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review article

Live-cell imaging powered by computation

Shroff, Hari
•
Testa, Ilaria
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Jug, Florian
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February 20, 2024
Nature Reviews Molecular Cell Biology

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Computational methods can help to address this challenge and are now shifting the boundaries of what is possible to capture in living systems. In this Review, we discuss these computational methods focusing on artificial intelligence-based approaches that can be layered on top of commonly used existing microscopies as well as hybrid methods that integrate computation and microscope hardware. We specifically discuss how computational approaches can improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.|The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preserving cell viability. Alongside developments of microscopy hardware, computational methods - especially those based on machine learning - are powerful tools to improve the signal-to-noise ratio, spatial resolution, temporal resolution and multi-colour capacity of live-cell imaging.

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Type
review article
DOI
10.1038/s41580-024-00702-6
Web of Science ID

WOS:001168926800001

Author(s)
Shroff, Hari
•
Testa, Ilaria
•
Jug, Florian
•
Manley, Suliana  
Date Issued

2024-02-20

Publisher

Nature Portfolio

Published in
Nature Reviews Molecular Cell Biology
Subjects

Life Sciences & Biomedicine

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Fluorescence Microscopy

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Superresolution Microscopy

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Long-Term

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Resolution

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Deep

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Deconvolution

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Tracking

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Nanoscopy

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Phototoxicity

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Software

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LEB  
FunderGrant Number

Howard Hughes Medical Institute (HHMI)

Ecole Polytechnique Federale de Lausanne (EPFL)

Available on Infoscience
March 18, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/206519
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