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Updated: Jun 13, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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Bin2cell reconstructs cells from high resolution Visium HD data.

Krzysztof Polański1,2, Raquel Bartolomé-Casado2,3, Ioannis Sarropoulos1,2

  • 1Cambridge Stem Cell Institute and Department of Medicine, University of Cambridge, Cambridge, CB2 0AW, United Kingdom.

Bioinformatics (Oxford, England)
|September 9, 2024
PubMed
Summary
This summary is machine-generated.

Bin2cell software reconstructs single cells from high-resolution spatial transcriptomics data, improving analysis of archived tissue samples. This method enhances downstream applications by accurately defining cellular boundaries from 2-micrometer bins.

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Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Bioinformatics

Background:

  • Visium HD offers sub-cellular resolution transcriptomic data from FFPE blocks.
  • Aggregating capture regions into single cells presents analytical challenges.

Purpose of the Study:

  • To develop a computational method (Bin2cell) for reconstructing single cells from high-resolution spatial transcriptomics data.
  • To improve downstream analysis by overcoming limitations of default binning strategies.

Main Methods:

  • Bin2cell utilizes morphology image segmentation and gene expression data.
  • Reconstruction is performed from 2-micrometer bins, leveraging sub-cellular resolution.
  • The software is compatible with existing Python single-cell and spatial transcriptomics tools.

Main Results:

  • Bin2cell efficiently reconstructs cells in minutes without GPU requirements.
  • Demonstrated improved downstream analysis using reconstructed cells compared to default 8-micrometer bins.
  • Validation performed on mouse brain and human colorectal cancer datasets.

Conclusions:

  • Bin2cell enables accurate single-cell reconstruction from high-resolution spatial transcriptomics.
  • The method enhances the utility of archived FFPE samples for transcriptomic analysis.
  • Bin2cell offers a computationally efficient solution for spatial biology research.