Experimentally recorded point cloud data, such as those generated by single-molecule localization microscopy, are continuously increasing in size and dimension. Gaining an intuitive understanding and facilitating the analysis of such multidimensional data remains challenging. Here we report a new open-source software platform, Genuage, that enables the easy perception of, interaction with and analysis of multidimensional point clouds in virtual reality. Genuage is compatible with arbitrary multidimensional data extending beyond single-molecule localization microscopy.
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Data are available in the GitHub repository (https://github.com/Genuage) and upon request. The data that were used in Supplementary Video 6 can be found at the following links: MNIST data (http://yann.lecun.com/exdb/mnist/), flocking birds data (https://figshare.com/s/3c572f91b07b06ed30aa; Flock1) and LiDAR data (http://www.semantic3d.net/view_dbase.php?chl=1; marketsquarefeldkirch4).
The reviewed source code for Genuage is available as Supplementary Software. Updated versions can be found at https://github.com/Genuage. The repository includes scripts for reading JSON files in MATLAB and for data exchange with Python and MATLAB.
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We acknowledge funding from the Fondation pour la recherche médicale (FRM; DEI20151234398) (B.H.), the Agence National de la recherche (ANR-19-CE42-0003-01) (B.H.), the LabEx CelTisPhyBio (ANR-10-LBX-0038, ANR-10-IDEX-0001-02) (B.H.) and the Institut Curie (B.H.). We recognize the support of France-BioImaging infrastructure grant ANR-10-INBS-04 (Investments for the future) (B.H.). We acknowledge the financial support of the Agence pour la Recherche sur le Cancer (ARC Foundation), ARC (B.H.) and DIM ELICIT (B.H.). We acknowledge funding from the Pasteur Institute (J.-B.M.), the sponsorships of CRPCEN, Gilead Science and foundation EDF (J.-B.M.), the ANR-17-CE23-0016 TRamWAy (J.-B.M.), the INCEPTION project (PIA/ANR- 16-CONV-0005, OG) (J.-B.M.), the programme d’investissement d’avenir supported by L’Agence Nationale de la Recherche ANR-19-P3IA-0001 Institut 3IA Prairie (J.-B.M.) and the support of the AVIRON grant from the Région Ile-de-France (DIM-ELICIT).
The authors declare no competing interests.
Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Notes 1–3.
General overview of the Genuage interface. Example of loading and manipulating two-color point cloud data followed by distance measurement. The two colors are obtained by sequential super-resolution STORM imaging of fission yeast cell wall during division labeled with Alexa 647, followed by PALM imaging of tubulin fibers expressing tdEOS fluorescent protein in multifocus microscopy (MFM).
Example of selection of a complex 3D form of point clouds in VR. The data were obtained by 3D STORM imaging of mitochondria using MFM.
Clipping plane tool in VR to explore the complex fiber network of point clouds generated by 3D super-resolution imaging of tubulin fibers of HeLa cells in MFM.
Using the profiler tool to measure the histogram of point clouds along a segment in 3D. The data correspond to 3D localizations obtained by STORM super-resolution imaging of TOM20 labeled with Alexa 647 dye. Such a task is commonly performed to check the real resolution power of a super-resolution experiment and to verify separation between point cloud features.
Trajectory analysis performed on localizations of beads injected in the nucleus of U2OS cells and imaged by MFM. Local analysis and diffusion coefficient calculation in a dense 4D point cloud.
Visualizing different types of point cloud data using Genuage. Show cases: 4D (3D with color) embedding of MNIST dataset (i), visualization of the trajectories of flocking birds (ii) and LiDAR point cloud data (iii).
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Blanc, T., El Beheiry, M., Caporal, C. et al. Genuage: visualize and analyze multidimensional single-molecule point cloud data in virtual reality. Nat Methods (2020). https://doi.org/10.1038/s41592-020-0946-1