Spatial transcriptomics of the nematode Caenorhabditis elegans using RNA tomography
| Autor: | Jonas Mars, Hendrik C. Korswagen, Marco C. Betist, Erik S. Schild, Annabel Ebbing, Judith Vivié |
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| Přispěvatelé: | Hubrecht Institute for Developmental Biology and Stem Cell Research |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Science (General)
Sequence analysis ved/biology.organism_classification_rank.species RNA-Seq Computational biology Biology General Biochemistry Genetics and Molecular Biology Transcriptome Q1-390 Model Organisms Gene expression Protocol Animals RNA Messenger Model organism Caenorhabditis elegans Tomography General Immunology and Microbiology ved/biology Sequence Analysis RNA General Neuroscience Gene Expression Profiling RNA MRNA Sequencing RNA extraction Single-Cell Analysis RNA-seq |
| Zdroj: | STAR Protocols, Vol 2, Iss 2, Pp 100411-(2021) STAR Protocols STAR protocols, 2(2). Cell Press |
| ISSN: | 2666-1667 |
| Popis: | Summary RNA tomography or tomo-seq combines mRNA sequencing and cryo-sectioning to spatially resolve gene expression. We have adapted this method for the nematode Caenorhabditis elegans to generate anteroposterior gene expression maps at near-cellular resolution. Here, we provide a detailed overview of the method and present two approaches: one that includes RNA isolation for maximum sensitivity and one that is suitable for partial automatization and is therefore less time-consuming. For complete details on the use and execution of this protocol, please refer to Ebbing et al. (2018). Graphical Abstract Highlights • Spatial transcriptomics method combining cryo-sectioning with mRNA sequencing • Separate protocols for maximal sensitivity versus partial automation • Bioinformatic analysis pipeline for quality control of mRNA sequencing data • Discussion of pitfalls and quality control issues RNA tomography or tomo-seq combines mRNA sequencing and cryo-sectioning to spatially resolve gene expression. We have adapted this method for the nematode Caenorhabditis elegans to generate anteroposterior gene expression maps at near-cellular resolution. Here, we provide a detailed overview of the method and present two approaches: one that includes RNA isolation for maximum sensitivity and one that is suitable for partial automatization and is therefore less time-consuming. |
| Databáze: | OpenAIRE |
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