Deep Visual Proteomics defines single-cell identity and heterogeneity
University of Copenhagen · Novo Nordisk Foundation · +16 more institutions
Abstract
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…
Citation impact
- FWCI
- 117.20
- Percentile
- 100%
- References
- 61
Authors
21- AMAndreas MundCorresponding
University of Copenhagen, Novo Nordisk Foundation
- FCFabian Coscia
University of Copenhagen, Max Delbrück Center, Novo Nordisk Foundation
- AKAndrás Kriston
HUN-REN Szegedi Biológiai Kutatóközpont, Hungarian Research Network
- RHRéka Hollandi
HUN-REN Szegedi Biológiai Kutatóközpont, Hungarian Research Network
- FKFerenc Kovács
HUN-REN Szegedi Biológiai Kutatóközpont, Hungarian Research Network
Topics & keywords
- Proteomics
- Proteome
- Biology
- Laser capture microdissection
- Computational biology
- Quantitative proteomics
- Context (archaeology)
- Phenotype
Funding
- ECEuropean CommissionAwards: 2018-342, H2020, 846795
- BFBundesministerium für Bildung und ForschungAward: 161L0222
- HSHungarian Scientific Research Fund
- LLundbeckfondenAward: NNF14CC0001
- MMax-Planck-Gesellschaft
- NNNovo NordiskAwards: NNF14CC0001, NNF15CC0001
- NNNovo Nordisk FondenAwards: NNF15CC0001, NNF14CC0001
- HLH. Lundbeck A/S
- MFMax-Planck-Institut für Bildungsforschung
- NINational Institutes of HealthAwards: R35CA264619, H2020
- H2Horizon 2020 Framework ProgrammeAward: 846795