CYTO Virtual Interactive 2021 Scientific Tutorial - Image-Based High-Throughput Screening, High-Content Analysis and High-Throughput Profiling
This tutorial session introduces basic concepts of imaging-based high-throughput screening (HTS) and high-content analysis (HCA). Conventional HTS assays are designed to evaluate a discrete cellular process and produce a single or small number of quantitative outputs. In contrast, imaging-based HTS/HCA approaches measure dozens to thousands of features and provide highly multiplexed quantitative results. Topics covered in this tutorial include (but are not limited to) considerations for imaging equipment, biological model selection, endpoint selection, imaging assay design, identification and use of positive control and reference treatments, methods for evaluating assays’ dynamic ranges, and approaches for assessing assays’ reproducibility, as well as informatics and data processing method allowing design and interpretation of results. In particular, we will focus on the use of modern machine learning and artificial intelligence in HCA/HTS in the context of predictive toxicology. We will discuss how the technologies have been applied in various disease fields and the challenges associated with the implementation of these methods. The attendees familiar with multiparametric flow cytometry techniques will gain a basic foundational knowledge of quantitative image-based single-cell measurements and recognize the similarities and differences between the HCA approaches and flow cytometry.
In summary, this presentation's goals are the following:
- Provide an overview of the HTS and HCA methods
- Understand how the combination of HTS/HCA and artificial intelligence can enhance understanding of complex biological processes and enable predictive toxicology
- Gain insights into the challenges associated with the implementation of HTS/HCS methods.
David Egan, PhD
Co-Founder and CEO
Core Life Analytics
Joshua Harrill, PhD
High-Throughput Phenotypic Profiling Hazard Screening
CMLE Credit: 1.0