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Unterauer, E.M.* ; Schentarra, E.M.* ; Jevdokimenko, K.* ; Shetab Boushehri, S. ; Marr, C. ; Opazo, F.* ; Fornasiero, E.F.* ; Jungmann, R.*

Protocol for SUM-PAINT spatial proteomic imaging generating neuronal architecture maps in rat hippocampal neurons.

STAR Protoc. 6:103637 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
To unravel the complexity of biological processes, it is necessary to resolve the underlying protein organization down to single proteins. Here, we present a protocol for secondary label-based unlimited multiplexed DNA-PAINT (SUM-PAINT), a DNA-PAINT-based super-resolution microscopy technique that is capable of resolving virtually unlimited protein species with single-protein resolution. We describe the steps to prepare neuronal cultures, troubleshoot and conduct SUM-PAINT experiments, and analyze the resulting feature-rich neuronal cell atlases using unsupervised machine learning approaches. For complete details on the use and execution of this protocol, please refer to Unterauer et al.1.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Cell Biology ; Cell Culture ; Microscopy ; Neuroscience; Microscopy
Language english
Publication Year 2025
HGF-reported in Year 2025
e-ISSN 2666-1667
Journal STAR Protocols
Quellenangaben Volume: 6, Issue: 1, Pages: , Article Number: 103637 Supplement: ,
Publisher Elsevier
Publishing Place Radarweg 29, 1043 Nx Amsterdam, Netherlands
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540007-001
Grants BMBF
Max Planck Foundation
Max Planck Society
IMPRS-ML graduate school
Deutsche Forschungsgemeinschaft (DFG)
CZI collaborative pair grant
Collaborative Research Center 1286 on Quantitative Synaptologie , Gottingen, Germany
F. Hoffmann-La Roche LTD
European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program
Helmholtz Association under the joint research school "Munich School for Data Science-MUDS"

European Research Council through an ERC Consolidator Grant (ReceptorPAINT)
DOI 10.1016/j.xpro.2025.103637
WOS ID 001441602400001
Scopus ID 85219535766
PubMed ID 40048420
Erfassungsdatum 2025-05-05
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