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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Connectogenomics: edges-first spatial biology.

Reem Yasser1, Mingrui Yu2, Nianchao Qian3

  • 1Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, USA.

Trends in Biotechnology
|November 7, 2025
PubMed
Summary
This summary is machine-generated.

We introduce connectogenomics, a new approach to spatial omics that maps cell types and their connections. This method combines spatial coordinates with direct contact measurements for a comprehensive understanding of cellular networks.

Keywords:
connectogenomicsgraph theoryspatial omicssystems biologytissue microenvironment

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Area of Science:

  • Molecular Biology
  • Genomics
  • Systems Biology

Background:

  • Spatial omics integrates cell types with their spatial context.
  • Current methods focus on spatial coordinates or proximity-based cell interactions.
  • A unified approach is needed to directly measure and analyze cellular contacts.

Purpose of the Study:

  • Introduce connectogenomics as a framework for sequencing assays that directly record cellular contacts.
  • Develop a tiered approach combining coordinate mapping and contact measurements.
  • Enable quantitative analysis of cellular network features like contact density and hub centrality.

Main Methods:

  • Directly sequencing molecular contacts between cells.
  • Integrating spatial coordinate data with contact network data.
  • Developing a four-tiered framework with a feedback loop for analysis.

Main Results:

  • Demonstrated the ability to verify direct cellular interactions using combined spatial and contact data.
  • Enabled quantitative readouts of network features such as contact density and hub centrality.
  • Validated the framework in diverse biological contexts including cancer immunotherapy, development, and genetic screens.

Conclusions:

  • Connectogenomics provides a robust framework for analyzing cellular interactions in spatial omics.
  • The integration of spatial mapping and contact sequencing offers deeper insights into cellular organization and function.
  • This approach has broad applicability in understanding complex biological systems.