Hit selection is the ultimate goal in many high-throughput screens. Various analytic methods are available for this purpose. Some commonly used ones are z score, z* score, strictly standardized mean difference (SSMD), SSMD*, and t statistic. It is critical to know how to use them correctly because the misusage of them can readily produce misleading results. Here, the author presents basic concepts, elaborates their commonality and difference, describes some common misusage that people should avoid, and uses simulated simple examples to illustrate how to use them correctly.

High-throughput screening (HTS) of large-scale RNA interference (RNAi) libraries has become an increasingly popular method of functional genomics in recent years. Cell-based assays used for RNAi screening often produce small dynamic ranges and significant variability because of the combination of cellular heterogeneity, transfection efficiency, and the intrinsic nature of the genes being targeted. These properties make reliable hit selection in the RNAi screen a difficult task. The use of robust methods based on median and median absolute deviation (MAD) has been suggested to improve hit selection in such cases, but mean and standard deviation (SD)—based methods are still predominantly used in many RNAi HTS. In an experimental approach to compare these 2 methods, a genome-scale small interfering RNA (siRNA) screen was performed, in which the identification of novel targets increasing the therapeutic index of the chemotherapeutic agent mitomycin C (MMC) was sought. MAD values were resistant to the presence of outliers, and the hits selected by the MAD-based method included all the hits that would be selected by SD-based method as well as a significant number of additional hits. When retested in triplicate, a similar percentage of these siRNAs were shown to genuinely sensitize cells to MMC compared with the hits shared between SD- and MAD-based methods. Confirmed hits were enriched with the genes involved in the DNA damage response and cell cycle regulation, validating the overall hit selection strategy. Finally, computer simulations showed the superiority and generality of the MAD-based method in various RNAi HTS data models. In conclusion, the authors demonstrate that the MAD-based hit selection method rescued physiologically relevant false negatives that would have been missed in the SD-based method, and they believe it to be the desirable 1st-choice hit selection method for RNAi screen results. ( Journal of Biomolecular Screening 2008:149-158)

Synaptic degeneration, including impairment of synaptic plasticity and loss of synapses, is an important feature of Alzheimer disease pathogenesis. Increasing evidence suggests that these degenerative synaptic changes are associated with an accumulation of soluble oligomeric assemblies of amyloid β (Aβ) known as ADDLs. In primary hippocampal cultures ADDLs bind to a subpopulation of neurons. However the molecular basis of this cell type-selective interaction is not understood. Here, using siRNA screening technology, we identified α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor subunits and calcineurin as candidate genes potentially involved in ADDL-neuron interactions. Immunocolocalization experiments confirmed that ADDL binding occurs in dendritic spines that express surface AMPA receptors, particularly the calcium-impermeable type II AMPA receptor subunit (GluR2). Pharmacological removal of the surface AMPA receptors or inhibition of AMPA receptors with antagonists reduces ADDL binding. Furthermore, using co-immunoprecipitation and photoreactive amino acid cross-linking, we found that ADDLs interact preferentially with GluR2-containing complexes. We demonstrate that calcineurin mediates an endocytotic process that is responsible for the rapid internalization of bound ADDLs along with surface AMPA receptor subunits, which then both colocalize with cpg2, a molecule localized specifically at the postsynaptic endocytic zone of excitatory synapses that plays an important role in activity-dependent glutamate receptor endocytosis. Both AMPA receptor and calcineurin inhibitors prevent oligomer-induced surface AMPAR and spine loss. These results support a model of disease pathogenesis in which Aβ oligomers interact selectively with neurotransmission pathways at excitatory synapses, resulting in synaptic loss via facilitated endocytosis. Validation of this model in human disease would identify therapeutic targets for Alzheimer disease.

RNA interference (RNAi) high-throughput screening (HTS) experiments carried out using large (>5000 short interfering [si]RNA) libraries generate a huge amount of data. In order to use these data to identify the most effective siRNAs tested, it is critical to adopt and develop appropriate statistical methods. To address the questions in hit selection of RNAi HTS, we proposed a quartile-based method which is robust to outliers, true hits and nonsymmetrical data. We compared it with the more traditional tests, mean +/- k standard deviation (SD) and median +/- 3 median of absolute deviation (MAD). The results suggested that the quartile-based method selected more hits than mean +/- k SD under the same preset error rate. The number of hits selected by median +/- k MAD was close to that by the quartile-based method. Further analysis suggested that the quartile-based method had the greatest power in detecting true hits, especially weak or moderate true hits. Our investigation also suggested that platewise analysis (determining effective siRNAs on a plate-by-plate basis) can adjust for systematic errors in different plates, while an experimentwise analysis, in which effective siRNAs are identified in an analysis of the entire experiment, cannot. However, experimentwise analysis may detect a cluster of true positive hits placed together in one or several plates, while platewise analysis may not. To display hit selection results, we designed a specific figure called a plate-well series plot. We thus suggest the following strategy for hit selection in RNAi HTS experiments. First, choose the quartile-based method, or median +/- k MAD, for identifying effective siRNAs. Second, perform the chosen method experimentwise on transformed/normalized data, such as percentage inhibition, to check the possibility of hit clusters. If a cluster of selected hits are observed, repeat the analysis based on untransformed data to determine whether the cluster is due to an artifact in the data. If no clusters of hits are observed, select hits by performing platewise analysis on transformed data. Third, adopt the plate-well series plot to visualize both the data and the hit selection results, as well as to check for artifacts.