Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-window data-independent acquisition.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
01 Feb 2024
01 Feb 2024
Historique:
received:
14
06
2023
accepted:
13
12
2023
medline:
2
2
2024
pubmed:
2
2
2024
entrez:
1
2
2024
Statut:
aheadofprint
Résumé
Mass spectrometry (MS)-based proteomics aims to characterize comprehensive proteomes in a fast and reproducible manner. Here we present the narrow-window data-independent acquisition (nDIA) strategy consisting of high-resolution MS1 scans with parallel tandem MS (MS/MS) scans of ~200 Hz using 2-Th isolation windows, dissolving the differences between data-dependent and -independent methods. This is achieved by pairing a quadrupole Orbitrap mass spectrometer with the asymmetric track lossless (Astral) analyzer which provides >200-Hz MS/MS scanning speed, high resolving power and sensitivity, and low-ppm mass accuracy. The nDIA strategy enables profiling of >100 full yeast proteomes per day, or 48 human proteomes per day at the depth of ~10,000 human protein groups in half-an-hour or ~7,000 proteins in 5 min, representing 3× higher coverage compared with current state-of-the-art MS. Multi-shot acquisition of offline fractionated samples provides comprehensive coverage of human proteomes in ~3 h. High quantitative precision and accuracy are demonstrated in a three-species proteome mixture, quantifying 14,000+ protein groups in a single half-an-hour run.
Identifiants
pubmed: 38302753
doi: 10.1038/s41587-023-02099-7
pii: 10.1038/s41587-023-02099-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Novo Nordisk Fonden (Novo Nordisk Foundation)
ID : NNF14CC0001
Organisme : Novo Nordisk Fonden (Novo Nordisk Foundation)
ID : NNF16CC0020906
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : MSmed-686547
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : EPIC-XS-823839
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : PUSHH-861389
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : HighResCells-810057
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : PUSHH-861389
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : MSmed-686547
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council)
ID : MSmed-686547
Informations de copyright
© 2024. The Author(s).
Références
Martinez-Val, A., Guzmán, U. H. & Olsen, J. V. Obtaining complete human proteomes. Annu. Rev. Genomics Hum. Genet. 23, 99–121 (2022).
pubmed: 35440146
doi: 10.1146/annurev-genom-112921-024948
Eliuk, S. & Makarov, A. Evolution of Orbitrap mass spectrometry instrumentation. Annu. Rev. Anal. Chem. https://doi.org/10.1146/annurev-anchem-071114-040325 (2015).
Sinitcyn, P. et al. Global detection of human variants and isoforms by deep proteome sequencing. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01714-x (2023).
doi: 10.1038/s41587-023-01714-x
pubmed: 36959352
pmcid: 10713452
Bekker-Jensen, D. B. et al. An optimized shotgun strategy for the rapid generation of comprehensive human proteomes. Cell Syst. 4, 587–599.e4 (2017).
pubmed: 28601559
pmcid: 5493283
doi: 10.1016/j.cels.2017.05.009
Branca, R. M. M. et al. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nat. Methods 11, 59–62 (2014).
pubmed: 24240322
doi: 10.1038/nmeth.2732
Van Puyvelde, B. et al. A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. Sci. Data 9, 126 (2022).
pubmed: 35354825
pmcid: 8967878
doi: 10.1038/s41597-022-01216-6
Fröhlich, K. et al. Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity. Nat. Commun. 13, 2622 (2022).
pubmed: 35551187
pmcid: 9098472
doi: 10.1038/s41467-022-30094-0
Messner, C. B. et al. Ultra-fast proteomics with Scanning SWATH. Nat. Biotechnol. 39, 846–854 (2021).
pubmed: 33767396
pmcid: 7611254
doi: 10.1038/s41587-021-00860-4
Skowronek, P. et al. Synchro-PASEF allows precursor-specific fragment ion extraction and interference removal in data-independent acquisition. Mol. Cell. Proteomics 22, 100489 (2023).
pubmed: 36566012
doi: 10.1016/j.mcpro.2022.100489
Meier, F. et al. Online parallel accumulation–serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer. Mol. Cell. Proteomics 17, 2534–2545 (2018).
pubmed: 30385480
pmcid: 6283298
doi: 10.1074/mcp.TIR118.000900
Yu, F. et al. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat. Commun. 14, 4154 (2023).
pubmed: 37438352
pmcid: 10338508
doi: 10.1038/s41467-023-39869-5
Stewart, H. I. et al. Parallelized acquisition of orbitrap and Astral analyzers enables high-throughput quantitative analysis. Anal. Chem. 95, 15656–15664 (2023).
Bekker-Jensen, D. B. et al. A compact quadrupole-Orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol. Cell. Proteomics 19, 716–729 (2020).
pubmed: 32051234
pmcid: 7124470
doi: 10.1074/mcp.TIR119.001906
Petrosius, V. et al. Evaluating the capabilities of the Astral mass analyzer for single-cell proteomics. Preprint at bioRxiv https://doi.org/10.1101/2023.06.06.543943 (2023).
Heil, L. R. et al. Evaluating the performance of the Astral mass analyzer for quantitative proteomics using data-independent acquisition. J. Proteome Res. 22, 3290–3300 (2023).
Olsen, J. V. et al. Higher-energy C-Trap dissociation for peptide modification analysis. Nat. Methods 4, 709–712 (2007).
pubmed: 17721543
doi: 10.1038/nmeth1060
Amodei, D. et al. Improving precursor selectivity in data-independent acquisition using overlapping windows. J. Am. Soc. Mass Spectrom. 30, 669–684 (2019).
pubmed: 30671891
pmcid: 6445824
doi: 10.1007/s13361-018-2122-8
Tabb, D. L. et al. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 9, 761–776 (2010).
Granholm, V., Noble, W. S. & Käll, L. On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics. J. Proteome Res. 10, 2671–2678 (2011).
pubmed: 21391616
pmcid: 3268674
doi: 10.1021/pr1012619
Scherl, A. et al. On the benefits of acquiring peptide fragment ions at high measured mass accuracy. J. Am. Soc. Mass Spectrom. 19, 891–901 (2008).
pubmed: 18417358
pmcid: 2459323
doi: 10.1016/j.jasms.2008.02.005
Michalski, A. et al. Ultra high resolution linear ion trap Orbitrap mass spectrometer (Orbitrap Elite) facilitates top down LC MS/MS and versatile peptide fragmentation modes. Mol. Cell. Proteomics 11, O111.013698 (2012).
pubmed: 22159718
doi: 10.1074/mcp.O111.013698
De Godoy, L. M. F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).
pubmed: 18820680
doi: 10.1038/nature07341
Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339–347 (2014).
pubmed: 24143002
doi: 10.1074/mcp.M113.034769
Kelstrup, C. D. et al. Optimized fast and sensitive acquisition methods for shotgun proteomics on a quadrupole Orbitrap mass spectrometer. J. Proteome Res. 11, 3487–3497 (2012).
pubmed: 22537090
doi: 10.1021/pr3000249
Feng, Y., Cappelletti, V. & Picotti, P. Quantitative proteomics of model organisms. Curr. Opin. Syst. Biol. https://doi.org/10.1016/j.coisb.2017.09.004 (2017).
Nielsen, M. L., Savitski, M. M. & Zubarev, R. A. Extent of modifications in human proteome samples and their effect on dynamic range of analysis in shotgun proteomics. Mol. Cell. Proteomics 5, 2384–2391 (2006).
pubmed: 17015437
doi: 10.1074/mcp.M600248-MCP200
Wang, Z. et al. High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS. eLife 11, e83947 (2022).
pubmed: 36449390
pmcid: 9711518
doi: 10.7554/eLife.83947
Tüshaus, J. et al. A region‐resolved proteomic map of the human brain enabled by high‐throughput proteomics. EMBO J. 2, e114665 (2023).
Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteomics. 13, 2513–2526 (2014).
pubmed: 24942700
pmcid: 4159666
doi: 10.1074/mcp.M113.031591
Ahrné, E., Molzahn, L., Glatter, T. & Schmidt, A. Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13, 2567–2578 (2013).
pubmed: 23794183
doi: 10.1002/pmic.201300135
Batth, T. S., Francavilla, C. & Olsen, J. V. Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J. Proteome Res. 13, 6176–6186 (2014).
pubmed: 25338131
doi: 10.1021/pr500893m
Kelstrup, C. D. et al. Rapid and deep proteomes by faster sequencing on a benchtop quadrupole ultra-high-field Orbitrap mass spectrometer. J. Proteome Res. 13, 6187–6195 (2014).
pubmed: 25349961
doi: 10.1021/pr500985w
Tsitsiridis, G. et al. CORUM: the comprehensive resource of mammalian protein complexes—2022. Nucleic Acids Res. 51, D539–D545 (2023).
pubmed: 36382402
doi: 10.1093/nar/gkac1015
Giurgiu, M. et al. CORUM: the comprehensive resource of mammalian protein complexes—2019. Nucleic Acids Res. 47, D559–D563 (2019).
pubmed: 30357367
doi: 10.1093/nar/gky973
de Godoy, L. M. F. et al. Status of complete proteome analysis by mass spectrometry: SILAC labeled yeast as a model system. Genome Biol. 7, R50 (2006).
pubmed: 16784548
pmcid: 1779535
doi: 10.1186/gb-2006-7-6-r50
Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–1594 (2014).
pubmed: 25159151
doi: 10.1016/j.celrep.2014.07.036
Olsen, J. V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010).
pubmed: 20068231
doi: 10.1126/scisignal.2000475
Martinez-Val, A. et al. Spatial-proteomics reveals phospho-signaling dynamics at subcellular resolution. Nat. Commun. 12, 7113 (2021).
pubmed: 34876567
pmcid: 8651693
doi: 10.1038/s41467-021-27398-y
Przybyla, L. & Gilbert, L. A. A new era in functional genomics screens. Nat. Rev. Genet. https://doi.org/10.1038/s41576-021-00409-w (2022).
Messner, C. B. et al. The proteomic landscape of genome-wide genetic perturbations. Cell 186, 2018–2034.e21 (2023).
Salvesen, L. et al. Neocortical neuronal loss in patients with multiple system atrophy: a stereological study. Cereb. Cortex 27, 400–410 (2017).
pubmed: 26464477
Monzio Compagnoni, G. & Di Fonzo, A. Understanding the pathogenesis of multiple system atrophy: state of the art and future perspectives. Acta Neuropathol. Commun. https://doi.org/10.1186/s40478-019-0730-6 (2019).
Rydbirk, R. et al. Brain proteome profiling implicates the complement and coagulation cascade in multiple system atrophy brain pathology. Cell. Mol. Life Sci. 79, 336 (2022).
pubmed: 35657417
pmcid: 9164190
doi: 10.1007/s00018-022-04378-z
Glat, M. J., Stefanova, N., Wenning, G. K. & Offen, D. Genes to treat excitotoxicity ameliorate the symptoms of the disease in mice models of multiple system atrophy. J. Neural Transm. 127, 205–212 (2020).
pubmed: 32065333
doi: 10.1007/s00702-020-02158-2
Xia, N., Cabin, D. E., Fang, F. & Reijo Pera, R. A. Parkinson’s disease: overview of transcription factor regulation, genetics, and cellular and animal models. Front. Neurosci. https://doi.org/10.3389/fnins.2022.894620 (2022).
Rafiee, M. R., Rohban, S., Davey, K., Ule, J. & Luscombe, N. M. RNA polymerase II-associated proteins reveal pathways affected in VCP-related amyotrophic lateral sclerosis. Brain 146, 2547–2556 (2023).
pubmed: 36789492
pmcid: 7614746
doi: 10.1093/brain/awad046
Meier, F. et al. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nat. Methods 17, 1229–1236 (2020).
pubmed: 33257825
doi: 10.1038/s41592-020-00998-0
Burns, A. P. et al. A universal and high-throughput proteomics sample preparation platform. Anal. Chem. 93, 8423–8431 (2021).
pubmed: 34110797
pmcid: 9876622
doi: 10.1021/acs.analchem.1c00265
Batth, T. S. et al. Protein aggregation capture on microparticles enables multipurpose proteomics sample preparation. Mol. Cell. Proteomics 18, 1027–1035 (2019).
pubmed: 30833379
pmcid: 6495262
doi: 10.1074/mcp.TIR118.001270
Chernushevich, I. V., Merenbloom, S. I., Liu, S. & Bloomfield, N. A W-geometry ortho-TOF MS with high resolution and up to 100% duty cycle for MS/MS. J. Am. Soc. Mass Spectrom. 28, 2143–2150 (2017).
pubmed: 28717932
doi: 10.1007/s13361-017-1742-8
Martínez-Val, A. et al. Hybrid-DIA: intelligent data acquisition integrates targeted and discovery proteomics to analyze phospho-signaling in single spheroids. Nat. Commun. 14, 3599 (2023).
pubmed: 37328457
pmcid: 10276052
doi: 10.1038/s41467-023-39347-y
Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J. & Gygi, S. P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2, 43–50 (2003).
pubmed: 12643542
doi: 10.1021/pr025556v
Washburn, M. P., Wolters, D. & Yates, J. R. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).
pubmed: 11231557
doi: 10.1038/85686
Thakur, S. S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell. Proteomics 10, M110.003699 (2011).
Nagaraj, N. et al. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol. Cell. Proteomics 11, M111.013722 (2012).
pubmed: 22021278
doi: 10.1074/mcp.M111.013722
Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).
pubmed: 31768060
doi: 10.1038/s41592-019-0638-x
Pham, T. V., Henneman, A. A. & Jimenez, C. R. iq: an R package to estimate relative protein abundances from ion quantification in DIA-MS-based proteomics. Bioinformatics 36, 2611–2613 (2020).
pubmed: 31909781
pmcid: 7178409
doi: 10.1093/bioinformatics/btz961
Bekker-Jensen, D. B. et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat. Commun. 11, 787 (2020).
pubmed: 32034161
pmcid: 7005859
doi: 10.1038/s41467-020-14609-1
Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
pubmed: 34557778
pmcid: 8454663