title: Cell-based fuzzy metrics enhance high-content screening (HCS) assay robustness.
abstract: High-content screening (HCS) allows the exploration of complex cellular phenotypes by automated microscopy and is increasingly being adopted for small interfering RNA 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 multiparametric 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 criterion based on 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 monoparametric assay tested, as revealed by an increase in Z' factor, Kolmogorov-Smirnov distance, and 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 multiparametric phenotypic assay sensitivity.
authors.affiliation: 1Cellomics Unit, Department of Vascular Biology and Inflammation, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
authors.forename: Hind
authors.initials: H
authors.lastname: Azegrouz
authors.forename: Gopal
authors.initials: G
authors.lastname: Karemore
authors.forename: Alberto
authors.initials: A
authors.lastname: Torres
authors.forename: Carlos M
authors.initials: CM
authors.lastname: Alaíz
authors.forename: Ana M
authors.initials: AM
authors.lastname: Gonzalez
authors.forename: Pedro
authors.initials: P
authors.lastname: Nevado
authors.forename: Alvaro
authors.initials: A
authors.lastname: Salmerón
authors.forename: Teijo
authors.initials: T
authors.lastname: Pellinen
authors.forename: Miguel A
authors.initials: MA
authors.lastname: del Pozo
authors.forename: José R
authors.initials: JR
authors.lastname: Dorronsoro
authors.forename: María C
authors.initials: MC
authors.lastname: Montoya
mesh_terms: D045744:Cell Line, Tumor; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D017143:Fuzzy Logic; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D018613:Microscopy, Confocal; D008856:Microscopy, Fluorescence; D012372:ROC Curve; D015203:Reproducibility of Results
keywords: cell-based assays; high-content screening; image analysis; phenotypic drug discovery; statistical analyses