ArticleNature communications2022
Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer's disease.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Graph-based contrastive learning enables unified integration and niche transfer across single-cell and spatial multi-omics.Briefings in bioinformatics · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Detecting chromatin state alterations in PBMCs associated with Type 2 Diabetes Mellitus.Communications medicine · 2026Article
- Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.Nature communications · 2026Article
- SpatioFreq: A Deep Learning Framework for Decoding Cellular and Tissue Landscapes Across Organisms Using Spatial Transcriptomics.Interdisciplinary sciences, computational life sciences · 2026Article
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- Balanced deep learning on multi-omics networks identifies molecular subgroups of pathological brain aging.medRxiv : the preprint server for health sciences · 2026Article
- From cells to pixels: A decision tree for designing bioimage analysis pipelines.Journal of microscopy · 2025Article
- sc4D: spatio-temporal single-cell transcriptomics analysis through embedded optimal transport identifies joint glial response to Alzheimer's disease pathology.bioRxiv : the preprint server for biology · 2025Article
- Bridging single cells to organs: Mesoscale modules as fundamental units of tissue function.Cell · 2025Review
- Data-driven fine-grained region discovery in the mouse brain with transformers.Nature communications · 2025Article
- A comprehensive comparison on clustering methods for multi-slice spatially resolved transcriptomics data analysis.Briefings in bioinformatics · 2025Article
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Article
- Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings.Nature communications · 2025Article
- SPaSE: Spatially resolved pathology scores using optimal transport on spatial transcriptomics data.Cell systems · 2025Article
- MuST: multiple-modality structure transformation for single-cell spatial transcriptomics.Briefings in bioinformatics · 2025Article
- Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies.Genome research · 2025Article
- Prediction of protein subcellular localization in single cells.Nature methods · 2025Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.