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SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data

The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A partic...

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Autores principales: Tiesmeyer, Sebastian, Sahay, Shashwat, Müller-Bötticher, Niklas, Eils, Roland, Mackowiak, Sebastian D., Ishaque, Naveed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918671/
https://www.ncbi.nlm.nih.gov/pubmed/35295943
http://dx.doi.org/10.3389/fgene.2022.785877
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author Tiesmeyer, Sebastian
Sahay, Shashwat
Müller-Bötticher, Niklas
Eils, Roland
Mackowiak, Sebastian D.
Ishaque, Naveed
author_facet Tiesmeyer, Sebastian
Sahay, Shashwat
Müller-Bötticher, Niklas
Eils, Roland
Mackowiak, Sebastian D.
Ishaque, Naveed
author_sort Tiesmeyer, Sebastian
collection PubMed
description The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data.
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spelling pubmed-89186712022-03-15 SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data Tiesmeyer, Sebastian Sahay, Shashwat Müller-Bötticher, Niklas Eils, Roland Mackowiak, Sebastian D. Ishaque, Naveed Front Genet Genetics The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data. Frontiers Media S.A. 2022-02-28 /pmc/articles/PMC8918671/ /pubmed/35295943 http://dx.doi.org/10.3389/fgene.2022.785877 Text en Copyright © 2022 Tiesmeyer, Sahay, Müller-Bötticher, Eils, Mackowiak and Ishaque. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Tiesmeyer, Sebastian
Sahay, Shashwat
Müller-Bötticher, Niklas
Eils, Roland
Mackowiak, Sebastian D.
Ishaque, Naveed
SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
title SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
title_full SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
title_fullStr SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
title_full_unstemmed SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
title_short SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data
title_sort ssam-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8918671/
https://www.ncbi.nlm.nih.gov/pubmed/35295943
http://dx.doi.org/10.3389/fgene.2022.785877
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