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An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation.

ABSTRACT: 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.

SUBMITTER: Dobrzycki T

PROVIDER: S-EPMC5936060 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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An optimised pipeline for parallel image-based quantification of gene expression and genotyping after <i>in situ</i> hybridisation.

Dobrzycki Tomasz T Krecsmarik Monika M Bonkhofer Florian F Patient Roger R Monteiro Rui R

Biology open 20180409 4

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 <i>in situ</i> 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, requ ...[more]

PMID: 29535102

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Project description:BackgroundMany important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular level resolution. Projects creating gene expression atlases at unprecedented scales for the embryonic fruit fly as well as the embryonic and adult mouse already involve the analysis of hundreds of thousands of high resolution experimental images mapping mRNA expression patterns. Challenges include accurate registration of highly deformed tissues, associating cells with known anatomical regions, and identifying groups of genes whose expression is coordinately regulated with respect to both concentration and spatial location. Solutions to these and other challenges will lead to a richer understanding of the complex system aspects of gene regulation in heterogeneous tissue.ResultsWe present an end-to-end approach for processing raw in situ expression imagery and performing subsequent analysis. We use a non-linear, information theoretic based image registration technique specifically adapted for mapping expression images to anatomical annotations and a method for extracting expression information within an anatomical region. Our method consists of coarse registration, fine registration, and expression feature extraction steps. From this we obtain a matrix for expression characteristics with rows corresponding to genes and columns corresponding to anatomical sub-structures. We perform matrix block cluster analysis using a novel row-column mixture model and we relate clustered patterns to Gene Ontology (GO) annotations.ConclusionResulting registrations suggest that our method is robust over intensity levels and shape variations in ISH imagery. Functional enrichment studies from both simple analysis and block clustering indicate that gene relationships consistent with biological knowledge of neuronal gene functions can be extracted from large ISH image databases such as the Allen Brain Atlas 1 and the Max-Planck Institute 2 using our method. While we focus here on imagery and experiments of the mouse brain our approach should be applicable to a variety of in situ experiments.

Dataset Information

An optimised pipeline for parallel image-based quantification of gene expression and genotyping after in situ hybridisation.

Publications

An optimised pipeline for parallel image-based quantification of gene expression and genotyping after <i>in situ</i> hybridisation.

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