Grid Screener: A Tool for Automated High-Throughput Screening on Biochemical and Biological Analysis Platforms

Abstract

Grid structures are common in high-throughput assays to parallelize experiments in biochemical or biological experiments. Manual analysis of grid images is laborious, time-consuming, expensive, and critical in terms of reproducibility. However, it is still common to do such analysis manually, as there is no standardized software for automated analysis. In this paper, we introduce a generic method to automatically detect grid structures in images and to perform flexible spot-wise analysis after successful grid detection. The deep learning-based approach of the grid structure detection allows being flexible concerning different grid types. The combination with a robust parameter estimation algorithm lowers the requirements of the detection quality and thus enhances robustness. Further, the method conducts semi-automated grid detection if a fully automated processing fails. An open-source software tool Grid Screener that implements the proposed methods is provided as a ready-for-use tool for researchers. The usability is demonstrated by taking different criteria into account, which are important for a successful application. We present the benefits of our proposed tool Grid Screener utilizing three different grid types in the context of high-throughput screening to show our contribution towards further lab automation. Our tool performs much faster than manual analysis, while maintaining or even enhancing accuracy.

Keywords

• Application software
• artificial neural networks
• automation
• biological systems
• chemical technology
• machine learning