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Related Experiment Video

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DeST-OT: Alignment of spatiotemporal transcriptomics data.

Peter Halmos1, Xinhao Liu1, Julian Gold2

  • 1Department of Computer Science, Princeton University, 35 Olden St., Princeton, NJ 08544, USA.

Cell Systems
|January 28, 2025
PubMed
Summary
This summary is machine-generated.

We developed a new method, developmental spatiotemporal optimal transport (DeST-OT), to align gene expression data across different developmental time points. This tool helps understand how cells grow and change in developing tissues.

Keywords:
alignmentdevelopmentdevelopmental biologygrowth ratesoptimal transportsemi-relaxed optimal transportspatially resolved transcriptomicsspatiotemporaltrajectory inference

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Area of Science:

  • Computational biology
  • Developmental biology
  • Genomics

Background:

  • Spatially resolved transcriptomics (SRT) provides gene expression data at a high resolution within tissues.
  • SRT data from different developmental stages are crucial for understanding tissue development but are challenging to align.
  • Existing methods struggle to accurately model the dynamic cellular processes occurring during development.

Purpose of the Study:

  • To introduce a novel computational method, developmental spatiotemporal optimal transport (DeST-OT), for aligning spatiotemporal transcriptomics data.
  • To enable the analysis of gene expression dynamics during organismal development.
  • To quantify cellular processes like growth, death, and differentiation within developing tissues.

Main Methods:

  • Developed DeST-OT, a method utilizing semi-relaxed optimal transport (OT) to align spatiotemporal transcriptomics data.
  • Incorporated modeling of cellular growth, death, and differentiation into the OT framework.
  • Derived quantitative metrics for growth distortion and cell migration to assess alignment plausibility.

Main Results:

  • DeST-OT successfully aligned spatiotemporal transcriptomics data from developing mouse kidney and axolotl brain.
  • The method demonstrated superior performance compared to existing alignment techniques.
  • Estimated growth rates from DeST-OT provided novel insights into gene expression programs driving development.

Conclusions:

  • DeST-OT is an effective computational tool for analyzing developmental transcriptomics data.
  • The method accurately models cellular dynamics and provides quantitative measures of developmental processes.
  • DeST-OT facilitates a deeper understanding of the spatial and temporal regulation of gene expression during development.