Abstract

Cell-based Fuzzy Metrics Enhance High Content Screening (HCS) Assay Robustness

Maria Montoya, Head of Cellomics Unit, National Center for Cardiovascular Research

HCS allows the exploration of complex cellular phenotypes by automated microscopy and is increasingly being adopted for siRNA genomic screening and phenotypic drug discovery. We introduce a series of cell-based evaluation metrics that have been implemented and validated in a mono-parametric HCS for regulators of the membrane trafficking protein Caveolin-1 (CAV1), and have also proved useful for the development of a multi-parametric phenotypic HCS for regulators of cytoskeletal reorganization. Imaging-metrics evaluate imaging quality such as staining and focus, whereas cell biology-metrics are fuzzy-logic based evaluators describing complex biological parameters such as sparseness, confluency and spreading. The evaluation metrics were implemented in a data mining pipeline, which first filters out cells that do not pass a quality criteria based in imaging-metrics and then uses cell-biology metrics to stratify cell samples to allow further analysis of homogeneous cell populations. Use of these metrics significantly improved the robustness of the mono-parametric assay tested, as revealed by an increase in both Z’ factor and SSMD (strict standard mean difference). Cell-biology evaluation metrics were also implemented in a novel supervised learning classification method that combines them with phenotypic features in a statistical model that exceeded conventional classification methods, thus improving multi-parametric phenotypic assay sensitivity.