Date: 16th October 2020
The Nobel Prize in Chemistry, 2014, was awarded to Eric Betzig, W.E. Moerner and Stefan Hell for the development of super-resolved fluorescence microscopy surpassing the limitations of the light microscope. The ground-breaking work brought optical microscopy into the nanodimension, producing cutting-edge knowledge of the tiniest components of life. However, there has been a limited ways to visualise, interact and analyse this complex data in three dimensions. Now scientists, present vLUME (visualization of the local universe in a micro environment), an immersive virtual reality (VR)-based visualisation software package purposefully designed to render large 3D- super-resolution microscopy datasets.
The software, vLUME, was created by scientists at the University of Cambridge, UK, and Lume VR Ltd, a 3D image analysis software company based in Oxford, UK, and was presented in Nature Methods this week. The software allowed super-resolution microscopy data to be visualised and analysed in virtual reality, and can be used to study everything from individual proteins to entire cells. vLUME could perform complex analysis on real three-dimensional biological samples that would otherwise be impossible by using regular flat-screen visualisation programs.
vLUME rapidly coverted large, multidimensional point-cloud datasets from 2D visualisation into an immersive 3D VR environment through a systematic workflow:
- The multidimensional, image stacks were processed with a standard fitting algorithm providing multiparameter outputs as .csv files.
- The resultant datasets was then loaded directly into the vLUME software and instantly visualised in VR.
- By anchoring at user-defined waypoints around these data, a smoothly interpolated fly-through video was created and exported, providing the user with a tool to effectively communicate their discoveries
So why use vLUME?
Well it offers four key features for user and aims to vastly reduce analysis time on such large and complex datasets that are generated from super-resolution microscopy. It can be used for data exploration and comparison, extracting 3D regions of interest from complex datasets. User-defined subregions can be custom analysed, and it can be used to export movies for publications and presentations.
The team here presented a case study using the software to visualise the periodic submembrane scaffold along axons of cultured neurons, and was used to segment, annotate and analyse complex subregions. It has also been used to study how antigen cells trigger an immune response in the body, allowing the researchers to support or challenge their hypotheses.
Conclusions and future applications
The researchers here present vLUME as an exciting, new immersive VR environment for exploring and analysing 3D super-resolution microscopy data. It enables researchers to make straightforward analytical sense of what is often highly complex 3D data, without the need for highly specialised skills. It is hoped that the software will enable sharing, exploration and interrogation of data in a unique and intuitive manner, offering users an entirely different perspective on their work.
In the future the team hope to see a multi-user function, and perhaps the incorporation of advanced computational imaging tools such as machine learning. Deep learning algorithms are powerful tools, in particular they lend themselves well to image analysis. We have recently seen their use for detection of metastases, and diagnosing breast cancer, and their ability to accelerate proteomic research. The combination of vLUME and AI would potentially accelerate drug targeting and analysis, reveal new disease pathologies and progress new treatments.
For more information please see the press release from Cambridge University
Spark, A., A. Kitching, D. Esteban-Ferrer, A. Handa, A. R. Carr, L.-M. Needham, A. Ponjavic, A. M. Santos, J. McColl, C. Leterrier, S. J. Davis, R. Henriques and S. F. Lee (2020). “vLUME: 3D virtual reality for single-molecule localization microscopy.” Nature Methods.
https://doi.org/10.1038/s41592-020-0962-1
