Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays

Area of Science:

• Life Sciences
• Biotechnology
• Drug Discovery

Background:

• High content screening (HCS) is a standard technology in drug discovery, generating large datasets.
• Data analysis and management in HCS face challenges, often creating bottlenecks in screening projects.
• Current HCS practices require efficient IT infrastructure for managing and analyzing extensive experimental data.

Purpose of the Study:

• To outline typical high content screening (HCS) workflows.
• To present IT infrastructure requirements for multi-well plate-based HCS.
• To highlight the benefits of a shared HCS data management and analysis infrastructure.

Main Methods:

• Review of current HCS data analysis and management practices.
• Identification of challenges in handling large HCS datasets.
• Description of IT infrastructure needs for multi-well plate HCS.

Main Results:

• HCS data analysis remains a significant bottleneck in many screening projects.
• A shared HCS data management and analysis infrastructure can optimize IT resource utilization.
• Implementing company-wide standards for data quality and results is achievable with shared infrastructure.

Conclusions:

• Efficient data management and analysis are crucial for high content screening (HCS) success.
• Shared IT infrastructure enhances resource efficiency and ensures data quality in HCS.
• Standardized workflows and infrastructure are essential for multi-well plate-based HCS.