Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Children's Research Institute, Departments of Pediatrics and Internal Medicine, Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
EBioMedicine. 2023 Aug;94:104698. doi: 10.1016/j.ebiom.2023.104698. Epub 2023 Jul 13.
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.
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.
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.
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.
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).
肝脏小叶、肾脏肾单位和肠道腺等组织表现出与不同细胞类型和功能相对应的复杂基因表达分带模式。为了定量理解分带，重要的是要测量细胞或遗传特征作为沿分带轴的位置的函数。虽然可以手动计数、描述和定位与分带轴有关的特征，但手动完成这项工作既费力又难以保持精确和准确。
我们通过开发一种基于深度学习的定量方法，即“组织定位系统”（TPS），解决了这一挑战，该方法可作为模型系统自动分析肝小叶的分带。
通过使用算法来识别血管、对血管进行分类，并根据沿门静脉至中央静脉轴的相对位置对区域进行分割，TPS 能够对具有特定区域报告基因的小鼠进行空间定量基因表达。
TPS 能够区分特定区域报告基因株、年龄和疾病状态之间的表达差异。TPS 还可以揭示以前认为随机定位的细胞的分带分布。TPS 的设计原则可以推广到其他组织，以探索分带的生物学。
CPRIT（RP190208、RP220614、RP230330）和 NIH（P30CA142543、R01AA028791、R01DK1253961、R01GM140012、1R01GM141519、1R01DE030656、1U01CA249245）。Pollack 基金会、Simmons 综合癌症中心癌症与肥胖转化试点奖以及 Mark 癌症研究基金会的新兴领导者奖（#21-003-ELA）。
