Raina Mauminah, Cheng Hao, Ferreira Ricardo Melo, Stansfield Treyden, Modak Chandrima, Cheng Ying-Hua, Suryadevara Hari Naga Sai Kiran, Xu Dong, Eadon Michael T, Ma Qin, Wang Juexin
Department of Biomedical Engineering and Informatics, Indiana University Indianapolis, Indianapolis, IN 46202, United States.
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, United States.
Bioinformatics. 2025 Jun 2;41(6). doi: 10.1093/bioinformatics/btaf303.
Chronic kidney disease (CKD) and acute kidney injury (AKI) are prominent public health concerns affecting more than 15% of the global population. The ongoing development of spatially resolved transcriptomics (SRT) technologies presents a promising approach for discovering the spatial distribution patterns of gene expression within diseased tissues. However, existing computational tools are predominantly calibrated and designed on the ribbon-like structure of the brain cortex, presenting considerable computational obstacles in discerning highly heterogeneous mosaic-like tissue architectures in the kidney. Consequently, timely and cost-effective acquisition of annotation and interpretation in the kidney remains a challenge in exploring the cellular and morphological changes within renal tubules and their interstitial niches.
We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), designed for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, REGNN integrates equivariance to handle n-dimensional symmetries of the spatial area, while additionally leveraging Positional Encoding to strengthen relative spatial relations of the nodes uniformly distributed in the lattice. Given the limited availability of well-labeled spatial data, this framework implements both graph autoencoder and graph self-supervised learning strategies. On heterogeneous samples from different kidney conditions, REGNN outperforms existing computational tools in identifying tissue architectures within the 10× Visium platform. This framework offers a powerful graph deep learning tool for investigating tissues within highly heterogeneous expression patterns and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases.
REGNN is publicly available at https://github.com/Mraina99/REGNN.
慢性肾脏病（CKD）和急性肾损伤（AKI）是突出的公共卫生问题，影响着全球超过15%的人口。空间分辨转录组学（SRT）技术的不断发展为发现疾病组织内基因表达的空间分布模式提供了一种有前景的方法。然而，现有的计算工具主要是在大脑皮层的带状结构上进行校准和设计的，在识别肾脏中高度异质的马赛克样组织结构时存在相当大的计算障碍。因此，在探索肾小管及其间质微环境内的细胞和形态变化时，及时且经济高效地获取肾脏中的注释和解释仍然是一个挑战。
我们提出了一个功能强大的图深度学习框架REGNN（关系等变图神经网络），用于对异质组织结构的SRT数据分析。为了使用图模型提高SRT晶格中的表达能力，REGNN集成了等变性来处理空间区域的n维对称性，同时还利用位置编码来加强均匀分布在晶格中的节点的相对空间关系。鉴于标记良好的空间数据可用性有限，该框架同时实现了图自动编码器和图自监督学习策略。在来自不同肾脏状况的异质样本上，REGNN在识别10× Visium平台内的组织结构方面优于现有的计算工具。该框架为研究高度异质表达模式下的组织提供了一个强大的图深度学习工具，并为查明导致复杂疾病进展的潜在病理机制铺平了道路。
