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Updated: Sep 12, 2025

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
Published on: October 19, 2021
Nanoscale spatiotemporal cluster analysis of expressed and endogenous proteins
Rachel S Gormal1, Tristan P Wallis1, Alex J McCann1
1Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, St Lucia, Queensland, Australia.
Abstract:
Super-resolution microscopy has revolutionized the ability to investigate biological structures and processes, which are now accessible at nanoscale resolution. Recent advances in single-particle tracking (SPT) approaches have enabled researchers to study the intermolecular dynamics of individual proteins within their native environments in live cells. Fluorescent intrabody localization microscopy expands on existing SPT approaches such as SPT photoactivated localization microscopy by granting access to the nanoclustering dynamics of intracellular endogenous proteins through the use of single-domain nanobodies that can also differentiate between the conformational states of proteins. Here we detail how to perform single-molecule imaging of expressed proteins and nanobodies raised against endogenous proteins. We provide a streamlined analytical pipeline utilizing newly established clustering algorithms for extracting meaningful biological information. Nanoclustering analysis using spatiotemporal indexing is an open-source program with a user interface that enables the extraction of a range of dynamic nanoclustering metrics, including spatial and temporal information, from SPT data. This Protocol combines these single-molecule tracking and spatiotemporal clustering approaches into a comprehensive guide for researchers to achieve the precise localization of expressed and endogenous proteins and the characterization of their conformation-specific clustering behavior within subcellular compartments at nanoscale resolution. The procedure requires 2-4 d and is suitable for users with some prior experience in super-resolution microscopy and microscopy data analysis.
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