Normalizing for individual cell population context in the analysis of high-content cellular screens.
Normalizing for individual cell population context in the analysis of high-content cellular screens.
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DOI：
10.1186/1471-2105-12-485
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发表时间：
2011-12-20
影响因子：
3
通讯作者：
Kaderali L
中科院分区：
文献类型：
--
作者：
Knapp B;Rebhan I;Kumar A;Matula P;Kiani NA;Binder M;Erfle H;Rohr K;Eils R;Bartenschlager R;Kaderali L
高含量、高通量的RNA干扰（RNAi）为阐明基因功能和参与生物过程提供了前所未有的可能性。基于显微镜的筛选允许在单个细胞水平上观察表型。最近的研究表明，细胞的群体背景会显着影响结果。然而，用于细胞筛选的标准分析方法目前不考虑单个细胞数据，除非这对于感兴趣的表型是重要的，即当研究细胞形态时。我们提出了一种方法，标准化和统计评分显微镜为基础的RNAi屏幕，利用单个细胞的信息，数百个细胞每敲低。每个细胞的个体群体背景被用于标准化。我们目前的结果对丙型肝炎和登革病毒的两个感染屏幕上，都表现出相当大的影响，由于人口背景下观察到的表型。此外，我们在非病毒筛选中显示，在不存在任何病毒的情况下，这些效应也可以在RNAi数据中发现。使用我们的方法来对这些影响进行标准化，与没有这种标准化和命中评分策略的分析相比，我们实现了更好的性能。此外，我们的方法比使用标准分析方法鉴定了丙型肝炎病毒复制中显著更丰富的途径。使用基于细胞的分析和群体背景的标准化，我们不仅在单个蛋白质水平上，而且特别是在途径水平上实现了提高的灵敏度和特异性。这导致了丙型肝炎病毒和登革热病毒的新的宿主依赖性因子的鉴定和更高的结果再现性。
High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
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影响因子：
12.3
作者：
Boutros M;Brás LP;Huber W
通讯作者：
Huber W
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
5.8
作者：
Rieber, Nora;Knapp, Bettina;Kaderali, Lars
通讯作者：
Kaderali, Lars
DOI：
10.1093/bioinformatics/btq398
发表时间：
2010-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Suratanee A;Rebhan I;Matula P;Kumar A;Kaderali L;Rohr K;Bartenschlager R;Eils R;König R
通讯作者：
König R
DOI：
10.1006/bbrc.1999.0460
发表时间：
1999-04-21
影响因子：
3.1
作者：
Kim, J;Lee, D;Choe, J
通讯作者：
Choe, J