Cell-based fuzzy metrics enhance high-content screening (HCS) assay robustness

TY - JOUR

T1 - Cell-based fuzzy metrics enhance high-content screening (HCS) assay robustness

AU - Azegrouz, Hind

AU - Karemore, Gopal

AU - Torres, Alberto

AU - Alaíz, Carlos M

AU - Gonzalez, Ana M

AU - Nevado, Pedro

AU - Salmerón, Alvaro

AU - Pellinen, Teijo

AU - del Pozo, Miguel A

AU - Dorronsoro, José R

AU - Montoya, María C

PY - 2013/12

Y1 - 2013/12

N2 - 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.

AB - 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.

KW - Cell Line, Tumor

KW - Drug Evaluation, Preclinical

KW - Fuzzy Logic

KW - High-Throughput Screening Assays

KW - Humans

KW - Microscopy, Confocal

KW - Microscopy, Fluorescence

KW - ROC Curve

KW - Reproducibility of Results

U2 - 10.1177/1087057113501554

DO - 10.1177/1087057113501554

M3 - Journal article

C2 - 24045580

SN - 2472-5552

VL - 18

SP - 1270

EP - 1283

JO - SLAS Discovery

JF - SLAS Discovery

IS - 10

ER -