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

Integrating spatial transcriptomics, which maps gene expression location within tissues, with single-cell multi-omics data, profiling gene expression and chromatin accessibility (or other epigenomic data) for the same cell, offers powerful insights into gene regulation. However, commercially available kits for simultaneous spatial multi-omics profiling are currently unavailable, hindering widespread data generation. Here, we present ISON (Integrated Spatial Omics Network), a unified computational method for integrative spatial multi-omics analysis from single cell multiome data and spatial transcriptomics data. ISON accurately predicts chromatin accessibility profiles for spatial spots and reconstructs spatially resolved gene regulatory networks, demonstrating scalability in both time and memory. Importantly, ISON’s chromatin accessibility prediction captures patterns consistent with cis- and trans- regulatory information and enables estimation of transcription factor (TF) activity at the spot level, distinguishing between TFs even within the same family, which is unique and is not present in approaches relying solely on chromatin accessibility data. The application of ISON to Alzheimer’s disease data reveals disease- and age-specific spatially variable gene regulatory modules, highlighting its potential to uncover spatially organized mechanisms driving complex biological processes. Obtaining matched spatial transcriptomic and epigenomic measurements from the same cell remains technically challenging. The authors develop a computational framework ISON to integrate widely available spatial transcriptomics data with sc-multiome profiles to infer spatial chromatin accessibility and gene regulatory networks.