Faculty, Staff and Student Publications
Language
English
Publication Date
8-1-2023
Journal
EBioMedicine
DOI
10.1016/j.ebiom.2023.104698
PMID
37453365
PMCID
PMC10365985
PubMedCentral® Posted Date
7-13-2023
PubMedCentral® Full Text Version
Post-print
Abstract
Background: Tissues such as the liver lobule, kidney nephron, and intestinal gland exhibit intricate patterns of zonated gene expression corresponding to distinct cell types and functions. To quantitatively understand zonation, it is important to measure cellular or genetic features as a function of position along a zonal axis. While it is possible to manually count, characterize, and locate features in relation to the zonal axis, it is labor-intensive and difficult to do manually while maintaining precision and accuracy.
Methods: We addressed this challenge by developing a deep-learning-based quantification method called the "Tissue Positioning System" (TPS), which can automatically analyze zonation in the liver lobule as a model system.
Findings: By using algorithms that identified vessels, classified vessels, and segmented zones based on the relative position along the portal vein to central vein axis, TPS was able to spatially quantify gene expression in mice with zone specific reporters.
Interpretation: TPS could discern expression differences between zonal reporter strains, ages, and disease states. TPS could also reveal the zonal distribution of cells previously thought to be positioned randomly. The design principles of TPS could be generalized to other tissues to explore the biology of zonation.
Funding: CPRIT (RP190208, RP220614, RP230330) and NIH (P30CA142543, R01AA028791, R01CA251928, R01DK1253961, R01GM140012, 1R01GM141519, 1R01DE030656, 1U01CA249245). The Pollack Foundation, Simmons Comprehensive Cancer Center Cancer & Obesity Translational Pilot Award, and the Emerging Leader Award from the Mark Foundation For Cancer Research (#21-003-ELA).
Keywords
Mice, Animals, Hepatocytes, Liver, Models, Biological, Protein Processing, Post-Translational, Tissue segmentation, Deep learning, Zonation, Expression pattern, Liver lobule
Published Open-Access
yes
Recommended Citation
Rong, Ruichen; Wei, Yonglong; Li, Lin; et al., "Image-Based Quantification of Histological Features as a Function of Spatial Location Using the Tissue Positioning System" (2023). Faculty, Staff and Student Publications. 6731.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6731
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