OME-Zarr: a cloud-optimized bioimaging file format with international community support

doi:10.1101/2023.02.17.528834

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[Preprint]. 2023 May 7:2023.02.17.528834.

doi: 10.1101/2023.02.17.528834.

OME-Zarr: a cloud-optimized bioimaging file format with international community support

Josh Moore¹, Daniela Basurto-Lozada², Sébastien Besson³, John Bogovic⁴, Jordão Bragantini⁵, Eva M Brown⁶, Jean-Marie Burel³, Xavier Casas Moreno⁷, Gustavo de Medeiros⁸, Erin E Diel⁹, David Gault³, Satrajit S Ghosh¹⁰, Ilan Gold¹¹, Yaroslav O Halchenko¹², Matthew Hartley¹³, Dave Horsfall², Mark S Keller¹¹, Mark Kittisopikul⁴, Gabor Kovacs¹⁴, Aybüke Küpcü Yoldaş¹³, Koji Kyoda¹⁵, Albane le Tournoulx de la Villegeorges¹⁶, Tong Li¹⁷, Prisca Liberali⁸, Dominik Lindner³, Melissa Linkert⁹, Joel Lüthi⁸, Jeremy Maitin-Shepard¹⁸, Trevor Manz¹¹, Luca Marconato¹⁹, Matthew McCormick²⁰, Merlin Lange⁵, Khaled Mohamed³, William Moore³, Nils Norlin²¹, Wei Ouyang⁷, Bugra Özdemir²², Giovanni Palla²³, Constantin Pape²⁴, Lucas Pelkmans²⁵, Tobias Pietzsch⁴, Stephan Preibisch⁴, Martin Prete¹⁷, Norman Rzepka¹⁶, Sameeul Samee²⁶, Nicholas Schaub²⁷, Hythem Sidky²⁶, Ahmet Can Solak⁵, David R Stirling⁹, Jonathan Striebel¹⁶, Christian Tischer²⁸, Daniel Toloudis⁶, Isaac Virshup²³, Petr Walczysko³, Alan M Watson²⁹, Erin Weisbart³⁰, Frances Wong³, Kevin A Yamauchi³¹, Omer Bayraktar¹⁷, Beth A Cimini³⁰, Nils Gehlenborg¹¹, Muzlifah Haniffa¹⁷, Nathan Hotaling²⁷, Shuichi Onami¹⁵, Loic A Royer⁵, Stephan Saalfeld⁴, Oliver Stegle¹⁹, Fabian J Theis²³, Jason R Swedlow³

Affiliations

¹ German BioImaging - Gesellschaft für Mikroskopie und Bildanalyse e.V., Konstanz, Germany.
² Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
³ Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK.
⁴ Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
⁵ Chan Zuckerberg Biohub, San Francisco, CA, United States.
⁶ Allen Institute for Cell Science, Seattle, WA, USA.
⁷ Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
⁸ Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland.
⁹ Glencoe Software Inc., Seattle, WA, USA.
¹⁰ Massachusetts Institute of Technology: Cambridge, MA, USA.
¹¹ Harvard Medical School, Boston, MA, USA.
¹² Dartmouth College, Hanover, NH, USA.
¹³ European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK.
¹⁴ Allen Institute for Neural Dynamics, Seattle, WA, USA.
¹⁵ RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
¹⁶ scalable minds GmbH, Potsdam, Germany.
¹⁷ Wellcome Sanger Institute, Hinxton, UK.
¹⁸ Google Research, Mountain View, CA, USA.
¹⁹ European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
²⁰ Kitware, Inc, Carrboro, NC, USA.
²¹ Department of Experimental Medical Science & Lund Bioimaging Centre, Lund University, Lund, Sweden.
²² Euro-BioImaging Bio-Hub, EMBL Heidelberg, Germany.
²³ Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
²⁴ Georg-August Universität Göttingen, Germany.
²⁵ University of Zurich, Zurich, Switzerland.
²⁶ Axle Research and Technology.
²⁷ Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health.
²⁸ EMBL Heidelberg, Germany.
²⁹ University of Pittsburgh, Pittsburgh, PA, USA.
³⁰ Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
³¹ Department of Biosystems Science and Engineering, ETH Zürich, Switzerland.

PMID: 36865282
PMCID: PMC9980008
DOI: 10.1101/2023.02.17.528834

OME-Zarr: a cloud-optimized bioimaging file format with international community support

Josh Moore et al. bioRxiv. 2023.

[Preprint]. 2023 May 7:2023.02.17.528834.

doi: 10.1101/2023.02.17.528834.

Authors

Affiliations

¹ German BioImaging - Gesellschaft für Mikroskopie und Bildanalyse e.V., Konstanz, Germany.
² Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
³ Divisions of Molecular Cell and Developmental Biology, and Computational Biology, University of Dundee, Dundee, Scotland, UK.
⁴ Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
⁵ Chan Zuckerberg Biohub, San Francisco, CA, United States.
⁶ Allen Institute for Cell Science, Seattle, WA, USA.
⁷ Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
⁸ Friedrich Miescher Institute for Biomedical Imaging, Basel, Switzerland.
⁹ Glencoe Software Inc., Seattle, WA, USA.
¹⁰ Massachusetts Institute of Technology: Cambridge, MA, USA.
¹¹ Harvard Medical School, Boston, MA, USA.
¹² Dartmouth College, Hanover, NH, USA.
¹³ European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Cambridge, UK.
¹⁴ Allen Institute for Neural Dynamics, Seattle, WA, USA.
¹⁵ RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
¹⁶ scalable minds GmbH, Potsdam, Germany.
¹⁷ Wellcome Sanger Institute, Hinxton, UK.
¹⁸ Google Research, Mountain View, CA, USA.
¹⁹ European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany.
²⁰ Kitware, Inc, Carrboro, NC, USA.
²¹ Department of Experimental Medical Science & Lund Bioimaging Centre, Lund University, Lund, Sweden.
²² Euro-BioImaging Bio-Hub, EMBL Heidelberg, Germany.
²³ Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
²⁴ Georg-August Universität Göttingen, Germany.
²⁵ University of Zurich, Zurich, Switzerland.
²⁶ Axle Research and Technology.
²⁷ Information Technology Branch, National Center for Advancing Translational Science, National Institutes of Health.
²⁸ EMBL Heidelberg, Germany.
²⁹ University of Pittsburgh, Pittsburgh, PA, USA.
³⁰ Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
³¹ Department of Biosystems Science and Engineering, ETH Zürich, Switzerland.

PMID: 36865282
PMCID: PMC9980008
DOI: 10.1101/2023.02.17.528834

Update in

OME-Zarr: a cloud-optimized bioimaging file format with international community support.
Moore J, Basurto-Lozada D, Besson S, Bogovic J, Bragantini J, Brown EM, Burel JM, Casas Moreno X, de Medeiros G, Diel EE, Gault D, Ghosh SS, Gold I, Halchenko YO, Hartley M, Horsfall D, Keller MS, Kittisopikul M, Kovacs G, Küpcü Yoldaş A, Kyoda K, le Tournoulx de la Villegeorges A, Li T, Liberali P, Lindner D, Linkert M, Lüthi J, Maitin-Shepard J, Manz T, Marconato L, McCormick M, Lange M, Mohamed K, Moore W, Norlin N, Ouyang W, Özdemir B, Palla G, Pape C, Pelkmans L, Pietzsch T, Preibisch S, Prete M, Rzepka N, Samee S, Schaub N, Sidky H, Solak AC, Stirling DR, Striebel J, Tischer C, Toloudis D, Virshup I, Walczysko P, Watson AM, Weisbart E, Wong F, Yamauchi KA, Bayraktar O, Cimini BA, Gehlenborg N, Haniffa M, Hotaling N, Onami S, Royer LA, Saalfeld S, Stegle O, Theis FJ, Swedlow JR. Moore J, et al. Histochem Cell Biol. 2023 Sep;160(3):223-251. doi: 10.1007/s00418-023-02209-1. Epub 2023 Jul 10. Histochem Cell Biol. 2023. PMID: 37428210 Free PMC article.

Abstract

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

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Conflict of interest statement

Competing Interests:

S.B., E.D., M.L., D.R.S. and J.R.S. are affiliated with Glencoe Software, a commercial company that builds, delivers, supports and integrates image data management systems across academic, biotech and pharmaceutical industries; J.M. and W.M. also hold equity in Glencoe Software. M.M. is affiliated with Kitware, Inc., a commercial company built around open-source platforms that provides advanced technical computing, state-of-the-art AI, and tailored software solutions to academic, government, and industrial customers. A.V., J.S. and N.R. are affiliated with Scalable Minds, a commercial company that builds, delivers, supports and integrates image analysis solutions. F.J.T. consults for Immunai Inc., Singularity Bio B.V., CytoReason Ltd, Cellarity, and Omniscope Ltd, and has ownership interest in Dermagnostix GmbH and Cellarity. N.A.H. and N.J.S. are contractors who work for Axle Research and Technology. S.B.S. and H.S. are affiliated with Axle Research and Technology and are contracted to the National Center for Advancing Translational Science, NIH. The remaining authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported.

Figures

**Fig. 1**
A common format enables a diverse set of use cases via a consistent API. A wide range of modalities can be converted into a representation that can be equally accessed by a variety of tools. This format can be used to download entire datasets for local processing, to stream pyramidal sub-resolutions for interactive viewing or to process entire resolutions in parallel. OME-Zarr data shown includes (A) idr0076 (Ali et al. 2020), (B) idr0101 (Payne et al. 2021), (C) idr0077 (Valuchova et al. 2020), (D) S-BIAD548 (Lim et al. 2023), (E) S-BIAD217 (de Boer et al. 2020), and (F) S-BIAD501 (Igarashi et al. 2015).

**Fig. 2**
By making use of an annotated hierarchy of arrays, OME-Zarr can represent complex relationships between images, capture the multiple resolutions of an image pyramid, and provide tunable chunk size and compression all within a single abstraction layer that can be saved as a directory of files on disk or shared remotely. (A) Each level of nested directories provides a different level of abstraction: the top-level directory can represent an entire 1 Terabyte plate with more than 100,000 pixels in the X and Y dimensions, while the lowest level directory represents individual chunks of N-dimensional data as small as 1 Megabyte. (B) In the example shown, a concatenation of low-resolution images produces a 2560 pixels x 1822 pixel representation of the entire plate, followed by similar examples of how many pixels must be loaded by a client at each zoom level.

**Fig. 3**
The original viewers of OME-Zarr published in Moore et al. 2021, from left to right BigDataViewer, napari, and Vizarr, here seen loading a view of the same EM volume of a 6 day old Platynereis larva from (Vergara et al. 2020) available at https://s3.embl.de/i2k-2020/platy-raw.ome.zarr. These three applications provided broad coverage over the most common bioimaging platforms like Fiji and napari but critically also a web viewer that could stream data on the fly.

**Fig. 4**
Advanced GPU Accelerated Volume Explorer (AGAVE) displaying a downsampled level from a multi-terabyte mouse brain OME-Zarr dataset. The number of pixels actually loaded is displayed at lower right. The full resolution data is 47310 × 20344 × 18471 which consumes about 33TB. The ability to quickly access multiresolution data makes low latency interactive visualization possible.

**Fig. 5**
ITKWidgets 3D rendering an OME-Zarr for IDR 0062A in Jupyter. Interactive features shown include volume rendering, slicing planes, and interactive widgets to adjust rendering parameters and slice planes indices.

**Fig. 6**
Neuroglancer rendering the same OME-Zarr from IDR 0062A as Fig. 5

**Fig. 7**
OME-NGFF Validator validating the same image from IDR 0062A on the left, and on the right providing a summary of the size of the data as well as providing a quick visualization of a single plane.

**Fig. 8**
webKnossos loading an EM volume of a 6 day old Platynereis larva from (Vergara et al. 2020) with a manually added segmentation. The web accessible version is accessible at https://wklink.org/6422.

**Fig. 9**
Allen Cell Volume Viewer displaying a multichannel fluorescence image of gene edited hiPSC cells via a downsampled level of an OME-Zarr converted from the dataset found at https://cfe.allencell.org/?dataset=variance_v1

**Fig. 10**
Example application of BigStitcher’s interest point based registration on one of the first “exaSPIM” lightsheet microscope datasets acquired at the Allen Institute for Neural Dynamics (sample 609281, available at the link in Table 2). The overlapping regions of two tiles are shown in BDV at their nominal (left) and aligned (right) locations. This large scale NGFF dataset consists of 54 tiles with dimensions of 24576 × 10656 × 2048 voxels (about 1 TB raw size) each.

**Fig. 11**
Visualization of a MERFISH mouse brain dataset (Allen Institute prototype MERFISH pipeline (Long et al. 2023)) via the napari-spatialdata plugin, featuring single-molecule transcripts (points) and their rasterized representation (image), polygonal ROIs, and annotated cells approximated as circles with variable radii. The dataset has been converted to OME-Zarr with the SpatialData APIs.

**Fig. 12**
NGFF-Converter GUI showing a sample of input formats being converted to OME-Zarr

**Fig. 13**
An OME-Zarr dataset on DANDI (ID: DANDI:000108) contains multiscale 5D datasets (time, channel, z, y, x), with metadata, scale, and transformations. A) DANDI:000108 includes multi-slab, multi-stain (NeuropeptideY-NPY, Calretinin-CR, YOYO1) data that are aligned and the coordinate transforms are stored in the Zarr files, allowing B) on-the-fly visualization and stitching of the slabs at multiple scales (two shown) using Neuroglancer. C) An HTML dashboard allows data submitter and any user on the Web to see the live status: samples+stains uploaded, their quality issues, and multiscale viewing of this 300+ TB dataset.

**Fig 14.**
(a) ‘Imaging’ tab of the zebrahub.org website showing the list of imaging datasets made available. (b) By clicking on a particular dataset, the user is directed to a neuroglancer instance that allows interactive exploration of the dataset.

**Fig. 15**
Multiscale OME-Zarr representation of a whole brain dataset (~6TB compressed) archived at BIL and visualized over the internet in napari via the napari-ome-zarr plugin. Data is stored at BIL in an alternative format and is dynamically converted to OME-Zarr chunk-by-chunk and delivered to clients upon request.

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References

1. Ali HR, Jackson HW, Zanotelli VRT, et al. (2020) Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer 1:163–175. 10.1038/s43018-020-0026-6 - DOI - PubMed
1. Allan C, Burel J-M, Moore J, et al. (2012) OMERO: flexible, model-driven data management for experimental biology. Nat Methods 9:245–253. 10.1038/nmeth.1896 - DOI - PMC - PubMed
1. Alted F (2010) Why Modern CPUs Are Starving and What Can Be Done about It. Comput Sci Eng 12:68–71. 10.1109/MCSE.2010.51 - DOI
1. Bahry E, Breimann L, Zouinkhi M, et al. (2022) RS-FISH: precise, interactive, fast, and scalable FISH spot detection. Nat Methods 19:1563–1567. 10.1038/s41592-022-01669-y - DOI - PMC - PubMed
1. Berman HM, Kleywegt GJ, Nakamura H, Markley JL (2012) The Protein Data Bank at 40: reflecting on the past to prepare for the future. Structure 20:391–396. 10.1016/j.str.2012.01.010 - DOI - PMC - PubMed

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[1] Ali HR, Jackson HW, Zanotelli VRT, et al. (2020) Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer 1:163–175. 10.1038/s43018-020-0026-6 - DOI - PubMed

[2] Ali HR, Jackson HW, Zanotelli VRT, et al. (2020) Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer 1:163–175. 10.1038/s43018-020-0026-6 - DOI - PubMed

[3] Allan C, Burel J-M, Moore J, et al. (2012) OMERO: flexible, model-driven data management for experimental biology. Nat Methods 9:245–253. 10.1038/nmeth.1896 - DOI - PMC - PubMed

[4] Allan C, Burel J-M, Moore J, et al. (2012) OMERO: flexible, model-driven data management for experimental biology. Nat Methods 9:245–253. 10.1038/nmeth.1896 - DOI - PMC - PubMed

[5] Alted F (2010) Why Modern CPUs Are Starving and What Can Be Done about It. Comput Sci Eng 12:68–71. 10.1109/MCSE.2010.51 - DOI

[6] Alted F (2010) Why Modern CPUs Are Starving and What Can Be Done about It. Comput Sci Eng 12:68–71. 10.1109/MCSE.2010.51 - DOI

[7] Bahry E, Breimann L, Zouinkhi M, et al. (2022) RS-FISH: precise, interactive, fast, and scalable FISH spot detection. Nat Methods 19:1563–1567. 10.1038/s41592-022-01669-y - DOI - PMC - PubMed

[8] Bahry E, Breimann L, Zouinkhi M, et al. (2022) RS-FISH: precise, interactive, fast, and scalable FISH spot detection. Nat Methods 19:1563–1567. 10.1038/s41592-022-01669-y - DOI - PMC - PubMed

[9] Berman HM, Kleywegt GJ, Nakamura H, Markley JL (2012) The Protein Data Bank at 40: reflecting on the past to prepare for the future. Structure 20:391–396. 10.1016/j.str.2012.01.010 - DOI - PMC - PubMed

[10] Berman HM, Kleywegt GJ, Nakamura H, Markley JL (2012) The Protein Data Bank at 40: reflecting on the past to prepare for the future. Structure 20:391–396. 10.1016/j.str.2012.01.010 - DOI - PMC - PubMed

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This is a preprint.

OME-Zarr: a cloud-optimized bioimaging file format with international community support

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OME-Zarr: a cloud-optimized bioimaging file format with international community support

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