Live-cell imaging in the deep learning era

Joanna W. Pylvänäinen; Estibaliz Gómez-de-Mariscal; Ricardo Henriques; Guillaume Jacquemet

doi:10.1016/j.ceb.2023.102271

Live-cell imaging in the deep learning era

Joanna W. Pylvänäinen
, Estibaliz Gómez-de-Mariscal
, Ricardo Henriques
, Guillaume Jacquemet

Biochemistry and Cell Biology

Research output: Contribution to journal › Review Article or Literature Review › peer-review

53 Citations (Scopus)

322 Downloads (Pure)

Abstract

Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.

Original language	English
Article number	102271
Number of pages	13
Journal	Current Opinion in Cell Biology
Volume	85
DOIs	https://doi.org/10.1016/j.ceb.2023.102271
Publication status	Published - Dec 2023
MoE publication type	A2 Review article in a scientific journal

Funding

This study was supported by the Academy of Finland ( 338537 to G.J.), the Sigrid Juselius Foundation (to G.J.), the Cancer Society of Finland (Syöpäjärjestöt; to G.J.), and the Solutions for Health strategic funding to Åbo Akademi University (to G.J.). E.G.M. and R.H. are supported by the Gulbenkian Foundation (Fundação Calouste Gulbenkian), the European Molecular Biology Organization Installation Grant ( EMBO -2020- IG4734 granted to R.H.) and Postdoctoral Fellowship (EMBO ALTF 174-2022 granted to E.G.M.), and the European Commission through the Horizon Europe program (AI4LIFE project, grant agreement 101057970-AI4LIFE to R.H.). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. R.H. also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement number 101001332 to R.H.), the Chan Zuckerberg Initiative Visual Proteomics Grant (vpi- 0000000044 ). This research was supported by InFLAMES Flagship Programme of the Academy of Finland (decision number: 337531 ). This study was supported by the Academy of Finland (338537 to G.J.), the Sigrid Juselius Foundation (to G.J.), the Cancer Society of Finland (Syöpäjärjestöt; to G.J.), and the Solutions for Health strategic funding to Åbo Akademi University (to G.J.). E.G.M. and R.H. are supported by the Gulbenkian Foundation (Fundação Calouste Gulbenkian), the European Molecular Biology Organization Installation Grant (EMBO-2020-IG4734 granted to R.H.) and Postdoctoral Fellowship (EMBO ALTF 174-2022 granted to E.G.M.), and the European Commission through the Horizon Europe program (AI4LIFE project, grant agreement 101057970-AI4LIFE to R.H.). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. R.H. also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement number 101001332 to R.H.), the Chan Zuckerberg Initiative Visual Proteomics Grant (vpi-0000000044). This research was supported by InFLAMES Flagship Programme of the Academy of Finland (decision number: 337531).

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10.1016/j.ceb.2023.102271

1-s2.0-S0955067423001205-mainFinal published version, 5.14 MBLicence: CC BY

https://urn.fi/URN:NBN:fi-fe20231127149339

Cite this

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title = "Live-cell imaging in the deep learning era",

abstract = "Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.",

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AU - Pylvänäinen, Joanna W.

AU - Gómez-de-Mariscal, Estibaliz

AU - Henriques, Ricardo

AU - Jacquemet, Guillaume

PY - 2023/12

Y1 - 2023/12

N2 - Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.

AB - Live imaging is a powerful tool, enabling scientists to observe living organisms in real time. In particular, when combined with fluorescence microscopy, live imaging allows the monitoring of cellular components with high sensitivity and specificity. Yet, due to critical challenges (i.e., drift, phototoxicity, dataset size), implementing live imaging and analyzing the resulting datasets is rarely straightforward. Over the past years, the development of bioimage analysis tools, including deep learning, is changing how we perform live imaging. Here we briefly cover important computational methods aiding live imaging and carrying out key tasks such as drift correction, denoising, super-resolution imaging, artificial labeling, tracking, and time series analysis. We also cover recent advances in self-driving microscopy.

UR - https://doi.org/10.1016/j.ceb.2023.102271

U2 - 10.1016/j.ceb.2023.102271

DO - 10.1016/j.ceb.2023.102271

M3 - Review Article or Literature Review

SN - 0955-0674

VL - 85

JO - Current Opinion in Cell Biology

JF - Current Opinion in Cell Biology

M1 - 102271

ER -

Live-cell imaging in the deep learning era

Abstract

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