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Updated: Aug 12, 2025

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Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics.

Frédéric Pont1,2,3, Juan Pablo Cerapio4,5,6, Pauline Gravelle4,5,6

  • 1CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France. frederic.pont@inserm.fr.

BMC Bioinformatics
|January 28, 2023
PubMed
Summary

A new open-source software, Single-Cell Spatial Explorer, enables powerful analysis of spatial transcriptomics data. This versatile tool aids researchers in exploring multimodal datasets from various technologies and tissues.

Keywords:
FreewareMultimodal analysisOpen-sourceSingle-cellSpatial transcriptomicsVisualization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell technologies generate large, multimodal datasets including transcriptomes, immunophenotypes, and spatial information.
  • Analysis of spatial transcriptomics data is challenging due to a scarcity of user-friendly, powerful, and free algorithmic tools.

Purpose of the Study:

  • To introduce an open-source software for the multimodal exploration and analysis of spatial transcriptomics data.
  • To provide researchers with a versatile and accessible tool for spatial transcriptomics research.

Main Methods:

  • Development of Single-Cell Spatial Explorer, an open-source software package.
  • Demonstration of the software's capabilities using 9 human and murine tissue datasets.
  • Application across 4 different spatial transcriptomics technologies.

Main Results:

  • Single-Cell Spatial Explorer facilitates the multimodal exploration of spatial transcriptomics data.
  • The software is exemplified with diverse datasets from human and murine tissues.
  • The tool supports analysis across multiple spatial transcriptomics technologies.

Conclusions:

  • Single-Cell Spatial Explorer is a powerful, versatile, and interoperable tool for spatial transcriptomics analysis.
  • The software addresses the need for accessible and effective analysis solutions in the field.
  • It empowers researchers to gain deeper insights from complex spatial transcriptomics datasets.