PXD023904-1

PXD023904 is an original dataset announced via ProteomeXchange.

Title	AI-driven Deep Visual Proteomics defines cell identity and heterogeneity
Description	The systems-wide analysis of biomolecules in time and space is key to our understanding of cellular function and heterogeneity in health and disease1. Remarkable technological progress in microscopy and multi-omics technologies enable increasingly data-rich descriptions of tissue heterogeneity2,3,4,5. Single cell sequencing, in particular, now routinely allows the mapping of cell types and states uncovering tremendous complexity6. Yet, an unaddressed challenge is the development of a method that would directly connect the visual dimension with the molecular phenotype and in particular with the unbiased characterization of proteomes, a close proxy for cellular function. Here we introduce Deep Visual Proteomics (DVP), which combines advances in artificial intelligence (AI)-driven image analysis of cellular phenotypes with automated single cell laser microdissection and ultra-high sensitivity mass spectrometry7. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. Individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and novel proteins. AI also discovered rare cells with distinct morphology, whose potential function was revealed by proteomics. In a 17-year-old primary melanoma tissue, DVP revealed spatio-temporal cancer-specific protein changes in the native tissue environment, including an interplay between tumor related mRNA splicing in the metastatic vertical growth phase, the immune system, and the ECM remodeling. Thus, DVP provides unprecedented molecular insights into disease progression and metastisis while retaining spatial information.
HostingRepository	PRIDE
AnnounceDate	2022-04-27
AnnouncementXML	Submission_2022-04-27_12:00:59.053.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Fabian Coscia
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	timsTOF Pro

Revision	Datetime	Status	ChangeLog Entry
0	2021-01-31 22:59:28	ID requested
⏵ 1	2022-04-27 12:00:59	announced
2	2023-06-16 02:42:22	announced	2023-06-16: Updated project metadata.
3	2023-11-14 09:01:08	announced	2023-11-14: Updated project metadata.

Dataset with its publication pending

submitter keyword: Proteomics, Microscopy, Laser Microdissection, Digital Pathology

Matthias Mann
contact affiliation	Prof. Dr. Matthias Mann Max Planck Institute of Biochemistry Am Klopferspitz 18 82152 Martinsried +49 89 8578-2557
contact email	mmann@biochem.mpg.de
lab head
Fabian Coscia
contact affiliation	University of Copenhagen NNF Center for Protein Research
contact email	fabian.coscia@cpr.ku.dk
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2022/04/PXD023904

PRIDE project URI

• PRIDE
  1. PXD023904
     1. Label: PRIDE project
     2. Name: AI-driven Deep Visual Proteomics defines cell identity and heterogeneity