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The spatial landscape of gene expression isoforms in tissue sections
In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164556/ https://www.ncbi.nlm.nih.gov/pubmed/36928528 http://dx.doi.org/10.1093/nar/gkad169 |
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author | Lebrigand, Kevin Bergenstråhle, Joseph Thrane, Kim Mollbrink, Annelie Meletis, Konstantinos Barbry, Pascal Waldmann, Rainer Lundeberg, Joakim |
author_facet | Lebrigand, Kevin Bergenstråhle, Joseph Thrane, Kim Mollbrink, Annelie Meletis, Konstantinos Barbry, Pascal Waldmann, Rainer Lundeberg, Joakim |
author_sort | Lebrigand, Kevin |
collection | PubMed |
description | In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context. |
format | Online Article Text |
id | pubmed-10164556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-101645562023-05-08 The spatial landscape of gene expression isoforms in tissue sections Lebrigand, Kevin Bergenstråhle, Joseph Thrane, Kim Mollbrink, Annelie Meletis, Konstantinos Barbry, Pascal Waldmann, Rainer Lundeberg, Joakim Nucleic Acids Res Methods Online In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized at spatial resolution with current transcriptome profiling methods. To that end, we introduce spatial isoform transcriptomics (SiT), an explorative method for characterizing spatial isoform variation and sequence heterogeneity using long-read sequencing. We show in mouse brain how SiT can be used to profile isoform expression and sequence heterogeneity in different areas of the tissue. SiT reveals regional isoform switching of Plp1 gene between different layers of the olfactory bulb, and the use of external single-cell data allows the nomination of cell types expressing each isoform. Furthermore, SiT identifies differential isoform usage for several major genes implicated in brain function (Snap25, Bin1, Gnas) that are independently validated by in situ sequencing. SiT also provides for the first time an in-depth A-to-I RNA editing map of the adult mouse brain. Data exploration can be performed through an online resource (https://www.isomics.eu), where isoform expression and RNA editing can be visualized in a spatial context. Oxford University Press 2023-03-17 /pmc/articles/PMC10164556/ /pubmed/36928528 http://dx.doi.org/10.1093/nar/gkad169 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Methods Online Lebrigand, Kevin Bergenstråhle, Joseph Thrane, Kim Mollbrink, Annelie Meletis, Konstantinos Barbry, Pascal Waldmann, Rainer Lundeberg, Joakim The spatial landscape of gene expression isoforms in tissue sections |
title | The spatial landscape of gene expression isoforms in tissue sections |
title_full | The spatial landscape of gene expression isoforms in tissue sections |
title_fullStr | The spatial landscape of gene expression isoforms in tissue sections |
title_full_unstemmed | The spatial landscape of gene expression isoforms in tissue sections |
title_short | The spatial landscape of gene expression isoforms in tissue sections |
title_sort | spatial landscape of gene expression isoforms in tissue sections |
topic | Methods Online |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164556/ https://www.ncbi.nlm.nih.gov/pubmed/36928528 http://dx.doi.org/10.1093/nar/gkad169 |
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