High-field asymmetric waveform ion mobility spectrometry for mass spectrometry-based proteomics

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.

Dual inhibition of ATR and DNA-PKcs radiosensitizes ATM-mutant prostate cancer

In advanced castration resistant prostate cancer (CRPC), mutations in the DNA damage response (DDR) gene ataxia telangiectasia mutated ( ATM ) are common. While poly(ADP-ribose) polymerase inhibitors are approved in this context, their clinical efficacy remains limited. Thus, there is a compelling need to identify alternative therapeutic avenues for ATM mutant prostate cancer patients. Here, we generated matched ATM-proficient and ATM-deficient CRPC lines to elucidate the impact of ATM loss on DDR in response to DNA damage via irradiation. Through unbiased phosphoproteomic screening, we unveiled that ATM-deficient CRPC lines maintain dependence on downstream ATM targets through activation of ATR and DNA-PKcs kinases. Dual inhibition of ATR and DNA-PKcs effectively inhibited downstream γH2AX foci formation in response to irradiation and radiosensitized ATM-deficient lines to a greater extent than either ATM-proficient controls or single drug treatment. Further, dual inhibition abrogated residual downstream ATM pathway signaling and impaired replication fork dynamics. To circumvent potential toxicity, we leveraged the RUVBL1/2 ATPase inhibitor Compound B, which leads to the degradation of both ATR and DNA-PKcs kinases. Compound B effectively radiosensitized ATM-deficient CRPC in vitro and in vivo , and impacted replication fork dynamics. Overall, dual targeting of both ATR and DNA-PKcs is necessary to block DDR in ATM-deficient CRPC, and Compound B could be utilized as a novel therapy in combination with irradiation in these patients.

Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given mass-to-charge range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

IMPLICATIONS

Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

Expanding Differential Ion Mobility Separations into the MegaDalton Range

Along with mass spectrometry (MS), ion mobility separations (IMS) are advancing to ever larger biomolecules. The emergence of electrospray ionization (ESI) and native MS enabled the IMS/MS analyses of proteins up to ∼100 kDa in the 1990s and whole protein complexes and viruses up to ∼10 MDa since the 2000s. Differential IMS (FAIMS) is substantially orthogonal to linear IMS based on absolute mobility K and offers exceptional resolution, unique selectivity, and steady filtering readily compatible with slower analytical methods such as electron capture or transfer dissociation (ECD/ETD). However, the associated MS stages had limited FAIMS to ions with m/z < 8000 and masses under ∼300 kDa. Here, we integrate high-definition FAIMS with the Q-Exactive Orbitrap UHMR mass spectrometer that can handle m/z up to 80,000 and MDa-size ions in the native ESI regime. In the initial evaluation, the oligomers of monoclonal antibody adalimumab (148 kDa) are size-selected up to at least the nonamers (1.34 MDa) with m/z values up to ∼17,000. This demonstrates the survival and efficient separation of noncovalent MDa assemblies in the FAIMS process, opening the door to novel analyses of the heaviest macromolecules.

Solvent Composition Can Have a Measurable Influence on the Ion Mobility-Derived Collision Cross Section of Small Molecules

Ion mobility (IM) is an important analytical technique for increasing identification coverage of metabolites in untargeted studies, especially when integrated into traditional liquid chromatography-mass spectrometry workflows. While there has been extensive work surrounding best practices to obtain and standardize collision cross section (CCS) measurements necessary for comparing across different IM techniques and laboratories, there has been little investigation into experimental factors beyond the mobility separation region that could potentially influence CCS measurements. The first-principles derived CCS of 15 chemical standards were evaluated across 27 aqueous:organic solvent compositions using a high-precision drift tube instrument. A small but measurable dependency of the CCS on the solvent composition was observed, with the larger analytes from this study (m/z > 400) exhibiting a characteristic increase in CCS at the intermediate (40-60%) solvent compositions. Parallels to the behavior of solvent viscosity and protonation site tautomers (protomers) were noted, although the origin of these solvent-dependent CCS trends is as yet unclear. Taken together, these findings document a solvent dependency on CCS, which, while minor (<0.5%), identifies an important need for reporting the solvent system when utilizing CCS in comparative ion mobility studies.

Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers

Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC-MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.

Proteomics-based mass spectrometry profiling of SARS-CoV-2 infection from human nasopharyngeal samples

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the on-going global pandemic of coronavirus disease 2019 (COVID-19) that continues to pose a significant threat to public health worldwide. SARS-CoV-2 encodes four structural proteins namely membrane, nucleocapsid, spike, and envelope proteins that play essential roles in viral entry, fusion, and attachment to the host cell. Extensively glycosylated spike protein efficiently binds to the host angiotensin-converting enzyme 2 initiating viral entry and pathogenesis. Reverse transcriptase polymerase chain reaction on nasopharyngeal swab is the preferred method of sample collection and viral detection because it is a rapid, specific, and high-throughput technique. Alternate strategies such as proteomics and glycoproteomics-based mass spectrometry enable a more detailed and holistic view of the viral proteins and host-pathogen interactions and help in detection of potential disease markers. In this review, we highlight the use of mass spectrometry methods to profile the SARS-CoV-2 proteome from clinical nasopharyngeal swab samples. We also highlight the necessity for a comprehensive glycoproteomics mapping of SARS-CoV-2 from biological complex matrices to identify potential COVID-19 markers.

Structural Elucidation of Intact Rough-type Lipopolysaccharides Using Field Asymmetric Ion Mobility Spectrometry and Kendrick Mass Defect Plots

Lipopolysaccharides (LPSs) are a hallmark virulence factor of Gram-negative bacteria. They are complex, structurally heterogeneous mixtures due to variations in number, type, and position of their simplest units: fatty acids and monosaccharides. Thus, LPS structural characterization by traditional mass spectrometry (MS) methods is challenging. Here, we describe the benefits of field asymmetric ion mobility spectrometry (FAIMS) for analysis of an intact R-type lipopolysaccharide complex mixture (lipooligosaccharide; LOS). Structural characterization was performed using Escherichia coli J5 (Rc mutant) LOS, a TLR4 agonist widely used in glycoconjugate vaccine research. FAIMS gas-phase fractionation improved the (S/N) ratio and number of detected LOS species. Additionally, FAIMS allowed the separation of overlapping isobars facilitating their tandem MS characterization and unequivocal structural assignments. In addition to FAIMS gas-phase fractionation benefits, extra sorting of the structurally related LOS molecules was further accomplished using Kendrick mass defect (KMD) plots. Notably, a custom KMD base unit of [Na-H] created a highly organized KMD plot that allowed identification of interesting and novel structural differences across the different LOS ion families, i.e., ions with different acylation degrees, oligosaccharides composition, and chemical modifications. Defining the composition of a single LOS ion by tandem MS along with the organized KMD plot structural network was sufficient to deduce the composition of 181 LOS species out of 321 species present in the mixture. The combination of FAIMS and KMD plots allowed in-depth characterization of the complex LOS mixture and uncovered a wealth of novel information about its structural variations.

TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOFIMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOFIMS-based immunopeptidomics, a novel extensive benignTOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.

Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

A fully automated FAIMS-DIA mass spectrometry-based proteomic pipeline

Here, we present a standardized, "off-the-shelf" proteomics pipeline working in a single 96-well plate to achieve deep coverage of cellular proteomes with high throughput and scalability. This integrated pipeline streamlines a fully automated sample preparation platform, a data-independent acquisition (DIA) coupled with high-field asymmetric waveform ion mobility spectrometer (FAIMS) interface, and an optimized library-free DIA database search strategy. Our systematic evaluation of FAIMS-DIA showing single compensation voltage (CV) at -35 V not only yields the deepest proteome coverage but also best correlates with DIA without FAIMS. Our in-depth comparison of direct-DIA database search engines shows that Spectronaut outperforms others, providing the highest quantifiable proteins. Next, we apply three common DIA strategies in characterizing human induced pluripotent stem cell (iPSC)-derived neurons and show single-shot mass spectrometry (MS) using single-CV (-35 V)-FAIMS-DIA results in >9,000 quantifiable proteins with <10% missing values, as well as superior reproducibility and accuracy compared with other existing DIA methods.

False-Positive Glycopeptide Identification via In-FAIMS Fragmentation

High-field asymmetric waveform ion mobility spectrometry (FAIMS) separates glycopeptides in the gas phase prior to mass spectrometry (MS) analysis, thus offering the potential to analyze glycopeptides without prior enrichment. Several studies have demonstrated the ability of FAIMS to enhance glycopeptide detection but have primarily focused on N-glycosylation. Here, we evaluated FAIMS for O-glycoprotein and mucin-domain glycoprotein analysis using samples of varying complexity. We demonstrated that FAIMS was useful in increasingly complex samples as it allowed for the identification of more glycosylated species. However, during our analyses, we observed a phenomenon called "in FAIMS fragmentation" (IFF) akin to in source fragmentation but occurring during FAIMS separation. FAIMS experiments showed a 2- to 5-fold increase in spectral matches from IFF compared with control experiments. These results were also replicated in previously published data, indicating that this is likely a systemic occurrence when using FAIMS. Our study highlights that although there are potential benefits to using FAIMS separation, caution must be exercised in data analysis because of prevalent IFF, which may limit its applicability in the broader field of O-glycoproteomics.

Exploiting ion-mobility mass spectrometry for unraveling proteome complexity

Ion mobility spectrometry-mass spectrometry (IMS-MS) is experiencing rapid growth in proteomic studies, driven by its enhancements in dynamic range and throughput, increasing the quantitation precision, and the depth of proteome coverage. The core principle of ion mobility spectrometry is to separate ions in an inert gas under the influence of an electric field based on differences in drift time. This minireview provides an introduction to IMS operation modes and a description of advantages and limitations is presented. Moreover, the principles of trapped IMS-MS (TIMS-MS), including parallel accumulation-serial fragmentation are discussed. Finally, emerging applications linked to TIMS focusing on sample throughput (in clinical proteomics) and sensitivity (single-cell proteomics) are reviewed, and the possibilities of intact protein analysis are discussed.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

Structural Elucidation of Intact Rough-Type Lipopolysaccharides using Field Asymmetric Ion Mobility Spectrometry and Kendrick Mass Defect Plots

Lipopolysaccharide (LPS) is a hallmark virulence factor of Gram-negative bacteria. It is a complex, structurally heterogeneous mixture due to variations in number, type, and position of its simplest units: fatty acids and monosaccharides. Thus, LPS structural characterization by traditional mass spectrometry (MS) methods is challenging. Here, we describe the benefits of field asymmetric ion mobility spectrometry (FAIMS) for analysis of intact R-type lipopolysaccharide complex mixture (lipooligosaccharide; LOS). Structural characterization was performed using Escherichia coli J5 (Rc mutant) LOS, a TLR4 agonist widely used in glycoconjugate vaccine research. FAIMS gas phase fractionation improved the (S/N) ratio and number of detected LOS species. Additionally, FAIMS allowed the separation of overlapping isobars facilitating their tandem MS characterization and unequivocal structural assignments. In addition to FAIMS gas phase fractionation benefits, extra sorting of the structurally related LOS molecules was further accomplished using Kendrick mass defect (KMD) plots. Notably, a custom KMD base unit of [NaH] created a highly organized KMD plot that allowed identification of interesting and novel structural differences across the different LOS ion families; i.e., ions with different acylation degrees, oligosaccharides composition, and chemical modifications. Defining the composition of a single LOS ion by tandem MS along with the organized KMD plot structural network was sufficient to deduce the composition of 179 LOS species out of 321 species present in the mixture. The combination of FAIMS and KMD plots allowed in-depth characterization of the complex LOS mixture and uncovered a wealth of novel information about its structural variations.

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.

High-resolution ion mobility spectrometry-mass spectrometry for isomeric separation of prostanoids after Girard's reagent T derivatization

RATIONALE

Isomeric separation of prostanoids is often a challenge and requires chromatography and time-consuming sample preparation. Multiple prostanoid isomers have distinct in vivo functions crucial for understanding the inflammation process, including prostaglandins E₂ (PGE₂ ) and D₂ (PGD₂ ). High-resolution ion mobility spectrometry (IMS) based on linear ion transport in low-to-moderate electric fields and nonlinear ion transport in strong electric fields emerges as a broad approach for rapid separations prior to mass spectrometry.

METHODS

Derivatization with Girard's reagent T (GT) was used to overcome inefficient ionization of prostanoids in negative ionization mode due to poor deprotonation of the carboxylic acid group. Three high-resolution IMS techniques, namely linear cyclic IMS, linear trapped IMS, and nonlinear high-field asymmetric waveform IMS, were compared for the isomeric separation and endogenous detection of prostanoids present in intestinal tissue.

RESULTS

Direct infusion of GT-derivatized prostanoids proved to increase the ionization efficiency in positive ionization mode by a factor of >10, which enabled detection of these molecules in endogenous concentration levels. The high-resolution IMS comparison revealed its potential for rapid isomeric analysis of biologically relevant prostanoids. Strengths and weaknesses of both linear and nonlinear IMS are discussed. Endogenous prostanoid detection in intestinal tissue extracts demonstrated the applicability of our approach in biomedical research.

CONCLUSIONS

The applied derivatization strategy offers high sensitivity and improved stereoisomeric separation for screening of complex biological systems. The high-resolution IMS comparison indicated that the best sensitivity and resolution are achieved by linear and nonlinear IMS, respectively.

Chemical isotope labeling for quantitative proteomics

Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.

Multidimensional separation and analysis of alpha-1-acid glycoprotein N-glycopeptides using high-field asymmetric waveform ion mobility spectrometry (FAIMS) and nano-liquid chromatography tandem mass spectrometry

Bottom-up nLC-MS/MS-based glycoprotein mass spectrometry workflows rely on the generation of a mixture of non-glycosylated and glycosylated peptides via proteolysis of glycoproteins. Such methods are challenged by suppression of hydrophilic glycopeptide ions by more abundant, hydrophobic, and readily ionizable non-glycosylated peptides. Commercially available high-field asymmetric waveform ion mobility spectrometry (FAIMS) devices have recently been introduced and present a potential benefit for glycoproteomic workflows by enabling orthogonal separation of non-glycosylated peptides and glycopeptides following chromatographic separation, and prior to MS/MS analysis. However, knowledge is lacking regarding optimal FAIMS conditions for glycopeptide analyses. Here, we document optimal FAIMS compensation voltages for the transmission and analysis of human alpha-1-acid glycoprotein (AGP) tryptic N-glycopeptide ions. Further, we evaluate the effect of FAIMS on AGP glycopeptide assignment confidence by comparing the number of assigned glycopeptides at different confidence levels using a standard nLC-MS/MS method or an otherwise identical method employing FAIMS. Optimized methods will potentiate glycoproteomic analyses by increasing the number of unique glycopeptide identifications and the confidence of glycopeptide assignments. Data are available via ProteomeXchange with identifier PXD036667. Analysis of alpha-1-acid glycoprotein (AGP) tryptic digests via nLC-FAIMS-MS/MS (top) led to the establishment of ideal FAIMS voltages for the analysis of AGP N-glycopeptides (bottom), suggesting that FAIMS can improve the depth of glycoproteome characterization. Pairs of CV magnitudes are shown along the x-axis.

Food metabolomics: Latest hardware-developments for nontargeted food authenticity and food safety testing

The analytical requirements for food testing have increased significantly in recent years. On the one hand, because food fraud is becoming an ever-greater challenge worldwide, and on the other hand because food safety is often difficult to monitor due to the far-reaching trade chains. In addition, the expectations of consumers on the quality of food have increased, and they are demanding extensive information. Cutting-edge analytical methods are required to meet these demands. In this context, non-targeted metabolomics strategies using mass and nuclear magnetic resonance spectrometers (mass spectrometry [MS]) have proven to be very suitable. MS-based approaches are of particular importance as they provide a comparatively high analytical coverage of the metabolome. Accordingly, the efficiency to address even challenging issues is high. A variety of hardware developments, which are explained in this review, have contributed to these advances. In addition, the potential of future developments is highlighted, some of which are currently not yet commercially available or only used to a comparatively small extent but are expected to gain in importance in the coming years.

Separation methods in single-cell proteomics: RPLC or CE?

Cellular heterogeneity is commonly investigated using single-cell genomics and transcriptomics to investigate biological questions such as disease mechanism, therapeutic screening, and genomic and transcriptomic diversity between cellular populations and subpopulations at the cellular level. Single-cell mass spectrometry (MS)-based proteomics enables the high-throughput examination of protein expression at the single-cell level with wide applicability, and with spatial and temporal resolution, applicable to the study of cellular development, disease, effect of treatment, etc. The study of single-cell proteomics has lagged behind genomics and transcriptomics largely because proteins from single-cell samples cannot be amplified as DNA and RNA can using well established techniques such as PCR. Therefore, analytical methods must be robust, reproducible, and sensitive enough to detect the very small amount of protein within a single cell. To this end, nearly every step of the proteomics process has been extensively altered and improved to facilitate the proteomics analysis of single cells including cell counting and sorting, lysis, protein digestion, sample cleanup, separation, MS data acquisition, and data analysis. Here, we have reviewed recent advances in single-cell protein separation using nano reversed phase liquid chromatography (nRPLC) and capillary electrophoresis (CE) to inform application driven selection of separation techniques in the laboratory setting.

Coupling High-Field Asymmetric Ion Mobility Spectrometry with Capillary Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry Improves Protein Identifications in Bottom-Up Proteomic Analysis of Low Nanogram Samples

In this work, we pioneered the assessment of coupling high-field asymmetric waveform ion mobility spectrometry (FAIMS) with ultrasensitive capillary electrophoresis hyphenated with tandem mass spectrometry (CE-MS/MS) to achieve deeper proteome coverage of low nanogram amounts of digested cell lysates. An internal stepping strategy using three or four compensation voltages per analytical run with varied cycle times was tested to determine optimal FAIMS settings and MS parameters for the CE-FAIMS-MS/MS method. The optimized method applied to bottom-up proteomic analysis of 1 ng of HeLa protein digest standard identified 1314 ± 30 proteins, 4829 ± 200 peptide groups, and 7577 ± 163 peptide spectrum matches (PSMs) corresponding to a 16, 25, and 22% increase, respectively, over CE-MS/MS alone, without FAIMS. Furthermore, the percentage of acquired MS/MS spectra that resulted in PSMs increased nearly 2-fold with CE-FAIMS-MS/MS. Label-free quantitation of proteins and peptides was also assessed to determine the precision of replicate analyses from FAIMS methods with increased cycle times. Our results also identified from 1 ng of HeLa protein digest without any prior enrichment 76 ± 9 phosphopeptides, 18% of which were multiphosphorylated. These results represent a 46% increase in phosphopeptide identifications over the control experiments without FAIMS yielding 2.5-fold more multiphosphorylated peptides.

Mass spectrometry and the cellular surfaceome

The collection of exposed plasma membrane proteins, collectively termed the surfaceome, is involved in multiple vital cellular processes, such as the communication of cells with their surroundings and the regulation of transport across the lipid bilayer. The surfaceome also plays key roles in the immune system by recognizing and presenting antigens, with its possible malfunctioning linked to disease. Surface proteins have long been explored as potential cell markers, disease biomarkers, and therapeutic drug targets. Despite its importance, a detailed study of the surfaceome continues to pose major challenges for mass spectrometry-driven proteomics due to the inherent biophysical characteristics of surface proteins. Their inefficient extraction from hydrophobic membranes to an aqueous medium and their lower abundance compared to intracellular proteins hamper the analysis of surface proteins, which are therefore usually underrepresented in proteomic datasets. To tackle such problems, several innovative analytical methodologies have been developed. This review aims at providing an extensive overview of the different methods for surfaceome analysis, with respective considerations for downstream mass spectrometry-based proteomics.

Different ion mobility-mass spectrometry coupling techniques to promote metabolomics

Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.

Mimicked synthetic ribosomal protein complex for benchmarking crosslinking mass spectrometry workflows

Cross-linking mass spectrometry has matured to a frequently used tool for the investigation of protein structures as well as interactome studies up to a system-wide level. The growing community generated a broad spectrum of applications, linker types, acquisition strategies and specialized data analysis tools, which makes it challenging to decide for an appropriate analysis workflow. Here, we report a large and flexible synthetic peptide library as reliable instrument to benchmark crosslink workflows. Additionally, we provide a tool, IMP-X-FDR, that calculates the real, experimentally validated, FDR, compares results across search engine platforms and analyses crosslink properties in an automated manner. We apply the library with 6 commonly used linker reagents and analyse the data with 6 established search engines. We thereby show that the correct algorithm and search setting choice is highly important to improve identification rate and reliability. We reach identification rates of up to ~70 % of the theoretical maximum (i.e. 700 unique lysine-lysine cross-links) while maintaining a real false-discovery-rate of <3 % at cross-link level with high reproducibility, representatively showing that our test system delivers valuable and statistically solid results.

Cross-linking mass spectrometry is widely used to elucidate protein structures and interactions. Here, the authors generate an extensive peptide library to benchmark the most common cross-link search engines with frequently used cross-linking reagents in low and high complex sample systems.

Indirect Enantioseparations: Recent Advances in Chiral Metabolomics for Biomedical Research

Chiral metabolomics is starting to become a well-defined research field, powered by the recent advances in separation techniques. This review aimed to cover the most relevant advances in indirect enantioseparations of endogenous metabolites that were published over the last 10 years, including improvements and development of new chiral derivatizing agents, along with advances in separation methodologies. Moreover, special emphasis is put on exciting advances in separation techniques combined with mass spectrometry, such as chiral discrimination by ion-mobility mass spectrometry together with untargeted strategies for profiling of chiral metabolites in complex matrices. These advances signify a leap in chiral metabolomics technologies that will surely offer a solid base to better understand the specific roles of enantiomeric metabolites in systems biology.

Assessing the Dipole Moments and Directional Cross Sections of Proteins and Complexes by Differential Ion Mobility Spectrometry

Ion mobility spectrometry (IMS) has become a mainstream approach to fractionate complex mixtures, separate isomers, and assign the molecular geometries. All modalities were grouped into linear IMS (based on the absolute ion mobility, K) and field asymmetric waveform IMS (FAIMS) relying on the evolution of K at a high normalized electric field (E/N) that induces strong ion heating. In the recently demonstrated low-field differential (LOD) IMS, the field is too weak for significant heating but locks the macromolecular dipoles to produce novel separations controlled by the relevant directional collision cross sections (CCSs). Here, we show LODIMS for mass-selected species, exploring the dipole alignment across charge states for the monomers and dimers of an exemplary protein, the alcohol dehydrogenase. Distinct conformational families for aligned species are revealed with directional CCS estimated from the field-dependent trend lines. We set up a model to extract the fractions of pendular conformers as a function of field intensity and translate them into dipole moment distributions. These developments make a critical step toward establishing LODIMS as a new tool for top-down proteomics and integrative structural biology.

Measuring change in glycoprotein structure

Biosynthetic enzymes in the secretory pathway create distributions of glycans at each glycosite that elaborate the biophysical properties and biological functions of glycoproteins. Because the biosynthetic glycosylation reactions do not go to completion, each protein glycosite is heterogeneous with respect to glycosylation. This heterogeneity means that it is not sufficient to measure protein abundance in omics experiments. Rather, it is necessary to sample the distribution of glycosylation at each glycosite to quantify the changes that occur during biological processes. On the one hand, the use of data-dependent acquisition methods to sample glycopeptides is limited by the instrument duty cycle and the missing value problem. On the other, stepped window data-independent acquisition samples all precursors, but ion abundances are limited by duty cycle. Therefore, the ability to quantify accurately the flux in glycoprotein glycosylation that occurs during biological processes requires the exploitation of emerging mass spectrometry technologies capable of deep, comprehensive sampling and selective high confidence assignment of the complex glycopeptide mixtures. This review summarizes recent technical advances and mass spectral glycoproteomics analysis strategies and how these developments impact our ability to quantify the changes in glycosylation that occur during biological processes. We highlight specific improvements to glycopeptide characterization through activated electron dissociation, ion mobility trends and instrumentation, and efficient algorithmic approaches for glycopeptide assignment. We also discuss the emerging need for unified standards to enable interlaboratory collaborations and effective monitoring of structural changes in glycoproteins.

Separation of Isomeric Metabolites and Gangliosides with High Performance (Drift Tube) Ion Mobility–Mass Spectrometry

The study of metabolites and gangliosides is increasingly important in drug discovery (1) and immunology (2). Accurate analysis of biologically relevant isomers is important because their structure affects their molecular properties. Typically, the isomers are separated using chromatography prior to mass spectrometry (MS) analysis. However, specialized chromatographic methods that distinguish isomers frequently require a complex setup and long runs. Techniques that allow accurate results to be acquired quickly and efficiently would be beneficial.

SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State-Dependent Redox-Sensitive Cysteines

Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation.

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High-coverage Cys oxidation state quantification using custom isotopic probes.

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FragPipe-IonQuant accurately quantifies Cys labeling comparably to Skyline.

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PTMProphet enables site-of-labeling localization for multi-Cys–containing peptides.

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SP3-Rox identifies changes in Cys oxidation during T cell activation.

Leveraging Parameter Dependencies in High-Field Asymmetric Waveform Ion-Mobility Spectrometry and Size Exclusion Chromatography for Proteome-wide Cross-Linking Mass Spectrometry

Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography-mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m/z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.

An overview of technologies for MS-based proteomics-centric multi-omics

INTRODUCTION

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.

AREAS COVERED

Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other 'omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other 'omics data, critical for enabling new discoveries driven by multi-omics.

EXPERT COMMENTARY

Multi-omics, centered on the integration of proteomics information with other 'omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across 'omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Getting more out of FLAG-Tag co-immunoprecipitation mass spectrometry experiments using FAIMS

Co-immunoprecipitation of proteins coupled to mass spectrometry is critical for the understanding of protein interaction networks. In instances where a suitable antibody is not available, it is common to graft synthetic tags onto a target protein sequence thereby allowing the use of commercially available antibodies for affinity purification. A common approach is through FLAG-Tag co-immunoprecipitation. To allow the selective elution of protein complexes, competitive displacement using a large molar excess of the tag peptides is often carried out. Yet, this creates downstream challenges for the mass spectrometry analysis due to the presence of large quantities of these peptides. Here, we demonstrate that Field Asymmetric Ion Mobility Spectrometry (FAIMS), a gas phase ion separation device prior to mass spectrometry analysis can be applied to FLAG-Tag co-immunoprecipitation experiments to increase the depth of protein coverage. By excluding these abundant tag peptides, we were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without the need for additional sample handling or altering sample preparation protocols. SIGNIFICANCE: We have shown that application of FAIMS separation in the gas phase can increase the proteome coverage of Flag-Tagged co-immunoprecipitation mass spectrometry experiments versus one without FAIMS. We were able to observe deeper coverage of interacting proteins and as a result, deeper biological insights, without additional sample handling, fractionation, machine run time or modifying the sample preparation protocol.

Advances in mass spectrometry-based glycoproteomics: An update covering the period 2017-2021

Protein glycosylation is one of the most common posttranslational modifications, and plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, host-pathogen interaction, and protein stability. Glycoproteomics is a proteomics subfield dedicated to identifying and characterizing the glycans and glycoproteins in a given cell or tissue. Aberrant glycosylation has been associated with various diseases such as Alzheimer's disease, viral infections, inflammation, immune deficiencies, congenital disorders, and cancers. However, glycoproteomic analysis remains challenging because of the low abundance, site-specific heterogeneity, and poor ionization efficiency of glycopeptides during LC-MS analyses. Therefore, the development of sensitive and accurate approaches to efficiently characterize protein glycosylation is crucial. Methods such as metabolic labeling, enrichment, and derivatization of glycopeptides, coupled with different mass spectrometry techniques and bioinformatics tools, have been developed to achieve sophisticated levels of quantitative and qualitative analyses of glycoproteins. This review attempts to update the recent developments in the field of glycoproteomics reported between 2017 and 2021.

UVPD Spectroscopy of Differential Mobility-Selected Prototropic Isomers of Rivaroxaban

Two ion populations of protonated Rivaroxaban, [C₁₉H₁₈ClN₃O₅S + H]⁺, are separated under pure N₂ conditions using differential mobility spectrometry prior to characterization in a hybrid triple quadrupole linear ion trap mass spectrometer. These populations are attributed to bare protonated Rivaroxaban and to a proton-bound Rivaroxaban-ammonia complex, which dissociates prior to mass-selecting the parent ion. Ultraviolet photodissociation (UVPD) and collision-induced dissociation (CID) studies indicate that both protonated Rivaroxaban ion populations are comprised of the computed global minimum prototropic isomer. Two ion populations are also observed when the collision environment is modified with 1.5% (v/v) acetonitrile. In this case, the protonated Rivaroxaban ion populations are produced by the dissociation of the ammonium complex and by the dissociation of a proton-bound Rivaroxaban-acetonitrile complex prior to mass selection. Again, both populations exhibit a similar CID behavior; however, UVPD spectra indicate that the two ion populations are associated with different prototropic isomers. The experimentally acquired spectra are compared with computed spectra and are assigned to two prototropic isomers that exhibit proton sharing between distal oxygen centers.

A mass spectrometry-based targeted assay for detection of SARS-CoV-2 antigen from clinical specimens

Background

The COVID-19 pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has overwhelmed health systems worldwide and highlighted limitations of diagnostic testing. Several types of diagnostic tests including RT-PCR-based assays and antigen detection by lateral flow assays, each with their own strengths and weaknesses, have been developed and deployed in a short time.

Methods

Here, we describe an immunoaffinity purification approach followed a by high resolution mass spectrometry-based targeted qualitative assay capable of detecting SARS-CoV-2 viral antigen from nasopharyngeal swab samples. Based on our discovery experiments using purified virus, recombinant viral protein and nasopharyngeal swab samples from COVID-19 positive patients, nucleocapsid protein was selected as a target antigen. We then developed an automated antibody capture-based workflow coupled to targeted high-field asymmetric waveform ion mobility spectrometry (FAIMS) - parallel reaction monitoring (PRM) assay on an Orbitrap Exploris 480 mass spectrometer. An ensemble machine learning-based model for determining COVID-19 positive samples was developed using fragment ion intensities from the PRM data.

Findings

The optimized targeted assay, which was used to analyze 88 positive and 88 negative nasopharyngeal swab samples for validation, resulted in 98% (95% CI = 0.922–0.997) (86/88) sensitivity and 100% (95% CI = 0.958–1.000) (88/88) specificity using RT-PCR-based molecular testing as the reference method.

Interpretation

Our results demonstrate that direct detection of infectious agents from clinical samples by tandem mass spectrometry-based assays have potential to be deployed as diagnostic assays in clinical laboratories, which has hitherto been limited to analysis of pure microbial cultures.

Deeper Protein Identification Using Field Asymmetric Ion Mobility Spectrometry in Top-Down Proteomics

Field asymmetric ion mobility spectrometry (FAIMS), when used in proteomics studies, provides superior selectivity and enables more proteins to be identified by providing additional gas-phase separation. Here, we tested the performance of cylindrical FAIMS for the identification and characterization of proteoforms by top-down mass spectrometry of heterogeneous protein mixtures. Combining FAIMS with chromatographic separation resulted in a 62% increase in protein identifications, an 8% increase in proteoform identifications, and an improvement in proteoform identification compared to samples analyzed without FAIMS. In addition, utilization of FAIMS resulted in the identification of proteins encoded by lower-abundance mRNA transcripts. These improvements were attributable, in part, to improved signal-to-noise for proteoforms with similar retention times. Additionally, our results show that the optimal compensation voltage of any given proteoform was correlated with the molecular weight of the analyte. Collectively these results suggest that the addition of FAIMS can enhance top-down proteomics in both discovery and targeted applications.

Recent technical progress in sample preparation and liquid-phase separation-mass spectrometry for proteomic analysis of mass-limited samples

Mass spectrometry (MS)-based proteomics has enabled the identification and quantification of thousands of proteins from complex proteomes in a single experiment. However, its performance for mass-limited proteome samples (e.g., single cells and tissue samples from laser capture microdissection) is still not satisfying. The development of novel proteomic methodologies with better overall sensitivity is vital. During the last several years, substantial technical progress has been achieved for the preparation and liquid-phase separation-MS characterization of mass-limited proteome samples. In this review, we summarize recent technological progress of sample preparation, liquid chromatography (LC)-MS, capillary zone electrophoresis (CZE)-MS and MS instrumentation for bottom-up proteomics of trace biological samples, highlight some exciting applications of the novel techniques for single-cell proteomics, and provide a very brief perspective about the field at the end.

Boosting MS1-only Proteomics with Machine Learning Allows 2000 Protein Identifications in Single-Shot Human Proteome Analysis Using 5 min HPLC Gradient

Proteome-wide analyses rely on tandem mass spectrometry and the extensive separation of proteolytic mixtures. This imposes considerable instrumental time consumption, which is one of the main obstacles in the broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on ultrashort LC gradients as well as MS1-only mass spectra acquisition and data processing. The method allows significant reduction of the proteome-wide analysis time to a few minutes at the depth of quantitative proteome coverage of 1000 proteins at 1% false discovery rate (FDR). In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine-learning area and applied the LightGBM decision tree boosting algorithm to the scoring of peptide feature matches when processing MS1 spectra. Furthermore, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve the performance of the DirectMS1 method are discussed and demonstrated, such as using FAIMS for gas-phase ion separation. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 min gradient LC-FAIMS/MS1 analysis. The data sets generated and analyzed during the current study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD023977.

SP3-FAIMS Chemoproteomics for High-Coverage Profiling of the Human Cysteinome*

Chemoproteomics has enabled the rapid and proteome-wide discovery of functional, redox-sensitive, and ligandable cysteine residues. Despite widespread adoption and considerable advances in both sample-preparation workflows and MS instrumentation, chemoproteomics experiments still typically only identify a small fraction of all cysteines encoded by the human genome. Here, we develop an optimized sample-preparation workflow that combines enhanced peptide labeling with single-pot, solid-phase-enhanced sample-preparation (SP3) to improve the recovery of biotinylated peptides, even from small sample sizes. By combining this improved workflow with on-line high-field asymmetric waveform ion mobility spectrometry (FAIMS) separation of labeled peptides, we achieve unprecedented coverage of >14000 unique cysteines in a single-shot 70 min experiment. Showcasing the wide utility of the SP3-FAIMS chemoproteomic method, we find that it is also compatible with competitive small-molecule screening by isotopic tandem orthogonal proteolysis-activity-based protein profiling (isoTOP-ABPP). In aggregate, our analysis of 18 samples from seven cell lines identified 34225 unique cysteines using only ∼28 h of instrument time. The comprehensive spectral library and improved coverage provided by the SP3-FAIMS chemoproteomics method will provide the technical foundation for future studies aimed at deciphering the functions and druggability of the human cysteineome.

Biosaur: An open-source Python software for liquid chromatography-mass spectrometry peptide feature detection with ion mobility support

RATIONALE

One of the important steps in initial data processing of peptide mass spectra is the detection of peptide features in full-range mass spectra. Ion mobility offers advantages over previous methods performing this detection by providing an additional structure-specific separation dimension. However, there is a lack of open-source software that utilizes these advantages and detects peptide features in mass spectra acquired along with ion mobility data using new instruments such as timsTOF and/or FAIMS-Orbitrap.

METHODS

Recently, a utility called Dinosaur was presented, which provides an efficient way for feature detection in peptide ion mass spectra. In this work we extended its functionality by developing Biosaur software to fully employ the additional information provided by ion mobility data. Biosaur was developed using the Python 3.8 programming language.

RESULTS

Biosaur supports the processing of data acquired using mass spectrometers with ion mobility capabilities, specifically timsTOF and FAIMS. In addition, it processes mass spectra obtained in negative ion mode and reports cosine correlation table for peptide features which is useful for differentiation between in-source fragments and semi-tryptic peptides.

CONCLUSIONS

Biosaur is a utility for detecting peptide features in liquid chromatography-mass spectra with ion mobility and negative ion supports. The software is distributed with an open-source APACHE 2.0 license and is freely available on Github: https://github.com/abdrakhimov1/Biosaur.

Improved Sensitivity of Ultralow Flow LC-MS-Based Proteomic Profiling of Limited Samples Using Monolithic Capillary Columns and FAIMS Technology

In this work, we pioneered a combination of ultralow flow (ULF) high-efficiency ultranarrow bore monolithic LC columns coupled to MS via a high-field asymmetric waveform ion mobility spectrometry (FAIMS) interface to evaluate the potential applicability for high sensitivity, robust, and reproducible proteomic profiling of low nanogram-level complex biological samples. As a result, ULF LC-FAIMS-MS brought unprecedented sensitivity levels and high reproducibility in bottom-up proteomic profiling. In addition, FAIMS improved the dynamic range, signal-to-noise ratios, and detection limits in ULF LC-MS-based measurements by significantly reducing chemical noise in comparison to the conventional nanoESI interface used with the same ULF LC-MS setup. Two, three, or four compensation voltages separated by at least 15 V were tested within a single LC-MS run using the FAIMS interface. The optimized ULF LC-ESI-FAIMS-MS/MS conditions resulted in identification of 2,348 ± 42 protein groups, 10,062 ± 285 peptide groups, and 15,734 ± 350 peptide-spectrum matches for 1 ng of a HeLa digest, using a 1 h gradient at the flow rate of 12 nL/min, which represents an increase by 38%, 91%, and 131% in respective identifications, as compared to the control experiment (without FAIMS). To evaluate the practical utility of the ULF LC-ESI-FAIMS-MS platform in proteomic profiling of limited samples, approximately 100, 1,000, and 10,000 U937 myeloid leukemia cells were processed, and a one-tenth of each sample was analyzed. Using the optimized conditions, we were able to reliably identify 251 ± 54, 1,135 ± 80, and 2,234 ± 25 protein groups from injected aliquots corresponding to ∼10, 100, and 1,000 processed cells.

Synchrotron UV photoactivation of trapped sodiated ions produced from poly(ethylene glycol) by electrospray ionization

RATIONALE

By taking advantage of the gas-phase decompositions of polymer ions, tandem mass spectrometry of polymers allows us to obtain more accurate structural information than from a simple mass measurement. Applied to a model polymer, the goal of this work was to evaluate the performances of an activation technique based on ultraviolet (UV) irradiation, as an alternative to conventional collisional activation.

METHODS

Sodiated poly(ethylene glycol) produced by electrospray ionization was isolated in a linear ion trap, then submitted to synchrotron UV irradiation over a range of wavelengths (52 to 248 nm). Fragmentation pathways resulting from UV photoactivation were investigated. The proposed mechanisms take into account: (i) the comparison with collision-induced dissociation (CID) product ions, (ii) the effect of wavelength-tunable UV activation, and (iii) deuterium-labeling and various other complementary experiments. For the highest molecular weight compounds, ion mobility spectrometry was used before UV photoactivation.

RESULTS

Synchrotron UV irradiation can induce dissociation of poly(ethylene glycol) sodiated ions without the requirement of the presence of a specific chromophore, if the photon energy is above 10 eV. UV photoactivation of poly(ethylene glycol) ions can yield fragmentations that differ from those in classical low-energy CID, especially from higher masses (>4000 g mol^-1 ). A successful coupling of UV photoactivation with ion mobility pre-filtering was presented.

CONCLUSIONS

UV activation combined or not with pre-filtering ion mobility is a promising alternative approach for the structural characterization of polymers. UV synchrotron radiation with a tunable wavelength was a great opportunity to study the effect of the photon energy, and to probe the mechanisms of ion decomposition from poly(ethylene glycol).

Simple Method to Quantify Protein Abundances from 1000 Cells

The rise of single-cell transcriptomics has created an urgent need for similar approaches that use a minimal number of cells to quantify expression levels of proteins. We integrated and optimized multiple recent developments to establish a proteomics workflow to quantify proteins from as few as 1000 mammalian stem cells. The method uses chemical peptide labeling, does not require specific equipment other than cell lysis tools, and quantifies >2500 proteins with high reproducibility. We validated the method by comparing mouse embryonic stem cells and in vitro differentiated motor neurons. We identify differentially expressed proteins with small fold changes and a dynamic range in abundance similar to that of standard methods. Protein abundance measurements obtained with our protocol compared well to corresponding transcript abundance and to measurements using standard inputs. The protocol is also applicable to other systems, such as fluorescence-activated cell sorting (FACS)-purified cells from the tunicate Ciona. Therefore, we offer a straightforward and accurate method to acquire proteomics data from minimal input samples.

Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry

Methodologies that facilitate high-throughput proteomic analysis are a key step toward moving proteome investigations into clinical translation. Data independent acquisition (DIA) has potential as a high-throughput analytical method due to the reduced time needed for sample analysis, as well as its highly quantitative accuracy. However, a limiting feature of DIA methods is the sensitivity of detection of low abundant proteins and depth of coverage, which other mass spectrometry approaches address by two-dimensional fractionation (2D) to reduce sample complexity during data acquisition. In this study, we developed a 2D-DIA method intended for rapid- and deeper-proteome analysis compared to conventional 1D-DIA analysis. First, we characterized 96 individual fractions obtained from the protein standard, NCI-7, using a data-dependent approach (DDA), identifying a total of 151,366 unique peptides from 11,273 protein groups. We observed that the majority of the proteins can be identified from just a few selected fractions. By performing an optimization analysis, we identified six fractions with high peptide number and uniqueness that can account for 80% of the proteins identified in the entire experiment. These selected fractions were combined into a single sample which was then subjected to DIA (referred to as 2D-DIA) quantitative analysis. Furthermore, improved DIA quantification was achieved using a hybrid spectral library, obtained by combining peptides identified from DDA data with peptides identified directly from the DIA runs with the help of DIA-Umpire. The optimized 2D-DIA method allowed for improved identification and quantification of low abundant proteins compared to conventional unfractionated DIA analysis (1D-DIA). We then applied the 2D-DIA method to profile the proteomes of two breast cancer patient-derived xenograft (PDX) models, quantifying 6,217 and 6,167 unique proteins in basal- and luminal- tumors, respectively. Overall, this study demonstrates the potential of high-throughput quantitative proteomics using a novel 2D-DIA method.