Azegrouz Hind, Karemore Gopal, Torres Alberto, Alaíz Carlos M, Gonzalez Ana M, Nevado Pedro, Salmerón Alvaro, Pellinen Teijo, del Pozo Miguel A, Dorronsoro José R, Montoya María C
1Cellomics Unit, Department of Vascular Biology and Inflammation, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain.
J Biomol Screen. 2013 Dec;18(10):1270-83. doi: 10.1177/1087057113501554. Epub 2013 Sep 17.
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.
高内涵筛选（HCS）可通过自动显微镜探索复杂的细胞表型，并且越来越多地被用于小干扰RNA基因组筛选和表型药物发现。我们介绍了一系列基于细胞的评估指标，这些指标已在用于膜转运蛋白小窝蛋白1（CAV1）调节剂的单参数HCS中实施并得到验证，并且已证明对开发用于细胞骨架重组调节剂的多参数表型HCS也很有用。成像指标评估诸如染色和聚焦等成像质量，而细胞生物学指标是基于模糊逻辑的评估器，用于描述诸如稀疏度、汇合度和铺展等复杂的生物学参数。评估指标在一个数据挖掘流程中实施，该流程首先根据成像指标筛选出未通过质量标准的细胞，然后使用细胞生物学指标对细胞样本进行分层，以便对同质细胞群体进行进一步分析。如Z'因子、柯尔莫哥洛夫-斯米尔诺夫距离和严格标准平均差的增加所示，使用这些指标显著提高了所测试的单参数测定的稳健性。细胞生物学评估指标还在一种新型监督学习分类方法中实施，该方法在一个超过传统分类方法的统计模型中将它们与表型特征相结合，从而提高了多参数表型测定的灵敏度。
