Artículos de revistas: "Data-Independent Acquisition (DIA)"

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Hu, Alex, William S. Noble y Alejandro Wolf-Yadlin. "Technical advances in proteomics: new developments in data-independent acquisition". F1000Research 5 (31 de marzo de 2016): 419. http://dx.doi.org/10.12688/f1000research.7042.1.

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The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.

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Pino, Lindsay K., Seth C. Just, Michael J. MacCoss y Brian C. Searle. "Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries". Molecular & Cellular Proteomics 19, n.º 7 (20 de abril de 2020): 1088–103. http://dx.doi.org/10.1074/mcp.p119.001913.

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Data independent acquisition (DIA) is an attractive alternative to standard shotgun proteomics methods for quantitative experiments. However, most DIA methods require collecting exhaustive, sample-specific spectrum libraries with data dependent acquisition (DDA) to detect and quantify peptides. In addition to working with non-human samples, studies of splice junctions, sequence variants, or simply working with small sample yields can make developing DDA-based spectrum libraries impractical. Here we illustrate how to acquire, queue, and validate DIA data without spectrum libraries, and provide a workflow to efficiently generate DIA-only chromatogram libraries using gas-phase fractionation (GPF). We present best-practice methods for collecting DIA data using Orbitrap-based instruments and develop an understanding for why DIA using an Orbitrap mass spectrometer should be approached differently than when using time-of-flight instruments. Finally, we discuss several methods for analyzing DIA data without libraries.

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Shah, Syed Muhammad Zaki, Arslan Ali, Muhammad Noman Khan, Adeeba Khadim, Mufarreh Asmari, Jalal Uddin y Syed Ghulam Musharraf. "Sensitive Detection of Pharmaceutical Drugs and Metabolites in Serum Using Data-Independent Acquisition Mass Spectrometry and Open-Access Data Acquisition Tools". Pharmaceuticals 15, n.º 7 (21 de julio de 2022): 901. http://dx.doi.org/10.3390/ph15070901.

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Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.

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Barbier Saint Hilaire, Pierre, Kathleen Rousseau, Alexandre Seyer, Sylvain Dechaumet, Annelaure Damont, Christophe Junot y François Fenaille. "Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics". Metabolites 10, n.º 4 (18 de abril de 2020): 158. http://dx.doi.org/10.3390/metabo10040158.

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Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition (DDA) and Sequential Window Acquisition of all THeoretical fragment ions (SWATH-type) Data Independent Acquisition (DIA) workflows on a high-field Orbitrap FusionTM TribridTM instrument for the robust identification and quantification of metabolites in human plasma. By using a set of 47 exogenous and 72 endogenous molecules, we compared the efficiency and complementarity of both approaches. We exploited the versatility of this mass spectrometer to collect meaningful MS/MS spectra at both high- and low-mass resolution and various low-energy collision-induced dissociation conditions under optimized DDA conditions. We also observed that complex and composite DIA-MS/MS spectra can be efficiently exploited to identify metabolites in plasma thanks to a reference tandem spectral library made from authentic standards while also providing a valuable data resource for further identification of unknown metabolites. Finally, we found that adding multi-event MS/MS acquisition did not degrade the ability to use survey MS scans from DDA and DIA workflows for the reliable absolute quantification of metabolites down to 0.05 ng/mL in human plasma.

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Lu, Yang Young, Jeff Bilmes, Ricard A. Rodriguez-Mias, Judit Villén y William Stafford Noble. "DIAmeter: matching peptides to data-independent acquisition mass spectrometry data". Bioinformatics 37, Supplement_1 (1 de julio de 2021): i434—i442. http://dx.doi.org/10.1093/bioinformatics/btab284.

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Abstract Motivation Tandem mass spectrometry data acquired using data independent acquisition (DIA) is challenging to interpret because the data exhibits complex structure along both the mass-to-charge (m/z) and time axes. The most common approach to analyzing this type of data makes use of a library of previously observed DIA data patterns (a ‘spectral library’), but this approach is expensive because the libraries do not typically generalize well across laboratories. Results Here, we propose DIAmeter, a search engine that detects peptides in DIA data using only a peptide sequence database. Although some existing library-free DIA analysis methods (i) support data generated using both wide and narrow isolation windows, (ii) detect peptides containing post-translational modifications, (iii) analyze data from a variety of instrument platforms and (iv) are capable of detecting peptides even in the absence of detectable signal in the survey (MS1) scan, DIAmeter is the only method that offers all four capabilities in a single tool. Availability and implementation The open source, Apache licensed source code is available as part of the Crux mass spectrometry analysis toolkit (http://crux.ms). Supplementary information Supplementary data are available at Bioinformatics online.

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Nijssen, Rosalie, Marco H. Blokland, Robin S. Wegh, Erik de Lange, Stefan P. J. van Leeuwen, Bjorn J. A. Berendsen y Milou G. M. van de Schans. "Comparison of Compound Identification Tools Using Data Dependent and Data Independent High-Resolution Mass Spectrometry Spectra". Metabolites 13, n.º 7 (21 de junio de 2023): 777. http://dx.doi.org/10.3390/metabo13070777.

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Liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS) is a frequently applied technique for suspect screening (SS) and non-target screening (NTS) in metabolomics and environmental toxicology. However, correctly identifying compounds based on SS or NTS approaches remains challenging, especially when using data-independent acquisition (DIA). This study assessed the performance of four HRMS-spectra identification tools to annotate in-house generated data-dependent acquisition (DDA) and DIA HRMS spectra of 32 pesticides, veterinary drugs, and their metabolites. The identification tools were challenged with a diversity of compounds, including isomeric compounds. The identification power was evaluated in solvent standards and spiked feed extract. In DDA spectra, the mass spectral library mzCloud provided the highest success rate, with 84% and 88% of the compounds correctly identified in the top three in solvent standard and spiked feed extract, respectively. The in silico tools MSfinder, CFM-ID, and Chemdistiller also performed well in DDA data, with identification success rates above 75% for both solvent standard and spiked feed extract. MSfinder provided the highest identification success rates using DIA spectra with 72% and 75% (solvent standard and spiked feed extract, respectively), and CFM-ID performed almost similarly in solvent standard and slightly less in spiked feed extract (72% and 63%). The identification success rates for Chemdistiller (66% and 38%) and mzCloud (66% and 31%) were lower, especially in spiked feed extract. The difference in success rates between DDA and DIA is most likely caused by the higher complexity of the DIA spectra, making direct spectral matching more complex. However, this study demonstrates that DIA spectra can be used for compound annotation in certain software tools, although the success rate is lower than for DDA spectra.

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Wang, Jian, Monika Tucholska, James D. R. Knight, Jean-Philippe Lambert, Stephen Tate, Brett Larsen, Anne-Claude Gingras y Nuno Bandeira. "MSPLIT-DIA: sensitive peptide identification for data-independent acquisition". Nature Methods 12, n.º 12 (9 de noviembre de 2015): 1106–8. http://dx.doi.org/10.1038/nmeth.3655.

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Koopmans, Frank, Jenny T. C. Ho, August B. Smit y Ka Wan Li. "Comparative Analyses of Data Independent Acquisition Mass Spectrometric Approaches: DIA, WiSIM-DIA, and Untargeted DIA". PROTEOMICS 18, n.º 1 (enero de 2018): 1700304. http://dx.doi.org/10.1002/pmic.201700304.

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Fierro-Monti, Ivo, Klemens Fröhlich, Christian Schori y Alexander Schmidt. "Assessment of Data-Independent Acquisition Mass Spectrometry (DIA-MS) for the Identification of Single Amino Acid Variants". Proteomes 12, n.º 4 (6 de noviembre de 2024): 33. http://dx.doi.org/10.3390/proteomes12040033.

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Proteogenomics integrates genomic and proteomic data to elucidate cellular processes by identifying variant peptides, including single amino acid variants (SAAVs). In this study, we assessed the capability of data-independent acquisition mass spectrometry (DIA-MS) to identify SAAV peptides in HeLa cells using various search engine pipelines. We developed a customised sequence database (DB) incorporating SAAV sequences from the HeLa genome and conducted searches using DIA-NN, Spectronaut, and Fragpipe-MSFragger. Our evaluation focused on identifying true positive SAAV peptides and false positives through entrapment DBs. This study revealed that DIA-MS provides reproducible and comprehensive coverage of the proteome, identifying a substantial proportion of SAAV peptides. Notably, the DIA-MS searches maintained consistent identification of SAAV peptides despite varying sizes of the entrapment DB. A comparative analysis showed that Fragpipe-MSFragger (FP-DIA) demonstrated the most conservative and effective performance, exhibiting the lowest false discovery match ratio (FDMR). Additionally, integrating DIA and data-dependent acquisition (DDA) MS data search outputs enhanced SAAV peptide identification, with a lower false discovery rate (FDR) observed in DDA searches. The validation using stable isotope dilution and parallel reaction monitoring (SID-PRM) confirmed the SAAV peptides identified by DIA-MS and DDA-MS searches, highlighting the reliability of our approach. Our findings underscore the effectiveness of DIA-MS in proteogenomic workflows for identifying SAAV peptides, offering insights into optimising search engine pipelines and DB construction for accurate proteomics analysis. These methodologies advance the understanding of proteome variability, contributing to cancer research and the identification of novel proteoform therapeutic targets.

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Ozorun, Gulsev, Alexander Eckersley, Eleanor Bradley, Rachel Watson, Michael Sherrat y Joe Swift. "P28 Data-independent acquisition mass spectrometry improves spatially resolved analysis of the human skin proteome". British Journal of Dermatology 190, n.º 6 (17 de mayo de 2024): e92-e92. http://dx.doi.org/10.1093/bjd/ljae105.050.

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Abstract Introduction and aims Proteomic analysis of the extracellular matrix (ECM) presents challenges because of the highly crosslinked and low-solubility nature of ECM proteins. Traditional homogenization and protein digestion approaches result in the loss of crucial information regarding protein localization and spatial relationships. To address this, spatially resolved proteomics emerges as a powerful tool for exploring heterogeneity within bulk tissues. This study aims to determine the minimum tissue volume required for comprehensive proteome coverage using data-independent acquisition mass spectrometry (DIA-MS) on skin tissue. The study focused on optimizing spatially resolved proteomic techniques to enhance depth-of-analysis while preserving spatial specificity. Methods Human abdominal skin biopsies were obtained from a single individual and subsequently cryosectioned. Histological assessment was performed through haematoxylin and eosin staining for visualization purposes. Laser-capture microdissection coupled with mass spectrometry facilitated the precise isolation of target regions. Comparative analyses were performed between data-dependent acquisition mass spectrometry (DDA-MS) and DIA-MS, with a particular emphasis on ECM proteins within the dermis. Results Our findings revealed an improvement in proteome coverage with DIA-MS compared with DDA-MS, in addition to clear scaling relationships between the depth-of-analysis and sample concentration. The Results demonstrated the superiority of DIA-MS in achieving robust and comprehensive proteomic profiles, even with minimal tissue volumes. Preliminary findings suggest the capability of DIA-MS in elucidating the complexities of the skin proteome with spatial resolution. Conclusions In conclusion, our study highlights the efficacy of DIA-MS in spatially resolved proteomics on skin tissue. The optimized approach presented here offers a reliable and efficient method for obtaining in-depth proteome information with minimal tissue requirements. These Results form the foundation for ongoing experiments, utilizing DIA-MS to advance spatially resolved proteomic analyses of human skin.

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Huang, Ting, Roland Bruderer, Jan Muntel, Yue Xuan, Olga Vitek y Lukas Reiter. "Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition". Molecular & Cellular Proteomics 19, n.º 2 (30 de diciembre de 2019): 421–30. http://dx.doi.org/10.1074/mcp.ra119.001705.

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In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

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Li, Yuanyue, Chuan-Qi Zhong, Xiaozheng Xu, Shaowei Cai, Xiurong Wu, Yingying Zhang, Jinan Chen, Jianghong Shi, Shengcai Lin y Jiahuai Han. "Group-DIA: analyzing multiple data-independent acquisition mass spectrometry data files". Nature Methods 12, n.º 12 (5 de octubre de 2015): 1105–6. http://dx.doi.org/10.1038/nmeth.3593.

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Kawashima, Yusuke, Eiichiro Watanabe, Taichi Umeyama, Daisuke Nakajima, Masahira Hattori, Kenya Honda y Osamu Ohara. "Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis". International Journal of Molecular Sciences 20, n.º 23 (26 de noviembre de 2019): 5932. http://dx.doi.org/10.3390/ijms20235932.

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Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.

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Tsou, Chih-Chiang, Dmitry Avtonomov, Brett Larsen, Monika Tucholska, Hyungwon Choi, Anne-Claude Gingras y Alexey I. Nesvizhskii. "DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics". Nature Methods 12, n.º 3 (19 de enero de 2015): 258–64. http://dx.doi.org/10.1038/nmeth.3255.

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Mun, Dong-Gi, Santosh Renuse, Mayank Saraswat, Anil Madugundu, Savita Udainiya, Hokeun Kim, Sung-Kyu Robin Park et al. "PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies". Analytical Chemistry 92, n.º 21 (20 de octubre de 2020): 14466–75. http://dx.doi.org/10.1021/acs.analchem.0c02513.

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van der Laan, Tom, Isabelle Boom, Joshua Maliepaard, Anne-Charlotte Dubbelman, Amy C. Harms y Thomas Hankemeier. "Data-Independent Acquisition for the Quantification and Identification of Metabolites in Plasma". Metabolites 10, n.º 12 (18 de diciembre de 2020): 514. http://dx.doi.org/10.3390/metabo10120514.

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A popular fragmentation technique for non-targeted analysis is called data-independent acquisition (DIA), because it provides fragmentation data for all analytes in a specific mass range. In this work, we demonstrated the strengths and weaknesses of DIA. Two types of chromatography (fractionation/3 min and hydrophilic interaction liquid chromatography (HILIC)/18 min) and three DIA protocols (variable sequential window acquisition of all theoretical mass spectra (SWATH), fixed SWATH and MSALL) were used to evaluate the performance of DIA. Our results show that fast chromatography and MSALL often results in product ion overlap and complex MS/MS spectra, which reduces the quantitative and qualitative power of these DIA protocols. The combination of SWATH and HILIC allowed for the correct identification of 20 metabolites using the NIST library. After SWATH window customization (i.e., variable SWATH), we were able to quantify ten structural isomers with a mean accuracy of 103% (91–113%). The robustness of the variable SWATH and HILIC method was demonstrated by the accurate quantification of these structural isomers in 10 highly diverse blood samples. Since the combination of variable SWATH and HILIC results in good quantitative and qualitative fragmentation data, it is promising for both targeted and untargeted platforms. This should decrease the number of platforms needed in metabolomics and increase the value of a single analysis.

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Sidoli, Simone, Rina Fujiwara, Katarzyna Kulej y Benjamin A. Garcia. "Differential quantification of isobaric phosphopeptides using data-independent acquisition mass spectrometry". Molecular BioSystems 12, n.º 8 (2016): 2385–88. http://dx.doi.org/10.1039/c6mb00385k.

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Zhang, Xiang, Ruitao Wu y Zhijian Qu. "A Cosine-Similarity-Based Deconvolution Method for Analyzing Data-Independent Acquisition Mass Spectrometry Data". Applied Sciences 13, n.º 10 (12 de mayo de 2023): 5969. http://dx.doi.org/10.3390/app13105969.

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Although data-independent acquisition (DIA) has the ability to identify and quantify all peptides in a sample, highly complex mixed mass spectra present difficulties for accurate peptide and protein identification. Additionally, the correspondence between the precursor and its fragments is broken, making it challenging to perform peptide identification directly using conventional DDA search engines. In this paper, we propose a cosine-similarity-based deconvolution method: CorrDIA. This is achieved by reconstructing the correspondence between precursor and fragment ions based on the consistency of extracted ion chromatograms (XICs). A deisotope peak cluster operation is added and centered on the MS/MS spectrum to improve the accuracy of spectrum interpretation and increase the number of identified peptides. The resulting MS/MS spectra can be identified using any data-dependent acquisition (DDA) sequencing software. The experimental results demonstrate that the number of peptide results increased by 12 percent and 21 percent respectively, and the repetition rate decreased by 12 percent. This reduces mass spectra complexity and difficulties in mass spectra analysis without the need for any mass spectra libraries.

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Zuo, Zeyuan, Liu Cao, Louis-Félix Nothia y Hosein Mohimani. "MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition". Bioinformatics 37, Supplement_1 (1 de julio de 2021): i231—i236. http://dx.doi.org/10.1093/bioinformatics/btab279.

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Abstract Motivation Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated. Results We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA. Availability and implementation MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS. Supplementary information Supplementary data are available at Bioinformatics online.

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Iwasaki, Mio, Rika Nishimura, Tatsuya Yamakawa, Yousuke Miyamoto, Tsuyoshi Tabata y Megumi Narita. "Differences in Uniquely Identified Peptides Between ddaPASEF and diaPASEF". Cells 13, n.º 22 (7 de noviembre de 2024): 1848. http://dx.doi.org/10.3390/cells13221848.

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Recent advancements in mass spectrometry-based proteomics have made it possible to conduct comprehensive protein analysis. In particular, the emergence of the data-independent acquisition (DIA) method powered by machine learning has significantly improved protein identification efficiency. However, compared with the conventional data-dependent acquisition (DDA) method, the degree to which peptides are uniquely identified by DIA and DDA has not been thoroughly examined. In this study, we identified over 10,000 proteins using the DDA and DIA methods and analyzed the characteristics of unique peptides identified by each method. Results showed that the number of peptides uniquely identified by DDA and DIA using the same column type was 19% and 32%, respectively, with shorter peptides preferentially detected by the DIA method. In addition, more DIA-specific peptides were identified, especially during the first 10% of elution time, and the overall 1/K0 and m/z shifted toward smaller values than in the DDA method. Furthermore, comparing the phosphorylation and ubiquitination proteome profiles with those of whole-cell lysates by DDA showed that the enrichment of post-translationally modified peptides resulted in wider m/z and 1/K0 ranges. Notably, the ubiquitin peptide-enriched samples displayed lower m/z values than the phospho-proteome. These findings suggest a bias in the types of peptides identified by the acquisition method and the importance of setting appropriate ranges for DIA based on the post-translational modification of peptide characteristics.

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Becker, Jonas P., Jonas D. Förster, Sven Blobner, David Weber, Sebastian Uhrig, Annika Baude, Johanna Wagner et al. "Abstract A036: Combining data-independent acquisition and targeted immunopeptidomics to enhance neoepitope discovery". Cancer Immunology Research 12, n.º 10_Supplement (18 de octubre de 2024): A036. http://dx.doi.org/10.1158/2326-6074.tumimm24-a036.

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Abstract Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor (TCR)-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen (HLA) class I molecules to cytotoxic T cells. Such neoepitopes arise from somatic mutations and their identification is crucial for the rational design of new immunotherapeutic interventions. In this study, we analyzed samples of dedifferentiated liposarcoma (DDLS), which is characterized by focal amplifications on chromosome 12. Using whole exome/whole genome and RNA sequencing, we previously found that these structural changes generate open reading frames and thus presumably transcribed and translated chimeric genes. The encoded neoepitopes are promising targets for epitope-centric individualized therapies if they are presented by HLA class I molecules. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is currently the only method to directly prove actual peptide presentation. We have recently developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM (optimized parallel reaction monitoring). In this study, we combined this workflow with an untargeted approach, data-independent acquisition with ion mobility separation by high-field asymmetric waveform ion mobility spectrometry (FAIMS-DIA). Using FAIMS-DIA on DDLS samples, we identified up to 16500 unique peptides per patient, including several neoepitope candidates. For the subsequent optiPRM analysis, candidate neoepitopes identified by FAIMS-DIA as well as additional candidates from variant calling with excellent predicted binding properties were synthesized as stable isotope-labeled (SIL) variant and assay-relevant parameters such as normalized collision energy (NCE) and exact retention times were determined relative to indexed retention time (iRT) peptides. Targeted analysis not only confirmed several of the FAIMS-DIA candidates but also yielded additional detections in 5 out of 6 analyzed patients. Using our complimentary FAIMS-DIA and PRM immunopeptidomics workflows, we have identified fusion-derived neoepitopes in 5 out of 8 DDLS patient samples. Importantly, the identifications contained both neoepitopes which would have been missed by the less sensitive FAIMS-DIA approach or which would have been excluded from the targeted analysis due to their poor predicted binding properties, highlighting the advantage of the combined approach. The detected neoepitopes, currently being functionally tested, represent candidates for the clinical development of personalized treatment options in liposarcoma such as cancer vaccines or TCR-based therapies. Citation Format: Jonas P Becker, Jonas D Förster, Sven Blobner, David Weber, Sebastian Uhrig, Annika Baude, Johanna Wagner, Isabel Poschke, Michael Platten, Stefan Fröhling, Peter Horak, Angelika B Riemer. Combining data-independent acquisition and targeted immunopeptidomics to enhance neoepitope discovery [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr A036.

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Bekker-Jensen, Dorte B., Ana Martínez-Val, Sophia Steigerwald, Patrick Rüther, Kyle L. Fort, Tabiwang N. Arrey, Alexander Harder, Alexander Makarov y Jesper V. Olsen. "A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients". Molecular & Cellular Proteomics 19, n.º 4 (12 de febrero de 2020): 716–29. http://dx.doi.org/10.1074/mcp.tir119.001906.

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State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 min LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument, we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.

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Tsai, Tsung-Heng, Meena Choi, Balazs Banfai, Yansheng Liu, Brendan X. MacLean, Tom Dunkley y Olga Vitek. "Selection of Features with Consistent Profiles Improves Relative Protein Quantification in Mass Spectrometry Experiments". Molecular & Cellular Proteomics 19, n.º 6 (31 de marzo de 2020): 944–59. http://dx.doi.org/10.1074/mcp.ra119.001792.

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In bottom-up mass spectrometry-based proteomics, relative protein quantification is often achieved with data-dependent acquisition (DDA), data-independent acquisition (DIA), or selected reaction monitoring (SRM). These workflows quantify proteins by summarizing the abundances of all the spectral features of the protein (e.g. precursor ions, transitions or fragments) in a single value per protein per run. When abundances of some features are inconsistent with the overall protein profile (for technological reasons such as interferences, or for biological reasons such as post-translational modifications), the protein-level summaries and the downstream conclusions are undermined. We propose a statistical approach that automatically detects spectral features with such inconsistent patterns. The detected features can be separately investigated, and if necessary, removed from the data set. We evaluated the proposed approach on a series of benchmark-controlled mixtures and biological investigations with DDA, DIA and SRM data acquisitions. The results demonstrated that it could facilitate and complement manual curation of the data. Moreover, it can improve the estimation accuracy, sensitivity and specificity of detecting differentially abundant proteins, and reproducibility of conclusions across different data processing tools. The approach is implemented as an option in the open-source R-based software MSstats.

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Martinković, Franjo, Marin Popović, Ozren Smolec, Vladimir Mrljak, Peter David Eckersall y Anita Horvatić. "Data Independent Acquisition Reveals In-Depth Serum Proteome Changes in Canine Leishmaniosis". Metabolites 13, n.º 3 (28 de febrero de 2023): 365. http://dx.doi.org/10.3390/metabo13030365.

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Comprehensive profiling of serum proteome provides valuable clues of health status and pathophysiological processes, making it the main strategy in biomarker discovery. However, the high dynamic range significantly decreases the number of detectable proteins, obstructing the insights into the underlying biological processes. To circumvent various serum enrichment methods, obtain high-quality proteome wide information using the next-generation proteomic, and study host response in canine leishmaniosis, we applied data-independent acquisition mass spectrometry (DIA-MS) for deep proteomic profiling of clinical samples. The non-depleted serum samples of healthy and naturally Leishmania-infected dogs were analyzed using the label-free 60-min gradient sequential window acquisition of all theoretical mass spectra (SWATH-MS) method. As a result, we identified 554 proteins, 140 of which differed significantly in abundance. Those were included in lipid metabolism, hematological abnormalities, immune response, and oxidative stress, providing valuable information about the complex molecular basis of the clinical and pathological landscape in canine leishmaniosis. Our results show that DIA-MS is a method of choice for understanding complex pathophysiological processes in serum and serum biomarker development.

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Phung, Toan K., Lucia F. Zacchi y Benjamin L. Schulz. "DIALib: an automated ion library generator for data independent acquisition mass spectrometry analysis of peptides and glycopeptides". Molecular Omics 16, n.º 2 (2020): 100–112. http://dx.doi.org/10.1039/c9mo00125e.

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Data Independent Acquisition (DIA) Mass Spectrometry (MS) workflows allow unbiased measurement of all detectable peptides from complex proteomes, but require ion libraries for interrogation of peptides of interest.

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26

Junjie, Hou, Wang Jifeng, Yang Fuquan y Xu Tao. "DIA-MS2pep: a library-free framework for comprehensive peptide identification from data-independent acquisition data". Biophysics Reports 8 (2022): 1–16. http://dx.doi.org/10.52601/bpr.2022.220011.

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Zhang, Hao, Gui-Yuan Zhang, Wei-Chao Su, Ya-Ting Chen, Yu-Feng Liu, Dong Wei, Yan-Xi Zhang et al. "High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis". Molecules 27, n.º 23 (23 de noviembre de 2022): 8155. http://dx.doi.org/10.3390/molecules27238155.

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Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.

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Chemonges, Saul. "Interrogation of an ovine serum peptide spectral library to annotate ambiguous clinicopathological biomarkers using data-independent acquisition". F1000Research 11 (5 de diciembre de 2022): 1433. http://dx.doi.org/10.12688/f1000research.128316.1.

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Background: The use of data-independent data acquisition mass spectrometry (DIA-MS) on biological samples from domestic animals is still uncommon. Here, sequential window acquisition of all theoretical mass spectra (SWATH-MS) – a variant of DIA-MS was used to analyse serum peptides of healthy sheep as compared with serum of sick sheep by interrogating a novel peptide spectral library (PSL). This approach enabled the detection and annotation of a wide range of proteins, than conventional clinical pathology protein assays. Methods: Serum samples from healthy sheep were obtained from a commercial source and normalised to represent a healthy sheep proteome background and then compared with serum samples of sheep suffering from a range of naturally-acquired illnesses submitted to The University of Queensland, Australia. Purified tryptic peptides were subjected to liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) on a quadrupole time-of-flight instrument (TripleTOF 5600+, SCIEX) set in a cyclic data-independent acquisition (DIA) mode using a generic (SWATH™, SCIEX) acquisition method. Data were processed using PeakView® v2.2 software with SWATH™ Acquisition MicroApp 2.0 (SCIEX) and MarkerView™ v1.3 software (SCIEX) pipeline to generate protein lists for downstream gene ontology annotation and pathway analysis of identified proteins. Results: There were distinct differences in peptide chromatographic features of sick sheep samples compared to those from healthy sheep. Healthy and sick sheep serum samples yielded 335 and 236 protein identifications (IDs), respectively. There were 96 protein IDs unique to sick sheep serum. A total of 431 protein IDs were annotated by combining healthy control and sick sheep protein IDs. Conclusions: SWATH analysis successfully aided in the detection some established clinicopathological serum biochemical analytes. This approach enabled the distinction of protein profiles of sick sheep samples from a healthy control sample, thereby providing a promising future perspective for the application of SWATH analysis in veterinary clinical use.

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Peris-Díaz, Manuel D., Shannon R. Sweeney, Olga Rodak, Enrique Sentandreu y Stefano Tiziani. "R-MetaboList 2: A Flexible Tool for Metabolite Annotation from High-Resolution Data-Independent Acquisition Mass Spectrometry Analysis". Metabolites 9, n.º 9 (17 de septiembre de 2019): 187. http://dx.doi.org/10.3390/metabo9090187.

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Technological advancements have permitted the development of innovative multiplexing strategies for data independent acquisition (DIA) mass spectrometry (MS). Software solutions and extensive compound libraries facilitate the efficient analysis of MS1 data, regardless of the analytical platform. However, the development of comparable tools for DIA data analysis has significantly lagged. This research introduces an update to the former MetaboList R package and a workflow for full-scan MS1 and MS/MS DIA processing of metabolomic data from multiplexed liquid chromatography high-resolution mass spectrometry (LC-HRMS) experiments. When compared to the former version, new functions have been added to address isolated MS1 and MS/MS workflows, processing of MS/MS data from stepped collision energies, performance scoring of metabolite annotations, and batch job analysis were incorporated into the update. The flexibility and efficiency of this strategy were assessed through the study of the metabolite profiles of human urine, leukemia cell culture, and medium samples analyzed by either liquid chromatography quadrupole time-of-flight (q-TOF) or quadrupole orbital (q-Orbitrap) instruments. This open-source alternative was designed to promote global metabolomic strategies based on recursive retrospective research of multiplexed DIA analysis.

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Jiang, Na, Yan Gao, Jia Xu, Fengting Luo, Xiangyang Zhang y Ruibing Chen. "A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells". Journal of Pharmaceutical and Biomedical Analysis 216 (julio de 2022): 114795. http://dx.doi.org/10.1016/j.jpba.2022.114795.

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Jiang, Na, Yan Gao, Jia Xu, Fengting Luo, Xiangyang Zhang y Ruibing Chen. "A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells". Journal of Pharmaceutical and Biomedical Analysis 216 (julio de 2022): 114795. http://dx.doi.org/10.1016/j.jpba.2022.114795.

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32

Borràs, Eva y Eduard Sabidó. "DIA+: A Data-Independent Acquisition Method Combining Multiple Precursor Charges to Improve Peptide Signal". Analytical Chemistry 90, n.º 21 (8 de octubre de 2018): 12339–41. http://dx.doi.org/10.1021/acs.analchem.8b03418.

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Searle, Brian C., Jarrett D. Egertson, James G. Bollinger, Andrew B. Stergachis y Michael J. MacCoss. "Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments". Molecular & Cellular Proteomics 14, n.º 9 (22 de junio de 2015): 2331–40. http://dx.doi.org/10.1074/mcp.m115.051300.

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Bersching, Katharina, Thomas Michna, Stefan Tenzer y Stefan Jacob. "Data-Independent Acquisition (DIA) Is Superior for High Precision Phospho-Peptide Quantification in Magnaporthe oryzae". Journal of Fungi 9, n.º 1 (31 de diciembre de 2022): 63. http://dx.doi.org/10.3390/jof9010063.

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The dynamic interplay of signaling networks in most major cellular processes is characterized by the orchestration of reversible protein phosphorylation. Consequently, analytic methods such as quantitative phospho-peptidomics have been pushed forward from a highly specialized edge-technique to a powerful and versatile platform for comprehensively analyzing the phosphorylation profile of living organisms. Despite enormous progress in instrumentation and bioinformatics, a high number of missing values caused by the experimental procedure remains a major problem, due to either a random phospho-peptide enrichment selectivity or borderline signal intensities, which both cause the exclusion for fragmentation using the commonly applied data dependent acquisition (DDA) mode. Consequently, an incomplete dataset reduces confidence in the subsequent statistical bioinformatic processing. Here, we successfully applied data independent acquisition (DIA) by using the filamentous fungus Magnaporthe oryzae as a model organism, and could prove that while maintaining data quality (such as phosphosite and peptide sequence confidence), the data completeness increases dramatically. Since the method presented here reduces the LC-MS/MS analysis from 3 h to 1 h and increases the number of phosphosites identified up to 10-fold in contrast to published studies in Magnaporthe oryzae, we provide a refined methodology and a sophisticated resource for investigation of signaling processes in filamentous fungi.

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Meng, Bo, Yuanyuan Huang, Ao Lu, Huanyue Liao, Rui Zhai, Xiaoyun Gong, Lianhua Dong et al. "Enhanced Analysis of Low-Abundance Proteins in Soybean Seeds Using Advanced Mass Spectrometry". International Journal of Molecular Sciences 26, n.º 3 (23 de enero de 2025): 949. https://doi.org/10.3390/ijms26030949.

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This study presents an advanced approach for the comprehensive analysis of low-abundance proteins in soybean seeds, addressing challenges posed by high-abundance storage proteins. We compared the effectiveness of Data-Dependent Acquisition (DDA), Data-Independent Acquisition (DIA), and BoxCar mass spectrometry techniques to identify low-abundance proteins in two types of soybean seeds: High-Oil and High-Protein seeds. The results indicate that the DIA method, and particularly the BoxCar methods, significantly improve the detection of low-abundance proteins compared to DDA, offering deeper insights into soybean seed biology. Specifically, BoxCar-based analysis revealed distinct proteomic differences between High-Oil and High-Protein seeds, highlighting more active metabolic processes in High-Oil seeds. Additionally, several key proteins were identified and annotated as uniquely expressed in either High-Oil or High-Protein seeds. These findings emphasize the importance of advanced proteomic techniques, such as BoxCar, in deepening our understanding of soybean seed biology and supporting breeding strategies to improve nutritional qualities.

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Wohlfahrt, Jessica, Jennifer Guergues y Stanley M. Stevens. "Deep Proteome Coverage of Microglia Using a Streamlined Data-Independent Acquisition-Based Proteomic Workflow: Method Consideration for a Phenotypically Diverse Cell Type". Proteomes 12, n.º 4 (27 de noviembre de 2024): 35. http://dx.doi.org/10.3390/proteomes12040035.

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As the primary innate immune cells of the brain, microglia play a key role in various homeostatic and disease-related processes. To carry out their numerous functions, microglia adopt a wide range of phenotypic states. The proteomic landscape represents a more accurate molecular representation of these phenotypes; however, microglia present unique challenges for proteomic analysis. This study implemented a streamlined liquid- and gas-phase fractionation method with data-dependent acquisition (DDA) and parallel accumulation–serial fragmentation (PASEF) analysis on a TIMS-TOF instrument to compile a comprehensive protein library obtained from adult-derived, immortalized mouse microglia with low starting material (10 µg). The empirical library consisted of 9140 microglial proteins and was utilized to identify an average of 7264 proteins/run from single-shot, data-independent acquisition (DIA)-based analysis microglial cell lysate digest (200 ng). Additionally, a predicted library facilitated the identification of 7519 average proteins/run from the same DIA data, revealing complementary coverage compared with the empirical library and collectively increasing coverage to approximately 8000 proteins. Importantly, several microglia-relevant pathways were uniquely identified with the empirical library approach. Overall, we report a simplified, reproducible approach to address the proteome complexity of microglia using low sample input and show the importance of library optimization for this phenotypically diverse cell type.

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Noel, Onika, Susan T. Weintraub, Harshit Garg, Furkan Dursun, Dharam Kaushik, Sammy Pardo, Dana Molleur et al. "DATA-INDEPENDENT ACQUISITION MASS SPECTROMETRY (DIA-MS) OF UROTHELIAL CARCINOMA RESULTS IN DISTINCT PROTEOMIC SIGNATURES". Urologic Oncology: Seminars and Original Investigations 42 (marzo de 2024): S64. http://dx.doi.org/10.1016/j.urolonc.2024.01.185.

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He, Guoqiang, Qingzu He, Jinyan Cheng, Rongwen Yu, Jianwei Shuai y Yi Cao. "ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction". International Journal of Molecular Sciences 25, n.º 13 (30 de junio de 2024): 7237. http://dx.doi.org/10.3390/ijms25137237.

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In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately forecast key features such as retention time (RT), ion intensity, and ion mobility (IM). Leveraging advanced techniques such as multi-head attention and BiLSTM for feature extraction, coupled with Nash-MTL for gradient coordination, ProPept-MT demonstrates superior prediction performance. Integrating ion mobility alongside RT, mass-to-charge ratio (m/z), and ion intensity forms 4D proteomics. Then, we outline a comprehensive workflow tailored for 4D DIA proteomics research, integrating the use of 4D in silico libraries predicted by ProPept-MT. Evaluation on a benchmark dataset showcases ProPept-MT’s exceptional predictive capabilities, with impressive results including a 99.9% Pearson correlation coefficient (PCC) for RT prediction, a median dot product (DP) of 96.0% for fragment ion intensity prediction, and a 99.3% PCC for IM prediction on the test set. Notably, ProPept-MT manifests efficacy in predicting both unmodified and phosphorylated peptides, underscoring its potential as a valuable tool for constructing high-quality 4D DIA in silico libraries.

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Capaci, Valeria, Feras Kharrat, Andrea Conti, Emanuela Salviati, Manuela Giovanna Basilicata, Pietro Campiglia, Nour Balasan et al. "The Deep Proteomics Approach Identified Extracellular Vesicular Proteins Correlated to Extracellular Matrix in Type One and Two Endometrial Cancer". International Journal of Molecular Sciences 25, n.º 9 (24 de abril de 2024): 4650. http://dx.doi.org/10.3390/ijms25094650.

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Among gynecological cancers, endometrial cancer is the most common in developed countries. Extracellular vesicles (EVs) are cell-derived membrane-surrounded vesicles that contain proteins involved in immune response and apoptosis. A deep proteomic approach can help to identify dysregulated extracellular matrix (ECM) proteins in EVs correlated to key pathways for tumor development. In this study, we used a proteomics approach correlating the two acquisitions—data-dependent acquisition (DDA) and data-independent acquisition (DIA)—on EVs from the conditioned medium of four cell lines identifying 428 ECM proteins. After protein quantification and statistical analysis, we found significant changes in the abundance (p < 0.05) of 67 proteins. Our bioinformatic analysis identified 26 pathways associated with the ECM. Western blotting analysis on 13 patients with type 1 and type 2 EC and 13 endometrial samples confirmed an altered abundance of MMP2. Our proteomics analysis identified the dysregulated ECM proteins involved in cancer growth. Our data can open the path to other studies for understanding the interaction among cancer cells and the rearrangement of the ECM.

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Dong, Mingming, Tung-Shing Mamie Lih, Minghui Ao, Yingwei Hu, Shao-Yung Chen, Rodrigo Vargas Eguez y Hui Zhang. "Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Intact N-Linked Glycopeptides". Analytical Chemistry 93, n.º 41 (8 de octubre de 2021): 13774–82. http://dx.doi.org/10.1021/acs.analchem.1c01659.

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41

Haynes, Sarah E., Jaimeen D. Majmudar y Brent R. Martin. "DIA-SIFT: A Precursor and Product Ion Filter for Accurate Stable Isotope Data-Independent Acquisition Proteomics". Analytical Chemistry 90, n.º 15 (10 de julio de 2018): 8722–26. http://dx.doi.org/10.1021/acs.analchem.8b01618.

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42

Tabb, David L., Mohammed Hanzala Kaniyar, Omar G. Rosas Bringas, Heaji Shin, Luciano Di Stefano, Martin S. Taylor, Shaoshuai Xie, Omer H. Yilmaz y John LaCava. "Interrogating data-independent acquisition LC–MS/MS for affinity proteomics". Journal of Proteins and Proteomics, 17 de septiembre de 2024. http://dx.doi.org/10.1007/s42485-024-00166-4.

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AbstractData-Independent Acquisition (DIA) LC–MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.

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Pietilä, Sami, Tomi Suomi y Laura L. Elo. "Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples". ISME Communications 2, n.º 1 (29 de junio de 2022). http://dx.doi.org/10.1038/s43705-022-00137-0.

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AbstractMass spectrometry-based metaproteomics is a relatively new field of research that enables the characterization of the functionality of microbiota. Recently, we demonstrated the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the previously used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the DDA-assisted DIA approach still required additional DDA data on the samples to assist the analysis. Here, we introduce, for the first time, an untargeted DIA metaproteomics tool that does not require any DDA data, but instead generates a pseudospectral library directly from the DIA data. This reduces the amount of required mass spectrometry data to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as a new open-source software package named glaDIAtor, including a modern web-based graphical user interface to facilitate wide use of the tool by the community.

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Yang, Yi, Xiaohui Liu, Chengpin Shen, Yu Lin, Pengyuan Yang y Liang Qiao. "In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics". Nature Communications 11, n.º 1 (9 de enero de 2020). http://dx.doi.org/10.1038/s41467-019-13866-z.

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AbstractData-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

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45

Chang, Jing Kai, Guoshou Teo, Yael Pewzner-Jung, Daniel J. Cuthbertson, Anthony H. Futerman, Markus R. Wenk, Hyungwon Choi y Federico Torta. "Q-RAI data-independent acquisition for lipidomic quantitative profiling". Scientific Reports 13, n.º 1 (7 de noviembre de 2023). http://dx.doi.org/10.1038/s41598-023-46312-8.

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AbstractUntargeted lipidomics has been increasingly adopted for hypothesis generation in a biological context or discovery of disease biomarkers. Most of the current liquid chromatography mass spectrometry (LC–MS) based untargeted methodologies utilize a data dependent acquisition (DDA) approach in pooled samples for identification and MS-only acquisition for semi-quantification in individual samples. In this study, we present for the first time an untargeted lipidomic workflow that makes use of the newly implemented Quadrupole Resolved All-Ions (Q-RAI) acquisition function on the Agilent 6546 quadrupole time-of-flight (Q-TOF) mass spectrometer to acquire MS2 spectra in data independent acquisition (DIA) mode. This is followed by data processing and analysis on MetaboKit, a software enabling DDA-based spectral library construction and extraction of MS1 and MS2 peak areas, for reproducible identification and quantification of lipids in DIA analysis. This workflow was tested on lipid extracts from human plasma and showed quantification at MS1 and MS2 levels comparable to multiple reaction monitoring (MRM) targeted analysis of the same samples. Analysis of serum from Ceramide Synthase 2 (CerS2) null mice using the Q-RAI DIA workflow identified 88 lipid species significantly different between CerS2 null and wild type mice, including well-characterized changes previously associated with this phenotype. Our results show the Q-RAI DIA as a reliable option to perform simultaneous identification and reproducible relative quantification of lipids in exploratory biological studies.

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Ye, Zilu y Sergey Y. Vakhrushev. "The role of Data-Independent Acquisition for Glycoproteomics". Molecular & Cellular Proteomics, 28 de diciembre de 2020, mcp.R120.002204. http://dx.doi.org/10.1074/mcp.r120.002204.

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Data independent acquisition (DIA) is now an emerging method in bottom-up proteomics and capable of achieving deep proteome coverage and accurate label-free quantification. However, for post-translational modifications (PTM), such as glycosylation, DIA methodology is still in the early stage of development. The full characterization of glycoproteins requires site specific glycan identification as well as subsequent quantification of glycan structures at each site. The tremendous complexity of glycosylation represents a significant analytical challenge in glycoproteomics. This review focuses on the development and perspectives of DIA methodology for N- and O- glycoproteomics and posits that DIA-based glycoproteomics could be a method of choice to address some of the challenging aspects of glycoproteomics. First, the current challenges in glycoproteomics and the basic principles of DIA is briefly introduced. DIA based glycoproteomics is then summarized and described into four aspects based on the actual samples. Lastly, we discussed the important challenges and future perspectives in the field. We believe that DIA can significantly facilitate glycoproteomic studies and contribute to the development of future advanced tools and approaches in the field of glycoproteomics.

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Rajczewski, Andrew T., J. Alfredo Blakeley‐Ruiz, Annaliese Meyer, Simina Vintila, Matthew R. McIlvin, Tim Van Den Bossche, Brian C. Searle et al. "Data‐Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome". PROTEOMICS, 10 de abril de 2025. https://doi.org/10.1002/pmic.202400187.

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ABSTRACTMass spectrometry (MS)‐based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being data‐dependent acquisition mass spectrometry (DDA‐MS). However, DDA‐MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA‐MS deficiencies, proteomics researchers have started using Data‐independent acquisition mass spectrometry (DIA‐MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA‐MS metaproteomic measurements relative to DDA‐MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA‐ and DIA‐MS acquisition methods. In this study, DIA‐MS yielded more protein and peptide identifications than DDA‐MS in each laboratory for the particular instruments and software parameters chosen. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA‐MS represents a promising strategy for MS‐based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

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Valmori, Marie, Vincent Marie, François Fenaille, Benoit Colsch y David Touboul. "Recent methodological developments in data-dependent analysis and data-independent analysis workflows for exhaustive lipidome coverage". Frontiers in Analytical Science 3 (10 de febrero de 2023). http://dx.doi.org/10.3389/frans.2023.1118742.

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Untargeted lipidomics applied to biological samples typically involves the coupling of separation methods to high-resolution mass spectrometry (HRMS). Getting an exhaustive coverage of the lipidome with a high confidence in structure identification is still highly challenging due to the wide concentration range of lipids in complex matrices and the presence of numerous isobaric and isomeric species. The development of innovative separation methods and HRMS(/MS) acquisition workflows helped improving the situation but issues still remain regarding confident structure characterization. To overcome these issues, thoroughly optimized MS/MS acquisition methods are needed. For this purpose, different methodologies have been developed to enable MS and MS/MS acquisition in parallel. Those methodologies, derived from the proteomics, are referred to Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA). In this context, this perspective paper presents the latest developments of DDA- and DIA-based lipidomic workflows and lists available bioinformatic tools for the analysis of resulting spectral data.

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Kitata, Reta Birhanu, Wai-Kok Choong, Chia-Feng Tsai, Pei-Yi Lin, Bo-Shiun Chen, Yun-Chien Chang, Alexey I. Nesvizhskii, Ting-Yi Sung y Yu-Ju Chen. "A data-independent acquisition-based global phosphoproteomics system enables deep profiling". Nature Communications 12, n.º 1 (5 de mayo de 2021). http://dx.doi.org/10.1038/s41467-021-22759-z.

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AbstractPhosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.

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Wandy, Joe, Ross McBride, Simon Rogers, Nikolaos Terzis, Stefan Weidt, Justin J. J. van der Hooft, Kevin Bryson, Rónán Daly y Vinny Davies. "Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics". Frontiers in Molecular Biosciences 10 (7 de marzo de 2023). http://dx.doi.org/10.3389/fmolb.2023.1130781.

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Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.

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