eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells.

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.

Integrative Approaches of Bioassay and Computational Analysis for Discovering Potential Bioactive Compounds and Predictive Toxicity.

Changes in eating habits are brought about by drastic changes in lifestyle and environment, and, it has been pointed out, are strongly involved in the increase in neurological diseases and onset of cancer in younger adult ages. There is a wide variety of chemical substances in food, and there is a need to analyze the effects of complex exposures on complex mechanisms of action and to develop methods for evaluating and predicting them. The power of molecular nutrition needs to create an integrated approach to human nutrition in line with the grand social challenges of diet-related illnesses.

StemPanTox: A fast and wide-target drug assessment system for tailor-made safety evaluations using personalized iPS cells.

An alternative model that reliably predicts human-specific toxicity is necessary because the translatability of effects on animal models for human disease is limited to context. Previously, we developed a method that accurately predicts developmental toxicity based on the gene networks of undifferentiated human embryonic stem (ES) cells. Here, we advanced this method to predict toxicities of 24 chemicals in six categories (neurotoxins, cardiotoxins, hepatotoxins, two types of nephrotoxins, and non-genotoxic carcinogens) and achieved high predictability (AUC = 0.