  Recently, various technologies have been introduced into digital pathology,
including artificial intelligence (AI) driven methods, in both areas of
pathological whole slide image (WSI) analysis and spatial transcriptomics (ST)
analysis. AI-driven WSI analysis utilizes the power of deep learning (DL),
expands the field of view for histopathological image analysis. On the other
hand, ST bridges the gap between tissue spatial analysis and biological
signals, offering the possibility to understand the spatial biology. However, a
major bottleneck in DL-based WSI analysis is the preparation of training
patterns, as hematoxylin & eosin (H&E) staining does not provide direct
biological evidence, such as gene expression, for determining the category of a
biological component. On the other hand, as of now, the resolution in ST is far
beyond that of WSI, resulting the challenge of further spatial analysis.
Although various WSI analysis tools, including QuPath, have cited the use of
WSI analysis tools in the context of ST analysis, its usage is primarily
focused on initial image analysis, with other tools being utilized for more
detailed transcriptomic analysis. As a result, the information hidden beneath
WSI has not yet been fully utilized to support ST analysis.
  To bridge this gap, we introduce QuST, a QuPath extension designed to bridge
the gap between H&E WSI and ST analyzing tasks. In this paper, we highlight the
importance of integrating DL-based WSI analysis and ST analysis in
understanding disease biology and the challenges in integrating these
modalities due to differences in data formats and analytical methods. The QuST
source code is hosted on GitHub and documentation is available at
(https://github.com/huangch/qust).
