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).
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365985 | PMC |
| http://dx.doi.org/10.1016/j.ebiom.2023.104698 | DOI Listing |
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