Abstract
Cartography of the plasma proteome remains technically challenging, primarily due to the abundance and dynamic range of plasma proteins and their concentrations, exceeding ten orders of magnitude, including low-abundant tissue-derived proteins in the pg/mL range. Data-independent acquisition mass spectrometry (DIA-MS) has seen advances in unbiased mass spectrometry-based proteomic analysis of the plasma proteome. Here, we describe a comprehensive proteomic workflow of human plasma from clinically relevant sample (10 μL) that includes anti-protein immunodepletion and highly sensitive sample preparation workflow, with optimized scheduled isolation DIA-MS and deep learning analysis. This approach results in over 960 proteins quantified from a single-shot analysis of broad dynamic range, across 8 orders of magnitude (8.2 ng/L to 0.67 g/L). We further compare data-dependent acquisition (DDA) MS to highlight the advantage in protein quantification and inter-sample variation. These developments have provided streamlined identification of the human plasma proteome, including low-abundant tissue-enriched proteins, and applications toward understanding the plasma proteome.
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References
Geyer PE, Holdt LM, Teupser D et al (2017) Revisiting biomarker discovery by plasma proteomics. Mol Syst Biol 13:942
Shishkova E, Coon JJ (2021) Rapid preparation of human blood plasma for bottom-up proteomics analysis. STAR Protoc 2:100856
Messner CB, Demichev V, Wendisch D et al (2020) Ultra-high-throughput clinical proteomics reveals classifiers of COVID-19 infection. Cell Syst 11:11–24.e4
Gautam SS, Singh RP, Karsauliya K et al (2022) Label-free plasma proteomics for the identification of the putative biomarkers of oral squamous cell carcinoma. J Proteome 259:104541
Bauer W, Weber M, Diehl-Wiesenecker E et al (2021) Plasma proteome fingerprints reveal distinctiveness and clinical outcome of SARS-CoV-2 infection. Viruses 13:2456
Gummesson A, Björnson E, Fagerberg L et al (2021) Longitudinal plasma protein profiling of newly diagnosed type 2 diabetes. EBioMedicine 63:103147
Liu Y, Buil A, Collins BC et al (2015) Quantitative variability of 342 plasma proteins in a human twin population. Mol Syst Biol 11:786
Keshishian H, Burgess MW, Specht H et al (2017) Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry. Nat Protoc 12:1683–1701
Blume JE, Manning WC, Troiano G et al (2020) Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. Nat Commun 11:1–14
Park J, Kim H, Kim SY et al (2020) In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients. Sci Rep 10:1–10
Kimura Y, Yanagimachi M, Ino Y et al (2017) Identification of candidate diagnostic serum biomarkers for Kawasaki disease using proteomic analysis. Sci Rep 7:1–12
Zhang S, Raedschelders K, Venkatraman V et al (2020) A dual workflow to improve the proteomic coverage in plasma using data-independent acquisition-MS. J Proteome Res 19:2828–2837
Sato H, Inoue Y, Kawashima Y et al (2022) In-depth serum proteomics by DIA-MS with in silico spectral libraries reveals dynamics during the active phase of systemic juvenile idiopathic arthritis. ACS Omega 7:7012–7023
Kimura Y, Nakai Y, Shin J et al (2021) Identification of serum prognostic biomarkers of severe COVID-19 using a quantitative proteomic approach. Sci Rep 11:1–9
Bruderer R, Bernhardt OM, Gandhi T et al (2016) High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation. Proteomics 16:2246–2256
Yang Y, Liu X, Shen C et al (2020) In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nat Commun 11:1–11
Tran NH, Qiao R, Xin L et al (2019) Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry. Nat Methods 16:63–66
Demichev V, Messner CB, Vernardis SI et al (2019) DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17:41–44
Wichmann C, Meier F, Winter SV et al (2019) MaxQuant. Live enables global targeting of more than 25,000 peptides. Mol Cell Proteomics 18:982–994
Hughes CS, Moggridge S, Müller T et al (2019) Single-pot, solid-phase-enhanced sample preparation for proteomics experiments. Nat Protoc 14:68–85
Deutsch EW, Omenn GS, Sun Z et al (2021) Advances and utility of the human plasma proteome. J Proteome Res 20(12):5241–5263
Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11:2301–2319
Tyanova S, Cox J (2018) Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. Cancer Syst Biol 1711:133–148
MacLean B, Tomazela DM, Shulman N et al (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26(7):966–968
Kompa AR, Greening DW, Kong AM et al (2021) Sustained subcutaneous delivery of secretome of human cardiac stem cells promotes cardiac repair following myocardial infarction. Cardiovasc Res 117:918–929
Claridge B, Rai A, Fang H et al (2021) Proteome characterisation of extracellular vesicles isolated from heart. Proteomics 21:2100026
Rai A, Fang H, Claridge B et al (2021) Proteomic dissection of large extracellular vesicle surfaceome unravels interactive surface platform. J Extracell Vesicles 10:e12164
Poh QH, Rai A, Carmichael II et al (2021) Proteome reprogramming of endometrial epithelial cells by human trophectodermal small extracellular vesicles reveals key insights into embryo implantation. Proteomics 21:2000210
Rai A, Fang H, Fatmous M et al (2021) A protocol for isolation, purification, characterization, and functional dissection of exosomes. In: Methods in molecular biology. Humana Press Inc, pp 105–149
Kristensen K, Henriksen JR, Andresen TL (2015) Adsorption of cationic peptides to solid surfaces of glass and plastic. PLoS One 10:e0122419
Goebel-Stengel M, Stengel A, Taché Y et al (2011) The importance of using the optimal plasticware and glassware in studies involving peptides. Anal Biochem 414:38–46
Rosenberger G, Koh CC, Guo T et al (2014) A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 1:1–15
Pino LK, Just SC, MacCoss MJ et al (2020) Acquiring and analyzing data independent acquisition proteomics experiments without spectrum libraries. Mol Cell Proteomics 19:1088–1103
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Fang, H., Greening, D.W. (2023). An Optimized Data-Independent Acquisition Strategy for Comprehensive Analysis of Human Plasma Proteome. In: Greening, D.W., Simpson, R.J. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 2628. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2978-9_7
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DOI: https://doi.org/10.1007/978-1-0716-2978-9_7
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