Cargando…
An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation
Advances in genome engineering have resulted in the generation of numerous zebrafish mutant lines. A commonly used method to assess gene expression in the mutants is in situ hybridisation (ISH). Because the embryos can be distinguished by genotype after ISH, comparing gene expression between wild-ty...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Company of Biologists Ltd
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5936060/ https://www.ncbi.nlm.nih.gov/pubmed/29535102 http://dx.doi.org/10.1242/bio.031096 |
_version_ | 1783320387038216192 |
---|---|
author | Dobrzycki, Tomasz Krecsmarik, Monika Bonkhofer, Florian Patient, Roger Monteiro, Rui |
author_facet | Dobrzycki, Tomasz Krecsmarik, Monika Bonkhofer, Florian Patient, Roger Monteiro, Rui |
author_sort | Dobrzycki, Tomasz |
collection | PubMed |
description | Advances in genome engineering have resulted in the generation of numerous zebrafish mutant lines. A commonly used method to assess gene expression in the mutants is in situ hybridisation (ISH). Because the embryos can be distinguished by genotype after ISH, comparing gene expression between wild-type and mutant siblings can be done blinded and in parallel. Such experimental design reduces the technical variation between samples and minimises the risk of bias. This approach, however, requires an efficient method of genomic DNA extraction from post-ISH fixed zebrafish samples to ascribe phenotype to genotype. Here we describe a method to obtain PCR-quality DNA from 95-100% of zebrafish embryos, suitable for genotyping after ISH. In addition, we provide an image analysis protocol for quantifying gene expression of ISH-probed embryos, adaptable for the analysis of different expression patterns. Finally, we show that intensity-based image analysis enables accurate representation of the variability of gene expression detected by ISH and that it can complement quantitative methods like qRT-PCR. By combining genotyping after ISH and computer-based image analysis, we have established a high-confidence, unbiased methodology to assign gene expression levels to specific genotypes, and applied it to the analysis of molecular phenotypes of newly generated lmo4a mutants. |
format | Online Article Text |
id | pubmed-5936060 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Company of Biologists Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-59360602018-05-22 An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation Dobrzycki, Tomasz Krecsmarik, Monika Bonkhofer, Florian Patient, Roger Monteiro, Rui Biol Open Methods & Techniques Advances in genome engineering have resulted in the generation of numerous zebrafish mutant lines. A commonly used method to assess gene expression in the mutants is in situ hybridisation (ISH). Because the embryos can be distinguished by genotype after ISH, comparing gene expression between wild-type and mutant siblings can be done blinded and in parallel. Such experimental design reduces the technical variation between samples and minimises the risk of bias. This approach, however, requires an efficient method of genomic DNA extraction from post-ISH fixed zebrafish samples to ascribe phenotype to genotype. Here we describe a method to obtain PCR-quality DNA from 95-100% of zebrafish embryos, suitable for genotyping after ISH. In addition, we provide an image analysis protocol for quantifying gene expression of ISH-probed embryos, adaptable for the analysis of different expression patterns. Finally, we show that intensity-based image analysis enables accurate representation of the variability of gene expression detected by ISH and that it can complement quantitative methods like qRT-PCR. By combining genotyping after ISH and computer-based image analysis, we have established a high-confidence, unbiased methodology to assign gene expression levels to specific genotypes, and applied it to the analysis of molecular phenotypes of newly generated lmo4a mutants. The Company of Biologists Ltd 2018-03-13 /pmc/articles/PMC5936060/ /pubmed/29535102 http://dx.doi.org/10.1242/bio.031096 Text en © 2018. Published by The Company of Biologists Ltd http://creativecommons.org/licenses/by/3.0This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. |
spellingShingle | Methods & Techniques Dobrzycki, Tomasz Krecsmarik, Monika Bonkhofer, Florian Patient, Roger Monteiro, Rui An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
title | An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
title_full | An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
title_fullStr | An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
title_full_unstemmed | An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
title_short | An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
title_sort | optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation |
topic | Methods & Techniques |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5936060/ https://www.ncbi.nlm.nih.gov/pubmed/29535102 http://dx.doi.org/10.1242/bio.031096 |
work_keys_str_mv | AT dobrzyckitomasz anoptimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT krecsmarikmonika anoptimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT bonkhoferflorian anoptimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT patientroger anoptimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT monteirorui anoptimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT dobrzyckitomasz optimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT krecsmarikmonika optimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT bonkhoferflorian optimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT patientroger optimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation AT monteirorui optimisedpipelineforparallelimagebasedquantificationofgeneexpressionandgenotypingafterinsituhybridisation |