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RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds

Accepted version
Peer-reviewed

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Authors

Brock, Emma 
Andrews, Rebecca 
Breiter, Jonathan 
Tian, Ru 

Abstract

Super-resolution and single-molecule microscopy are increasingly applied to complex biological systems. A major challenge of this approach is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false positive puncta that other analysis methods detect, and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art in precision and speed, using image gradients to separate Gaussian-shaped objects from background. We demonstrate RASP's power by showing it can extract spatial correlations between microglia, neurons, and alpha-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments with a sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as supplementary files and links to third-party repositories.

Description

Keywords

40 Engineering, 34 Chemical Sciences, 51 Physical Sciences, Neurosciences

Journal Title

Journal of Physical Chemistry B

Conference Name

Journal ISSN

1520-6106
1520-5207

Volume Title

Publisher

American Chemical Society
Sponsorship
Michael J. Fox Foundation (MJFF) (via University College London (UCL)) (WT4152838)
Aligning Science Across Parkinson's [ASAP-000509]
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