Singh Shantanu, Carpenter Anne E, Genovesio Auguste
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
J Biomol Screen. 2014 Jun;19(5):640-50. doi: 10.1177/1087057114528537. Epub 2014 Apr 7.
Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising. In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60-80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information. We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.
与表型筛选方法相比，基于靶点的高通量筛选（HTS）最近因其相对较低的产出率而受到批评。一种表型筛选类型，即基于图像的高内涵筛选（HCS），被认为特别有前景。在本文中，我们评估HCS是否已达到其所能达到的高内涵程度。我们分析了HCS相关出版物，发现尽管每年发表的HCS实验数量持续稳步增长，但信息含量却滞后。我们发现，到目前为止发表的大多数高内涵筛选（60%-80%）仅利用了从每个样本中测量的一两个基于图像的特征，而忽略了这些特征在每个细胞群体中的分布。我们讨论了几种可能的解释，重点关注数据分析传统应为此负责的假设。这包括与管理大型多维HCS数据集相关的实际问题，以及将HTS的分析质量统计方法应用于HCS。这两者都可能导致对携带复杂且有价值表型信息的分析方法进行简化或系统性摒弃。我们预测，能够为整个细胞群体收集完整多参数数据的先进数据分析方法将使HCS最终发挥其潜力。
