SRRF: Universal live-cell super-resolution microscopy DOI Creative Commons
S J Culley, Kalina L. Tosheva, Pedro M. Pereira

et al.

The International Journal of Biochemistry & Cell Biology, Journal Year: 2018, Volume and Issue: 101, P. 74 - 79

Published: May 28, 2018

Super-resolution microscopy techniques break the diffraction limit of conventional optical to achieve resolutions approaching tens nanometres. The major advantage such is that they provide close those obtainable with electron while maintaining benefits light as a wide palette high specificity molecular labels, straightforward sample preparation and live-cell compatibility. Despite this, application super-resolution dynamic, living samples has thus far been limited often requires specialised, complex hardware. Here we demonstrate how novel analytical approach, Super-Resolution Radial Fluctuations (SRRF), able make accessible wider range researchers. We show its applicability live expressing GFP using commercial confocal well laser- LED-based widefield microscopes, latter achieving long-term timelapse imaging minimal photobleaching.

Language: Английский

Deep learning enables structured illumination microscopy with low light levels and enhanced speed DOI Creative Commons
Luhong Jin, Bei Liu, Fenqiang Zhao

et al.

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: April 22, 2020

Abstract Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over limited microscopy. However, it requires both intense multiple acquisitions to produce single high-resolution image. Using deep learning augment SIM, we obtain five-fold reduction number of raw images required for super-resolution generate under extreme low light conditions (at least 100× fewer photons). We validate performance neural networks on different cellular structures achieve multi-color, live-cell imaging with greatly reduced photobleaching.

Language: Английский

Citations

207
DeepImageJ: A user-friendly environment to run deep learning models in ImageJ DOI
Estibaliz Gómez‐de‐Mariscal,

Carlos García-López-de-Haro,

Wei Ouyang

et al.

Nature Methods, Journal Year: 2021, Volume and Issue: 18(10), P. 1192 - 1195

Published: Sept. 30, 2021

Language: Английский

Citations

195
Deep learning enables fast and dense single-molecule localization with high accuracy DOI
Artur Speiser, Lucas-Raphael Müller, Philipp Hoess

et al.

Nature Methods, Journal Year: 2021, Volume and Issue: 18(9), P. 1082 - 1090

Published: Sept. 1, 2021

Language: Английский

Citations

189
CLIJ: GPU-accelerated image processing for everyone DOI Open Access
Robert Haase, Loïc A. Royer, Peter Steinbach

et al.

Nature Methods, Journal Year: 2019, Volume and Issue: 17(1), P. 5 - 6

Published: Nov. 18, 2019

Language: Английский

Citations

187
SRRF: Universal live-cell super-resolution microscopy DOI Creative Commons
S J Culley, Kalina L. Tosheva, Pedro M. Pereira

et al.

The International Journal of Biochemistry & Cell Biology, Journal Year: 2018, Volume and Issue: 101, P. 74 - 79

Published: May 28, 2018

Super-resolution microscopy techniques break the diffraction limit of conventional optical to achieve resolutions approaching tens nanometres. The major advantage such is that they provide close those obtainable with electron while maintaining benefits light as a wide palette high specificity molecular labels, straightforward sample preparation and live-cell compatibility. Despite this, application super-resolution dynamic, living samples has thus far been limited often requires specialised, complex hardware. Here we demonstrate how novel analytical approach, Super-Resolution Radial Fluctuations (SRRF), able make accessible wider range researchers. We show its applicability live expressing GFP using commercial confocal well laser- LED-based widefield microscopes, latter achieving long-term timelapse imaging minimal photobleaching.

Language: Английский

Citations

179