Data-independent Acquisition Method Research Articles

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 -35V 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.

One-Pot Analytical Pipeline for Efficient and Sensitive Proteomic Analysis of Extracellular Vesicles.

Extracellular vesicle (EV) proteomics emerges as an effective tool for discovering potential biomarkers for disease diagnosis, monitoring, and therapeutics. However, the current workflow of mass spectrometry-based EV proteome analysis is not fully compatible in a clinical setting due to inefficient EV isolation methods and a tedious sample preparation process. To streamline and improve the efficiency of EV proteome analysis, here we introduce a one-pot analytical pipeline integrating a robust EV isolation approach, EV total recovery and purification (EVtrap), with in situ protein sample preparation, to detect urinary EV proteome. By incorporating solvent-driven protein capture and fast on-bead digestion, the one-pot pipeline enabled the whole EV proteome analysis to be completed within one day. In comparison with the existing workflow, the one-pot pipeline was able to obtain better peptide yield and identify the equivalent number of unique EV proteins from 1 mL of urine. Finally, we applied the one-pot pipeline to profile proteomes in urinary EVs of bladder cancer patients. A total of 2774 unique proteins were identified in 53 urine samples using a 15 min gradient library-free data-independent acquisition method. Taken altogether, our novel one-pot analytical pipeline demonstrated its potential for routine and robust EV proteomics in biomedical applications.

Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application.

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.

Serial and multi-level proteome analysis for microscale protein samples

Post-translational modifications (PTMs), such as phosphorylation and ubiquitination, play key roles in signal transduction and protein homeostasis. The crosstalk of PTMs greatly expands the components of proteome and protein functions. Multi-level proteome analysis, which involves proteome investigations of total lysate and PTMs in this context, provides a comprehensive approach to explore the PTM crosstalk of a biological system under diverse disturbances. However, multi-level proteome practice remains technically challenging. Here we intended to build a strategy for multi-level proteome analysis, in which we focus on the serial profiling the total proteome, ubiquitinome and phosphoproteome from the microscale of starting material. We started by evaluating five common lysis buffers and found that the sodium deoxycholate buffer provided the best overall performance. We then developed an approach for serial enrichment and profiling of the multi-level proteome. To expand the depth of identification, we customized the variable windows to perform data-independent acquisition (DIA) sequencing for each proteome. In total, we identified 6465 proteins, ∼20,000 GlyGly sites (class 1), and ∼ 19,000 phosphosites (class 1) sequentially using 1 mg of HeLa digest by three DIA measurements. We applied this strategy to analyze MG132-treated HeLa cells and observed the crosstalk between ubiquitination and phosphorylation. Our method can be referenced for other multi-level proteome studies with microscale samples. SignificanceLysis buffer containing sodium deoxycholate provided the best overall performance in multi-level proteome analysis. One step of ubiquitination enrichment before phosphorylation enrichment does not reduce the reproducibility of phosphoproteome. Customized isolation windows were established for DIA analysis on each level of proteome. Combined the serial enrichment approach and the customized single-shot DIA method enabled the multi-level proteome of microscale protein samples.

Dynamic Data-Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment.

Data-independent acquisition (DIA) mass spectrometry has grown in popularity in recent years, because of the reproducibility and quantitative rigor of a systematic tandem mass spectrometry (MS/MS) sampling method. However, traditional DIA methods may spend valuable instrument time acquiring MS/MS spectra with no usable information in them, affecting sensitivity and quantitative performance. We developed a DIA strategy that dynamically adjusts the MS/MS windows during the chromatographic separation. The method focuses MS/MS acquisition on the most relevant mass range at each point in time─increasing the quantitative sensitivity by increasing the time spent on each DIA window. We demonstrate an improved lower limit of quantification, on average, without sacrificing the number of peptides detected.

Proteomic analysis identifies subgroups of patients with active systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is a clinically and biologically heterogenous autoimmune disease. We aimed to investigate the plasma proteome of patients with active SLE to identify novel subgroups, or endotypes, of patients.MethodPlasma was collected from patients with active SLE who were enrolled in the British Isles Lupus Assessment Group Biologics Registry (BILAG-BR). The plasma proteome was analysed using a data-independent acquisition method, Sequential Window Acquisition of All theoretical mass spectra mass spectrometry (SWATH-MS). Unsupervised, data-driven clustering algorithms were used to delineate groups of patients with a shared proteomic profile.ResultsIn 223 patients, six clusters were identified based on quantification of 581 proteins. Between the clusters, there were significant differences in age (p = 0.012) and ethnicity (p = 0.003). There was increased musculoskeletal disease activity in cluster 1 (C1), 19/27 (70.4%) (p = 0.002) and renal activity in cluster 6 (C6) 15/24 (62.5%) (p = 0.051). Anti-SSa/Ro was the only autoantibody that significantly differed between clusters (p = 0.017). C1 was associated with p21-activated kinases (PAK) and Phospholipase C (PLC) signalling. Within C1 there were two sub-clusters (C1A and C1B) defined by 49 proteins related to cytoskeletal protein binding. C2 and C6 demonstrated opposite Rho family GTPase and Rho GDI signalling. Three proteins (MZB1, SND1 and AGL) identified in C6 increased the classification of active renal disease although this did not reach statistical significance (p = 0.0617).ConclusionsUnsupervised proteomic analysis identifies clusters of patients with active SLE, that are associated with clinical and serological features, which may facilitate biomarker discovery. The observed proteomic heterogeneity further supports the need for a personalised approach to treatment in SLE.

Spatial proteomics landscape and immune signature analysis of renal sample of lupus nephritis based on laser-captured microsection

ObjectiveWe aimed to reveal a spatial proteomic and immune signature of kidney function regions in lupus nephritis (LN).Material and methodsThe laser capture microdissection (LCM) was used to isolate the glomerulus, tubules, and interstitial of the kidney from paraffin samples. The data-independent acquisition (DIA) method was used to collect proteomics data. The bioinformatic analysis was performed.ResultsA total of 49,658 peptides and 4056 proteins were quantitated. Our results first showed that a high proportion of activated NK cells, naive B cells, and neutrophils in the glomerulus, activated NK cells in interstitial, and resting NK cells were accumulated in tubules in LN. The immune-related function analysis of differential expression proteins in different regions indicated that the glomerulus and interstitial were major sites of immune disturbance and regulation connected with immune response activation. Furthermore, we identified 7, 8, and 9 hub genes in LN’s glomerulus, renal interstitial, and tubules. These hub genes were significantly correlated with the infiltration of immune cell subsets. We screened out ALB, CTSB, LCN2, A2M, CDC42, VIM, LTF, and CD14, which show higher performance as candidate biomarkers after correlation analysis with clinical indexes. The function within three regions of the kidney was analyzed. The differential expression proteins (DEGs) between interstitial and glomerulus were significantly enriched in the immune-related biological processes, and myeloid leukocyte-mediated immunity and cellular response to hormone stimulus. The DEGs between tubules and glomerulus were significantly enriched in cell activation and leukocyte-mediated immunity. While the DEGs between tubules and interstitial were enriched in response to lipid, antigen processing, and presentation of peptide antigen response to oxygen-containing compound, the results indicated a different function within kidney regions.ConclusionsCollectively, we revealed spatial proteomics and immune signature of LN kidney regions by combined using LCM and DIA.

SAPrIm, a semi-automated protocol for mid-throughput immunopeptidomics.

Human leukocyte antigen (HLA) molecules play a crucial role in directing adaptive immune responses based on the nature of their peptide ligands, collectively coined the immunopeptidome. As such, the study of HLA molecules has been of major interest in the development of cancer immunotherapies such as vaccines and T-cell therapies. Hence, a comprehensive understanding and profiling of the immunopeptidome is required to foster the growth of these personalised solutions. We herein describe SAPrIm, an Immunopeptidomics tool for the Mid-Throughput era. This is a semi-automated workflow involving the KingFisher platform to isolate immunopeptidomes using anti-HLA antibodies coupled to a hyper-porous magnetic protein A microbead, a variable window data independent acquisition (DIA) method and the ability to run up to 12 samples in parallel. Using this workflow, we were able to concordantly identify and quantify ~400 - 13000 unique peptides from 5e5 - 5e7 cells, respectively. Overall, we propose that the application of this workflow will be crucial for the future of immunopeptidome profiling, especially for mid-size cohorts and comparative immunopeptidomics studies.

A microfluidics-enabled automated workflow of sample preparation for MS-based immunopeptidomics

Mass spectrometry (MS)-based immunopeptidomics is an attractive antigen discovery method with growing clinical implications. However, the current experimental approach to extract HLA-restricted peptides requires a bulky sample source, which remains a challenge for obtaining clinical specimens. We present an innovative workflow that requires a low sample volume, which streamlines the immunoaffinity purification (IP) and C18 peptide cleanup on a single microfluidics platform with automated liquid handling and minimal sample transfers, resulting in higher assay sensitivity. We also demonstrate how the state-of-the-art data-independent acquisition (DIA) method further enhances the depth of tandem MS spectra-based peptide sequencing. Consequently, over 4,000 and 5,000 HLA-I-restricted peptides were identified from as few as 0.2 million RA957 cells and a melanoma tissue of merely 5mg, respectively. We also identified multiple immunogenic tumor-associated antigens and hundreds of peptides derived from non-canonical protein sources. This workflow represents a powerful tool for identifying the immunopeptidome of sparse samples.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry

Monitoring of host cell proteins (HCPs) during the manufacturing of monoclonal antibodies (mAb) has become a critical requirement to provide effective and safe drug products. Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities. However, this technique has several limitations and does, among others, not enable the precise identification of proteins. In this context, mass spectrometry (MS) became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs. However, in order to be routinely implemented in biopharmaceutical companies, liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification. Here, we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard, the HCP Profiler solution, with a spectral library-based data-independent acquisition (DIA) method and strict data validation criteria. The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical data-dependent acquisition on a series of samples produced at various stages of the manufacturing process. While we also explored spectral library-free DIA interpretation, the spectral library-based approach still showed highest accuracy and reproducibility (coefficients of variation < 10%) with a sensitivity down to the sub-ng/mg mAb level. Thus, this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation

Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications.

Development of a Spectral Library for the Discovery of Altered Genomic Events in Mycobacterium avium Associated With Virulence Using Mass Spectrometry–Based Proteogenomic Analysis

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using (i) a protein database for Mycobacterium tuberculosis, (ii) an M. avium genome six-frame–translated database, and (iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2954 proteins. Further, proteogenomic analyses aided in identifying 1301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Quantitative proteomics identifies and validates urinary biomarkers of rhabdomyosarcoma in children

BackgroundRhabdomyosarcoma (RMS) is the most common soft tissue sarcoma with poor prognosis in children. The 5-year survival rate for early RMS has improved, whereas it remains unsatisfactory for advanced patients. Urine can rapidly reflect changes in the body and identify low-abundance proteins. Early screening of tumor markers through urine in RMS allows for earlier treatment, which is associated with better outcomes.MethodsRMS patients under 18 years old, including those newly diagnosed and after surgery, were enrolled. Urine samples were collected at the time points of admission and after four cycles of chemotherapy during follow-up. Then, a two-stage workflow was established. (1) In the discovery stage, differential proteins (DPs) were initially identified in 43 RMS patients and 12 healthy controls (HCs) using a data-independent acquisition method. (2) In the verification stage, DPs were further verified as biomarkers in 54 RMS patients and 25 HCs using parallel reaction monitoring analysis. Furthermore, a receiver operating characteristic (ROC) curve was used to construct the protein panels for the diagnosis of RMS. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software were used to perform bioinformatics analysis.ResultsA total of 251 proteins were significantly altered in the discovery stage, most of which were enriched in the head, neck and urogenital tract, consistent with the most common sites of RMS. The most overrepresented biological processes from GO analysis included immunity, inflammation, tumor invasion and neuronal damage. Pathways engaging the identified proteins revealed 33 common pathways, including WNT/β-catenin signaling and PI3K/AKT signaling. Finally, 39 proteins were confirmed as urinary biomarkers for RMS, and a diagnostic panel composed of 5 candidate proteins (EPS8L2, SPARC, HLA-DRB1, ACAN, and CILP) was constructed for the early screening of RMS (AUC: 0.79, 95%CI = 0.66 ~ 0.92).ConclusionsThese findings provide novel biomarkers in urine that are easy to translate into clinical diagnosis of RMS and illustrate the value of global and targeted urine proteomics to identify and qualify candidate biomarkers for noninvasive molecular diagnosis.

Improving SWATH-MS analysis by deep-learning.

Data-independent acquisition (DIA) of tandem mass spectrometry spectra has emerged as a promising technology to improve coverage and quantification of proteins in complex mixtures. The success of DIA experiments is dependent on the quality of spectral libraries used for data base searching. Frequently, these libraries need to be generated by labor and time intensive data dependent acquisition (DDA) experiments. Recently, several algorithms have been published that allow the generation of theoretical libraries by an efficient prediction of retention time and intensity of the fragment ions. Sequential windowed acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) is a DIA method that can be applied at an unprecedented speed, but the fragmentation spectra suffer from a lower quality than data acquired on Orbitrap instruments. To reliably generate theoretical libraries that can be used in SWATH experiments, we developed deep-learning for SWATH analysis (dpSWATH), to improve the sensitivity and specificity of data generated by Q-TOF mass spectrometers. The theoretical library built by dpSWATH allowed us to increase the identification rate of proteins compared to traditional or library-free methods. Based on our analysis we conclude that dpSWATH is a superior prediction framework for SWATH-MS measurements than other algorithms based on Orbitrap data.

Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition

Data-independent acquisition (DIA) methods have become increasingly popular in mass spectrometry–based proteomics because they enable continuous acquisition of fragment spectra for all precursors simultaneously. However, these advantages come with the challenge of correctly reconstructing the precursor–fragment relationships in these highly convoluted spectra for reliable identification and quantification. Here, we introduce a scan mode for the combination of trapped ion mobility spectrometry with parallel accumulation—serial fragmentation (PASEF) that seamlessly and continuously follows the natural shape of the ion cloud in ion mobility and peptide precursor mass dimensions. Termed synchro-PASEF, it increases the detected fragment ion current several-fold at sub-second cycle times. Consecutive quadrupole selection windows move synchronously through the mass and ion mobility range. In this process, the quadrupole slices through the peptide precursors, which separates fragment ion signals of each precursor into adjacent synchro-PASEF scans. This precisely defines precursor–fragment relationships in ion mobility and mass dimensions and effectively deconvolutes the DIA fragment space. Importantly, the partitioned parts of the fragment ion transitions provide a further dimension of specificity via a lock-and-key mechanism. This is also advantageous for quantification, where signals from interfering precursors in the DIA selection window do not affect all partitions of the fragment ion, allowing to retain only the specific parts for quantification. Overall, we establish the defining features of synchro-PASEF and explore its potential for proteomic analyses.

Applying Data‐Independent Acquisition Mass Spectrometry on Cerebrospinal Fluid Samples from a Neurology Clinic for Biomarker Discovery in Alzheimer’s Disease

AbstractBackgroundDiagnosis of Alzheimer’s disease (AD) primarily relies on cognitive assessments combined with imaging and limited fluid biomarkers. These biomarkers do not correlate with cognitive prognosis. Analysis of larger sets of biomarkers could improve diagnosis and prognosis of AD and differentiate between comorbid pathophysiologies, and measure treatment efficacy. Here, we report analysis of a Data‐independent Acquisition (DIA) mass‐spectrometry method used to simultaneously quantify hundreds of proteins in a large‐scale patient cohort. We aim to define markers that are able to stratify and diagnose AD in this clinically complex cohort.MethodSamples from 408 patients spanning various dementia diagnoses were collected from the Massachusetts General Hospital Lumbar Puncture clinic. The patient cohort consisted of ATN‐verified cognitively‐unimpaired (n = 81), mild‐cognitive impairment from AD (n = 116), mild‐cognitive impairment from other causes (n = 78), dementia from AD (n = 65) and dementia from other causes (n = 31). Samples were analyzed on an Orbitrap Fusion using a DIA method and raw files were searched using Scaffold‐DIA.ResultAnalyses of the method’s technical performance showed a median intrabatch CV of 24.7% which decreased to 17.5% after filtering for peptides expressed in 95% of all samples. The mean interbatch CV was 73.7% which reduced to 28.8% after ComBat batch‐correction. After filtering for missing values and selecting only the peptides with a mean intrabatch CV below 25%, our dataset consists of 1761 unique peptide sequences belonging to 516 proteins.To determine the differential abundance of peptides we fit a linear regression model to the data. 572 peptides were found to be differentially abundant across all diagnoses compared to AD (padj &lt; 0.05). 11 of these peptides belonging to 5 proteins (PKM, ALDOA, GUAD, BASP1 and CH3L1) were differentially abundant between AD and all non‐AD diagnoses, suggesting specificity for AD. PKM and ALDOA, are involved in regulation of balance between glycolysis and oxidative phosphorylation, potentially indicating a shift in brain metabolism in AD.ConclusionTechnically robust unbiased mass‐spectrometry can highlight novel AD specific biomarkers which may reflect pathophysiological processes key to AD progression.

Quantitative proteomic analysis of exosomes from umbilical cord mesenchymal stem cells and rat bone marrow stem cells.

Exosomes derived from mesenchymal stem cells (MSCs) have been used for cancer treatment, however, an in-depth analysis of the exosomal proteomes is lacking. In this manuscript, we use the diaPASEF (parallel accumulation serial fragmentation combined with the data-independent acquisition) method to quantify exosomes derived from human umbilical cord mesenchymal stem cells (UCMSCs) and rat bone marrow stem cells (BMSCs), resulting in identification of 4200 human proteins and 5362 rat proteins. Comparison of human exosomal proteins and total cellular proteins reveals that some proteins exist in the exosomes exclusively that can be served as potential markers for exosomes. Quantitative proteomic analysis of exosomes from different passages of BMSCs shows that the proteins involved in TGF-β signaling pathway are regulated in abundance, which could be markers for the therapeutic ability of BMSC exosomes. Collectively, the data presented by this study can be a resource for further study of exosome research.

Enhancing Biomolecule Analysis and 2DMS Experiments by Implementation of (Activated Ion) 193 nm UVPD on a FT-ICR Mass Spectrometer.

Ultraviolet photodissociation is a fast, photon-mediated fragmentation method that yields high sequence coverage and informative cleavages of biomolecules. In this work, 193 nm UVPD was coupled with a 12 Tesla FT-ICR mass spectrometer and 10.6 μm infrared multi-photon dissociation to provide gentle slow-heating of UV-irradiated ions. No internal instrument hardware modifications were required. Adjusting the timing of laser pulses to the ion motion within the ICR cell provided consistent fragmentation yield shot-to-shot and may also be used to monitor ion positions within the ICR cell. Single-pulse UVPD of the native-like 5+ charge state of ubiquitin resulted in 86.6% cleavage coverage. Additionally, IR activation post UVPD doubled the overall fragmentation yield and boosted the intensity of UVPD-specific x-type fragments up to 4-fold. This increased yield effect was also observed for the 6+ charge state of ubiquitin, albeit less pronounced. This indicates that gentle slow-heating serves to sever tethered fragments originating from non-covalently linked compact structures and makes activation post UVPD an attractive option to boost fragmentation efficiency for top-down studies. Lastly, UVPD was implemented and optimized as a fragmentation method for 2DMS, a data-independent acquisition method. UVPD-2DMS was demonstrated to be a viable method using BSA digest peptides as a model system.

Quantitating Nrf2-regulated cytoprotective proteins using an LC-MS/MS data-independent acquisition method

Quantitating Nrf2-regulated cytoprotective proteins using an LC-MS/MS data-independent acquisition method