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For many applications in oncology, pathologists inspect 2D tissue sections of resected tumor samples to assess tumor morphology, identify infiltrating lymphocytes, and predict disease progression. While 3D tissue models obtained with confocal or light-sheet microscopes promise added value over 2D scans, data-handling and visualization of whole-slide scans at cell-resolution becomes computationally demanding and sometimes even impossible on consumer-grade hardware. To enable the simultaneous display of large tissue volumes and individual cell phenotypes with a small memory footprint, we created Cell2Voxel, a light-weight, cell-based 3D tissue model in which every cell is represented by a single voxel on a coarse, regular grid. As an alternative to confocal and light-sheet microscopy, we present a workflow including widefield imaging of multiplex 2D tissue sections, cell segmentation and phenotyping, image registration, conflict-free assignment of cell-voxels to the target grid, and the creation of the final 3D tissue model. As a showcase, we present 3D models of human colon cancer samples and murine pancreatic islets. We show that Cell2Voxel can reduce the memory footprint compared to other methods by 2-3 orders of magnitude and enables the simultaneous visualization of millions of individual cells in whole-slide tissue volumes on conventional computer hardware. While our final 3D model can be inspected using open-source software on all major operating systems, we additionally present an interactive data viewer tailored to our data format. Cell2Voxel can be created from any 2D widefield image stack, making it an inexpensive, easy-to-use alternative for large-volume 3D tissue visualization. |
