Novel computational model of gastrula morphogenesis to identify spatial discriminator genes by self-organizing map (SOM) clustering

Haruka Takaoka,Junko Yamane,Tomoya Mori,Wataru Fujibuchi,Cantas Alev

doi:10.1038/s41598-019-49031-1

Haruka Takaoka, Junko Yamane + Show 3 more

Open Access

https://doi.org/10.1038/s41598-019-49031-1

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Abstract

Deciphering the key mechanisms of morphogenesis during embryonic development is crucial to understanding the guiding principles of the body plan and promote applications in biomedical research fields. Although several computational tissue reconstruction methods using cellular gene expression data have been proposed, those methods are insufficient with regard to arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Here, we report SPRESSO, a new in silico three-dimensional (3D) tissue reconstruction method using stochastic self-organizing map (stochastic-SOM) clustering, to estimate the spatial domains of cells in tissues or organs from only their gene expression profiles. With only five gene sets defined by Gene Ontology (GO), we successfully demonstrated the reconstruction of a four-domain structure of mid-gastrula mouse embryo (E7.0) with high reproducibility (success rate = 99%). Interestingly, the five GOs contain 20 genes, most of which are related to differentiation and morphogenesis, such as activin A receptor and Wnt family member genes. Further analysis indicated that Id2 is the most influential gene contributing to the reconstruction. SPRESSO may provide novel and better insights on the mechanisms of 3D structure formation of living tissues via informative genes playing a role as spatial discriminators.

Highlights

The reconstruction of three-dimensional (3D) tissues such as organoids and organ-like structures from human induced pluripotent stem cells[1] is one of the most exciting technologies in the field of regenerative medicine
Current principal component analysis (PCA)-based methods are used for 3D visualization, an ab initio approach that does not depend on the spatial information of marker genes obtained by in situ hybridization is promising for 3D reconstruction
In order to expand the capability of our preliminary research, we further developed a novel 3D reconstruction method using stochastic self-organizing map clustering, or SPRESSO (SPatial REconstruction by Stochastic-SOM), which features gene selections based on Gene Ontology (GO)[25,26]

Summary

Introduction

The reconstruction of three-dimensional (3D) tissues such as organoids and organ-like structures from human induced pluripotent stem (iPS) cells[1] is one of the most exciting technologies in the field of regenerative medicine. Several computational methods to reconstruct 3D tissues by estimating the spatial positions of individual cells in tissues with gene expression data obtained by single-cell RNA-seq have been reported[16,17,18,19,20,21,22]. These methods may be roughly divided into two types: the landmark approach and the ab initio approach. The method yielded high success rates and demonstrated a remarkable ability to find spatial discriminator genes that contribute to differentiation and tissue morphogenesis

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