RT Journal Article
SR Electronic
T1 Deep learning enables fast and dense single-molecule localization with high accuracy
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.10.26.355164
DO 10.1101/2020.10.26.355164
A1 Artur Speiser
A1 Lucas-Raphael Müller
A1 Ulf Matti
A1 Christopher J. Obara
A1 Wesley R. Legant
A1 Anna Kreshuk
A1 Jakob H. Macke
A1 Jonas Ries
A1 Srinivas C. Turaga
YR 2020
UL http://biorxiv.org/content/early/2020/10/26/2020.10.26.355164.abstract
AB Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.