Data-independent Acquisition Method Research Articles

B-146 Unlocking the potential of large-cohort proteomics studies with the Orbitrap Astral mass platform

Abstract Background Large-cohort proteomics analysis using mass spectrometry is a powerful approach to discover and validate new biomarkers. In combination with clinical data and computational analysis, large-cohort proteomics study brings opportunities in improving early diagnosis, refining patient stratification, and predicting/monitoring treatment response. Yet, to achieve meaningful biological insights in large-cohort studies, robust, reproducible, and comprehensive proteome profiling in a high-throughput manner still remains challenging. Here, we use a high-resolution accurate mass (HRAM) Oribtrap Astral platform to enable high-quality and robust protein quantification across thousands of LC-MS/MS analyses. With a throughput of 100 samples/day, we can reproducibly profile ∼9000 proteins from human cell line and ∼800 proteins from undepleted plasma across multiple instruments and more than 10 consecutive days in a 24/7 operation mode. Methods Experiments were performed on multiple Orbitrap Astral mass spectrometers with and without FAIMS Pro interface. Chromatographic separations were performed using a trap/elute method on Vanquish Neo system at a 11-minute gradient and a flow rate of 1ul/min, resulting in a throughput of 100 samples/day. Undepleted plasma digest was analyzed in a 24/7 mode for > 1000 injections on each LC-MS/MS setup. In addition, HeLa digest as quality control (QC) was analyzed every 12hours. The Oribtrap Astral platform was operated in data-independent acquisition (DIA) mode using a full scan with m/z 380-980, and DIA MS/MS scans with an isolation window of 2Th. Resulting DIA raw files were automatically transferred and processed using Chimerys in a beta version of Proteome Discoverer software. Results Multiple LC-MS/MS systems were operated in DIA mode either with or without FAIMS Pro device in a 24/7 operation mode at a throughput of 100SPD. Undepleted plasma digest was analyzed with >1000 injections on each LC-MS/MS setup. To monitor the baseline performance, HeLa digest served as QC were analyzed periodically every 12 hours with 3 technical replicates each time. To effectively manage and analyze these thousands of data files generated, we developed an automated data transfer and analysis pipeline. Automatically, the resulting DIA raw data files were immediately transferred to a server, then processed by state-of-the-art intelligent search algorithm Chimerys. Benefiting from the ultra-high scan speed of up to 200Hz on the Orbitrap Astral mass spectrometer, a much narrower isolation window width of 2Th was applied in the DIA method, comparing to 10-20Th on the classic DIA method. As a result, ∼9000 proteins from HeLa digest and ∼800 proteins from undepleted plasma digest were identified within only a 11-minutes gradient, respectively. More than 80% of the plasma proteins were reproducibly identified and quantified from all the runs on each LC-MS/MS setup, indicating a great reproducibility from run-to-run longitudinally. Stable and robust peptide quantitation was observed by extracting peptides with high, medium, and low abundant across the runs. Importantly, QC showed no performance degradation throughout the entire study, indicating high robustness of the entire LC-MS/MS setup. Conclusions Orbitrap Astral mass platform can comprehensively analyze the proteome of >1000s of sample robustly and reproducibly in a high-throughput manner, addressing the needs in large-cohort studies.

Data-Independent Acquisition Shortens the Analytical Window of Single-Cell Proteomics to Fifteen Minutes in Capillary Electrophoresis Mass Spectrometry.

Separation in single-cell mass spectrometry (MS) improves molecular coverage and quantification; however, it also elongates measurements, thus limiting analytical throughput to study large populations of cells. Here, we advance the speed of bottom-up proteomics by capillary electrophoresis (CE) high-resolution mass spectrometry (MS) for single-cell proteomics. We adjust the applied electrophoresis potential to readily control the duration of electrophoresis. On the HeLa proteome standard, shorter separation times curbed proteome detection using data-dependent acquisition (DDA) but not data-independent acquisition (DIA) on an Orbitrap analyzer. This DIA method identified 1161 proteins vs 401 proteins by the reference DDA within a 15 min effective separation from single HeLa-cell-equivalent (∼200 pg) proteome digests. Label-free quantification found these exclusively DIA-identified proteins in the lower domain of the concentration range, revealing sensitivity improvement. The approach also significantly advanced the reproducibility of quantification, where ∼76% of the DIA-quantified proteins had <20% coefficient of variation vs ∼43% by DDA. As a proof of principle, the method allowed us to quantify 1242 proteins in subcellular niches in a single, neural-tissue fated cell in the live Xenopus laevis (frog) embryo, including many canonical components of organelles. DIA integration enhanced throughput by ∼2-4 fold and sensitivity by a factor of ∼3 in single-cell (subcellular) CE-MS proteomics.

Comparative analysis of feature annotation methods for SESI-HRMS in exhaled breath analysis

Secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a powerful method for the analysis of exhaled breath in real time. However, feature annotation is challenging due to the flow-injection nature of the technique. To evaluate alternative methods for enhancing feature annotation, a study was conducted where the exhaled breath of sixteen subjects was condensed and analyzed using dynamic headspace vacuum in-trap extraction gas chromatography-mass spectrometry (DHS-V-ITEX-GC–MS) and liquid chromatography coupled to mass spectrometry (LC-MS) using polar and reverse-phase conditions along with a data-independent MS2-acquisition method based on multiple injections. The annotation results obtained from these methods were compared to those from SESI-HRMS. The use of these techniques on breath condensate is unprecedented. The GC–MS method primarily detected compounds of exogenous origin, particularly additives in oral hygiene products like menthol. On the other hand, LC-MS detected a vast number of features, especially with the utilized data-independent acquisition method. Chemical classes to these features were assigned in-silico. In positive ion mode, mostly amino acids and amines were detected, while the largest group in negative ion mode consisted of carboxylic acids. Approximately 25% and 5% of SESI features had a corresponding match with LC-MS and GC–MS. While both GC–MS and LC-MS methods partially overlapped with the SESI features, there was limited overlap of both in the mass-to-charge range from 150 to 200. In conclusion, both GC–MS and LC-MS analysis of breath condensate can serve as supplementary tools for annotating features obtained from SESI-MS. However, to increase confidence in the annotation results, combining these methods with additional on-line fragmentation techniques is recommended.

In vitro and in vivo evaluation of antibacterial activity and mechanism of luteolin from Humulus scandens against Escherichia coli from chicken

Resistance of Escherichia coli (E.coli) to antibiotics has steadily increased over time; hence, there is an urgent need to develop safer alternatives to antibiotics. The present study aimed to evaluate the effect of luteolin (Lut) on E. coli from chicken. The bioactive compound Lut from Humulus scandens was selected by network pharmacology and molecular docking analyses. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and confocal laser scanning microscopy (CLSM) were used to observe the effects of Lut on the morphology and structure of E. coli cells. The data-independent acquisition (DIA) method was used to analyze protein expression level of E. coli before and after Lut treatment. The in vivo evaluation of the antibacterial, anti-inflammatory, and oxidative effects of Lut on E.coli was conducted using E.coli isolated strains infected the SPF chicken model. The network pharmacology analysis revealed 19 distinctive bioactive compounds such as Lut and β-sitosterol in H. scandens; furthermore, 30 core targets were selected from H. scandens. The KEGG enrichment analysis showed that the PI3K-Akt, TNF, MAPK, IL-17, JAK-STAT, and HIF-1 pathways were related from H. scandens. Based on the results of the network pharmacology analysis, Lut was subjected to screening by molecular docking analysis to determine its antibacterial effect on E. coli and the associated mechanism of action. The minimum inhibitory concentration (MIC) of Lut against E. coli standard strains was 500 µg/mL. SEM, TEM, and CLSM results indicated that Lut damaged the cell wall and cell membrane of E. coli strains and destroyed the cell structure, leading to cell death.The expression level of membrane structure, Phenylalanine metabolism and some other metabolic pathways in E.coli changed after treatment with Lut (P < 0.05). In vivo experiments in the SPF chicken model showed that Lut treatment alleviated the decline in the growth performance of chickens (P < 0.05), prevented pathological changes in the correspond ding organs and suppressed the inflammatory response induced by E. coli infection (P < 0.05), improved the immunity and antioxidant capacity of chickens (P < 0.05), and protected them against infection with E. coli strains. To summarize, Lut from H. scandens can inhibit E. coli growth by damaging the cell membrane structureand affecting the expression level of some metabolic proteins. In vivo experiments also showed that Lut can significantly reduce the damage caused by E. coli isolates on SPF chickens, improve their antioxidant capacity and immunity, and reduce inflammatory responses following E. coli infection.

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer

BackgroundThe five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.ResultsWe conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC.ConclusionsThis study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

Cerebrospinal Fluid Proteomics Identifies Potential Biomarkers for Early-Onset Alzheimer’s Disease

Background: Early-onset Alzheimer’s disease (EOAD) exhibits a notable degree of heterogeneity as compared to late-onset Alzheimer’s disease (LOAD). The proteins and pathways contributing to the pathophysiology of EOAD still need to be completed and elucidated. Objective: Using correlation network analysis and machine learning to analyze cerebrospinal fluid (CSF) proteomics data to identify potential biomarkers and pathways associated with EOAD. Methods: We employed mass spectrometry to conduct CSF proteomic analysis using the data-independent acquisition method in a Chinese cohort of 139 CSF samples, including 40 individuals with normal cognition (CN), 61 patients with EOAD, and 38 patients with LOAD. Correlation network analysis of differentially expressed proteins was performed to identify EOAD-associated pathways. Machine learning assisted in identifying crucial proteins differentiating EOAD. We validated the results in an Western cohort and examined the proteins expression by enzyme-linked immunosorbent assay (ELISA) in additional 9 EOAD, 9 LOAD, and 9 CN samples from our cohort. Results: We quantified 2,168 CSF proteins. Following adjustment for age and sex, EOAD exhibited a significantly greater number of differentially expressed proteins than LOAD compared to CN. Additionally, our data indicates that EOAD may exhibit more pronounced synaptic dysfunction than LOAD. Three potential biomarkers for EOAD were identified: SH3BGRL3, LRP8, and LY6 H, of which SH3BGRL3 also accurately classified EOAD in the Western cohort. LY6 H reduction was confirmed via ELISA, which was consistent with our proteomic results Conclusions: This study provides a comprehensive profile of the CSF proteome in EOAD and identifies three potential EOAD biomarker proteins.

Pharmacological inhibition of RUNX1 reduces infarct size after acute myocardial infarction in rats and underlying mechanism revealed by proteomics implicates repressed cathepsin levels

Myocardial infarction (MI) results in prolonged ischemia and the subsequent cell death leads to heart failure which is linked to increased deaths or hospitalizations. New therapeutic targets are urgently needed to prevent cell death and reduce infarct size among patients with MI. Runt-related transcription factor-1 (RUNX1) is a master-regulator transcription factor intensively studied in the hematopoietic field. Recent evidence showed that RUNX1 has a critical role in cardiomyocytes post-MI. The increased RUNX1 expression in the border zone of the infarct heart contributes to decreased cardiac contractile function and can be therapeutically targeted to protect against adverse cardiac remodelling. This study sought to investigate whether pharmacological inhibition of RUNX1 function has an impact on infarct size following MI. In this work we demonstrate that inhibiting RUNX1 with a small molecule inhibitor (Ro5-3335) reduces infarct size in an in vivo rat model of acute MI. Proteomics study using data-independent acquisition method identified increased cathepsin levels in the border zone myocardium following MI, whereas heart samples treated by RUNX1 inhibitor present decreased cathepsin levels. Cathepsins are lysosomal proteases which have been shown to orchestrate multiple cell death pathways. Our data illustrate that inhibition of RUNX1 leads to reduced infarct size which is associated with the suppression of cathepsin expression. This study demonstrates that pharmacologically antagonizing RUNX1 reduces infarct size in a rat model of acute MI and unveils a link between RUNX1 and cathepsin-mediated cell death, suggesting that RUNX1 is a novel therapeutic target that could be exploited clinically to limit infarct size after an acute MI.

Differential protein expression and metabolite profiling in glaucoma: Insights from a multi-omics analysis.

Various substances within the aqueous humor (AH) can directly or indirectly impact intraocular tissues associated with intraocular pressure (IOP), a critical factor in glaucoma development. This study aims to investigate individual changes in these AH substances and the interactions among altered components through a multi-omics approach. LC/MS analysis was conducted on AH samples from patients with exfoliation syndrome (XFS, n = 5), exfoliation glaucoma (XFG, n = 4), primary open-angle glaucoma (POAG, n = 11), and cataracts (control group, n = 7). Subsequently, differentially expressed proteins and metabolites among groups, alterations in their network interactions, and their biological functions were examined. Both data-independent acquisition and data-dependent acquisition methods were employed to analyze the AH proteome and metabolome, and the results were integrated for a comprehensive analysis. In the proteomics analysis, proteins upregulated in both the XFG and POAG groups were associated with lipid metabolism, complement activation, and extracellular matrix regulation. Metabolomic analysis highlighted significant changes in amino acids related to antioxidant processes in the glaucoma groups. Notably, VTN, APOA1, C6, and L-phenylalanine exhibited significant alterations in the glaucoma groups. Integration of individual omics analyses demonstrated that substances associated with inflammation and lipid metabolism, altered in the glaucoma groups, showed robust interactions within a complex network involving PLG, APOA1, and L-phenylalanine or C3, APOD, and L-valine. These findings offer valuable insights into the molecular mechanisms governing IOP regulation and may contribute to the development of new biomarkers for managing glaucoma.

O02 Ubiquitomics of CYLD-deficient skin tumours reveals dysregulation of hair follicle keratinocyte processes

Abstract Introduction and aims CYLD cutaneous syndrome (CCS) is an inherited hair follicle tumour syndrome where biallelic loss of function of the deubiquitinase enzyme CYLD gives rise to cylindroma skin tumours. The role of CYLD in skin biology is not well characterized but it is known to regulate several cell signalling pathways through deubiquitination of effector proteins. We aimed to determine how loss of CYLD function can lead to CCS skin tumour formation by studying the ubiquitome of CCS tumours and healthy skin. Methods Healthy skin (n = 5) and CCS tumour tissue (n = 5) were collected from flash frozen samples. Lysed homogenates were trypsin-digested and the resulting peptides underwent diglycine immunopurification for subsequent liquid chromatography–tandem mass spectrometry analysis on a Bruker Tims-TOF HT (Billerica, MA, USA) mass spectrometer. We utilized a data-independent acquisition method adapted for diglycine-containing peptides to generate mass spectrometry data. Peptide identification was performed by searching mass spectrometry data against an in silico neural network derived spectral library with diglycine modification. Results We identified 1273 ubiquitination sites across 649 proteins in CCS tumour and control samples. After performing t-tests (P &amp;lt; 0.05), we found 305 ubiquitination sites that were either unique to their respective condition or were differentially regulated. Analysis of ubiquitinated peptide sequences uncovered cell surface receptors with increased ubiquitination in CCS tumour samples including the insulin receptor, which had ubiquitination modifications at its kinase domain, suggesting increased insulin signalling in CCS tumours. Gene set enrichment analysis of CCS tumour ubiquitinated substrates revealed increased ubiquitination of secretory granules and extracellular exosomes, suggesting that loss of CYLD may disrupt regulation of protein secretion processes. Conclusions Our findings describe the ubiquitome of CCS keratinocytes, providing insights into how loss of CYLD function can affect biological processes in skin.

Nasal Lavage Fluid Proteomics Reveals Potential Biomarkers of Asthma Associated with Disease Control.

Little research has explored the proteomic characteristics of nasal lavage fluid from asthmatic patients. This study aims to investigate whether differentially expressed proteins (DEPs) in nasal lavage fluid can serve as a biomarker to differentiate asthma patients from healthy controls (HCs) and to discern between individuals with well controlled and poorly controlled asthma. We enrolled patients with allergic rhinitis (AR), asthma, or both conditions, and HCs in this study. We recorded patients' demographic and medical history data and administered asthma quality of life questionnaire (AQLQ) and asthma control questionnaire (ACQ). Nasal fluid samples were collected, followed by protein measurements, and proteomic analysis utilizing the data-independent acquisition (DIA) method. Twenty-four with asthma, 27 with combined asthma+ AR, 25 with AR, and 12 HCs were enrolled. Four proteins, superoxide dismutase 2 (SOD2), serpin B7 (SERPINB7), kallikrein-13 (KLK13), and bleomycin hydrolase (BLMH) were significantly upregulated in nasal lavage fluid samples of asthma without AR, compared to HCs (Fold change ≥2.0, false-discovery rate [FDR] <0.05). Conversely, 56 proteins including secretoglobin family 2A member 1 (SCGB2A1) were significantly downregulated (fold change ≥2.0, FDR <0.05). Furthermore, 96.49% of DEPs including peptidase inhibitor 3 (PI3) and C-X-C motif chemokine 17 (CXCL17) were upregulated in poorly controlled asthma patients without AR relative those with well- or partly controlled asthma (fold change ≥1.5, FDR <0.05). Search tool for the retrieval of interacting genes/proteins (STRING) analysis showed that PI3, with 18 connections, may be pivotal in asthma control. The study revealed significant alteration in the nasal lavage proteome in asthma without AR patients. Moreover, our results indicated a potential association between the expression of proteome in the upper airway and the level of asthma control. Specifically, PI3 appears to be a key role in the regulation of asthma without AR.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Changes in milk fat globule membrane proteins along lactation stage of Laoshan dairy goat

The milk fat globule membrane (MFGM) is a complex structure with numerous functions, and its composition is affected by many factors. There have been few systematic investigations on goat MFGM proteome profiling during lactation. Individual milk samples from 15 healthy dairy goats were obtained at six lactation time points for investigation of the MFGM proteome using both data-independent acquisition (DIA) and data-dependent acquisition (DDA) proteomics techniques combined with multivariate statistical analysis. Using the DIA method, 890 variably abundant MFGM proteins were discovered throughout the lactation cycle. From 1 to 240 d, butyrophilin subfamily 1 member A1, lipoprotein lipase, perilipin-2, and adipose triglyceride lipase were upregulated, while APOE, complement C3, clusterin, and IgG were downregulated. Furthermore, from 1 to 90 d, annexin A1, annexin A2, and antithrombin-III were downregulated, then upregulated by d 240. Albumin had a high degree of connectedness, indicating that it was a key protein, according to protein–protein interaction research. Overall, our findings gave new insights into the biological features of MFGM protein in goat milk throughout lactation, which may aid in the creation of specialized MFGM products and infant formula.

Abstract 1161: Deep spatial proteomics: A new approach for obtaining insight into the glioblastoma microenvironment

Abstract Glioblastoma is the most frequent and malignant brain tumor, with a median survival of approximately 14 months. The tumor microenvironment is composed of different cell types and is known to support the glioblastoma cells and contribute to poor patient prognosis. Since accumulating studies report discordance between mRNA and protein abundances both on population and single-cell levels, and since proteins are the effector molecules of the cells and the actual target molecules of most drugs, our aim is to dissect the tumor microenvironment by spatial LC-MS based proteomics. Glioblastoma formalin-fixed paraffin-embedded (FFPE) tissue was sectioned and placed on membrane glass slides suitable for laser capture microdissection (LCM), followed by precise isolation of single cells from the heterogeneous tumor microenvironment. We used the Zeiss Palm MicroBeam laser microdissection instrument, combined with an Orbitrap Eclipse Tribrid Mass Spectrometer running our in-house sensitivity tailored Data Independent Acquisition method (WISH-DIA) to analyze and quantify the proteomes. We obtained up to 3000 proteins out of only 100 cells and were able to demonstrate the feasibility of collecting and analyzing various cell types at the single-cell level. The proteins identified belonged to many different classes/families of proteins and were able to distinguish the different cell types. In conclusion, this on-going study demonstrates our ability to isolate single cells from the highly heterogeneous tumor microenvironment of glioblastoma with ultra-high sensitivity, thereby allowing the characterization of the proteome of the glioblastoma microenvironment followed by identification of potential therapeutic and/or diagnostic targets. Citation Format: Kartikey Saxena, Rune Daucke, Vilde Pedersen, Pia Helene Klausen, Erwin Schoof, Bjarne Winther Kristensen. Deep spatial proteomics: A new approach for obtaining insight into the glioblastoma microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1161.

Automating data analysis for hydrogen/deuterium exchange mass spectrometry using data-independent acquisition methodology

We present a hydrogen/deuterium exchange workflow coupled to tandem mass spectrometry (HX-MS2) that supports the acquisition of peptide fragment ions alongside their peptide precursors. The approach enables true auto-curation of HX data by mining a rich set of deuterated fragments, generated by collisional-induced dissociation (CID), to simultaneously confirm the peptide ID and authenticate MS1-based deuteration calculations. The high redundancy provided by the fragments supports a confidence assessment of deuterium calculations using a combinatorial strategy. The approach requires data-independent acquisition (DIA) methods that are available on most MS platforms, making the switch to HX-MS2 straightforward. Importantly, we find that HX-DIA enables a proteomics-grade approach and wide-spread applications. Considerable time is saved through auto-curation and complex samples can now be characterized and at higher throughput. We illustrate these advantages in a drug binding analysis of the ultra-large protein kinase DNA-PKcs, isolated directly from mammalian cells.

Robust, Precise, and Deep Proteome Profiling Using a Small Mass Range and Narrow Window Data-Independent-Acquisition Scheme.

In recent years, a plethora of different data-independent acquisition methods have been developed for proteomics to cover a wide range of requirements. Current deep proteome profiling methods rely on fractionations, elaborate chromatography, and mass spectrometry setups or display suboptimal quantitative precision. We set out to develop an easy-to-use one shot DIA method that achieves high quantitative precision and high proteome coverage. We achieve this by focusing on a small mass range of 430-670 m/z using small isolation windows without overlap. With this new method, we were able to quantify >9200 protein groups in HEK lysates with an average coefficient of variance of 3.2%. To demonstrate the power of our newly developed narrow mass range method, we applied it to investigate the effect of PGC-1α knockout on the skeletal muscle proteome in mice. Compared to a standard data-dependent acquisition method, we could double proteome coverage and, most importantly, achieve a significantly higher quantitative precision, as compared to a previously proposed DIA method. We believe that our method will be especially helpful in quantifying low abundant proteins in samples with a high dynamic range. All raw and result files are available at massive.ucsd.edu (MSV000092186).

Plasma Proteomic Analysis Based on 4D-DIA Evaluates the Clinical Response to Imrecoxib in the Early Treatment of Osteoarthritis.

Nonsteroidal anti-inflammatory drugs (NSAIDs) are the primary treatment for osteoarthritis (OA), but prolonged use has adverse effects and varying efficacy. Among NSAIDs, imrecoxib, a selective cyclooxygenase-2 (COX-2) inhibitor, reduces side effects yet remains ineffective for half of the patient population. This study aims to identify biomarkers for early evaluation of imrecoxib efficacy in OA for personalized therapy optimization. From September 2021 to January 2022, imrecoxib was administered to patients with OA at Nanjing Drum Tower Hospital. Plasma samples from these patients underwent proteomic analysis through the four-dimensional data-independent acquisition (4D-DIA) method, followed by bioinformatics analysis. Potential differentially expressed proteins (DEPs) were validated using enzyme-linked immunosorbent assays (ELISA). Sixty-six patients with knee OA were included and divided into responders (n = 35) and non-responders (n = 31). Proteomic analysis was conducted on 15 patients from each group, with ELISA validation for every patient. We found 140 DEPs between the two groups after imrecoxib treatment, characterized by 29 proteins showing upregulation and 111 displaying downregulation (P < 0.05, fold change > ± 1.2). Galectin-1 (LGALS1), galectin-3 (LGALS3), and cluster of differentiation 44 (CD44) were identified as potential markers for evaluating clinical response to imrecoxib in OA following ELISA validation. This study successfully identified biomarkers for evaluating imrecoxib's clinical response in patients with OA using 4D-DIA technology. These biomarkers may play a vital role in future personalized OA treatment strategies, pending further confirmation.

Changes in the astronaut serum proteome during prolonged spaceflight.

The molecular mechanisms associated with spaceflight-induced biological adaptations that may affect many healthy tissue functions remain poorly understood. In this study, we analyzed temporal changes in the serum proteome of six astronauts during prolonged spaceflight missions using quantitative comprehensive proteome analysis performed with the data-independent acquisition method of mass spectrometry (DIA-MS). All six astronauts participated in a spaceflight mission for approximately 6 months and showed a decreasing trend in T-scores at almost all sites where dual-energy X-ray absorptiometry scans were performed. DIA-MS successfully identified 624 nonredundant proteins in sera and further quantitative analysis for each sampling point provided information on serum protein profiles closely related to several time points before (pre-), during (in-), and after (post-) spaceflight. Changes in serum protein levels between spaceflight and on the ground suggest that abnormalities in bone metabolism are induced in astronauts during spaceflight. Furthermore, changes in the proteomic profile occurring during spaceflight suggest that serum levels of bone metabolism-related proteins, namely ALPL, COL1A1, SPP1, and POSTN, could serve as highly responsive indicators of bone metabolism status in spaceflight missions. This study will allow us to accelerate research to improve our understanding of the molecular mechanisms of biological adaptations associated with prolonged spaceflight.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation–enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Development and comparison of parallel reaction monitoring and data-independent acquisition methods for quantitative analysis of hydrophilic compounds in white tea

In the present work, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) methods were developed for the accurate quantitation of amino acids, alkaloids nucleosides and nucleotides in tea. The quality peaks were significantly enhanced by optimizing the LC elution procedure, HCD voltage, MS resolution, and scanning event. Both methods were validated with good liner linearity (0.004–200 μg/mL), LODs (0.001–0.309 μg/mL for PRM and 0.001–0.564 μg/mL for DIA). Applied to white tea sample, the contents of these hydrophilic compounds were range from 34,655.39 to 70,586.14 mg/kg, and caffeine (32,529.02 mg/kg) and theanine (5483.46 mg/kg) were determined as the most abundant ones. Based on the quantitation data set, the white tea samples from Puer, Lincang and Xishuangbanna were clearly discriminated using multivariate data analysis. The results of the present works show that PRM and DIA have great potential in quantitative analysis of multiple hydrophilic compounds in food samples.