Perspectives and opinions from scientific leaders on the evolution of data-independent acquisition for quantitative proteomics and novel biological applications
Christie L. Hunter
A , Joanna Bons B and Birgit Schilling B *
+ Author Affiliations
- Author Affiliations
A SCIEX, Redwood City, CA, USA.
B Buck Institute for Research on Aging, 8001 Redwood Boulevard, Novato, CA 94945, USA.
Dr. Christie Hunter is the Chief Scientist, Application Development at SCIEX. Christie is focused on developing innovative MS workflows for the quantitative analysis of proteins and peptides, working in the SCIEX R&D department, and working collaboratively with researchers in the field. Over the years, she has developed workflows for MRM analysis of peptides, advanced data independent acquisition strategies, and most recently, ultra-high throughput quantification workflows for peptides/proteins using Acoustic Ejection Mass Spectrometry. Christie received her PhD in protein biochemistry from the University of British Columbia (Canada). |
Dr. Joanna Bons is a postdoctoral fellow in the laboratory of Dr. Birgit Schilling at the Buck Institute for Research on Aging. After an engineer degree in Biotechnology, she joined the team of Dr. Christine Carapito at the BioOrganic Mass Spectrometry Laboratory in Strasbourg, France, where she specialized in quantitative mass spectrometry-based proteomics method development (SRM, PRM, DIA) for proteome quantification and characterization. She received her PhD in Analytical Chemistry in 2019, and then joined Dr. Birgit Schilling s laboratory. She focuses on developing and optimizing innovative DIA and targeted strategies for deciphering proteome and PTM remodeling in various collaborative projects, spanning neurodegenerative diseases, cancer, and metabolism dysfunction and diseases. |
Dr. Birgit Schilling works at the Buck Institute for Research on Aging in the San Francisco Bay Area since 2000 as Professor and Director of the Mass Spectrometry Technology Center, specifically focusing on data-independent acquisition technologies and large-scale proteome quantification. Dr. Schilling received her PhD in Germany, and then moved to the University of California San Francisco (UCSF) as postdoctoral fellow. Dr. Schilling is interested in translational research and research that may aim towards therapeutic interventions to improve human aging or age-related diseases, specifically osteoarthritis and cancer. Dr. Schilling uses modern proteomic technologies to investigate mechanisms of aging, senescence and cancer, and using this knowledge to develop biomarkers and targets for interventions. |
Australian Journal of Chemistry 76(8) 379-398 https://doi.org/10.1071/CH23039
Submitted: 22 February 2023 Accepted: 22 May 2023 Published: 19 July 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
The methodology of data-independent acquisition (DIA) within mass spectrometry (MS) was developed into a method of choice for quantitative proteomics, to capture the depth and dynamics of biological systems, and to perform large-scale protein quantification. DIA provides deep quantitative proteome coverage with high sensitivity, high quantitative accuracy, and excellent acquisition-to-acquisition reproducibility. DIA workflows benefited from the latest advancements in MS instrumentation, acquisition/isolation schemes, and computational algorithms, which have further improved data quality and sample throughput. This powerful DIA-MS scan type selects all precursor ions contained in pre-determined isolation windows, and systematically fragments all precursor ions from each window by tandem mass spectrometry, subsequently covering the entire precursor ion m/z range. Comprehensive proteolytic peptide identification and label-free quantification are achieved post-acquisition using spectral library-based or library-free approaches. To celebrate the > 10 years of success of this quantitative DIA workflow, we interviewed some of the scientific leaders who have provided crucial improvements to DIA, to the quantification accuracy and proteome depth achieved, and who have explored DIA applications across a wide range of biology. We discuss acquisition strategies that improve specificity using different isolation schemes, and that reduce complexity by combining DIA with sophisticated chromatography or ion mobility separation. Significant leaps forward were achieved by evolving data processing strategies, such as library-free processing, and machine learning to interrogate data more deeply. Finally, we highlight some of the diverse biological applications that use DIA-MS methods, including large-scale quantitative proteomics, post-translational modification studies, single-cell analysis, food science, forensics, and small molecule analysis.
Keywords: data-independent acquisition, food science, forensics, immunopeptidomics, ion mobility, machine learning, metabolomics, microflow chromatography, protein turnover, proteomics, quantification, reproducibility, single-cell proteomics.
References
[1] R Aebersold, M Mann, Mass-spectrometric exploration of proteome structure and function. Nature 2016, 537, 347.| Mass-spectrometric exploration of proteome structure and function.Crossref | GoogleScholarGoogle Scholar |
[2] B Schilling, MJ Rardin, BX MacLean, AM Zawadzka, BE Frewen, MP Cusack, et al. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol Cell Proteomics 2012, 11, 202.
| Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation.Crossref | GoogleScholarGoogle Scholar |
[3] J Cox, MY Hein, CA Luber, I Paron, N Nagaraj, M Mann, Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 2014, 13, 2513.
| Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.Crossref | GoogleScholarGoogle Scholar |
[4] P Picotti, R Aebersold, Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods 2012, 9, 555.
| Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions.Crossref | GoogleScholarGoogle Scholar |
[5] L Anderson, CL Hunter, Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol Cell Proteomics 2006, 5, 573.
| Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins.Crossref | GoogleScholarGoogle Scholar |
[6] H Zhang, Q Liu, LJ Zimmerman, A-JL Ham, RJC Slebos, J Rahman, et al. Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry. Mol Cell Proteomics 2011, 10, M110.006593.
| Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry.Crossref | GoogleScholarGoogle Scholar |
[7] S Gallien, E Duriez, C Crone, M Kellmann, T Moehring, B Domon, Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol Cell Proteomics 2012, 11, 1709.
| Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer.Crossref | GoogleScholarGoogle Scholar |
[8] AC Peterson, JD Russell, DJ Bailey, MS Westphall, JJ Coon, Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics 2012, 11, 1475.
| Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics.Crossref | GoogleScholarGoogle Scholar |
[9] B Schilling, B MacLean, JM Held, AK Sahu, MJ Rardin, DJ Sorensen, et al. Multiplexed, Scheduled, High-Resolution Parallel Reaction Monitoring on a Full Scan QqTOF Instrument with Integrated Data-Dependent and Targeted Mass Spectrometric Workflows. Anal Chem 2015, 87, 10222.
| Multiplexed, Scheduled, High-Resolution Parallel Reaction Monitoring on a Full Scan QqTOF Instrument with Integrated Data-Dependent and Targeted Mass Spectrometric Workflows.Crossref | GoogleScholarGoogle Scholar |
[10] A Doerr, Targeted proteomics. Nat Methods 2009, 7, 34.
| Targeted proteomics.Crossref | GoogleScholarGoogle Scholar |
[11] LC Gillet, P Navarro, S Tate, H Röst, N Selevsek, L Reiter, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 2012, 11, O111.016717.
| Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Crossref | GoogleScholarGoogle Scholar |
[12] JG Meyer, B Schilling, Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 2017, 14, 419.
| Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques.Crossref | GoogleScholarGoogle Scholar |
[13] JD Venable, MQ Dong, J Wohlschlegel, A Dillin, JR Yates, Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 2004, 1, 39.
| Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra.Crossref | GoogleScholarGoogle Scholar |
[14] JC Silva, R Denny, CA Dorschel, M Gorenstein, IJ Kass, GZ Li, et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem 2005, 77, 2187.
| Quantitative proteomic analysis by accurate mass retention time pairs.Crossref | GoogleScholarGoogle Scholar |
[15] RB Kitata, JC Yang, YJ Chen, Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. Mass Spectrom Rev 2022, e21781.
| Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape.Crossref | GoogleScholarGoogle Scholar |
[16] P Picotti, O Rinner, R Stallmach, F Dautel, T Farrah, B Domon, et al. High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nat Methods 2010, 7, 43.
| High-throughput generation of selected reaction-monitoring assays for proteins and proteomes.Crossref | GoogleScholarGoogle Scholar |
[17] L Reiter, O Rinner, P Picotti, R Hüttenhain, M Beck, MY Brusniak, et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 2011, 8, 430.
| mProphet: automated data processing and statistical validation for large-scale SRM experiments.Crossref | GoogleScholarGoogle Scholar |
[18] H Röst, L Malmström, R Aebersold, A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol Cell Proteomics 2012, 11, 540.
| A computational tool to detect and avoid redundancy in selected reaction monitoring.Crossref | GoogleScholarGoogle Scholar |
[19] HL Röst, G Rosenberger, P Navarro, L Gillet, SM Miladinović, OT Schubert, et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 2014, 32, 219.
| OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.Crossref | GoogleScholarGoogle Scholar |
[20] OT Schubert, LC Gillet, BC Collins, P Navarro, G Rosenberger, WE Wolski, et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 2015, 10, 426.
| Building high-quality assay libraries for targeted analysis of SWATH MS data.Crossref | GoogleScholarGoogle Scholar |
[21] C Ludwig, L Gillet, G Rosenberger, S Amon, BC Collins, R Aebersold, Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 2018, 14, e8126.
| Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial.Crossref | GoogleScholarGoogle Scholar |
[22] C Escher, L Reiter, B MacLean, R Ossola, F Herzog, J Chilton, et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 2012, 12, 1111.
| Using iRT, a normalized retention time for more targeted measurement of peptides.Crossref | GoogleScholarGoogle Scholar |
[23] G Rosenberger, CC Koh, T Guo, HL Röst, P Kouvonen, BC Collins, et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 2014, 1, 140031.
| A repository of assays to quantify 10,000 human proteins by SWATH-MS.Crossref | GoogleScholarGoogle Scholar |
[24] Y Zhang, A Bilbao, T Bruderer, J Luban, C Strambio-De-Castillia, F Lisacek, et al. The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition. J Proteome Res 2015, 14, 4359.
| The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition.Crossref | GoogleScholarGoogle Scholar |
[25] B Schilling, BW Gibson, CL Hunter, Generation of High-Quality SWATH® Acquisition Data for Label-free Quantitative Proteomics Studies Using TripleTOF® Mass Spectrometers. Methods Mol Biol 2017, 1550, 223.
| Generation of High-Quality SWATH® Acquisition Data for Label-free Quantitative Proteomics Studies Using TripleTOF® Mass Spectrometers.Crossref | GoogleScholarGoogle Scholar |
[26] N Selevsek, CY Chang, LC Gillet, P Navarro, OM Bernhardt, L Reiter, et al. Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry. Mol Cell Proteomics 2015, 14, 739.
| Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-mass spectrometry.Crossref | GoogleScholarGoogle Scholar |
[27] L Krasny, P Bland, J Burns, NC Lima, PT Harrison, L Pacini, et al. A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts. Dis Model Mech 2020, 13, dmm044586.
| A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts.Crossref | GoogleScholarGoogle Scholar |
[28] T Zhu, Y Zhu, Y Xuan, H Gao, X Cai, SR Piersma, et al. DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery. Genomics Proteomics Bioinformatics 2020, 18, 104.
| DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery.Crossref | GoogleScholarGoogle Scholar |
[29] K Barkovits, S Pacharra, K Pfeiffer, S Steinbach, M Eisenacher, K Marcus, et al. Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition. Mol Cell Proteomics 2020, 19, 181.
| Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition.Crossref | GoogleScholarGoogle Scholar |
[30] BC Collins, CL Hunter, Y Liu, B Schilling, G Rosenberger, SL Bader, et al. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nat Commun 2017, 8, 291.
| Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry.Crossref | GoogleScholarGoogle Scholar |
[31] R Sun, C Hunter, C Chen, W Ge, N Morrice, S Liang, et al. Accelerated Protein Biomarker Discovery from FFPE Tissue Samples Using Single-Shot, Short Gradient Microflow SWATH MS. J Proteome Res 2020, 19, 2732.
| Accelerated Protein Biomarker Discovery from FFPE Tissue Samples Using Single-Shot, Short Gradient Microflow SWATH MS.Crossref | GoogleScholarGoogle Scholar |
[32] TA Addona, SE Abbatiello, B Schilling, SJ Skates, DR Mani, DM Bunk, et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol 2009, 27, 633.
| Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma.Crossref | GoogleScholarGoogle Scholar |
[33] DL Tabb, L Vega-Montoto, PA Rudnick, AM Variyath, A-JL Ham, DM Bunk, et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography–tandem mass spectrometry. J Proteome Res 2010, 9, 761.
| Repeatability and reproducibility in proteomic identifications by liquid chromatography–tandem mass spectrometry.Crossref | GoogleScholarGoogle Scholar |
[34] G Rosenberger, I Bludau, U Schmitt, M Heusel, CL Hunter, Y Liu, et al. Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses. Nat Methods 2017, 14, 921.
| Statistical control of peptide and protein error rates in large-scale targeted data-independent acquisition analyses.Crossref | GoogleScholarGoogle Scholar |
[35] P Navarro, J Kuharev, LC Gillet, OM Bernhardt, B MacLean, HL Röst, et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat Biotechnol 2016, 34, 1130.
| A multicenter study benchmarks software tools for label-free proteome quantification.Crossref | GoogleScholarGoogle Scholar |
[36] Y Xuan, NW Bateman, S Gallien, S Goetze, Y Zhou, P Navarro, et al. Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies. Nat Commun 2020, 11, 5248.
| Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies.Crossref | GoogleScholarGoogle Scholar |
[37] RC Poulos, PG Hains, R Shah, N Lucas, D Xavier, SS Manda, et al. Strategies to enable large-scale proteomics for reproducible research. Nat Commun 2020, 11, 3793.
| Strategies to enable large-scale proteomics for reproducible research.Crossref | GoogleScholarGoogle Scholar |
[38] DB Bekker-Jensen, A Martínez-Val, S Steigerwald, P Rüther, KL Fort, TN Arrey, et al. A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients. Mol Cell Proteomics 2020, 19, 716.
| A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients.Crossref | GoogleScholarGoogle Scholar |
[39] M Tognetti, K Sklodowski, S Müller, D Kamber, J Muntel, R Bruderer, et al. Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area. J Proteome Res 2022, 21, 1718.
| Biomarker Candidates for Tumors Identified from Deep-Profiled Plasma Stem Predominantly from the Low Abundant Area.Crossref | GoogleScholarGoogle Scholar |
[40] L Reilly, L Peng, E Lara, D Ramos, M Fernandopulle, CB Pantazis, et al. A fully automated FAIMS-DIA proteomic pipeline for high-throughput characterization of iPSC-derived neurons [Preprint]. bioRxiv 2021, 2021.11.24.469921.
| A fully automated FAIMS-DIA proteomic pipeline for high-throughput characterization of iPSC-derived neurons [Preprint].Crossref | GoogleScholarGoogle Scholar |
[41] F Meier, AD Brunner, M Frank, A Ha, I Bludau, E Voytik, et al. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition. Nat Methods 2020, 17, 1229.
| diaPASEF: parallel accumulation-serial fragmentation combined with data-independent acquisition.Crossref | GoogleScholarGoogle Scholar |
[42] DG Mun, PM Vanderboom, AK Madugundu, K Garapati, S Chavan, JA Peterson, et al. DIA-Based Proteome Profiling of Nasopharyngeal Swabs from COVID-19 Patients. J Proteome Res 2021, 20, 4165.
| DIA-Based Proteome Profiling of Nasopharyngeal Swabs from COVID-19 Patients.Crossref | GoogleScholarGoogle Scholar |
[43] F Meier, MA Park, M Mann, Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics. Mol Cell Proteomics 2021, 20, 100138.
| Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics.Crossref | GoogleScholarGoogle Scholar |
[44] CB Messner, V Demichev, N Bloomfield, JSL Yu, M White, M Kreidl, et al. Ultra-fast proteomics with Scanning SWATH. Nat Biotechnol 2021, 39, 846.
| Ultra-fast proteomics with Scanning SWATH.Crossref | GoogleScholarGoogle Scholar |
[45] P Skowronek, F Krohs, M Lubeck, G Wallmann, ECM Itang, P Koval, et al. Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition. Mol Cell Proteomics 2023, 22, 100489.
| Synchro-PASEF Allows Precursor-Specific Fragment Ion Extraction and Interference Removal in Data-Independent Acquisition.Crossref | GoogleScholarGoogle Scholar |
[46] Z Wang, M Mülleder, I Batruch, A Chelur, K Textoris-Taube, T Schwecke, et al. High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS. Elife 2022, 11, e83947.
| High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS.Crossref | GoogleScholarGoogle Scholar |
[47] J Vowinckel, A Zelezniak, R Bruderer, M Mülleder, L Reiter, M Ralser, Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition. Sci Rep 2018, 8, 4346.
| Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition.Crossref | GoogleScholarGoogle Scholar |
[48] A Zelezniak, J Vowinckel, F Capuano, CB Messner, V Demichev, N Polowsky, et al. Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts. Cell Syst 2018, 7, 269.
| Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.Crossref | GoogleScholarGoogle Scholar |
[49] CB Messner, V Demichev, D Wendisch, L Michalick, M White, A Freiwald, et al. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection. Cell Syst 2020, 11, 11.
| Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.Crossref | GoogleScholarGoogle Scholar |
[50] R Bruderer, J Muntel, S Müller, OM Bernhardt, T Gandhi, O Cominetti, et al. Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance. Mol Cell Proteomics 2019, 18, 1242.
| Analysis of 1508 Plasma Samples by Capillary-Flow Data-Independent Acquisition Profiles Proteomics of Weight Loss and Maintenance.Crossref | GoogleScholarGoogle Scholar |
[51] Covey TR, Schneider BB, Javaheri H, LeBlanc JCY, Ivosev G, Corr JJ, et al. ESI, APCI, and MALDI a Comparison of the Central Analytical Figures of Merit: Sensitivity, Reproducibility, and Speed. In: Cole RB, editor. Electrospray and MALDI Mass Spectrometry. John Wiley & Sons, Ltd; 2010. pp. 441–90.
[52] E Shishkova, AS Hebert, JJ Coon, Now, More Than Ever, Proteomics Needs Better Chromatography. Cell Syst 2016, 3, 321.
| Now, More Than Ever, Proteomics Needs Better Chromatography.Crossref | GoogleScholarGoogle Scholar |
[53] Y Bian, M The, P Giansanti, J Mergner, R Zheng, M Wilhelm, et al. Identification of 7000–9000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC–MS/MS. Anal Chem 2021, 93, 8687.
| Identification of 7000–9000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC–MS/MS.Crossref | GoogleScholarGoogle Scholar |
[54] Y Bian, R Zheng, FP Bayer, C Wong, YC Chang, C Meng, et al. Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS. Nat Commun 2020, 11, 157.
| Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS.Crossref | GoogleScholarGoogle Scholar |
[55] Y Bian, FP Bayer, YC Chang, C Meng, S Hoefer, N Deng, et al. Robust Microflow LC-MS/MS for Proteome Analysis: 38000 Runs and Counting. Anal Chem 2021, 93, 3686.
| Robust Microflow LC-MS/MS for Proteome Analysis: 38000 Runs and Counting.Crossref | GoogleScholarGoogle Scholar |
[56] Y Bian, C Gao, B Kuster, On the potential of micro-flow LC-MS/MS in proteomics. Expert Rev Proteomics 2022, 19, 153.
| On the potential of micro-flow LC-MS/MS in proteomics.Crossref | GoogleScholarGoogle Scholar |
[57] CB Messner, V Demichev, Z Wang, J Hartl, G Kustatscher, M Mülleder, et al. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023, 23, e2200013.
| Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology.Crossref | GoogleScholarGoogle Scholar |
[58] EL Boys, J Liu, PJ Robinson, RR Reddel, Clinical applications of mass spectrometry-based proteomics in cancer: where are we? Proteomics 2023, 23, e2200238.
| Clinical applications of mass spectrometry-based proteomics in cancer: where are we?Crossref | GoogleScholarGoogle Scholar |
[59] V Demichev, CB Messner, SI Vernardis, KS Lilley, M Ralser, DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 2020, 17, 41.
| DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.Crossref | GoogleScholarGoogle Scholar |
[60] AT Kong, FV Leprevost, DM Avtonomov, D Mellacheruvu, AI Nesvizhskii, MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods 2017, 14, 513.
| MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics.Crossref | GoogleScholarGoogle Scholar |
[61] C Gotti, F Roux-Dalvai, C Joly-Beauparlant, L Mangnier, M Leclercq, A Droit, Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard. J Proteome Res 2021, 20, 4801.
| Extensive and Accurate Benchmarking of DIA Acquisition Methods and Software Tools Using a Complex Proteomic Standard.Crossref | GoogleScholarGoogle Scholar |
[62] K Fröhlich, E Brombacher, M Fahrner, D Vogele, L Kook, N Pinter, et al. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nat Commun 2022, 13, 2622.
| Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity.Crossref | GoogleScholarGoogle Scholar |
[63] LL Xu, A Young, A Zhou, HL Röst, Machine Learning in Mass Spectrometric Analysis of DIA Data. Proteomics 2020, 20, e1900352.
| Machine Learning in Mass Spectrometric Analysis of DIA Data.Crossref | GoogleScholarGoogle Scholar |
[64] R Bouwmeester, R Gabriels, T Van Den Bossche, L Martens, S Degroeve, The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows. Proteomics 2020, 20, e1900351.
| The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows.Crossref | GoogleScholarGoogle Scholar |
[65] JG Meyer, Deep learning neural network tools for proteomics. Cell Rep Methods 2021, 1, 100003.
| Deep learning neural network tools for proteomics.Crossref | GoogleScholarGoogle Scholar |
[66] P Sinitcyn, H Hamzeiy, F Salinas Soto, D Itzhak, F McCarthy, C Wichmann, et al. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol 2021, 39, 1563.
| MaxDIA enables library-based and library-free data-independent acquisition proteomics.Crossref | GoogleScholarGoogle Scholar |
[67] R Bruderer, OM Bernhardt, T Gandhi, SM Miladinović, LY Cheng, S Messner, et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics 2015, 14, 1400.
| Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues.Crossref | GoogleScholarGoogle Scholar |
[68] S Gessulat, T Schmidt, DP Zolg, P Samaras, K Schnatbaum, J Zerweck, et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods 2019, 16, 509.
| Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.Crossref | GoogleScholarGoogle Scholar |
[69] G Teo, S Kim, CC Tsou, B Collins, AC Gingras, AI Nesvizhskii, et al. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics 2015, 129, 108.
| mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry.Crossref | GoogleScholarGoogle Scholar |
[70] DL Plubell, L Käll, BJ Webb-Robertson, LM Bramer, A Ives, NL Kelleher, et al. Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics? J Proteome Res 2022, 21, 891.
| Putting Humpty Dumpty Back Together Again: What Does Protein Quantification Mean in Bottom-Up Proteomics?Crossref | GoogleScholarGoogle Scholar |
[71] KW Li, MA Gonzalez-Lozano, F Koopmans, AB Smit, Recent Developments in Data Independent Acquisition (DIA) Mass Spectrometry: Application of Quantitative Analysis of the Brain Proteome. Front Mol Neurosci 2020, 13, 564446.
| Recent Developments in Data Independent Acquisition (DIA) Mass Spectrometry: Application of Quantitative Analysis of the Brain Proteome.Crossref | GoogleScholarGoogle Scholar |
[72] K-T Tshilenge, CG Aguirre, J Bons, N Basisty, S Song, J Rose, et al. Proteomic Analysis of Huntington’s Disease Medium Spiny Neurons Identifies Alterations in Lipid Droplets. Mol Cell Proteomics 2023, 22, 100534.
| Proteomic Analysis of Huntington’s Disease Medium Spiny Neurons Identifies Alterations in Lipid Droplets.Crossref | GoogleScholarGoogle Scholar |
[73] O Karayel, S Virreira Winter, S Padmanabhan, YI Kuras, DT Vu, I Tuncali, et al. Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson’s disease. Cell Rep Med 2022, 3, 100661.
| Proteome profiling of cerebrospinal fluid reveals biomarker candidates for Parkinson’s disease.Crossref | GoogleScholarGoogle Scholar |
[74] L Krasny, PH Huang, Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol Omics 2021, 17, 29.
| Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology.Crossref | GoogleScholarGoogle Scholar |
[75] J Bons, D Pan, S Shah, R Bai, C Chen-Tanyolac, X Wang, et al. Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas. Proteomics 2022, 23, e2200021.
| Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas.Crossref | GoogleScholarGoogle Scholar |
[76] E Gonçalves, RC Poulos, Z Cai, S Barthorpe, SS Manda, N Lucas, et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 2022, 40, 835.
| Pan-cancer proteomic map of 949 human cell lines.Crossref | GoogleScholarGoogle Scholar |
[77] K Nakamura, M Hirayama-Kurogi, S Ito, T Kuno, T Yoneyama, W Obuchi, et al. Large-scale multiplex absolute protein quantification of drug-metabolizing enzymes and transporters in human intestine, liver, and kidney microsomes by SWATH-MS: comparison with MRM/SRM and HR-MRM/PRM. Proteomics 2016, 16, 2106.
| Large-scale multiplex absolute protein quantification of drug-metabolizing enzymes and transporters in human intestine, liver, and kidney microsomes by SWATH-MS: comparison with MRM/SRM and HR-MRM/PRM.Crossref | GoogleScholarGoogle Scholar |
[78] J Li, LS Smith, HJ Zhu, Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters. Drug Discov Today Technol 2021, 39, 49.
| Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters.Crossref | GoogleScholarGoogle Scholar |
[79] J DeBoer, MS Wojtkiewicz, N Haverland, Y Li, E Harwood, E Leshen, et al. Proteomic profiling of HIV-infected T-cells by SWATH mass spectrometry. Virology 2018, 516, 246.
| Proteomic profiling of HIV-infected T-cells by SWATH mass spectrometry.Crossref | GoogleScholarGoogle Scholar |
[80] C Lozano, L Grenga, F Gallais, G Miotello, L Bellanger, J Armengaud, Mass spectrometry detection of monkeypox virus: Comprehensive coverage for ranking the most responsive peptide markers. Proteomics 2023, 23, e2200253.
| Mass spectrometry detection of monkeypox virus: Comprehensive coverage for ranking the most responsive peptide markers.Crossref | GoogleScholarGoogle Scholar |
[81] Bons J, Rose J, O’Broin A, Schilling B. Advanced mass spectrometry-based methods for protein molecular-structural biologists. In: Tripathi T, Kumar Dubey V, editors. Advances in Protein Molecular and Structural Biology Methods. Academic Press; 2022. pp. 311–26.
[82] S Doll, AL Burlingame, Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chem Biol 2015, 10, 63.
| Mass spectrometry-based detection and assignment of protein posttranslational modifications.Crossref | GoogleScholarGoogle Scholar |
[83] J Brandi, R Noberini, T Bonaldi, D Cecconi, Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications. J Chromatogr A 2022, 1678, 463352.
| Advances in enrichment methods for mass spectrometry-based proteomics analysis of post-translational modifications.Crossref | GoogleScholarGoogle Scholar |
[84] RB Kitata, WK Choong, CF Tsai, PY Lin, BS Chen, YC Chang, et al. A data-independent acquisition-based global phosphoproteomics system enables deep profiling. Nat Commun 2021, 12, 2539.
| A data-independent acquisition-based global phosphoproteomics system enables deep profiling.Crossref | GoogleScholarGoogle Scholar |
[85] DG Christensen, JG Meyer, JT Baumgartner, AK D’Souza, WC Nelson, SH Payne, et al. Identification of Novel Protein Lysine Acetyltransferases in Escherichia coli. mBio 2018, 9, e01905-18.
| Identification of Novel Protein Lysine Acetyltransferases in Escherichia coli.Crossref | GoogleScholarGoogle Scholar |
[86] J Bons, J Rose, R Zhang, JB Burton, C Carrico, E Verdin, et al. In-depth analysis of the Sirtuin 5-regulated mouse brain malonylome and succinylome using library-free data-independent acquisitions. Proteomics 2023, 23, e2100371.
| In-depth analysis of the Sirtuin 5-regulated mouse brain malonylome and succinylome using library-free data-independent acquisitions.Crossref | GoogleScholarGoogle Scholar |
[87] J Fert-Bober, V Venkatraman, CL Hunter, R Liu, EL Crowgey, R Pandey, et al. Mapping Citrullinated Sites in Multiple Organs of Mice Using Hypercitrullinated Library. J Proteome Res 2019, 18, 2270.
| Mapping Citrullinated Sites in Multiple Organs of Mice Using Hypercitrullinated Library.Crossref | GoogleScholarGoogle Scholar |
[88] A Stachowicz, N Sundararaman, V Venkatraman, J Van Eyk, J Fert-Bober, pH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC–MS/MS Analysis for Confident Identification of Citrullinated Peptides. Methods Mol Biol 2022, 2420, 107.
| pH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC–MS/MS Analysis for Confident Identification of Citrullinated Peptides.Crossref | GoogleScholarGoogle Scholar |
[89] J Fert-Bober, JT Giles, RJ Holewinski, JA Kirk, H Uhrigshardt, EL Crowgey, et al. Citrullination of myofilament proteins in heart failure. Cardiovasc Res 2015, 108, 232.
| Citrullination of myofilament proteins in heart failure.Crossref | GoogleScholarGoogle Scholar |
[90] V Romero, J Fert-Bober, PA Nigrovic, E Darrah, UJ Haque, DM Lee, et al. Immune-mediated pore-forming pathways induce cellular hypercitrullination and generate citrullinated autoantigens in rheumatoid arthritis. Sci Transl Med 2013, 5, 209ra150.
| Immune-mediated pore-forming pathways induce cellular hypercitrullination and generate citrullinated autoantigens in rheumatoid arthritis.Crossref | GoogleScholarGoogle Scholar |
[91] A Stachowicz, R Pandey, N Sundararaman, V Venkatraman, JE Van Eyk, J Fert-Bober, Protein arginine deiminase 2 (PAD2) modulates the polarization of THP-1 macrophages to the anti-inflammatory M2 phenotype. J Inflamm (Lond) 2022, 19, 20.
| Protein arginine deiminase 2 (PAD2) modulates the polarization of THP-1 macrophages to the anti-inflammatory M2 phenotype.Crossref | GoogleScholarGoogle Scholar |
[92] Z Jin, Z Fu, J Yang, J Troncosco, AD Everett, JE Van Eyk, Identification and characterization of citrulline-modified brain proteins by combining HCD and CID fragmentation. Proteomics 2013, 13, 2682.
| Identification and characterization of citrulline-modified brain proteins by combining HCD and CID fragmentation.Crossref | GoogleScholarGoogle Scholar |
[93] Fert-Bober J, Pandey R, Dardov VJ, Van Meter TE, Edmonds DJ, Van Eyk JE. Traumatic brain injury: glial fibrillary acidic protein posttranslational modification. In: Wu AHB, Peacock WF, editors. Biomarkers for Traumatic Brain Injury. Academic Press; 2020. pp. 77–91.
[94] DB Bekker-Jensen, OM Bernhardt, A Hogrebe, A Martinez-Val, L Verbeke, T Gandhi, et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 2020, 11, 787.
| Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries.Crossref | GoogleScholarGoogle Scholar |
[95] B MacLean, DM Tomazela, N Shulman, M Chambers, GL Finney, B Frewen, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 2010, 26, 966.
| Skyline: an open source document editor for creating and analyzing targeted proteomics experiments.Crossref | GoogleScholarGoogle Scholar |
[96] JG Meyer, S Mukkamalla, H Steen, AI Nesvizhskii, BW Gibson, B Schilling, PIQED: automated identification and quantification of protein modifications from DIA-MS data. Nat Methods 2017, 14, 646.
| PIQED: automated identification and quantification of protein modifications from DIA-MS data.Crossref | GoogleScholarGoogle Scholar |
[97] CC Tsou, D Avtonomov, B Larsen, M Tucholska, H Choi, AC Gingras, et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 2015, 12, 258.
| DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics.Crossref | GoogleScholarGoogle Scholar |
[98] B Salovska, H Zhu, T Gandhi, M Frank, W Li, G Rosenberger, et al. Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation. Mol Syst Biol 2020, 16, e9170.
| Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation.Crossref | GoogleScholarGoogle Scholar |
[99] RE Foreman, AL George, F Reimann, FM Gribble, RG Kay, Peptidomics: A Review of Clinical Applications and Methodologies. J Proteome Res 2021, 20, 3782.
| Peptidomics: A Review of Clinical Applications and Methodologies.Crossref | GoogleScholarGoogle Scholar |
[100] K DeLaney, AR Buchberger, L Atkinson, S Gründer, A Mousley, L Li, New techniques, applications and perspectives in neuropeptide research. J Exp Biol 2018, 221, jeb151167.
| New techniques, applications and perspectives in neuropeptide research.Crossref | GoogleScholarGoogle Scholar |
[101] I Lyapina, V Ivanov, I Fesenko, Peptidome: Chaos or Inevitability. Int J Mol Sci 2021, 22, 13128.
| Peptidome: Chaos or Inevitability.Crossref | GoogleScholarGoogle Scholar |
[102] JP Becker, AB Riemer, The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies. Front Immunol 2022, 13, 883989.
| The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies.Crossref | GoogleScholarGoogle Scholar |
[103] NP Croft, SA Smith, YC Wong, CT Tan, NL Dudek, IEA Flesch, et al. Kinetics of antigen expression and epitope presentation during virus infection. PLoS Pathog 2013, 9, e1003129.
| Kinetics of antigen expression and epitope presentation during virus infection.Crossref | GoogleScholarGoogle Scholar |
[104] JW Yewdell, LC Antón, JR Bennink, Defective ribosomal products (DRiPs): a major source of antigenic peptides for MHC class I molecules? J Immunol 1996, 157, 1823.
| Defective ribosomal products (DRiPs): a major source of antigenic peptides for MHC class I molecules?Crossref | GoogleScholarGoogle Scholar |
[105] NP Croft, DA de Verteuil, SA Smith, YC Wong, RB Schittenhelm, DC Tscharke, et al. Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry. Mol Cell Proteomics 2015, 14, 1361.
| Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry.Crossref | GoogleScholarGoogle Scholar |
[106] NP Croft, SA Smith, J Pickering, J Sidney, B Peters, P Faridi, et al. Most viral peptides displayed by class I MHC on infected cells are immunogenic. Proc Natl Acad Sci U S A 2019, 116, 3112.
| Most viral peptides displayed by class I MHC on infected cells are immunogenic.Crossref | GoogleScholarGoogle Scholar |
[107] DG Bracewell, R Francis, CM Smales, The future of host cell protein (HCP) identification during process development and manufacturing linked to a risk‐based management for their control. Biotechnol Bioeng 2015, 112, 1727.
| The future of host cell protein (HCP) identification during process development and manufacturing linked to a risk‐based management for their control.Crossref | GoogleScholarGoogle Scholar |
[108] G Husson, A Delangle, J O’Hara, S Cianferani, A Gervais, A Van Dorsselaer, et al. Dual Data-Independent Acquisition Approach Combining Global HCP Profiling and Absolute Quantification of Key Impurities during Bioprocess Development. Anal Chem 2018, 90, 1241.
| Dual Data-Independent Acquisition Approach Combining Global HCP Profiling and Absolute Quantification of Key Impurities during Bioprocess Development.Crossref | GoogleScholarGoogle Scholar |
[109] N Pythoud, J Bons, G Mijola, A Beck, S Cianférani, C Carapito, Optimized Sample Preparation and Data Processing of Data-Independent Acquisition Methods for the Robust Quantification of Trace-Level Host Cell Protein Impurities in Antibody Drug Products. J Proteome Res 2021, 20, 923.
| Optimized Sample Preparation and Data Processing of Data-Independent Acquisition Methods for the Robust Quantification of Trace-Level Host Cell Protein Impurities in Antibody Drug Products.Crossref | GoogleScholarGoogle Scholar |
[110] OT Schubert, J Mouritsen, C Ludwig, HL Röst, G Rosenberger, PK Arthur, et al. The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis. Cell Host Microbe 2013, 13, 602.
| The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis.Crossref | GoogleScholarGoogle Scholar |
[111] T Kockmann, C Trachsel, C Panse, A Wahlander, N Selevsek, J Grossmann, et al. Targeted proteomics coming of age – SRM, PRM and DIA performance evaluated from a core facility perspective. Proteomics 2016, 16, 2183.
| Targeted proteomics coming of age – SRM, PRM and DIA performance evaluated from a core facility perspective.Crossref | GoogleScholarGoogle Scholar |
[112] CS Ejsing, E Duchoslav, J Sampaio, K Simons, R Bonner, C Thiele, et al. Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. Anal Chem 2006, 78, 6202.
| Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning.Crossref | GoogleScholarGoogle Scholar |
[113] B Simons, D Kauhanen, T Sylvänne, K Tarasov, E Duchoslav, K Ekroos, Shotgun Lipidomics by Sequential Precursor Ion Fragmentation on a Hybrid Quadrupole Time-of-Flight Mass Spectrometer. Metabolites 2012, 2, 195.
| Shotgun Lipidomics by Sequential Precursor Ion Fragmentation on a Hybrid Quadrupole Time-of-Flight Mass Spectrometer.Crossref | GoogleScholarGoogle Scholar |
[114] G Hopfgartner, D Tonoli, E Varesio, High-resolution mass spectrometry for integrated qualitative and quantitative analysis of pharmaceuticals in biological matrices. Anal Bioanal Chem 2012, 402, 2587.
| High-resolution mass spectrometry for integrated qualitative and quantitative analysis of pharmaceuticals in biological matrices.Crossref | GoogleScholarGoogle Scholar |
[115] R Bonner, G Hopfgartner, SWATH data independent acquisition mass spectrometry for metabolomics. Trends Analyt Chem 2019, 120, 115278.
| SWATH data independent acquisition mass spectrometry for metabolomics.Crossref | GoogleScholarGoogle Scholar |
[116] F Klont, S Jahn, C Grivet, S König, R Bonner, G Hopfgartner, SWATH data independent acquisition mass spectrometry for screening of xenobiotics in biological fluids: Opportunities and challenges for data processing. Talanta 2020, 211, 120747.
| SWATH data independent acquisition mass spectrometry for screening of xenobiotics in biological fluids: Opportunities and challenges for data processing.Crossref | GoogleScholarGoogle Scholar |
[117] M Raetz, R Bonner, G Hopfgartner, SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis. Metabolomics 2020, 16, 71.
| SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis.Crossref | GoogleScholarGoogle Scholar |
[118] T Martins-Marques, SI Anjo, P Pereira, B Manadas, H Girão, Interacting Network of the Gap Junction (GJ) Protein Connexin43 (Cx43) is Modulated by Ischemia and Reperfusion in the Heart. Mol Cell Proteomics 2015, 14, 3040.
| Interacting Network of the Gap Junction (GJ) Protein Connexin43 (Cx43) is Modulated by Ischemia and Reperfusion in the Heart.Crossref | GoogleScholarGoogle Scholar |
[119] Mendes VM, Coelho M, Manadas B. Untargeted Metabolomics Relative Quantification by SWATH Mass Spectrometry Applied to Cerebrospinal Fluid. In: Santamaría E, Fernández-Irigoyen J, editors. Cerebrospinal Fluid (CSF) Proteomics: Methods and Protocols. New York, NY: Springer New York; 2019. pp. 321–36.
[120] AJ Krotulski, SJ Varnum, BK Logan, Sample Mining and Data Mining: Combined Real-Time and Retrospective Approaches for the Identification of Emerging Novel Psychoactive Substances. J Forensic Sci 2020, 65, 550.
| Sample Mining and Data Mining: Combined Real-Time and Retrospective Approaches for the Identification of Emerging Novel Psychoactive Substances.Crossref | GoogleScholarGoogle Scholar |
[121] B Furtwängler, N Üresin, K Motamedchaboki, R Huguet, D Lopez-Ferrer, V Zabrouskov, et al. Real-Time Search-Assisted Acquisition on a Tribrid Mass Spectrometer Improves Coverage in Multiplexed Single-Cell Proteomics. Mol Cell Proteomics 2022, 21, 100219.
| Real-Time Search-Assisted Acquisition on a Tribrid Mass Spectrometer Improves Coverage in Multiplexed Single-Cell Proteomics.Crossref | GoogleScholarGoogle Scholar |
[122] EM Schoof, B Furtwängler, N Üresin, N Rapin, S Savickas, C Gentil, et al. Quantitative single-cell proteomics as a tool to characterize cellular hierarchies. Nat Commun 2021, 12, 3341.
| Quantitative single-cell proteomics as a tool to characterize cellular hierarchies.Crossref | GoogleScholarGoogle Scholar |
[123] JM Fulcher, LM Markillie, HD Mitchell, SM Williams, KM Engbrecht, RJ Moore, et al. Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting. bioRxiv 2022, 2022.05.17.492137.[Preprint]
| Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting.Crossref | GoogleScholarGoogle Scholar |
[124] B Budnik, E Levy, G Harmange, N Slavov, SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol 2018, 19, 161.
| SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation.Crossref | GoogleScholarGoogle Scholar |
[125] V Petrosius, EM Schoof, Recent advances in the field of single-cell proteomics. Transl Oncol 2023, 27, 101556.
| Recent advances in the field of single-cell proteomics.Crossref | GoogleScholarGoogle Scholar |
[126] AD Brunner, M Thielert, C Vasilopoulou, C Ammar, F Coscia, A Mund, et al. Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation. Mol Syst Biol 2022, 18, e10798.
| Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation.Crossref | GoogleScholarGoogle Scholar |
[127] N Slavov, Driving Single Cell Proteomics Forward with Innovation. J Proteome Res 2021, 20, 4915.
| Driving Single Cell Proteomics Forward with Innovation.Crossref | GoogleScholarGoogle Scholar |
[128] J Derks, N Slavov, Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA. J Proteome Res 2023, 22, 697.
| Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA.Crossref | GoogleScholarGoogle Scholar |
[129] N Slavov, Framework for multiplicative scaling of single-cell proteomics. Nat Biotechnol 2022, 41, 23.
| Framework for multiplicative scaling of single-cell proteomics.Crossref | GoogleScholarGoogle Scholar |
[130] L Szyrwiel, L Sinn, M Ralser, V Demichev, Slice-PASEF: fragmenting all ions for maximum sensitivity in proteomics [Preprint]. bioRxiv 2022, 2022.10.31.514544.
| Slice-PASEF: fragmenting all ions for maximum sensitivity in proteomics [Preprint].Crossref | GoogleScholarGoogle Scholar |
[131] N Bache, PE Geyer, DB Bekker-Jensen, O Hoerning, L Falkenby, PV Treit, et al. A Novel LC System Embeds Analytes in Pre-formed Gradients for Rapid, Ultra-robust Proteomics. Mol Cell Proteomics 2018, 17, 2284.
| A Novel LC System Embeds Analytes in Pre-formed Gradients for Rapid, Ultra-robust Proteomics.Crossref | GoogleScholarGoogle Scholar |
[132] R Chen, M Snyder, Promise of personalized omics to precision medicine. Wiley Interdiscip Rev Syst Biol Med 2013, 5, 73.
| Promise of personalized omics to precision medicine.Crossref | GoogleScholarGoogle Scholar |
