Spatial and stoichiometric in situ analysis of biomolecular oligomerization at single-protein resolution

Latest advances in super-resolution microscopy allow the study of subcellular features at the level of single proteins, which could lead to discoveries in fundamental biological processes, specifically in cell signaling mediated by membrane receptors. Despite these advances, accurately extracting quantitative information on molecular arrangements of proteins at the 1-20 nm scale through rigorous image analysis remains a significant challenge. Here, we present SPINNA (Single-Protein Investigation via Nearest-Neighbor Analysis): an analysis framework that compares nearest-neighbor distances from experimental single-protein position data with those obtained from realistic simulations based on a user-defined model of protein oligomerization states. We demonstrate SPINNA in silico, in vitro, and in cells. In particular, we quantitatively assess the oligomerization of the epidermal growth factor receptor (EGFR) upon EGF treatment and investigate the dimerization of CD80 and PD-L1, key surface ligands involved in immune cell signaling. Importantly, we offer an open-source Python implementation and a GUI to facilitate SPINNA's widespread use in the scientific community.

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Publication Details

Journal
Nature Communications
Published
2025-05-06
DOI
https://doi.org/10.1038/s41467-025-59500-z
Citations
11
Primary Topic
Advanced Fluorescence Microscopy Techniques
Type
article
Field-Weighted Citation Impact
17.97

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article

Spatial and stoichiometric in situ analysis of biomolecular oligomerization at single-protein resolution

Rafal Kowalewski, Ian A. Parish, Maartje M. C. Bastings, Ross A. Dickins et al.
11 citations
Nature Communications
Advanced Fluorescence Microscopy Techniques
17.97
article

Spatial and stoichiometric in situ analysis of biomolecular oligomerization at single-protein resolution

Rafal Kowalewski, Ian A. Parish, Maartje M. C. Bastings, Ross A. Dickins, S. Reinhardt, Heinrich Grabmayr, Philipp R. Steen, Ralf Jungmann, Isabelle Pachmayr, Jisoo Kwon, Luciano A. Masullo, Ethan P. Oxley, Larissa Heinze, Ana Perovic, Monique Honsa, Shuhan Xu
article en
11 citations

Abstract

Latest advances in super-resolution microscopy allow the study of subcellular features at the level of single proteins, which could lead to discoveries in fundamental biological processes, specifically in cell signaling mediated by membrane receptors. Despite these advances, accurately extracting quantitative information on molecular arrangements of proteins at the 1-20 nm scale through rigorous image analysis remains a significant challenge. Here, we present SPINNA (Single-Protein Investigation via Nearest-Neighbor Analysis): an analysis framework that compares nearest-neighbor distances from experimental single-protein position data with those obtained from realistic simulations based on a user-defined model of protein oligomerization states. We demonstrate SPINNA in silico, in vitro, and in cells. In particular, we quantitatively assess the oligomerization of the epidermal growth factor receptor (EGFR) upon EGF treatment and investigate the dimerization of CD80 and PD-L1, key surface ligands involved in immune cell signaling. Importantly, we offer an open-source Python implementation and a GUI to facilitate SPINNA's widespread use in the scientific community.

Nature CommunicationsVol. 16(1)
The University of Melbourne (AU), Peter MacCallum Cancer Centre (AU), Center for NanoScience (DE), Australian Centre for HIV and Hepatitis Virology Research (AU), Max Planck Institute of Psychiatry (DE), Max Planck Institute of Biochemistry (DE), École Polytechnique Fédérale de Lausanne (CH), Monash University (AU), Ludwig-Maximilians-Universität München (DE)
Australian Government, Max-Planck-Förderstiftung, European Commission, National Research Foundation, Volkswagen Foundation, Danmarks Grundforskningsfond, Bundesministerium für Bildung und Forschung, Max-Planck-Gesellschaft, International Max Planck Research School for Advanced Methods in Process and Systems Engineering
Openalex Percentile: Top 1%
Advanced Fluorescence Microscopy Techniques
17.97
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