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Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface

Nature Computational Science volume 3, pages 777–788 (2023)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

The spatiotemporal organization of membrane-associated molecules is central to the regulation of cellular signals. Powerful new microscopy techniques enable the three-dimensional visualization of localization and activation of these molecules; however, the quantitative interpretation and comparison of molecular organization on the three-dimensional cell surface remains challenging because cells themselves vary greatly in morphology. Here we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology-invariant manner. We validated the framework by analyzing synthetic signaling patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the framework’s versatility, we further compared the spatial organization of cell surface signals both within, and between, cell populations, and powered an upstream machine-learning-based analysis of signaling motifs.

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Fig. 1: Spatial organization of molecular distributions on the 3D cell surface.
Fig. 2: Cellular harmonics for the analysis of molecular distributions on the 3D cell surface.
Fig. 3: Validation and robustness of spectral decomposition analysis.
Fig. 4: Decomposing the spatial organization of cell surface signaling on melanoma cells.
Fig. 5: Evaluating the spatial redistribution of cell surface signaling on melanoma cells.

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Data availability

All image data that support the findings of this study are available in a Zenodo repository (https://doi.org/10.5281/zenodo.8166238)54. This repository comprises a collection of high-resolution 3D cell images captured using light-sheet microscopy. These images have been previously published, and the origin of the dataset is referenced in the Methods section. Source data for Figs. 2–5 are provided with this manuscript.

Code availability

All analysis was performed in a custom software package (u-signal3D) written in MATLAB (Mathworks), which is available as a frozen version in a Zenodo repository (https://doi.org/10.5281/zenodo.8222824)55 and as an updated repository at (https://github.com/DanuserLab/u-signal3D).

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Acknowledgements

We would like to thank M. Ovsjanikov, J. Noh, F. Zhou and J. Huh for fruitful discussions, and A. Weems, E. Welf, R. Fiolka and K. Dean for data collection. This work was supported by grant no. K99GM123221 to M.K.D., and grant nos. R35GM136428 and RM1GM145399 to G.D.

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Author notes

  1. Meghan K. Driscoll

    Present address: Department of Pharmacology, University of Minnesota, Minneapolis, MN, USA

Authors and Affiliations

  1. Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Hanieh Mazloom-Farsibaf, Qiongjing Zou, Rebecca Hsieh, Gaudenz Danuser & Meghan K. Driscoll

  2. Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Hanieh Mazloom-Farsibaf, Gaudenz Danuser & Meghan K. Driscoll

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Contributions

H.M-.F. and M.K.D. conceived and designed the project. M.K.D. and G.D supervised the project. H.M.-F. wrote the codes for the package and M.K.D, Q.Z., and R.H. helped with writing and debugging the codes. H.M.-F. wrote the manuscript. M.K.D. and G.D helped with writing and editing the manuscript. All authors read and approved the final manuscript

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Correspondence to Gaudenz Danuser or Meghan K. Driscoll.

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Nature Computational Science thanks Bruno Goud, Kristine Schauer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10.

Reporting Summary

Supplementary Video 1

Molecular distribution over time: surface rendering of an MV3 melanoma cell expressing GFP-AktPH, a marker of PI3K activity. The red color indicates regions of high signaling activity and yellow color regions of low activity (see the color bar in Fig. 1b).

Supplementary Video 2

Three-dimensional molecular organization of cytosol marker and CyOFP-tractin at the surface of an MV3 melanoma cell. Surface rendering of an MV3 melanoma cell expressing cytosolic GFP and CyOFP-tractin (see Fig. 4a).

Supplementary Video 3

Three-dimensional raw image of cytosol marker and CyOFP-tractin on an MV3 melanoma cell. Maximum intensity projection along the y-axis of raw volumetric of cytosolic GFP and CyOFP-tractin signals onto the cell surface (see Fig. 4a). Both signals display patchy organization, however, the CyOFP-tractin signal is more structured, as expected.

Supplementary Video 4

PI3K activity redistribution on an MV3 melanoma cell under PI3K inhibition. Surface rendering of an MV3 melanoma cell expressing GFP-AktPH, a marker of PI3K activity, after adding PI3K inhibitor (left). Energy density spectra of the PI3K activity redistribution under PI3K inhibition.

Supplementary Data

Statistical source data for Supplementary Figs. 2, 3 and 6–10.

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Source Data Fig. 4

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Source Data Fig. 5

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Mazloom-Farsibaf, H., Zou, Q., Hsieh, R. et al. Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface. Nat Comput Sci 3, 777–788 (2023). https://doi.org/10.1038/s43588-023-00512-4

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