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A unified pipeline for FISH spatial transcriptomics

High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house...

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Detalles Bibliográficos
Autores principales: Cisar, Cecilia, Keener, Nicholas, Ruffalo, Mathew, Paten, Benedict
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504669/
https://www.ncbi.nlm.nih.gov/pubmed/37719153
http://dx.doi.org/10.1016/j.xgen.2023.100384
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author Cisar, Cecilia
Keener, Nicholas
Ruffalo, Mathew
Paten, Benedict
author_facet Cisar, Cecilia
Keener, Nicholas
Ruffalo, Mathew
Paten, Benedict
author_sort Cisar, Cecilia
collection PubMed
description High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house tools that are often poorly documented and not generalizable. To address this, we have expanded and improved the starfish library and used those tools to create PIPEFISH, a semi-automated and generalizable pipeline that performs transcript annotation for fluorescence in situ hybridization (FISH)-based spatial transcriptomics. We used this pipeline to annotate transcript locations from three real datasets from three different common types of FISH image-based experiments, MERFISH, seqFISH, and targeted in situ sequencing (ISS), and verified that the results were high quality using the internal quality metrics of the pipeline and also a comparison with an orthogonal method of measuring RNA expression. PIPEFISH is a publicly available and open-source tool.
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spelling pubmed-105046692023-09-17 A unified pipeline for FISH spatial transcriptomics Cisar, Cecilia Keener, Nicholas Ruffalo, Mathew Paten, Benedict Cell Genom Article High-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and its effects on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write their own in-house tools that are often poorly documented and not generalizable. To address this, we have expanded and improved the starfish library and used those tools to create PIPEFISH, a semi-automated and generalizable pipeline that performs transcript annotation for fluorescence in situ hybridization (FISH)-based spatial transcriptomics. We used this pipeline to annotate transcript locations from three real datasets from three different common types of FISH image-based experiments, MERFISH, seqFISH, and targeted in situ sequencing (ISS), and verified that the results were high quality using the internal quality metrics of the pipeline and also a comparison with an orthogonal method of measuring RNA expression. PIPEFISH is a publicly available and open-source tool. Elsevier 2023-08-21 /pmc/articles/PMC10504669/ /pubmed/37719153 http://dx.doi.org/10.1016/j.xgen.2023.100384 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cisar, Cecilia
Keener, Nicholas
Ruffalo, Mathew
Paten, Benedict
A unified pipeline for FISH spatial transcriptomics
title A unified pipeline for FISH spatial transcriptomics
title_full A unified pipeline for FISH spatial transcriptomics
title_fullStr A unified pipeline for FISH spatial transcriptomics
title_full_unstemmed A unified pipeline for FISH spatial transcriptomics
title_short A unified pipeline for FISH spatial transcriptomics
title_sort unified pipeline for fish spatial transcriptomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504669/
https://www.ncbi.nlm.nih.gov/pubmed/37719153
http://dx.doi.org/10.1016/j.xgen.2023.100384
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