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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

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Inference of spatial chromatin accessibility via integration of spatial transcriptomics and single-cell multi-omics

Ishita Debnath¹, Zhana Duren²

¹Center for Computational Biology and Bioinformatics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.

Nature Communications

|June 4, 2026

Summary

This study introduces ISON, a computational method that integrates spatial transcriptomics with single-cell multi-omics data. ISON predicts chromatin accessibility and reconstructs spatial gene regulatory networks, advancing our understanding of gene regulation in tissues.

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Spatial transcriptomics and single-cell multi-omics offer powerful insights into gene regulation.
Simultaneous spatial multi-omics profiling kits are currently unavailable, limiting data generation.

Purpose of the Study:

To present ISON, a unified computational method for integrative spatial multi-omics analysis.
To enable accurate prediction of chromatin accessibility profiles for spatial spots.
To reconstruct spatially resolved gene regulatory networks.

Main Methods:

Development of ISON, a computational method for integrating single-cell multiome and spatial transcriptomics data.
Scalable analysis in terms of time and memory.
Prediction of chromatin accessibility and estimation of transcription factor activity at the spot level.

Main Results:

ISON accurately predicts chromatin accessibility profiles for spatial spots.
Reconstruction of spatially resolved gene regulatory networks.
Estimation of transcription factor activity, distinguishing between TFs within the same family.
Application to Alzheimer's disease data revealed disease- and age-specific spatially variable gene regulatory modules.

Conclusions:

ISON is a scalable and effective computational method for integrative spatial multi-omics analysis.
ISON enables the estimation of transcription factor activity at the spot level, offering unique insights.
The method has potential for uncovering spatially organized mechanisms in complex biological processes, such as Alzheimer's disease.