Huang Wendong, Hu Yaofeng, Wang Lequn, Wu Guangsheng, Zhang Chuanchao, Shi Qianqian
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf021.
Spatially resolved transcriptomics (SRT) technologies facilitate the exploration of cell fates or states within tissue microenvironments. Despite these advances, the field has not adequately addressed the regulatory heterogeneity influenced by microenvironmental factors. Here, we propose a novel Spatially Aligned Graph Transfer Learning (SpaGTL), pretrained on a large-scale multi-modal SRT data of about 100 million cells/spots to enable inference of context-specific spatial gene regulatory networks across multiple scales in data-limited settings. As a novel cross-dimensional transfer learning architecture, SpaGTL aligns spatial graph representations across gene-level graph transformers and cell/spot-level manifold-dominated variational autoencoder. This alignment facilitates the exploration of microenvironmental variations in cell types and functional domains from a molecular regulatory perspective, all within a self-supervised framework. We verified SpaGTL's precision, robustness, and speed over existing state-of-the-art algorithms and show SpaGTL's potential that facilitates the discovery of novel regulatory programs that exhibit strong associations with tissue functional regions and cell types. Importantly, SpaGTL could be extended to process multi-slice SRT data and map molecular regulatory landscape associated with three-dimensional spatial-temporal changes during development.
空间分辨转录组学（SRT）技术有助于探索组织微环境中的细胞命运或状态。尽管取得了这些进展，但该领域尚未充分解决受微环境因素影响的调控异质性问题。在此，我们提出了一种新颖的空间对齐图转移学习（SpaGTL）方法，它在约1亿个细胞/斑点的大规模多模态SRT数据上进行预训练，以便在数据有限的情况下推断跨多个尺度的特定背景空间基因调控网络。作为一种新颖的跨维度转移学习架构，SpaGTL在基因级图变换器和细胞/斑点级流形主导的变分自编码器之间对齐空间图表示。这种对齐有助于从分子调控的角度探索细胞类型和功能域中的微环境变化，且均在自监督框架内进行。我们验证了SpaGTL相对于现有最先进算法的精度、稳健性和速度，并展示了SpaGTL在促进发现与组织功能区域和细胞类型具有强关联的新型调控程序方面的潜力。重要的是，SpaGTL可以扩展以处理多层SRT数据，并绘制与发育过程中三维时空变化相关的分子调控景观。
