Deep Visual Proteomics defines single-cell identity and heterogeneity.
Journal
Nature biotechnology
ISSN: 1546-1696
Titre abrégé: Nat Biotechnol
Pays: United States
ID NLM: 9604648
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
08
03
2022
accepted:
30
03
2022
pubmed:
20
5
2022
medline:
16
8
2022
entrez:
19
5
2022
Statut:
ppublish
Résumé
Despite the availabilty of imaging-based and mass-spectrometry-based methods for spatial proteomics, a key challenge remains connecting images with single-cell-resolution protein abundance measurements. Here, we introduce Deep Visual Proteomics (DVP), which combines artificial-intelligence-driven image analysis of cellular phenotypes with automated single-cell or single-nucleus laser microdissection and ultra-high-sensitivity mass spectrometry. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. By individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and uncharacterized proteins. In an archived primary melanoma tissue, DVP identified spatially resolved proteome changes as normal melanocytes transition to fully invasive melanoma, revealing pathways that change in a spatial manner as cancer progresses, such as mRNA splicing dysregulation in metastatic vertical growth that coincides with reduced interferon signaling and antigen presentation. The ability of DVP to retain precise spatial proteomic information in the tissue context has implications for the molecular profiling of clinical samples.
Identifiants
pubmed: 35590073
doi: 10.1038/s41587-022-01302-5
pii: 10.1038/s41587-022-01302-5
pmc: PMC9371970
doi:
Substances chimiques
Proteome
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1231-1240Subventions
Organisme : NCI NIH HHS
ID : R35 CA264619
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2022. The Author(s).
Références
Hériché, J.-K., Alexander, S. & Ellenberg, J. Integrating imaging and omics: computational methods and challenges. Annu. Rev. Biomed. Data Sci. 2, 175–197 (2019).
doi: 10.1146/annurev-biodatasci-080917-013328
Brunner, A. et al. Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation. Mol. Syst. Biol. 18, e10798 (2022).
pubmed: 35226415
pmcid: 8884154
doi: 10.15252/msb.202110798
Hollandi, R. et al. nucleAIzer: a parameter-free deep learning framework for nucleus segmentation using image style transfer. Cell Syst. 10, 453–458 (2020).
pubmed: 34222682
pmcid: 8247631
doi: 10.1016/j.cels.2020.04.003
Smith, K. & Horvath, P. Active learning strategies for phenotypic profiling of high-content screens. J. Biomol. Screen. 19, 685–695 (2014).
pubmed: 24643256
doi: 10.1177/1087057114527313
Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. Preprint at https://arxiv.org/abs/1611.07004 (2016).
Caicedo, J. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019).
pubmed: 31636459
pmcid: 6919559
doi: 10.1038/s41592-019-0612-7
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2020).
pubmed: 33318659
doi: 10.1038/s41592-020-01018-x
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
pubmed: 17076895
pmcid: 1794559
doi: 10.1186/gb-2006-7-10-r100
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
pubmed: 31570887
doi: 10.1038/s41592-019-0582-9
Conrad, C. et al. Micropilot: automation of fluorescence microscopy-based imaging for systems biology. Nat. Methods 8, 246–249 (2011).
pubmed: 21258339
pmcid: 3086017
doi: 10.1038/nmeth.1558
Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).
pubmed: 34912115
doi: 10.1038/s41586-021-04217-4
Lengyel, E. Ovarian cancer development and metastasis. Am. J. Pathol. 177, 1053–1064 (2010).
pubmed: 20651229
pmcid: 2928939
doi: 10.2353/ajpath.2010.100105
Kurnit, K. C., Fleming, G. F. & Lengyel, E. Updates and new options in advanced epithelial ovarian cancer treatment. Obstet. Gynecol. 137, 108–121 (2021).
pubmed: 33278287
doi: 10.1097/AOG.0000000000004173
Sakaue-Sawano, A. et al. Visualizing spatiotemporal dynamics of multicellular cell-cycle progression. Cell 132, 487–498 (2008).
pubmed: 18267078
doi: 10.1016/j.cell.2007.12.033
Altelaar, A. M. & Heck, A. J. Trends in ultrasensitive proteomics. Curr. Opin. Chem. Biol. 16, 206–213 (2012).
pubmed: 22226769
doi: 10.1016/j.cbpa.2011.12.011
Coscia, F. et al. A streamlined mass spectrometry-based proteomics workflow for large‐scale FFPE tissue analysis. J. Pathol. 251, 100–112 (2020).
pubmed: 32154592
doi: 10.1002/path.5420
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
Lundberg, E. & Borner, G. H. H. Spatial proteomics: a powerful discovery tool for cell biology. Nat. Rev. Mol. Cell Biol. 20, 285–302 (2019).
pubmed: 30659282
doi: 10.1038/s41580-018-0094-y
Mahdessian, D. et al. Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature 590, 649–654 (2021).
pubmed: 33627808
doi: 10.1038/s41586-021-03232-9
Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419–1260419 (2015).
pubmed: 25613900
doi: 10.1126/science.1260419
Venturini, V. et al. The nucleus measures shape changes for cellular proprioception to control dynamic cell behavior. Science 370, eaba2644 (2020).
Arias-Garcia, M., Rickman, R., Sero, J., Yuan, Y. & Bakal, C. The cell–cell adhesion protein JAM3 determines nuclear deformability by regulating microtubule organization. Preprint at https://www.biorxiv.org/content/10.1101/689737v2.full (2020).
Kokkat, T. J., Patel, M. S., McGarvey, D., Livolsi, V. A. & Baloch, Z. W. Archived formalin-fixed paraffin-embedded (FFPE) blocks: a valuable underexploited resource for extraction of DNA, RNA, and protein. Biopreserv. Biobank 11, 101–106 (2013).
pubmed: 24845430
pmcid: 4077003
doi: 10.1089/bio.2012.0052
Niazi, M. K. K., Parwani, A. V. & Gurcan, M. N. Digital pathology and artificial intelligence. Lancet Oncol. 20, e253–e261 (2019).
pubmed: 31044723
pmcid: 8711251
doi: 10.1016/S1470-2045(19)30154-8
Zhu, S., Schuerch, C. & Hunt, J. Review and updates of immunohistochemistry in selected salivary gland and head and neck tumors. Arch. Pathol. Lab. Med. 139, 55–66 (2015).
pubmed: 25549144
doi: 10.5858/arpa.2014-0167-RA
Kim, L. C., Song, L. & Haura, E. B. Src kinases as therapeutic targets for cancer. Nat. Rev. Clin. Oncol. 6, 587–595 (2009).
pubmed: 19787002
doi: 10.1038/nrclinonc.2009.129
Shain, A. H. et al. The genetic evolution of melanoma from precursor lesions. N. Engl. J. Med. 373, 1926–1936 (2015).
pubmed: 26559571
doi: 10.1056/NEJMoa1502583
Pollock, P. M. et al. High frequency of BRAF mutations in nevi. Nat. Genet. 33, 19–20 (2003).
pubmed: 12447372
doi: 10.1038/ng1054
Raamsdonk, C. D. V. et al. Frequent somatic mutations of GNAQ in uveal melanoma and blue naevi. Nature 457, 599–602 (2009).
pubmed: 19078957
doi: 10.1038/nature07586
Wang, Z. et al. CD146, from a melanoma cell adhesion molecule to a signaling receptor. Signal Transduct. Target Ther. 5, 148 (2020).
pubmed: 32782280
pmcid: 7421905
doi: 10.1038/s41392-020-00259-8
Kumar, P. R., Moore, J. A., Bowles, K. M., Rushworth, S. A. & Moncrieff, M. D. Mitochondrial oxidative phosphorylation in cutaneous melanoma. Br. J. Cancer 124, 115–123 (2021).
pubmed: 33204029
doi: 10.1038/s41416-020-01159-y
Eddy, K. & Chen, S. Overcoming immune evasion in melanoma. Int. J. Mol. Sci. 21, 8984 (2020).
pmcid: 7730443
doi: 10.3390/ijms21238984
Winkler, J., Abisoye-Ogunniyan, A., Metcalf, K. J. & Werb, Z. Concepts of extracellular matrix remodelling in tumour progression and metastasis. Nat. Commun. 11, 5120 (2020).
pubmed: 33037194
pmcid: 7547708
doi: 10.1038/s41467-020-18794-x
Zhang, Y., Qian, J., Gu, C. & Yang, Y. Alternative splicing and cancer: a systematic review. Signal Transduct. Target Ther. 6, 78 (2021).
pubmed: 33623018
pmcid: 7902610
doi: 10.1038/s41392-021-00486-7
Frankiw, L., Baltimore, D. & Li, G. Alternative mRNA splicing in cancer immunotherapy. Nat. Rev. Immunol. 19, 675–687 (2019).
pubmed: 31363190
doi: 10.1038/s41577-019-0195-7
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Benediktsson, A. M., Schachtele, S. J., Green, S. H. & Dailey, M. E. Ballistic labeling and dynamic imaging of astrocytes in organotypic hippocampal slice cultures. J. Neurosci. Methods 141, 41–53 (2005).
pubmed: 15585287
doi: 10.1016/j.jneumeth.2004.05.013
Stadler, C., Skogs, M., Brismar, H., Uhlén, M. & Lundberg, E. A single fixation protocol for proteome-wide immunofluorescence localization studies. J. Proteomics 73, 1067–1078 (2010).
pubmed: 19896565
doi: 10.1016/j.jprot.2009.10.012
Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).
pubmed: 31932730
doi: 10.1038/s41587-019-0392-8
Goodfellow, J. P.-A. I. J. & Bengio, Y. Generative adversarial networks. Proc. International Conference on Neural Information Processing Systems 2672–2680 (2014).
Hollandi, R., Diosdi, A., Hollandi, G., Moshkov, N. & Horvath, P. AnnotatorJ: an ImageJ plugin to ease hand annotation of cellular compartments. Mol. Biol. Cell 31, 2179–2186 (2020).
pubmed: 32697683
pmcid: 7550707
doi: 10.1091/mbc.E20-02-0156
Kulak, N. A., Geyer, P. E. & Mann, M. Loss-less nano-fractionator for high sensitivity, high coverage proteomics*. Mol. Cell Proteomics 16, 694–705 (2017).
pubmed: 28126900
pmcid: 5383787
doi: 10.1074/mcp.O116.065136
Prianichnikov, N. et al. MaxQuant software for ion mobility enhanced shotgun proteomics*. Mol. Cell Proteomics 19, 1058–1069 (2020).
pubmed: 32156793
pmcid: 7261821
doi: 10.1074/mcp.TIR119.001720
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
pubmed: 19029910
doi: 10.1038/nbt.1511
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
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
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).
pubmed: 28394336
pmcid: 5409104
doi: 10.1038/nmeth.4256
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
pubmed: 27348712
doi: 10.1038/nmeth.3901
Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116–5121 (2001).
pubmed: 11309499
pmcid: 33173
doi: 10.1073/pnas.091062498
Yu, G. & He, Q.-Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2015).
doi: 10.1039/C5MB00663E
Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z., & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).
pubmed: 31114916
pmcid: 6602449
doi: 10.1093/nar/gkz401
Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019).
pubmed: 30395289
doi: 10.1093/nar/gky1106
Sarkans, U. et al. The BioStudies database—one stop shop for all data supporting a life sciences study. Nucleic Acids Res. 46, D1266–D1270 (2017).
Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).
pubmed: 30476243
doi: 10.1093/nar/gky1131