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Updated: May 7, 2026

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

STORIES: learning cell fate landscapes from spatial transcriptomics using optimal transport.

Geert-Jan Huizing¹, Jules Samaran¹, Daniele Capocefalo¹

¹Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France.

|November 3, 2025

Summary

This summary is machine-generated.

We introduce STORIES, a novel computational method for analyzing spatial transcriptomics data over time. This approach enhances understanding of cell fate trajectories and spatial organization in dynamic biological processes.

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

Genomics
Computational Biology
Developmental Biology

Background:

Spatial transcriptomics revolutionizes the study of tissue organization.
Inferring cell fate trajectories from spatiotemporal data is a critical goal.
Existing computational methods struggle with spatially resolved transcriptomic data.

Purpose of the Study:

To develop a novel computational method for analyzing spatiotemporal transcriptomic data.
To improve the inference of cell fate trajectories with spatial information.
To address the limitations of current gradient flow learning methods in spatial transcriptomics.

Main Methods:

Propose STORIES, a method extending Optimal Transport to learn a spatially informed potential.
Utilize Wasserstein gradient flow learning framework.
Benchmark against existing approaches using large Stereo-seq spatiotemporal atlases.

Main Results:

STORIES demonstrates superior spatial coherence compared to existing methods.
Successfully applied to axolotl neural regeneration and mouse gliogenesis.
Identified gene trends for known markers (Nptx1, Aldh1l1) and putative drivers.

Conclusions:

STORIES provides a powerful new tool for analyzing dynamic biological processes using spatial transcriptomics.
The method enhances the understanding of tissue spatial organization and cell differentiation.
Enables discovery of novel gene markers and drivers in complex biological systems.