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Updated: Apr 18, 2026

Spatial and Temporal Control of Murine Melanoma Initiation from Mutant Melanocyte Stem Cells

Spatial and Temporal Control of Murine Melanoma Initiation from Mutant Melanocyte Stem Cells

Published on: June 7, 2019

Spatially Anchored Regulatory State Inference in Melanoma.

Jagan Mohan Reddy Dwarampudi¹, Veena Kochat², Suresh Satpati²

¹Department of Electrical and Computer Engineering, University of Houston, Houston, USA.

Biorxiv : the Preprint Server for Biology

|April 17, 2026

Summary

This summary is machine-generated.

This study introduces a new framework to integrate spatial transcriptomics and single-cell multiome data, revealing spatially resolved gene regulatory programs within tissue architecture for enhanced biological insights.

Keywords:

Multimodal integration Regulatory inference Single-cell multiome Spatial transcriptomics Visium

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Spatial transcriptomics (ST) provides gene expression data in tissue context but lacks regulatory details.
Single-cell multiome assays offer transcriptional and chromatin data but lack spatial information.

Purpose of the Study:

To develop a framework for spatially anchored regulatory inference by integrating ST and single-cell multiome data.
To infer and analyze spatially resolved regulatory programs within tissue microenvironments.

Main Methods:

Integration of Visium ST with single-cell multiome data using a GraphST-based framework.
Spatially regularized cell-to-spot mapping to propagate chromatin accessibility and transcription factor motif activity into tissue space.
Joint differential expression and accessibility testing at the spatial domain level for regulatory analysis.

Main Results:

The framework successfully infers spatially localized regulatory programs in melanoma tissue sections.
Demonstration that cell assignment strategy significantly impacts the stability of downstream regulatory inferences.
Generation of interpretable gene-, peak-, and transcription factor-level outputs for multimodal spatial analysis.

Conclusions:

The developed framework enables comprehensive multimodal spatial analysis by integrating gene expression and chromatin accessibility data.
This approach advances the understanding of tissue-specific regulatory mechanisms and their spatial organization.
The modular design facilitates detailed investigation of regulatory elements within their native tissue context.