生物传感器与生物电子学

Discerning Data Analysis Methods to Clarify Agonistic/Antagonistic Actions on the Ion Flux Assay of Ligand-Gated Ionotropic Glutamate Receptor on Engineered Post-Synapse Model Cells

Abstract

Akito Tateishi, Michael Cauchi, Chisato Tanoue, Satoshi Migita, Sarah K. Coleman, Shinya Ikeno, Kari Keinänen, Conrad Bessant and Tetsuya Haruyama

Cell-based experiments provide the efficacy of chemicals through the biological function. The authors have described post-synapse model cell-based assay based on qualified analysis for neural drug discoveries. However, in general, cell-based assays often include data fluctuation. This may be a barrier preventing the performance for various practical purposes. In this study, we tried discerning data analysis for clarify the chemical action to the ionotoropic glutamate receptor (GluR), whereby an ion-flux assay of post-synapse model cells is performed and are analyzed based on a chemometrics approach. The dynamic behavior of the GluR of post-synapse model cell was assayed with multivariate data analysis methods namely hierarchical cluster analysis (HCA) and principal component analysis (PCA). By using HCA, we can identify and remove the non-responding samples. By using PCA, the effect of chemicals on the dynamic behavior of ion flux through GluR can be recognized clearly; as either agonist or antagonist. As shown in the results, the GluR-based assay by post-synapse model cell with data analysis methods provide a sodium influx profile which is derived by an agonists or antagonists application. By employing the data analysis methods, PCA and HCA, it is possible to develop a smart cellular biosensing system for qualified analysis.