QuST: QuPath Extension for Integrative Whole Slide Image and Spatial Transcriptomics Analysis

TL;DR

QuST, a tool that bridges the gap between WSI and ST, is introduced, underscoring the transformative power of this integrated approach in disease biology.

Abstract

The integration of AI in digital pathology, particularly in whole slide image (WSI) and spatial transcriptomics (ST) analysis, holds immense potential for enhancing our understanding of diseases. Despite challenges such as training pattern preparation and resolution disparities, the convergence of these technologies can unlock new insights. We introduce QuST, a tool that bridges the gap between WSI and ST, underscoring the transformative power of this integrated approach in disease biology.

Figures (14)

• Figure 1: QuST workflow includes: (a) users begin by importing ST data into QuPath using QuST. This step may require additional spatial alignment data which can be obtained via FIJI, if the user is working on Xenium dataset (see text). (b) once the ST data is successfully loaded, users can perform analysis and visualization via QuPath and QuST. (c) given the biological evidences provided by ST, users can generate the training set for image based cell classification and region segmentation based on H&E. Finally, the result generated using the DL module can be further analyzed using the functions described in (b).
• Figure 2: Workflow for DAPI-H&E image registration.
• Figure 3: Image example for analyzing the performance of image registration.
• Figure 4: Statistics for cell displacement with and without image registration.
• Figure 5: Results showing functions of spatial profiling provided by QuST: (a) Neighboring cell connectivity based on Delaunay clustering. Various single cell analyses available in QuST are based on the neighboring cell connectivity. (b) QuST's cellular spatial profiling generates a heat map indicating the distance to boundary of a specific cell type, e.g., tumor-epithelial cells to the corresponding tumor boundary.