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Updated: Jul 13, 2026
10:41
An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues
Published on: April 5, 2018
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Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues.
Pengfei Guo1, Liran Mao2,3,4, Yufan Chen5
1Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. pengfei.guo@pennmedicine.upenn.edu.
Nature Methods
|January 27, 2025
Summary
Spatial multi-omics profiling reveals cellular complexity. The new spatial-Mux-seq technology simultaneously analyzes five molecular layers, offering a comprehensive view of tissue identity and spatiotemporal relationships in mouse embryos and brains.
Area of Science:
- Molecular Biology
- Genomics
- Neuroscience
Background:
- Cellular phenotypes are governed by a hierarchy of molecular layers including the genome, epigenome, transcriptome, proteome, and metabolome.
- Existing spatial omics methods are often limited to one or two modalities, providing an incomplete picture of cellular identity within tissues.
Purpose of the Study:
- To introduce spatial-Mux-seq, a novel multimodal spatial technology.
- To enable simultaneous profiling of five molecular modalities at tissue and cellular resolution.
- To comprehensively map cellular heterogeneity and spatiotemporal relationships in complex biological systems.
Main Methods:
- Development and application of spatial-Mux-seq, a multimodal spatial technology.
- Simultaneous profiling of two histone modifications, chromatin accessibility, whole transcriptome, and protein panels.
- Application to mouse embryos and mouse brains for generating detailed multimodal tissue maps.
Main Results:
- Spatial-Mux-seq identified more cell types and states than unimodal approaches.
- Uncovered spatiotemporal relationships between epigenetic modifications, gene expression, and protein levels during neuron differentiation.
- Revealed a radial glia niche with dynamic spatial epigenetic signals.
Conclusions:
- Multimodal spatial omics provides a more complete understanding of tissue and cellular heterogeneity.
- Spatial-Mux-seq advances the characterization of complex biological systems by integrating multiple molecular layers.
- This technology opens new avenues for exploring spatiotemporal dynamics in development and disease.
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