Department of Physics and Institute for Physical Science and Technology, University of Maryland, College Park, MD, 20742, USA.
National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
BMC Bioinformatics. 2018 Nov 16;19(1):427. doi: 10.1186/s12859-018-2454-1.
Image-based high-throughput screening (HTS) reveals a high level of heterogeneity in single cells and multiple cellular states may be observed within a single population. Currently available high-dimensional analysis methods are successful in characterizing cellular heterogeneity, but suffer from the "curse of dimensionality" and non-standardized outputs.
Here we introduce RefCell, a multi-dimensional analysis pipeline for image-based HTS that reproducibly captures cells with typical combinations of features in reference states and uses these "typical cells" as a reference for classification and weighting of metrics. RefCell quantitatively assesses heterogeneous deviations from typical behavior for each analyzed perturbation or sample.
We apply RefCell to the analysis of data from a high-throughput imaging screen of a library of 320 ubiquitin-targeted siRNAs selected to gain insights into the mechanisms of premature aging (progeria). RefCell yields results comparable to a more complex clustering-based single-cell analysis method; both methods reveal more potential hits than a conventional analysis based on averages.
基于图像的高通量筛选（HTS）揭示了单细胞中的高度异质性，并且可能在单个群体中观察到多个细胞状态。目前可用的高维分析方法在表征细胞异质性方面取得了成功，但存在“维度诅咒”和非标准化输出的问题。
在这里，我们介绍了 RefCell，这是一种用于基于图像的 HTS 的多维分析管道，它可重现地捕获具有参考状态下典型特征组合的细胞，并将这些“典型细胞”用作分类和加权度量的参考。RefCell 定量评估了每个分析扰动或样本中典型行为的异质偏离程度。
我们将 RefCell 应用于对 320 个泛素靶向 siRNA 文库的高通量成像筛选数据的分析，旨在深入了解早衰（早衰症）的机制。RefCell 的结果可与更复杂的基于聚类的单细胞分析方法相媲美；这两种方法都比基于平均值的传统分析揭示了更多的潜在靶点。
