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SCS: cell segmentation for high-resolution spatial transcriptomics

Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10–15 cells per spot, recent technologies provide a much denser spot placement...

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Detalles Bibliográficos
Autores principales: Chen, Hao, Li, Dongshunyi, Bar-Joseph, Ziv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312435/
https://www.ncbi.nlm.nih.gov/pubmed/37398213
http://dx.doi.org/10.1101/2023.01.11.523658
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author Chen, Hao
Li, Dongshunyi
Bar-Joseph, Ziv
author_facet Chen, Hao
Li, Dongshunyi
Bar-Joseph, Ziv
author_sort Chen, Hao
collection PubMed
description Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10–15 cells per spot, recent technologies provide a much denser spot placement leading to sub-cellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcriptomics. Here we present SCS, which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new sub-cellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells, and provided more realistic cell size estimation. Sub-cellular analysis of RNAs using SCS spots assignments provides information on RNA localization and further supports the segmentation results.
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spelling pubmed-103124352023-07-01 SCS: cell segmentation for high-resolution spatial transcriptomics Chen, Hao Li, Dongshunyi Bar-Joseph, Ziv bioRxiv Article Spatial transcriptomics promises to greatly improve our understanding of tissue organization and cell-cell interactions. While most current platforms for spatial transcriptomics only offer multi-cellular resolution, with 10–15 cells per spot, recent technologies provide a much denser spot placement leading to sub-cellular resolution. A key challenge for these newer methods is cell segmentation and the assignment of spots to cells. Traditional image-based segmentation methods are limited and do not make full use of the information profiled by spatial transcriptomics. Here we present SCS, which combines imaging data with sequencing data to improve cell segmentation accuracy. SCS assigns spots to cells by adaptively learning the position of each spot relative to the center of its cell using a transformer neural network. SCS was tested on two new sub-cellular spatial transcriptomics technologies and outperformed traditional image-based segmentation methods. SCS achieved better accuracy, identified more cells, and provided more realistic cell size estimation. Sub-cellular analysis of RNAs using SCS spots assignments provides information on RNA localization and further supports the segmentation results. Cold Spring Harbor Laboratory 2023-06-16 /pmc/articles/PMC10312435/ /pubmed/37398213 http://dx.doi.org/10.1101/2023.01.11.523658 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Chen, Hao
Li, Dongshunyi
Bar-Joseph, Ziv
SCS: cell segmentation for high-resolution spatial transcriptomics
title SCS: cell segmentation for high-resolution spatial transcriptomics
title_full SCS: cell segmentation for high-resolution spatial transcriptomics
title_fullStr SCS: cell segmentation for high-resolution spatial transcriptomics
title_full_unstemmed SCS: cell segmentation for high-resolution spatial transcriptomics
title_short SCS: cell segmentation for high-resolution spatial transcriptomics
title_sort scs: cell segmentation for high-resolution spatial transcriptomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312435/
https://www.ncbi.nlm.nih.gov/pubmed/37398213
http://dx.doi.org/10.1101/2023.01.11.523658
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