Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics.
Humans
Proteomics
/ methods
Paraffin Embedding
/ methods
Tissue Fixation
/ methods
Organelles
/ metabolism
Laser Capture Microdissection
/ methods
Single-Cell Analysis
/ methods
Colorectal Neoplasms
/ pathology
Reproducibility of Results
Mass Spectrometry
/ methods
Formaldehyde
/ chemistry
Hydrogels
/ chemistry
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Oct 2024
30 Oct 2024
Historique:
received:
09
05
2024
accepted:
21
10
2024
medline:
31
10
2024
pubmed:
31
10
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Hydrogel-based tissue expansion combined with mass spectrometry (MS) offers an emerging spatial proteomics approach. Here, we present a filter-aided expansion proteomics (FAXP) strategy for spatial proteomics analysis of archived formalin-fixed paraffin-embedded (FFPE) specimens. Compared to our previous ProteomEx method, FAXP employed a customized tip device to enhance both the stability and throughput of sample preparation, thus guaranteeing the reproducibility and robustness of the workflow. FAXP achieved a 14.5-fold increase in volumetric resolution. It generated over 8 times higher peptide yield and a 255% rise in protein identifications while reducing sample preparation time by 50%. We also demonstrated the applicability of FAXP using human colorectal FFPE tissue samples. Furthermore, for the first time, we achieved bona fide single-subcellular proteomics under image guidance by integrating FAXP with laser capture microdissection.
Identifiants
pubmed: 39477916
doi: 10.1038/s41467-024-53683-7
pii: 10.1038/s41467-024-53683-7
doi:
Substances chimiques
Formaldehyde
1HG84L3525
Hydrogels
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9378Informations de copyright
© 2024. The Author(s).
Références
Mund, A., Brunner, A. D. & Mann, M. Unbiased spatial proteomics with single-cell resolution in tissues. Mol. Cell 82, 2335–2349 (2022).
doi: 10.1016/j.molcel.2022.05.022
pubmed: 35714588
Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).
doi: 10.1038/s41580-023-00615-w
pubmed: 37280296
Taylor, M. J., Lukowski, J. K. & Anderton, C. R. Spatially resolved mass spectrometry at the single cell: recent innovations in proteomics and metabolomics. J. Am. Soc. Mass Spectrom. 32, 872–894 (2021).
doi: 10.1021/jasms.0c00439
pubmed: 33656885
pmcid: 8033567
Moore, J. L., Patterson, N. H., Norris, J. L. & Caprioli, R. M. Prospective on imaging mass spectrometry in clinical diagnostics. Mol. Cell. Proteom. 22, 100576 (2023).
doi: 10.1016/j.mcpro.2023.100576
Claes, B. S. R. et al. MALDI-IHC-guided in-depth spatial proteomics: targeted and untargeted MSI combined. Anal. Chem. 95, 2329–2338 (2023).
doi: 10.1021/acs.analchem.2c04220
pubmed: 36638208
pmcid: 9893213
Mund, A. et al. Deep visual proteomics defines single-cell identity and heterogeneity. Nat. Biotechnol. 40, 1231–1240 (2022).
doi: 10.1038/s41587-022-01302-5
pubmed: 35590073
pmcid: 9371970
Makhmut, A. et al. A framework for ultra-low-input spatial tissue proteomics. Cell Syst. 14, 1002–1014.e1005 (2023).
doi: 10.1016/j.cels.2023.10.003
pubmed: 37909047
Rosenberger, F. A. et al. Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome. Nat. Methods 20, 1530–1536 (2023).
doi: 10.1038/s41592-023-02007-6
pubmed: 37783884
pmcid: 10555842
Xu, R. et al. Spatial-resolution cell type proteome profiling of cancer tissue by fully integrated proteomics technology. Anal. Chem. 90, 5879–5886 (2018).
doi: 10.1021/acs.analchem.8b00596
pubmed: 29641186
Nordmann, T. M. et al. Spatial proteomics identifies JAKi as treatment for a lethal skin disease. Nature. https://doi.org/10.1038/s41586-024-08061-0 (2024).
Kabatnik, S. et al. Spatial characterization and stratification of colorectal adenomas by deep visual proteomics. iScience 27, 110620 (2024).
Kwon, Y. et al. Hanging drop sample preparation improves sensitivity of spatial proteomics. Lab Chip 22, 2869–2877 (2022).
doi: 10.1039/D2LC00384H
pubmed: 35838077
pmcid: 9320080
Zhu, Y. et al. Nanodroplet processing platform for deep and quantitative proteome profiling of 10-100 mammalian cells. Nat. Commun. 9, 882 (2018).
Ma, M. et al. In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP). Nat. Commun. 13, 7736 (2022).
Bhatia, H. S. et al. Spatial proteomics in three-dimensional intact specimens. Cell 185, 5040–5058 (2022).
M’Saad, O. & Bewersdorf, J. Light microscopy of proteins in their ultrastructural context. Nat. Commun. 11, 3850 (2020).
doi: 10.1038/s41467-020-17523-8
pubmed: 32737322
pmcid: 7395138
Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347, 543–548 (2015).
doi: 10.1126/science.1260088
pubmed: 25592419
pmcid: 4312537
Ku, T. et al. Multiplexed and scalable super-resolution imaging of three-dimensional protein localization in size-adjustable tissues. Nat. Biotechnol. 34, 973–981 (2016).
doi: 10.1038/nbt.3641
pubmed: 27454740
pmcid: 5070610
Tillberg, P. W. et al. Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies. Nat. Biotechnol. 34, 987–992 (2016).
Drelich, L. et al. Toward high spatially resolved proteomics using expansion microscopy. Anal. Chem. 93, 12195–12203 (2021).
doi: 10.1021/acs.analchem.0c05372
pubmed: 34449217
Li, L. et al. Spatially resolved proteomics via tissue expansion. Nat. Commun. 13, 7242 (2022).
doi: 10.1038/s41467-022-34824-2
pubmed: 36450705
pmcid: 9712279
Bai, Y. H. et al. Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nat. Commun. 14, 4013 (2023).
Chan, Y. H. et al. Gel-assisted mass spectrometry imaging enables sub-micrometer spatial lipidomics. Nat. Commun. 15, 5036 (2024).
doi: 10.1038/s41467-024-49384-w
pubmed: 38866734
pmcid: 11169460
Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).
doi: 10.1038/nmeth.1322
pubmed: 19377485
Ye, Z. et al. One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes. Nat. Commun. 15, 2474 (2024).
doi: 10.1038/s41467-024-46777-9
pubmed: 38503780
pmcid: 10951212
Chen, W. et al. Simple and integrated spintip-based technology applied for deep proteome profiling. Anal. Chem. 88, 4864–4871 (2016).
doi: 10.1021/acs.analchem.6b00631
pubmed: 27062885
Shevchenko, A., Tomas, H., Havli, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2006).
doi: 10.1038/nprot.2006.468
pubmed: 17406544
Damstra, H. G. J. et al. Correction: Visualizing cellular and tissue ultrastructure using ten-fold robust expansion microscopy (TREx). eLife 11, e85169 (2022).
doi: 10.7554/eLife.85169
pubmed: 36444779
pmcid: 9708063
Suttapitugsakul, S., Xiao, H. P., Smeekens, J. & Wu, R. H. Evaluation and optimization of reduction and alkylation methods to maximize peptide identification with MS- based proteomics. Mol. Biosyst. 13, 2574–2582 (2017).
doi: 10.1039/C7MB00393E
pubmed: 29019370
pmcid: 5698164
Hammad, S. et al. Protocols for staining of bile canalicular and sinusoidal networks of human, mouse and pig livers, three-dimensional reconstruction and quantification of tissue microarchitecture by image processing and analysis. Arch. Toxicol. 88, 1161–1183 (2014).
doi: 10.1007/s00204-014-1243-5
pubmed: 24748404
pmcid: 3996365
Zhang, Z. et al. Deficiency of ASGR1 promotes liver injury by increasing GP73-mediated hepatic endoplasmic reticulum stress. Nat. Commun. 15, 1908 (2024).
doi: 10.1038/s41467-024-46135-9
pubmed: 38459023
pmcid: 10924105
Shao, Y. K. et al. Proteomics profiling of colorectal cancer progression identifies PLOD2 as a potential therapeutic target. Cancer Commun. 42, 164–169 (2022).
doi: 10.1002/cac2.12240
Boedigheimer, M. J. et al. Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories. BMC Genomics 9, 285 (2008).
doi: 10.1186/1471-2164-9-285
pubmed: 18549499
pmcid: 2453529
Shen, L. et al. ADCdb: the database of antibody–drug conjugates. Nucleic Acids Res. 52, D1097–D1109 (2024).
doi: 10.1093/nar/gkad831
pubmed: 37831118
Ordoñez, C., Screaton, R. A., Ilantzis, C. & Stanners, C. P. Human carcinoembryonic antigen functions as a general inhibitor of anoikis1. Cancer Res. 60, 3419–3424 (2000).
pubmed: 10910050
Blumenthal, R. D., Hansen, H. J. & Goldenberg, D. M. Inhibition of adhesion, invasion, and metastasis by antibodies targeting CEACAM6 (NCA-90) and CEACAM5 (carcinoembryonic antigen). Cancer Res. 65, 8809–8817 (2005).
doi: 10.1158/0008-5472.CAN-05-0420
pubmed: 16204051
Bury, A. G. et al. A subcellular cookie cutter for spatial genomics in human tissue. Anal. Bioanal. Chem. 414, 5483–5492 (2022).
doi: 10.1007/s00216-022-03944-5
pubmed: 35233697
pmcid: 9242960
Yuan, Z. N. et al. Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments. Mol. Cancer. 22, 48 (2023).
Karlsson, S. & Nystrom, H. The extracellular matrix in colorectal cancer and its metastatic settling-alterations and biological implications. Crit. Rev. Oncol. Hematol. 175, 103712 (2022).
doi: 10.1016/j.critrevonc.2022.103712
pubmed: 35588938
Baker, A. M. et al. Evolutionary history of human colitis-associated colorectal cancer. Gut 68, 985–995 (2019).
doi: 10.1136/gutjnl-2018-316191
pubmed: 29991641
Wang, W. et al. Molecular subtyping of colorectal cancer: recent progress, new challenges and emerging opportunities. Semin. Cancer Biol. 55, 37–52 (2019).
doi: 10.1016/j.semcancer.2018.05.002
pubmed: 29775690
Valdes, P. A. et al. Improved immunostaining of nanostructures and cells in human brain specimens through expansion-mediated protein decrowding. Sci. Transl. Med.16, eabo0049 (2024).
Tsai, C.-F. et al. Surfactant-assisted one-pot sample preparation for label-free single-cell proteomics. Commun. Biol. 4, 265 (2021).
Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat. Methods 14, 513–520 (2017).
doi: 10.1038/nmeth.4256
pubmed: 28394336
pmcid: 5409104
Yu, F. et al. Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant. Mol. Cell. Proteomics 19, 1575–1585 (2020).
doi: 10.1074/mcp.TIR120.002048
pubmed: 32616513
pmcid: 7996969
Demichev, V. et al. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat. Commun. 13, 3944 (2022).
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).
doi: 10.1038/s41592-019-0638-x
pubmed: 31768060
Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).
doi: 10.1093/nar/gkab1038
pubmed: 34723319
Dong, Z. et al. Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics. Zenodo. https://doi.org/10.5281/zenodo.13843661 (2024).