Technology

SQUIRREL (Super-resolution Quantitative Image Rating and Reporting of Error Locations) is an analytical approach developed to provide quantitative assessment of super-resolution image quality. It allows mapping of local image errors in super-resolution reconstructions, highlighting defects and guiding optimization of imaging parameters.

The key premise behind SQUIRREL is that a good super-resolution image should be a high-precision representation of the underlying sample structure. By comparing a super-resolution image to a diffraction-limited reference image of the same volume, errors and anomalies can be identified.

The workflow involves aligning a super-resolution image with its corresponding diffraction-limited reference image, generally acquired under widefield illumination. The super-resolution image is then convolved with a representative resolution scaling function (RSF) to convert it into a diffraction-limited equivalent - the ‘resolution-scaled image’. The RSF can be automatically estimated or provided by the user. A pixel-wise comparison between the reference image and resolution-scaled image then allows generation of a quantitative error map highlighting discrepancies.

Two global metrics are also calculated by SQUIRREL – the resolution-scaled error (RSE) and resolution-scaled Pearson coefficient (RSP). The RSE represents the overall root-mean-square error between reference and resolution-scaled images. The RSP is a correlation coefficient between -1 and 1 comparing reference and resolution-scaled images, providing a quality score that enables comparison between different imaging modalities.

By highlighting regions of high dissimilarity in an intuitive heat map format, SQUIRREL allows easy identification of missing structures, incorrectly merged objects, and bright aggregates in super-resolution data. It does not rely on prior expectations of sample properties. However, SQUIRREL cannot detect very small-scale (<150 nm) artifacts due to the diffraction-limited reference image. Using a higher resolution reference image from another super-resolution technique can enable detection of smaller anomalies.

As an analytical tool, SQUIRREL has diverse applications for improving super-resolution imaging:

1) Comparing reconstructions from the same single-molecule localization microscopy (SMLM) data using different algorithms – error maps highlight issues in each reconstruction and quality metrics can rank approaches. High quality regions from different images can then be fused to create an optimal representation.

2) Empirically optimizing imaging conditions – for example finding the ideal DNA-PAINT imager strand concentration or number of frames for a dSTORM acquisition.

3) Continually monitoring image quality during acquisition for automated feedback and adaptation of system parameters.

4) Identifying and troubleshooting defects introduced at different stages of the super-resolution workflow – sample preparation, labeling, imaging conditions or analytical methods.

SQUIRREL enables robust quantitative evaluation of super-resolution image quality through comparison to a diffraction-limited standard. By mapping local inaccuracies and anomalies, it facilitates optimization of data acquisition and analysis to improve representation of the nanoscale structure. Together with quality metrics that report global error and similarity, SQUIRREL provides an analytical framework for super-resolution image validation.

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Publications featuring NanoJ-SQUIRREL

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, January 2024 Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ-eSRRF, NanoJ-SQUIRREL, NanoJ-SRRF and ZeroCostDL4Mic Funded by: CZI, EMBO, ERC, H2021 and H2022 DOI: 10.1242/jcs.261545

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, January 2023 Technologies: 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: Photonics.com, The Science Times, Optics.org and Phys.org Blogs: Springer Nature Protocols and Methods Community DOI: 10.1038/s41592-023-02057-w

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, January 2023 Technologies: NanoJ, NanoJ-eSRRF, NanoJ-SQUIRREL, NanoJ-SRRF, NanoJ-VirusMapper and NanoPyx Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022 DOI: 10.1101/2023.08.13.553080

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches Christoph Spahn, Estibaliz Gómez-de-Mariscal, Romain F. Laine, Pedro M. Pereira, Lucas von Chamier, Mia Conduit, Mariana G. Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques Paper published in Communications Biology, January 2022 Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ, NanoJ-SQUIRREL and ZeroCostDL4Mic Funded by: CZI, ERC, FCT and Wellcome Trust DOI: 10.1038/s42003-022-03634-z

Nanoscale colocalization of NK cell activating and inhibitory receptors controls signal integration David Tomaz, Pedro Matos Pereira, Nadia Guerra, Julian Dyson, Keith Gould, Ricardo Henriques Paper published in Frontiers in Immunology, January 2022 Technologies: NanoJ and NanoJ-SQUIRREL Funded by: BBSRC, EMBO and ERC DOI: 10.3389/fimmu.2022.868496

Democratising deep learning for microscopy with ZeroCostDL4Mic Lucas von Chamier, Romain F. Laine, Johanna Jukkala, Christoph Spahn, Daniel Krentzel, Elias Nehme, Martina Lerche, Sara Hernández-Pérez, Pieta K. Mattila, Eleni Karinou, Séamus Holden, Ahmet Can Solak, Alexander Krull, Tim-Oliver Buchholz, Martin L. Jones, Loïc A. Royer, Christophe Leterrier, Yoav Shechtman, Florian Jug, Mike Heilemann, Guillaume Jacquemet, Ricardo Henriques Paper published in Nature communications, January 2021 Technologies: CARE, NanoJ, NanoJ-SQUIRREL, NanoJ-SRRF and ZeroCostDL4Mic Funded by: EMBO, ERC and Wellcome Trust News: AZO Life Sciences, Drug Target Review, Nanotechnology Now and The Medical News Blogs: Microbiome Digest - Bik's Picks DOI: 10.1038/s41467-021-22518-0

An Introduction to Live-Cell Super-Resolution Imaging Siân Culley, Pedro Matos Pereira, Romain F Laine, Ricardo Henriques Book chapter published in Imaging from Cells to Animals In Vivo, January 2020 Technologies: CARE, NanoJ, NanoJ-Fluidics, NanoJ-SQUIRREL and QuickPALM DOI: 10.1201/9781315174662-4

Super‐beacons - Open‐source probes with spontaneous tuneable blinking compatible with live‐cell super‐resolution microscopy Pedro M Pereira, Nils Gustafsson, Mark Marsh, Musa M Mhlanga, Ricardo Henriques Paper published in Traffic, January 2020 Technologies: NanoJ, NanoJ-Fluidics, NanoJ-SQUIRREL, NanoJ-SRRF and Super-Beacons Funded by: BBSRC and Wellcome Trust DOI: 10.1111/tra.12728

Fluctuation-based super-resolution traction force microscopy Aki Stubb, Romain F Laine, Mitro Miihkinen, Hellyeh Hamidi, Camilo Guzmán, Ricardo Henriques, Guillaume Jacquemet, Johanna Ivaska Paper published in Nano letters, January 2020 Technologies: FBSR-TFM, NanoJ, NanoJ-SQUIRREL and NanoJ-SRRF Funded by: BBSRC DOI: 10.1021/acs.nanolett.9b04083

Automating multimodal microscopy with NanoJ-Fluidics Pedro Almada, Pedro M. Pereira, Siân Culley, Ghislaine Caillol, Fanny Boroni-Rueda, Christina L. Dix, Guillaume Charras, Buzz Baum, Romain F. Laine, Christophe Leterrier, Ricardo Henriques Paper published in Nature communications, January 2019 Technologies: NanoJ, NanoJ-Fluidics, NanoJ-SQUIRREL, NanoJ-SRRF and NanoJ-VirusMapper Funded by: BBSRC and Wellcome Trust News: Technology Times, MSN, DNYUZ and Express Informer DOI: 10.1038/s41467-019-09231-9

Artificial intelligence for microscopy - what you should know Lucas von Chamier, Romain F. Laine, Ricardo Henriques Review published in Biochemical Society Transactions, January 2019 Technologies: CARE, NanoJ, NanoJ-Fluidics, NanoJ-SQUIRREL and NanoJ-SRRF Funded by: BBSRC and Wellcome Trust News: Azooptics.com DOI: 10.1042/bst20180391

NanoJ - a high-performance open-source super-resolution microscopy toolbox Romain F Laine, Kalina L Tosheva, Nils Gustafsson, Robert D M Gray, Pedro Almada, David Albrecht, Gabriel T Risa, Fredrik Hurtig, Ann-Christin Lindås, Buzz Baum, Jason Mercer, Christophe Leterrier, Pedro M Pereira, Siân Culley, Ricardo Henriques Paper published in Journal of physics D - Applied physics, January 2019 Technologies: CARE, NanoJ, NanoJ-Fluidics, NanoJ-SQUIRREL, NanoJ-SRRF, NanoJ-VirusMapper and QuickPALM Funded by: BBSRC and Wellcome Trust DOI: 10.1088/1361-6463/ab0261

Fix your membrane receptor imaging - actin cytoskeleton and CD4 membrane organization disruption by chemical fixation Pedro M Pereira, David Albrecht, Siân Culley, Caron Jacobs, Mark Marsh, Jason Mercer, Ricardo Henriques Published in Frontiers in immunology, January 2019 Technologies: NanoJ, NanoJ-Fluidics and NanoJ-SQUIRREL Funded by: BBSRC and Wellcome Trust DOI: 10.3389/fimmu.2019.00675

Nuclear pores as versatile reference standards for quantitative superresolution microscopy Jervis Vermal Thevathasan, Maurice Kahnwald, Konstanty Cieśliński, Philipp Hoess, Sudheer Kumar Peneti, Manuel Reitberger, Daniel Heid, Krishna Chaitanya Kasuba, Sarah Janice Hoerner, Yiming Li, Yu-Le Wu, Markus Mund, Ulf Matti, Pedro Matos Pereira, Ricardo Henriques, Bianca Nijmeijer, Moritz Kueblbeck, Vilma Jimenez Sabinina, Jan Ellenberg, Jonas Ries Paper published in Nature methods, January 2019 Technologies: NanoJ, NanoJ-SQUIRREL, NanoJ-SRRF and Nuclear-Pores as references Funded by: BBSRC and Wellcome Trust News: Mirage News DOI: 10.1038/s41592-019-0574-9

Quantitative mapping and minimization of super-resolution optical imaging artifacts Siân Culley, David Albrecht, Caron Jacobs, Pedro Matos Pereira, Christophe Leterrier, Jason Mercer, Ricardo Henriques Paper published in Nature methods, January 2018 Technologies: NanoJ, NanoJ-SQUIRREL and QuickPALM Funded by: BBSRC and Wellcome Trust News: physicsworld.com DOI: 10.1038/nmeth.4605

SRRF - Universal live-cell super-resolution microscopy Siân Culley, Kalina L Tosheva, Pedro Matos Pereira, Ricardo Henriques Paper published in The international journal of biochemistry & cell biology, January 2018 Technologies: NanoJ, NanoJ-SQUIRREL and NanoJ-SRRF Funded by: BBSRC and Wellcome Trust DOI: 10.1016/j.biocel.2018.05.014

Content-aware image restoration - pushing the limits of fluorescence microscopy Martin Weigert, Uwe Schmidt, Tobias Boothe, Andreas Müller, Alexandr Dibrov, Akanksha Jain, Benjamin Wilhelm, Deborah Schmidt, Coleman Broaddus, Siân Culley, Mauricio Rocha-Martins, Fabián Segovia-Miranda, Caren Norden, Ricardo Henriques, Marino Zerial, Michele Solimena, Jochen Rink, Pavel Tomancak, Loic Royer, Florian Jug, Eugene W. Myers Paper published in Nature methods, January 2018 Technologies: CARE and NanoJ-SQUIRREL Funded by: BBSRC and Wellcome Trust News: Technology Networks, VBIO, Innovations Report and Informationsdienst Wissenschaft DOI: 10.1038/s41592-018-0216-7

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Funding contributing to NanoJ-SQUIRREL

How does membrane topology influence T-cell activation and HIV infection? Simao Coelho Funded by: FCT - Exploratory Research Projects Duration: March 2023 - September 2024

Cutting-edge super-resolution image analysis in napari through NanoJ Bruno Saraiva, Ricardo Henriques Funded by: CZI - Applications - napari Plugin Foundations grants Duration: January 2023 - December 2023 Publications: 3

Real-Time high-content Super-Resolution Imaging of ES Cell States Eran Meshorer, Ricardo Henriques, Anna Kreshuk, Sandrine Lévêque-Fort, Nicolas Bourg, Genevieve Almouzni Alias: RT-SuperES Funded by: H2022 - EIC Pathfinder Open Duration: April 2022 - March 2027 Publications: 10

Enabling Live-Cell 4D Super-Resolution Microscopy Guided by Artificial Intelligence Ricardo Henriques Alias: SelfDriving4DSR Funded by: ERC - Consolidator Duration: September 2021 - September 2026 Publications: 28

Optial Biology PhD programme Michael Hausser, Ricardo Henriques, Antonella Riccio Funded by: Wellcome Trust - 4-year PhD Programme in Science Duration: August 2021 - August 2025

Unveiling live-cell viral replication at the nanoscale Ricardo Henriques Funded by: EMBO - Installation Grant Duration: January 2021 - January 2026 Publications: 21