Real-Time high-content Super-Resolution Imaging of ES Cell States

Funding Image
Alias: RT-SuperES
Agency: H2022
Type: EIC Pathfinder Open
Principal Investigator: Eran Meshorer
Investigators: Eran Meshorer, Ricardo Henriques, Anna Kreshuk, Sandrine Lévêque-Fort, Nicolas Bourg, Genevieve Almouzni
Start-date: April 2022
End-date: March 2027
DOI: 10.3030/101099654

RT-SuperES is a project that creates a novel microscope equipped with machine learning-based automated decision making, allowing high-content imaging of embryonic stem cells in real-time. By combining conventional and super-resolution imaging, it explores cellular processes during differentiation at a nanoscale level. It will use cutting-edge technologies such as SNAP-tagging, SRRF, SMLM, SIM, NanoJ-Fluidics, and AI algorithms. The project brings together a multidisciplinary team across four countries to establish this groundbreaking and affordable imaging technology.

Technology explored

BioImage Model Zoo BioImage Model Zoo
NanoJ-Fluidics NanoJ-Fluidics
Super-Beacons Super-Beacons
ZeroCostDL4Mic ZeroCostDL4Mic

Supported publications

Harnessing artificial intelligence to reduce phototoxicity in live imaging
Estibaliz Gómez-de-Mariscal, Mario Del Rosario, Joanna W. Pylvänäinen, Guillaume Jacquemet, Ricardo Henriques
Perspective published in Journal of Cell Science, February 2024
Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ-SRRF and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC, H2021 and H2022
DOI: 10.1242/jcs.261545
The Rise of Data-Driven Microscopy powered by Machine Learning
Leonor Morgado, Estibaliz Gómez-de-Mariscal, Hannah S Heil, Ricardo Henriques
Preprint published in arXiv, January 2024
Technologies: BioImage Model Zoo, DL4MicEverywhere, NanoJ, NanoJ-Fluidics and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022
DOI: 10.48550/arXiv.2401.05282
DL4MicEverywhere - Deep learning for microscopy made flexible, shareable, and reproducible
Iván Hidalgo-Cenalmor, Joanna W Pylvänäinen, Mariana G Ferreira, Craig T Russell, Ignacio Arganda-Carreras, AI4Life Consortium, Guillaume Jacquemet, Ricardo Henriques, Estibaliz Gómez-de-Mariscal
Preprint published in bioRxiv, November 2023
Technologies: BioImage Model Zoo, DeepBacs, DL4MicEverywhere and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC, H2021 and H2022
DOI: 10.1101/2023.11.19.567606
High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation
Romain F. Laine, Hannah S. Heil, Simao Coelho, Jonathon Nixon-Abell, Angélique Jimenez, Theresa Wiesner, Damián Martínez, Tommaso Galgani, Louise Régnier, Aki Stubb, Gautier Follain, Samantha Webster, Jesse Goyette, Aurelien Dauphin, Audrey Salles, Siân Culley, Guillaume Jacquemet, Bassam Hajj, Christophe Leterrier, Ricardo Henriques
Paper published in Nature Methods, November 2023
Technologies: CARE, NanoJ, NanoJ-eSRRF, NanoJ-SQUIRREL, NanoJ-SRRF, NanoPyx and Nuclear-Pores as references
Funded by: CZI, EMBO, ERC, FCT, H2021, H2022, InnOValley and Wellcome Trust
News:, The Science Times, and
Blogs: Springer Nature Protocols and Methods Community
DOI: 10.1038/s41592-023-02057-w
Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth
Christoph Spahn, Stuart Middlemiss, Estibaliz Gómez-de-Mariscal, Ricardo Henriques, Helge B. Bode, Séamus Holden, Mike Heilemann
Preprint published in bioRxiv, October 2023
Technologies: CARE, DeepBacs and ZeroCostDL4Mic
Funded by: EMBO, ERC, H2021 and H2022
DOI: 10.1101/2023.10.16.562172
NanoPyx - super-fast bioimage analysis powered by adaptive machine learning
Bruno M. Saraiva, Inês M. Cunha, António D. Brito, Gautier Follain, Raquel Portela, Robert Haase, Pedro M. Pereira, Guillaume Jacquemet, Ricardo Henriques
Preprint published in bioRxiv, August 2023
Technologies: CARE, NanoJ, NanoJ-eSRRF, NanoJ-SQUIRREL, NanoJ-SRRF, NanoJ-VirusMapper and NanoPyx
Funded by: CZI, EMBO, ERC, H2021 and H2022
DOI: 10.1101/2023.08.13.553080