Wang Lequn, Bai Xiaosheng, Zhang Chuanchao, Shi Qianqian, Chen Luonan
Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China.
Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
Small Methods. 2024 Dec 2:e2401163. doi: 10.1002/smtd.202401163.
Spatially Resolved Transcriptomics (SRT) offers unprecedented opportunities to elucidate the cellular arrangements within tissues. Nevertheless, the absence of deconvolution methods that simultaneously model multi-modal features has impeded progress in understanding cellular heterogeneity in spatial contexts. To address this issue, SpaDA is developed, a novel spatially aware domain adaptation method that integrates multi-modal data (i.e., transcriptomics, histological images, and spatial locations) from SRT to accurately estimate the spatial distribution of cell types. SpaDA utilizes a self-expressive variational autoencoder, coupled with deep spatial distribution alignment, to learn and align spatial and graph representations from spatial multi-modal SRT data and single-cell RNA sequencing (scRNA-seq) data. This strategy facilitates the transfer of cell type annotation information across these two similarity graphs, thereby enhancing the prediction accuracy of cell type composition. The results demonstrate that SpaDA surpasses existing methods in cell type deconvolution and the identification of cell types and spatial domains across diverse platforms. Moreover, SpaDA excels in identifying spatially colocalized cell types and key marker genes in regions of low-quality measurements, exemplified by high-resolution mouse cerebellum SRT data. In conclusion, SpaDA offers a powerful and flexible framework for the analysis of multi-modal SRT datasets, advancing the understanding of complex biological systems.
空间分辨转录组学（SRT）为阐明组织内的细胞排列提供了前所未有的机会。然而，缺乏能同时对多模态特征进行建模的反卷积方法阻碍了在空间背景下理解细胞异质性方面的进展。为解决这一问题，开发了SpaDA，这是一种新颖的空间感知域适应方法，它整合了来自SRT的多模态数据（即转录组学、组织学图像和空间位置），以准确估计细胞类型的空间分布。SpaDA利用一个自表达变分自编码器，结合深度空间分布对齐，从空间多模态SRT数据和单细胞RNA测序（scRNA-seq）数据中学习并对齐空间和图形表示。这种策略促进了细胞类型注释信息在这两个相似性图之间的传递，从而提高了细胞类型组成的预测准确性。结果表明，SpaDA在细胞类型反卷积以及跨不同平台识别细胞类型和空间域方面优于现有方法。此外，以高分辨率小鼠小脑SRT数据为例，SpaDA在识别低质量测量区域中空间共定位的细胞类型和关键标记基因方面表现出色。总之，SpaDA为多模态SRT数据集的分析提供了一个强大且灵活的框架，推动了对复杂生物系统的理解。
