Published November 6, 2020
| Version v1
Publication
The spatial landscape of gene expression isoforms in tissue sections
Contributors
Others:
- Centre National de la Recherche Scientifique (CNRS)
- Université Côte d'Azur (UCA)
- Institut de pharmacologie moléculaire et cellulaire (IPMC) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS) ; COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)
- Fondation Recherche Médicale DEQ20180339158
- National Infrastructure France Génomique (Commissariat aux Grands Investissements, ANR-10-INBS-09-03, ANR-10-INBS-09-02)
- Inserm Cross-cutting Scientific Program HuDeCA 2018
- ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
- European Project: 874656,discovAIR
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 with current methods. Here, we introduce Spatial Isoform Transcriptomics (SiT), an explorative method for characterizing spatial isoform and sequence heterogeneity in tissue sections, and show how it can be used to profile isoform expression and sequence heterogeneity in a tissue context
Additional details
Identifiers
- URL
- https://hal.archives-ouvertes.fr/hal-02992334
- URN
- urn:oai:HAL:hal-02992334v1
Origin repository
- Origin repository
- UNICA