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Quantification of gene expression patterns to reveal the origins of abnormal morphogenesis

Neus Martinez-Abadias, Roger Mateu Estivill, Jaume Sastre Tomas, Susan Motch Perrine, Melissa Yoon, Alex Robert-Moreno, Jim Swoger, Lucia Russo, Kazuhiko Kawasaki, Joan Richtsmeier, James Sharpe

Preprint posted on January 11, 2018 https://www.biorxiv.org/content/early/2018/01/11/246256

Geometric morphometrics for quantifying developmental defects – analysis of spatio-temporal gene expression in numbers

Selected by Teresa Rayon

Categories: developmental biology

Summary

In this pre-print, the Sharpe lab develops a tool to quantitatively characterize gene expression patterns in whole embryos. They identify a limb defect in a mouse model of Apert syndrome carrying a missense mutation on the Fibroblast Growth Factor Receptor 2. In particular, quantifying downstream target Dusp6 allows them to spot the appearance of the limb defects, which have previously remained difficult to discern in mouse models of Apert syndrome.

 

Why I chose the paper:
One of my mantras is that new discoveries arise when biological phenomena are revisited in quantitative terms, even though they could have been previously studied on the basis of qualitative analysis; this preprint nicely provides a new look at gene expression.
The description of gene expression patterns over time during development is the pillar for our understanding of how genes work. In situ hybridisation is as a long existing technique that can be performed in any organism that contains RNA, and it has been used to unravel where and when genes are expressed. However, this technique has always been considered qualitative, difficult to analyse in whole embryos, and subjective at times. Neus Martinez-Abadias and colleagues develop a tool that allows them to accurately measure and determine subtle spatial changes in gene expression patterns in mouse mutants by combining image analysis, segmentation, and geometric morphometrics. There are very few examples where morphometric approaches have been applied to study morphological variability, and precisely relate it to gene expression. In the method, the authors first image and segment the shape of the tissue, and the expression pattern of Dusp6. Then, they describe its shape in 3D as a set of landmark coordinates. In this way, they objectively compare tissue morphology with a given gene expression pattern across embryos at various developmental stages to identify previously unrecognised limb defects at the molecular level.

 

How this work moves the field forward:
This preprint advances our understanding of how spatio-temporal changes in gene expression culminate in phenotypic variation, and builds the path towards quantitative developmental biology.

 

Figure 1. A new quantitative analysis method for 3D gene expression data, based on geometric morphometrics and Dusp6 expression analysis. An overview of the steps required for geometric morphometrics (1-3), and the outcome of the gene expression analysis. Images taken from figures 3 and 4f

 

Questions to the authors:
Since the technique is very powerful, I wonder the amount of time the authors dedicated to process the data, and how easily could it be applied in other labs.
Which do the authors think are the limitations of the technique?
How subtle can the changes be to define a given phenotype?
Have they tried to do the analysis with more than one gene?

Tags: gene expression

Read preprint (2 votes)




  • Author's response

    Neus Martinez-Abadias shared

    Questions to the authors:

    Since the technique is very powerful, I wonder the amount of time the authors dedicated to process the data, and how easily could it be applied in other labs.

    As we have developed this technique from scratch, we worked on this project for 2-3 years. Now that all the technical issues have been sorted out, we could reprocess all the data in just 2-3 months. We have optimized the experimentation, imaging, segmentation and landmark processing of the samples. All the details of the “recipe” are now described in the paper. Our next goal is to fully automatize the whole pipeline.

     

    The technique is indeed powerful and adaptable to other organs and model systems. It would be great to see other labs applying our approach and discovering new processes and mechanisms that the eyes just can’t see! Whole mount in situ hybridization (WISH) is a technique that is already in use in most molecular labs around the world. Many groups may already have in their “drawers” data on gene expression patterns ready to be quantified. Geometric morphometrics (GM) is also a discipline within everyone’s reach, with a long history, plenty of available resources, user-friendly free software and a big community of users (www.morphometrics.org). It was a pity that GM had not reached the developmental biology community before. Hopefully, our work will eventually bridge these two fields. OPT scanning is perhaps the least accessible technology, but OPT scanners are gradually getting to more and more labs and at EMBL we are always open to collaborate and share our expertise!

     

    Which do the authors think are the limitations of the technique?

    First, our method is limited to genes that can be labeled by WISH. Then, the limits are those of GM and the type of shape changes occurring during development. GM can only deal with gene expression domains that can be reliably represented as a unique continuously changing shape. However, some gene expression domains have inherently ill-defined shapes, lack homologous landmarks, and/or dramatically change their shape even over short periods of time, with emergence or loss of regions. In those cases, landmark free approaches could offer an alternative.

     

    How subtle can the changes be to define a given phenotype?

    Very subtle. Indeed, since the shape changes we are searching for are so subtle, we learnt in the process that in order to minimize variations due to experimentation, it is critical to process all the litters in the same experiment and strictly apply the same protocols. Even so, our first results were discouraging. We did not find any differences between the limbs and the gene expression patterns in wildtype and Apert syndrome mice when we first analyzed the pool of samples. Only after analyzing separately forelimbs and hindlimbs from different litters, the “magic” started. It was when we managed to properly control the different sources of variation that we were able to clearly separate between wildtype and mutant mice and detect when the differences first appeared. The resolution of the analysis was so high that we could even detect that altered gene expression occurs first and precedes the limb defects, which emerged only a few hours later.

     

    Have they tried to do the analysis with more than one gene?

    We have done the analysis with other genes, such as Hoxa11 and Hoxa13 in 2D (Martínez-Abadías et al 2016, https://academic.oup.com/sysbio/article/65/2/194/2427013), and Hand2 in 3D. All the analyses successfully revealed insights into normal and disease-altered limb development. We are also applying the approach to other organs, such as the heart, the face and the brain. The quantitative approach is time and resource-consuming, as it requires large samples of embryos over a short time window, but the effort is totally worth. Otherwise, it would be impossible to detect such critical changes about the origin of diseases and further understand how development translates genetic into phenotypic variation, which is a key process of Developmental Biology.

     

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