vLUME: 3D virtual reality for single-molecule localization microscopy

International audiencevLUME is a virtual reality software package designed to render large three-dimensional single-molecule localization micros-copy datasets. vLUME features include visualization, seg-mentation, bespoke analysis of complex local geometries and exporting features. vLUME can perform complex analysis on real three-dimensional biological samples that would otherwise be impossible by using regular flat-screen visualization programs. Super-resolution microscopy based on three-dimensional single-molecule localization microscopy (3D-SMLM) is now well established 1,2 , and its widespread adoption has led to the development of more than 36 software packages dedicated to quantitative evaluation of the spatial and temporal detection of fluorophore photoswitching 3. While the initial emphasis in the 3D-SMLM field has clearly been on improving resolution and data quality, there is now a marked absence of 3D visualization approaches that enable the straightforward, high-fidelity exploration of this type of data. Inspired by the horological phosphorescence points that illuminate watch-faces in the dark, we present vLUME (visualization of the local universe in a micro environment, pronounced 'volume'), an immer-sive virtual reality (VR)-based visualization software package purposefully designed to render large 3D-SMLM datasets. It is free for academic use. vLUME enables robust visualization, segmentation, annotation and quantification of millions of fluorescence puncta from any 3D-SMLM technique. vLUME has an intuitive user interface and is compatible with all commercial gaming VR hardware (Oculus Rift/Rift S and HTC Vive/Vive Pro; Supplementary Video 1). Although other microscopy data (that is, confocal) visualiza-tion tools have previously explored VR technology using volumet-ric representations 4,5 , vLUME has been specifically and purposefully created for SMLM. It accelerates the analysis of highly complex 3D point-cloud data and the rapid identification of defects that are otherwise neglected in global quality metrics. (A comparison with other VR and non-VR tools can be found in Supplementary Table 1.) vLUME is a point-cloud based 3D-SMLM data visualization tool able to render all pointillism-based multidimensional data-sets. It differs from other 3D tools for 3D-SMLM visualization such as ViSP 6 by providing a complete VR interactive environment and intuitive interface for life scientists, dedicated to data visual-ization, segmentation and analysis. Users load multidimensional particle-list datasets into vLUME (.csv files; Fig. 1a), such as those generated by commonly used 3D-SMLM software 7,8. This allows users to comprehend the spatial and temporal relation between points comprising a 3D structure. In time-lapse data, 3D reconstructions update for each time-point under user control. vLUME is built with the industry-standard cross-platform UnityEngine, providing a high-performance rendering framework that scales with future advances in graphics performance. vLUME has four key features: (1) Data exploration and comparison. The configurable user interface allows researchers, without need for programming, to seamlessly switch back and forth from a global view of the entire captured sample to detailed nanoscale views of molecular elements in any arbitrary orientation, faster than with conventional flat screens 9. Doing so allows the easy local selection of data for further analysis (Supplementary Videos 2 and 3). The software can be used to leverage the human capacity to quickly interpret local features in these data, such as global and local artifacts (Fig. 1b), that are more difficult to trace by automated software without the ground truth being known 10. In addition, it is easy to quickly evaluate and compare different processing software side by side, such as, for example, QuickPALM versus ThunderSTORM (Fig. 1c). We include example datasets with different sample types, using various SMLM-based super-resolution methods and from different international super-resolution laboratories to demonstrate its broad applicability (Fig. 1b-e and Methods). (2) Extracting 3D regions of interest (ROI) from complex datasets. Complex biological interactions occur in intricate 3D geom-etries, with the evaluation of interaction data often requiring the extraction and analysis of specific subselections of a dataset. To demonstrate this capacity of vLUME, we carried out complex segmentation tasks where users needed to identify and select small local features (tens to hundreds of localizations) in data of large dimensions (millions of localizations, Fig. 1d). A single microtubule can be easily extracted from a complex 3D tangle of microtubules within a eukaryotic cell (Fig. 1d, right). This process can be performed in less than 1 min and the ROI exported for further analysis (Methods). Once uploaded, these data subsets can be scaled, highlighted, colored and selected in three dimensions via VR controls (Supplementary Video 4). (3) Custom analysis of user-defined subregions. Quantitative bio-imaging not only relies on high-quality images but quantitative evaluation using bespoke code. Recognizing this, we included a user-definable script interpreter written in the multiparadigm language C# (Methods). These data can be easily evaluated t