vLUME: 3D Virtual Reality for Single-molecule Localization Microscopy
Posted on: 7 March 2020
Preprint posted on 21 January 2020
Article now published in Nature Methods at http://dx.doi.org/10.1038/s41592-020-0962-1
Categories: biophysics, cell biology, molecular biology
Background
Super-resolution microscopy refers to techniques that allow images to be obtained with a resolution higher than that imposed by the diffraction limit. Super-resolution microscopy based on 3D single molecule localization microscopy (SMLM) is now well established- including methods such as PALM, STORM, dSTORM, SMI, and SPDM. The widespread adoption of 3D SMLM has led to the development of multiple software packages for quantitative evaluation of the spatial and temporal detection of photoswitching.
While significant advances have taken place with respect to various aspects of super-resolution microscopy, an important gap is the lack of 3D visualization approaches that enable high fidelity exploration of this type of data. Spark et al here present vLUME (Figure 1), a free visualization software package that enables robust visualization, segmentation and quantification of millions of fluorescent puncta from any 3D SMLM technique.
Key findings and developments
General features of vLUME
- Compatibility with all commercial VR hardware.
- Higher efficiency and accuracy for analysis of highly complex 3D point-cloud data.
- Ability to render all pointillism-based multidimensional datasets.
- Provides a complete VR interactive environment and intuitive interface for users, dedicated to data visualization and segmentation.
- Accepts multiple data formats.
Software key features
- 1) Data exploration and comparison.
- The user interface allows detailed nanoscale views of molecular elements in any arbitrary orientation, and data selection.
- In their preprint, Spark et al include various sample types, using various SR methods, from different international SR labs to demonstrate vLUME’s broad applicability and reproducibility. These included investigation of the T cell plasma membrane, microtubules in U-2 OS cells, nuclear pore complex in U-2 OS cells, Caulobacter crescentus bacteria, and microtubules and clathrin in COS cells.
- 2) Extracting 3D regions of interest (ROI) from complex data sets.
- Complex biological interactions occur in intricate 3D geometries, which often require further evaluation of interaction data within specific sub-selections of a data set. vLUME allows complex segmentation tasks at multiple scales.
- The ROIs can be exported for further analysis. Once uploaded, these data subsets can be scaled, highlighted, coloured and selected in 3D via VR controls.
- 3) Custom analysis of user-defined subregions.
- Quantitative evaluation is required for image analysis. For this purpose, Spark et al have included in their work, a user-definable script interpreter written in the multi-paradigm language C#.
- These data can be easily evaluated to give the user instant quantitative feedback about the specific sub-region of their data set.
- 4) Exporting movies for publications and presentations.
- As well as allowing customization of data for presentation purposes vLUME also allows automatic generation of a ‘fly-through’ video to allow researchers to articulate their scientific discoveries.
- Altogether, vLUME provides a new immersive environment for exploring and analysing 3D-SMLM data. It enables scientists with any level of expertise to make straightforward analyses of highly complex 3D data.
What I like about this preprint
I think this is an extraordinary advance for data exploration. I like this preprint and the method it presents because a) it is open access; b) it addresses an important gap of tools currently existing for a microscopy area which is advancing at a very fast pace; c) it allows a wide range of analyses; d) it has a modern and user friendly interface. I also like and that the authors made their code and helpful materials available (https://github.com/lumevr/vLume/releases) and that they include in their supplementary material very useful tools, particularly thoroughly and carefully done videos on the general features of vLUME; how to select and annotate data; how to load and filter data; how to manipulate data; and how to select data and run scripts. Altogether, this is fully in line with open science!
Open questions
- Throughout your preprint you mention vLUME ensure high fidelity exploration of the data. One question that arose for me with the text, and the tutorial videos, is how does vLUME help distinguishing local and global artefacts?
- What are the data storage demands for data acquired by/analyses performed with vLUME? You mention in your work you can even explore data acquired as time-lapse…
- You show in the supplementary material how vLUME was tested in multiple labs, with multiple samples, and using multiple super-resolution techniques. Was there no difference in your features 1,2, and 3, when using different microscopy and/or sample preparation methods to acquire the data? Altogether, is there an optimal set of conditions whereby vLUME gives the best outputs for your first three criteria?
- The applicability of vLUME to super-resolution microscopy-derived data is extremely exciting. With equally exciting developments in microscopy at multiple scales, do you envisage that vLUME can eventually integrate data at various scales, allowing visualization and analysis of data arising from full bodies/cells, to single molecules?
References
- Spark A, et al, vLUME: 3D virtual reality for single-molecule localization microscopy, bioRxiv, 2020
doi: https://doi.org/10.1242/prelights.17487
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