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Related Experiment Video

Updated: Oct 16, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

Cell segmentation in imaging-based spatial transcriptomics.

Viktor Petukhov^1,2, Rosalind J Xu^3,4,5, Ruslan A Soldatov¹

¹Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Nature Biotechnology

|October 15, 2021

Summary

This summary is machine-generated.

Baysor, a new segmentation method, accurately defines cell boundaries in spatial transcriptomics data. It improves cell counting and reduces artifacts, enhancing tissue organization analysis.

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Last Updated: Oct 16, 2025

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

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Published on: April 30, 2019

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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

Area of Science:

Spatial transcriptomics
Bioinformatics
Cell biology

Background:

Single-molecule spatial transcriptomics provides high-resolution tissue organization insights.
Accurate cell boundary segmentation is crucial but challenging in these datasets.
Existing methods often rely on nuclei stains, limiting precision.

Purpose of the Study:

To develop and evaluate Baysor, a novel computational method for precise cell segmentation in spatial transcriptomics.
To improve the accuracy and completeness of cell identification in imaging-based spatial transcriptomics data.
To provide a versatile tool applicable across diverse spatial transcriptomics protocols.

Main Methods:

Baysor employs a joint likelihood model integrating transcriptional composition and cell morphology for 2D/3D segmentation.
The method can utilize co-stains or segment based solely on detected transcripts.
Performance was benchmarked using extended multiplexed error-robust fluorescence in situ hybridization (MERFISH) with immunostained boundaries and other protocols.

Main Results:

Baysor significantly improves cell segmentation accuracy compared to existing tools.
The method can nearly double the number of identified cells in some datasets.
Baysor effectively reduces segmentation artifacts and demonstrates robustness across five different spatial transcriptomics protocols.

Conclusions:

Baysor offers a significant advancement in cell segmentation for spatial transcriptomics.
Its ability to leverage transcriptomic and morphological data enhances cell boundary definition.
Baysor is a versatile and powerful tool for analyzing complex tissue architectures in spatial transcriptomics studies.