasing the additional experimental cost, which significantly enhances the experimental effect of FAIMS spectral for the analysis of complex mixtures.

Theoretical and experimental study on the effect of scanning speed on FAIMS peaks

High-Field Asymmetric Ion Mobility Spectrometry (FAIMS) is a technique for ion separation and detection based on ion mobility variation under high electronic field. While compensation voltage scanning speed is a fundamental parameter in FAIMS, its impact on spectra remains unclear. In this work, a function referred to as F-EMG is introduced to describe the impact of compensation voltage scanning speed on FAIMS spectra, and the properties of the function are studied. Theoretical analysis emphasizes the impact of the scanning speed on peak height, position, and symmetry, as well as the capability of the F-EMG function to progressively approach Gaussian function at lower scanning speeds. Furthermore, the function indicates that spectra obtained in positive and negative scanning modes exhibits symmetry. An experimental validation, conducted with a custom FAIMS setup and analyzing hydrogen sulfide, ethylbenzene, toluene, styrene, benzene and ammonia, confirms the model's influence on peak features, fitting accuracy, and exhibits a closer alignment with the Gaussian function at lower scanning speeds. Additionally, the experimental data indicate that the spectra show symmetry in positive and negative scanning models. This work not only improves understanding of FAIMS spectral analysis but also introduces a robust method for enhancing data accuracy across varying scanning speeds.

Rapid method for accurate peak position extraction in high-field asymmetric ion mobility spectrometry

Rapid method for accurate peak position extraction in high-field asymmetric ion mobility spectrometry

Mass Spectrometry Imaging Reveals Abnormalities in Cardiolipin Composition and Distribution in Astrocytoma Tumor Tissues.

Cardiolipin (CL) is a mitochondrial lipid with diverse roles in cellular respiration, signaling, and organelle membrane structure. CL content and composition are essential for proper mitochondrial function. Deranged mitochondrial energy production and signaling are key components of glial cell cancers and altered CL molecular species have been observed in mouse brain glial cell xenograft tumors. The objective of this study was to describe CL structural diversity trends in human astrocytoma tumors of varying grades and correlate these trends with histological regions within the heterogeneous astrocytoma microenvironment. To this aim, we applied desorption electrospray ionization coupled with high field asymmetric ion mobility mass spectrometry (DESI-FAIMS-MS) to map CL molecular species in human normal cortex (N = 29), lower-grade astrocytoma (N = 19), and glioblastoma (N = 28) tissues. With this platform, we detected 46 CL species and 12 monolysocardiolipin species from normal cortex samples. CL profiles detected from glioblastoma tissues lacked diversity and abundance of longer chain polyunsaturated fatty acid containing CL species when compared to CL detected from normal and lower-grade tumors. CL profiles correlated with trends in tumor viability and tumor infiltration. Structural characterization of the CL species by tandem MS experiments revealed differences in fatty acid and double bond isomer composition among astrocytoma tissues compared with normal cortex and glioblastoma tissues. The GlioVis platform was used to analyze astrocytoma gene expression data from the CGGA dataset. Decreased expression of several mitochondrial respiratory enzyme encoding-genes was observed for higher-grade versus lower-grade tumors, however no significant difference was observed for cardiolipin synthesis enzyme CRLS1.

Advanced mass spectrometry workflows for accurate quantification of trace-level host cell proteins in drug products: Benefits of FAIMS separation and gas-phase fractionation DIA.

Therapeutic monoclonal antibodies (mAb) production relies on multiple purification steps before release as a drug product (DP). A few host cell proteins (HCPs) may co-purify with the mAb. Their monitoring is crucial due to the considerable risk they represent for mAb stability, integrity, and efficacy and their potential immunogenicity. Enzyme-linked immunosorbent assays (ELISA) commonly used for global HCP monitoring present limitations in terms of identification and quantification of individual HCPs. Therefore, liquid chromatography tandem mass spectrometry (LC-MS/MS) has emerged as a promising alternative. Challenging DP samples show an extreme dynamic range requiring high performing methods to detect and reliably quantify trace-level HCPs. Here, we investigated the benefits of adding high-field asymmetric ion mobility spectrometry (FAIMS) separation and gas phase fractionation (GPF) prior to data independent acquisition (DIA). FAIMS LC-MS/MS analysis allowed the identification of 221 HCPs among which 158 were reliably quantified for a global amount of 880ng/mg of NIST mAb Reference Material. Our methods have also been successfully applied to two FDA/EMA approved DPs and allowed digging deeper into the HCP landscape with the identification and quantification of a few tens of HCPs with sensitivity down to the sub-ng/mg of mAb level.

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing.

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time.

Expanding the Epitranscriptomic RNA Sequencing and Modification Mapping Mass Spectrometry Toolbox with Field Asymmetric Waveform Ion Mobility and Electrochemical Elution Liquid Chromatography.

Post-transcriptional modifications of RNA strongly influence the RNA structure and function. Recent advances in RNA sequencing and mass spectrometry (MS) methods have identified over 140 of these modifications on a wide variety of RNA species. Most next-generation sequencing approaches can only map one RNA modification at a time, and while MS can assign multiple modifications simultaneously in an unbiased manner, MS cannot accurately catalog and assign RNA modifications in complex biological samples due to limitations in the fragment length and coverage depth. Thus, a facile method to identify novel RNA modifications while simultaneously locating them in the context of their RNA sequences is still lacking. We combined two orthogonal modes of RNA ion separation before MS identification: high-field asymmetric ion mobility separation (FAIMS) and electrochemically modulated liquid chromatography (EMLC). FAIMS RNA MS increases both coverage and throughput, while EMLC LC-MS orthogonally separates RNA molecules of different lengths and charges. The combination of the two methods offers a broadly applicable platform to improve the length and depth of MS-based RNA sequencing while providing contextual access to the analysis of RNA modifications.

Identification of proteoforms by top-down proteomics using two-dimensional low/low pH reversed-phase liquid chromatography-mass spectrometry.

In top-down (TD) proteomics, efficient proteoform separation is crucial to reduce the sample complexity and increase the depth of the analysis. Here, we developed a two-dimensional low pH/low pH reversed-phase liquid chromatography separation scheme for TD proteomics. The first dimension for offline fractionation was performed using a polymeric reversed-phase (PLRP-S) column with trifluoroacetic acid as ion-pairing reagent. The second dimension, a C4 nanocolumn with formic acid as ion-pairing reagent, was coupled online with a high-field asymmetric ion mobility spectrometry (FAIMS) Orbitrap Tribrid mass spectrometer. For both dimensions several parameters were optimized, such as the adaption of the LC gradients in the second dimension according to the elution time (i.e., fraction number) in the first dimension. Avoidance of elevated temperatures and prolonged exposure to acidic conditions minimized cleavage of acid labile aspartate-proline peptide bonds. Furthermore, a concatenation strategy was developed to reduce the total measurement time. We compared our low/low pH with a previously published high pH (C4, ammonium formate)/low pH strategy and found that both separation strategies led to complementary proteoform identifications, mainly below 20kDa, with a higher number of proteoforms identified by the low/low pH separation. With the optimized separation scheme, more than 4900 proteoforms from 1250 protein groups were identified in Caco-2 cells.

Integrated Lithium Battery-Powered High-Field Asymmetric Ion Mobility Spectrometer (FAIMS) for Molecular Structure Fingerprinting and Deep Learning

A portable high-field asymmetric ion transfer spectrometer (FAIMS) was miniaturized to improve the design. The prototype consists of a main controller, a galvanometer, a high-voltage square-wave power supply, a Raspberry Pi module, and integrated circuitry. The new design eliminates a main power supply and meets the high- and low-voltage requirements. Qt, a cross-platform software package for creating graphical user interfaces, was used to map the molecular structure fingerprint. The timing, circuit structure, and mechanical assembly were optimized. The dimensions were 45 cm × 48 cm × 23 cm. 16 s were required to collect 1000 data points; the usage time exceeded 8 h, and the background noise was approximately 0.2 pA. The prototype offers excellent efficiency, stability, and multiple applications. The accuracy of FAIMS with the deep-learning model for data acquisition is good. This system is anticipated to evaluate the concentrations of volatiles in diverse types of samples.

Atmospheric pressure photoionization – High-field asymmetric ion mobility spectrometry (APPI-FAIMS) studies for on-site monitoring of aromatic volatile organic compounds (VOCs) in groundwater

A continuously operating system for monitoring groundwater contamination by aromatic VOCs has been developed. For this purpose, a novel gas-water separation unit was to be used in combination with APPI-FAIMS. The gas-water separation unit successfully reduced the humidity in the sample flow to ≤1.6 ppmv prior to analyte ionization. Initially, toluene was selected as a model aromatic VOC. The quantitative response of toluene, as a single VOC in water (LOD <1 mg L−1), was used to investigate the feasibility of the monitoring system and the effect of humidity on the signal produced by the APPI-FAIMS. With humidity increase (up to 400 ppmv) an increase of the toluene signal for about 30% was observed, including the possible formation and detection of water clusters and toluene-water clusters. Similar effects were noted in the case of benzene. However, for the detection of single contaminants such as indane and trimethylbenzenes (TMBs) this was not observed even at relative high humidity (500 ppmv). Additionally, on-site, continuous, groundwater monitoring of the aromatic VOCs contamination was carried out successfully with the gas-water separation APPI-FAIMS at low humidity (0.3–1.6 ppmv) allowing simplified monitoring of a specific, total aromatic VOCs signal in groundwater.

Mass Spectrometry Based Identification of Novel HLA Class I Restricted Peptides in Merkel Cell Carcinoma

Highly polymorphic class I human leukocyte antigen (HLA‐I) molecules present endogenous peptides to CD8+ T cells, an essential part of immune regulation. This mechanism allows for the presentation of thousands of epitopes per allele and individual. Liquid chromatography‐mass spectrometry (LC‐MS/MS) is a high throughput method that can be utilized to identify endogenously processed and presented peptides. Merkel cell carcinoma (MCC) is a rare and aggressive form of neuroendocrine skin cancer. A major subtype of this cancer is caused by the Merkel cell Polyomavirus (MCPyV), so MCPyV+ MCC is an informative model system to explore virally derived cancer‐specific epitopes. MCC, however, often downregulates or eliminates HLA‐I antigen presentation to evade recognition by the immune system. We hypothesized that reversing the mechanisms of immune evasion will allow us to detect novel epitopes. Here, we used optimized LC‐MS/MS methods to characterize the immunopeptidome of MCC in a native and perturbed state. First, we compared the immunopeptidome of primary tumor samples and tumor‐derived MCC cell lines. Peptide identities presented on primary tumors and tumor‐derived cell lines overlapped by 50% or more, indicating that the tumor‐derived cell lines retain their innate tumor properties and are a useful model to study immune presentation in MCC. Next, we investigated mechanisms to restore HLA‐I presentation in MCC, such as IFNγ treatment and knockout or inhibition of components of the Polycomb repressive complex (PRC1.1). We found that IFNγ treatment increased peptide yield up to 8‐fold across seven cell lines while also increasing presentation on HLA‐B alleles. Using tandem mass tag labeling, we observed relative quantitative changes in the immunopeptidome of an MCPyV+ cell line when PRC1.1 components were knocked down. We discovered increased HLA‐I presentation upon USP7 inhibitor treatment in comparison to the control. In contrast to IFNγ treatment, USP7 inhibition did not alter the fraction of observed peptides assigned to alleles. Finally, use of High Field Asymmetric Ion Mobility Spectrometry (FAIMS) to analyze samples with reinstated HLA presentation enabled identification of a number of novel antigens, including three MCPyV viral epitopes. The immunogenicity of these viral peptides was determined by ELISpot assays with peripheral blood mononuclear cells (PBMCs). These findings can be translated into medical treatments for patients diagnosed with this cancer.

Monolithic integration of tandem-FAIMS based on overlapping ion peak separation and weak ion peak discovery for gas detection

High-field asymmetric ion mobility spectroscopy (FAIMS) distinguishes and separates gas-phase ions according to the nonlinear change in ion mobility in a high electric field. However, since the electric field strength cannot be increased infinitely, multiple ion peaks may overlap in a complex sample detection, which can increase the difficulty of identifying ions. This paper designed a chip called tandem-FAIMS and proposed a method to separate ions in a complex sample. The tandem-FAIMS can separate overlapping ion peaks through two-stage dispersion electrodes and draw the corresponding spectrum, which we call the fingerprint. Combining fingerprint and multipeak fitting can lead to higher detection sensitivity. In this paper, benzene, styrene, and their mixture are used as samples to evaluate the effectiveness of the proposed method. The experimental results show that there are overlapping ion peaks in the spectrum under low dispersion voltage (DV=480V,550V), and the proposed method can accurately separate the overlapping ion peaks and obtain ion peak position information. The peak position error is within ±0.5V.

FAIMS Enhances the Detection of PTM Crosstalk Sites.

Protein post-translational modifications (PTMs) enable cells to rapidly change in response to biological stimuli. With hundreds of different PTMs, understanding these control mechanisms is complex. To date, efforts have focused on investigating the effect of a single PTM on protein function. Yet, many proteins contain multiple PTMs. Moreover, one PTM can alter the prevalence of another, a phenomenon termed PTM crosstalk. Understanding PTM crosstalk is critical; however, its detection is challenging since PTMs occur substoichiometrically. Here, we develop an enrichment-free, label-free proteomics method that utilizes high-field asymmetric ion mobility spectrometry (FAIMS) to enhance the detection of PTM crosstalk. We show that by searching for multiple combinations of dynamic PTMs on peptide sequences, a 6-fold increase in candidate PTM crosstalk sites is identified compared with that of standard liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflows. Additionally, by cycling through FAIMS compensation voltages within a single LC-FAIMS-MS/MS run, we show that our LC-FAIMS-MS/MS workflow can increase multi-PTM-containing peptide identifications without additional increases in run times. With 159 novel candidate crosstalk sites identified, we envisage LC-FAIMS-MS/MS to play an important role in expanding the repertoire of multi-PTM identifications. Moreover, it is only by detecting PTM crosstalk that we can “see” the full picture of how proteins are regulated.

Identification of Specific Substances in the FAIMS Spectra of Complex Mixtures Using Deep Learning.

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.

Helium-assisted enhanced discharge in a hollow needle for high-field asymmetric ion mobility spectrometry (FAIMS).

A carrier gas mixture of nitrogen and helium has been employed to improve the resolving power at the expense of sensitivity for planar high-field asymmetric ion mobility spectrometry (FAIMS) in previous work. In this paper, a new hollow needle-to-ring ion source was developed, where the helium and nitrogen enter from the hollow needle and ring, respectively. It was found that the signal strengths of acetone, ethanol, and ethyl acetate increased by 8.5, 2.0, and 3.3 times for helium ratios of 20%, 20%, and 10%, respectively. At the same time, the absolute value of compensation voltage and the number of ion peaks increases. It shows that adding an appropriate helium ratio to nitrogen simultaneously improved the sensitivity and resolving power of planar FAIMS, which is reported for the first time.

Remodeling nanoDESI Platform with Ion Mobility Spectrometry to Expand Protein Coverage in Cancerous Tissue.

Nanospray desorption electrospray ionization mass spectrometry is an ambient ionization technique that is capable of mapping proteins in tissue sections. However, high-abundant molecules or isobaric interference in biological samples hampers its broad applications in probing low-abundant proteins. To address this challenge, herein we demonstrated an integrated module that coupled pneumatic-assisted nanospray desorption electrospray ionization mass spectrometry with high-field asymmetric ion mobility spectrometry. Using this module to analyze mouse brain sections, the protein coverage was significantly increased. This improvement allowed the mapping of low-abundant proteins in tissue sections with a 5 μm spatial resolution enabled by computationally assisted fusion with optical microscopic images. Moreover, the module was successfully applied to characterize melanoma in skin tissues based on the enhanced protein profiles. The results suggested that this integrating module will be potentially applied to discover novel proteins in cancers.

IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19–38 times faster; 6–18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell.

We report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

Isobaric Tag-Based Protein Profiling across Eight Human Cell Lines Using High-Field Asymmetric Ion Mobility Spectrometry and Real-Time Database Searching.

A vast number of human cell lines are available for cell culture model-based studies, and as such the potential exists for discrepancies in findings due to cell line selection. To investigate this concept, the authors determine the relative protein abundance profiles of a panel of eight diverse, but commonly studied human cell lines. This panel includes HAP1, HEK293T, HeLa, HepG2, Jurkat, Panc1, SH-SY5Y, and SVGp12. A mass spectrometry-based proteomics workflow designed to enhance quantitative accuracy while maintaining analytical depth is used. To this end, this strategy leverages TMTpro16-based sample multiplexing, high-field asymmetric ion mobility spectrometry, and real-time database searching. The data show that the differences in the relative protein abundance profiles reflect cell line diversity. The authors also determine several hundred proteins to be highly enriched for a given cell line, and perform gene ontology and pathway analysis on these cell line-enriched proteins. An R Shiny application is designed to query protein abundance profiles and retrieve proteins with similar patterns. The workflows used herein can be applied to additional cell lines to aid cell line selection for addressing a given scientific inquiry or for improving an experimental design.