Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease

• Published in: Nature communications 2022-12, Vol.13 (1), p.7480-17, Article 7480
• Main Authors: Zhang, Xinyi, Wang, Xiao, Shivashankar, G. V., Uhler, Caroline
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2401; 631/1647/2017/1947; 631/1647/245/2225; 692/53/2422; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - genetics; Alzheimer's disease; Biomarkers; Chromatin; Chromatin - genetics; Computer applications; Gene expression; Humanities and Social Sciences; Humans; Medical imaging; Morphology; multidisciplinary; Neurodegenerative diseases; Parameterization; Science; Science (multidisciplinary); Spatial analysis; Spatial data; Technology; Tissues; Transcriptome - genetics; Transcriptomics
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35233-1

Tissue development and disease lead to changes in cellular organization, nuclear morphology, and gene expression, which can be jointly measured by spatial transcriptomic technologies. However, methods for jointly analyzing the different spatial data modalities in 3D are still lacking. We present a computational framework to integrate Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues. STACI incorporates multiple modalities in a single representation for downstream tasks, enables the prediction of spatial transcriptomic data from nuclear images in unseen tissue sections, and provides built-in batch correction of gene expression and tissue morphology through over-parameterization. We apply STACI to analyze the spatio-temporal progression of Alzheimer’s disease and identify the associated nuclear morphometric and coupled gene expression features. Collectively, we demonstrate the importance of characterizing disease progression by integrating multiple data modalities and its potential for the discovery of disease biomarkers. Methods for jointly analysing the different spatial data modalities in 3D are lacking. Here the authors report the computational framework STACI (Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data) which they apply to an Alzheimer’s disease mouse model.