Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

Abstract

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.

singlecellVR: interactive visualization of single-cell data in virtual reality

“…To showcase the functionality and generalizability of scvr across data types, we have preprocessed a collection of 17 published datasets, which includes both scRNA-seq as well as scATAC-seq and single cell proteomic data and made them available for immediate VR visualization. Taken together we believe this step addresses a key limitations of previously-developed VR tools mentioned above, and a formal comparison is presented below (Yang et al, 2018; Legetth et al, 2019; Bressan et al, 2021).…”

“…Finally, we extend our framework to computational methods that derive the RNA velocity of single cells for visualization in VR(La Manno et al, 2018; Bergen et al, 2020). With the recent advances in spatially-resolved transcriptomics (Welch et al, 2019) and corresponding analysis methods (Hao et al, 2021; Miller et al, 2021), visualization of such data has already been extended to a VR framework (Bressan et al, 2021). We believe this new sort of three-dimensional VR will also become especially useful once made available to the general research community via inexpensive hardware and facile data preprocessing and preparation for VR visualization.…”

“…To showcase the functionality and generalizability of scvr across data types, we have preprocessed a collection of 17 published datasets, which includes both scRNA-seq as well as scATAC-seq and single cell proteomic data and made them available for immediate VR visualization. Taken together we believe this step addresses a key limitations of previously-developed VR tools mentioned above, and a formal comparison is presented below (Yang et al, 2018;Legetth et al, 2019;Bressan et al, 2021). Excitingly, given the small footprint of the files obtained with scvr, we are offering users the ability to easily submit their processed data to the singlecellVR GitHub Repository (see Supplementary Figure 1) to make the tool a general resource for the field.…”

“…VR visualization methods for single cell data have been recently proposed (Yang et al, 2018; Legetth et al, 2019; Bressan et al, 2021). However, these methods require either expensive hardware or specific data inputs that mandate intermediate to advanced computational skills.…”

DataSheet1.docx

“…As mentioned above, there are currently three unpublished reports of VR tools created to visualize single-cell data: CellexalVR (Legetth et al, 2019), starmap (Yang et al, 2018), and Theia (Bressan et al, 2021). In this section, we compare these tools to singlecellVR on two axes: 1) ease of use and 2) overall performance for visualization and analysis in VR.…”