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Unterauer, E.M.* ; Shetab Boushehri, S. ; Jevdokimenko, K.* ; Masullo, L.A.* ; Ganji, M.* ; Sograte-Idrissi, S.* ; Kowalewski, R.* ; Strauss, S.* ; Reinhardt, S.C.M.* ; Perovic, A.* ; Marr, C. ; Opazo, F.* ; Fornasiero, E.F.* ; Jungmann, R.*

Spatial proteomics in neurons at single-protein resolution.

Cell 187, 1785-1800.e16 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
To understand biological processes, it is necessary to reveal the molecular heterogeneity of cells by gaining access to the location and interaction of all biomolecules. Significant advances were achieved by super-resolution microscopy, but such methods are still far from reaching the multiplexing capacity of proteomics. Here, we introduce secondary label-based unlimited multiplexed DNA-PAINT (SUM-PAINT), a high-throughput imaging method that is capable of achieving virtually unlimited multiplexing at better than 15 nm resolution. Using SUM-PAINT, we generated 30-plex single-molecule resolved datasets in neurons and adapted omics-inspired analysis for data exploration. This allowed us to reveal the complexity of synaptic heterogeneity, leading to the discovery of a distinct synapse type. We not only provide a resource for researchers, but also an integrated acquisition and analysis workflow for comprehensive spatial proteomics at single-protein resolution.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Dna-paint ; Excitatory Synapses ; Inhibitory Synapses ; Multiplexing ; Neuron Atlas ; Neuron Imaging ; Proteomics ; Spatial-omics ; Super-resolution Microscopy ; Synapse ; Synapse Diversity ; Synaptic Proteins; Superresolution Microscopy; Dna-paint; Release; Binding
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0092-8674
e-ISSN 1097-4172
Zeitschrift Cell
Quellenangaben Band: 187, Heft: 7, Seiten: 1785-1800.e16 Artikelnummer: , Supplement: ,
Verlag Cell Press
Verlagsort Cambridge, Mass.
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
Förderungen ERC
BMBF
Max Planck Foundation
Max Planck Society
QBM graduate school
IMPRS-ML graduate school
European Union
Deutsche Forschungsgemeinschaft (DFG)
Schram Stiftung
Hightech Agenda Bayern
Collaborative Research Center 1286 on Quantitative Synaptologie Gottingen, Germany
F. Hoffmann-La Roche
Helmholtz Association
European Research Council
DOI 10.1016/j.cell.2024.02.045
WOS ID 001228149900001
Scopus ID 85188558512
PubMed ID 38552614
Erfassungsdatum 2024-04-25
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