Author:
Mori Tomoya,Takase Toshiro,Lan Kuan-Chun,Yamane Junko,Alev Cantas,Kimura Azuma,Osafune Kenji,Yamashita Jun K.,Akutsu Tatsuya,Kitano Hiroaki,Fujibuchi Wataru
Abstract
Abstract
Background
Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided.
Results
This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations.
Conclusions
eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations.
Funder
Ministry of Education, Culture, Sports, Science and Technology
Japan Agency for Medical Research and Development
iPS Cell Research Fund
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Structural Biology