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Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web

Nature Methods volume 19, pages 515–516 (2022)Cite this article

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To the Editor — Advances in highly multiplexed imaging have enabled the comprehensive profiling of complex tissues in healthy and diseased states, facilitating the study of fundamental biology and human disease at spatially resolved, subcellular resolution1,2. Although the rapid innovation of biological imaging brings significant scientific value, the proliferation of technologies without unification of interoperable standards has created challenges that limit the analysis and sharing of results. The adoption of community-designed next-generation file formats (NGFF) is a proposed solution to promote bioimaging interoperability at scale3. Here we introduce Viv (https://github.com/hms-dbmi/viv), an open-source bioimaging visualization library that supports OME-TIFF4 and OME-NGFF3 directly on the web. Viv addresses a critical limitation of most web-based bioimaging viewers by removing a dependency on server-side rendering, offering a flexible toolkit for browsing multi-terabyte datasets on both mobile and desktop devices—without software installation.

Viv implements purely client-side rendering to decouple the browser from the server and still offer the flexibility of on-demand multichannel rendering. Existing web viewers also leverage graphical processing unit (GPU)-accelerated rendering but are generally tailored toward single-channel volumetric datasets and, crucially, lack the ability to reuse and compose features for existing or novel applications5,6. In contrast, Viv’s modularity allows core functionality to be repurposed and extended. The library consists of two primary components: (i) data-loading modules for OME-TIFF and OME-NGFF and (ii) configurable GPU programs that render full-bit-depth primary data on user devices.

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Fig. 1: Overview of data flow and rendering approaches for web-based bioimage data visualization and Viv features.

Code availability

Viv is open source and available under an MIT license at https://github.com/hms-dbmi/viv and at https://www.npmjs.com/package/@hms-dbmi/viv.

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Acknowledgements

We would like to thank members of the OME community for their guidance, as well as M. deCaestecker, E. Neumann and M. Brewer from Vanderbilt University and Vanderbilt University Medical Center for their efforts generating the imaging mass spectrometry and microscopy data highlighted in the referenced the use cases. Viv was developed with funding from the US National Institutes of Health (NIH; OT2OD026677, T15LM007092, T32HG002295) and National Science Foundation (NSF; DGE1745303) and the Harvard Stem Cell Institute (CF-0014-17-03). The use cases in this manuscript were supported with additional funding from the NIH (U54DK120058, 2P41GM103391, OT2OD026671) and NSF (CBET1828299).

Author information

Authors and Affiliations

  1. Harvard Medical School, Boston, MA, USA

    Trevor Manz, Ilan Gold, Chuck McCallum, Mark S. Keller & Nils Gehlenborg

  2. Vanderbilt University, Nashville, TN, USA

    Nathan Heath Patterson & Jeffrey M. Spraggins

  3. Indiana University, Bloomington, IN, USA

    Bruce W. Herr II & Katy Börner

Authors
  1. Trevor Manz
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  2. Ilan Gold
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  3. Nathan Heath Patterson
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  4. Chuck McCallum
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  9. Nils Gehlenborg
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Contributions

N.G., T.M. and I.G. conceived the project. N.G. coordinated the author team and oversaw the project. T.M. and I.G. developed the software. N.G., T.M., I.G., M.S.K., C.M., N.H.P., B.W.H., K.B. and J.M.S. tested and offered user feedback for the software. N.H.P. and J.M.S. provided datasets for testing the software. T.M., I.G., N.H.P., M.S.K., C.M. and B.W.H. developed applications with the software for supporting use cases. N.G., T.M., N.H.P. and I.G. wrote the manuscript. All authors approved the final manuscript.

Corresponding author

Correspondence to Nils Gehlenborg.

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Competing interests

N.G. is a co-founder and equity owner of Datavisyn. All other authors declare no competing interests

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Nature Methods thanks Ulrik Günther and Nikita Vladimirov for their contribution to the peer review of this work.

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Manz, T., Gold, I., Patterson, N.H. et al. Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web. Nat Methods 19, 515–516 (2022). https://doi.org/10.1038/s41592-022-01482-7

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