Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping

Ha H. Park; Bowen Wang; Suhong Moon; Tyler Jepson; Ke Xu

doi:10.1038/s42003-023-04729-x

Communications Biology (Mar 2023)

Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping

Ha H. Park,
Bowen Wang,
Suhong Moon,
Tyler Jepson,
Ke Xu

Affiliations

Ha H. Park: Department of Chemistry, University of California
Bowen Wang: Department of Chemistry, University of California
Suhong Moon: Department of Electrical Engineering and Computer Sciences, University of California
Tyler Jepson: QB3-Berkeley, University of California
Ke Xu: Department of Chemistry, University of California

DOI: https://doi.org/10.1038/s42003-023-04729-x
Journal volume & issue: Vol. 6, no. 1
pp. 1 – 8

Abstract

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A machine-learning-enabled approach, pixels-to-diffusivity (Pix2D), directly extracts the diffusion coefficient D from single molecule images and enables super-resolved D spatial mapping.

Published in Communications Biology

ISSN: 2399-3642 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science: Biology (General)
Website: https://www.nature.com/commsbio/

About the journal