@inproceedings{
song2023atat,
title={{ATAT}: Automated Tissue Alignment and Traversal},
author={Steven Song and Emaan Mohsin and Andrey Kuznetsov and Christopher Weber and Robert Grossman and Aly Khan},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=PNM9mojudC}
}
The spatial geometry of tissue biopsies reveals complex landscapes of cellular interactions. With the advent of spatial transcriptomics (ST), the ability to measure RNA across these intricate terrains has significantly advanced. However, without a pathologist’s insight to delineate regions of interest, modeling gene expression transitions across specific regions becomes a daunting task. A case in point is grading the severity of inflammatory bowel disease (IBD) across the intestinal wall while identifying the organization of immune cell types across the tissue layers; such characterization will be essential in the push for precision medicine. Yet the challenge to harness ST data to decipher spatially dependent transcriptional programs in a scalable and automated manner remains a well acknowledged barrier to wider implementation. Our study aims to: (1) Utilize hematoxylin and eosin (H\&E) stained images for automated segmentation of histological regions and (2) Simulate the gene expression transition across these histological layers within a single algorithmic framework. To these ends, we present ATAT: Automated Tissue Alignment and Traversal. With our approach, we automate the integration of H\&E stained images with spatial transcriptomics and simplify the investigation of important biomedical questions, such as characterization of inflammatory conditions across intestinal walls.
ATAT: Automated Tissue Alignment and Traversal
Steven Song Emaan Mohsin Andrey Kuznetsov Christopher Weber Robert L. Grossman Aly A Khan