Screening
pounds or samples having similar phenotypes (Figure 3). Performing image analysis with algo- rithms generating many different parameters and the subsequent analysis of the data generated require a strong computational power. Generally this will not be performed on the computers delivered with the imagers, requiring images and data to be transferred to dedicated databases before analysis. These processes are time- and resource-consuming, which can restrict multi- parametric analysis to selected subsets (eg pri- mary hits based on uni-variate readout). Compared to hit identification with uni-variate readout, we have recently observed a clear reduc- tion of the number of false positives when apply- ing multi-parametric image analysis (eg calculat- ing the Mahalanobis distance to positive controls based on more than 100 parameters).
Figure 3
Analysis of multi-parametric HCS data. Data pre-processing consists of correction for plate pattern and plate effects, parameter selection by
dimension reduction and well summary. The parameters are being further analysed by either classification or
clustering. Visualisation of clustering results in self- organising maps helps to identify similar phenotypes
images or image conversion step is required. However, the most important is to use robust analysis scripts to account for plate-to-plate varia- tion in intensity of staining. In addition, data analysis software for assay quality control in order to recognise quality issues as early as possible is needed. A special requirement for HCS is the link from data to images, allowing a prompt visualisa- tion of the images to quickly identify staining issues or assay artifacts (Figure 2).
Multi-parametric data analysis
In HTS assays, a rather small number of project- specific readout parameters (<10) are collected. However, HCS is able to provide much more information with data sets containing readouts on multiple cellular parameters. To exploit the high content of images through multi-parametric data analysis, more sophisticated software tools are needed. Using our recently developed in- house software tool we are able to classify sam- ples into hits or inactive based on a multitude of readouts6. Another analysis type can cluster sam- ple responses into groups similar to control com-
What are the challenges?
High-content screening in high throughput as described above is now well established in the lead finding department of NIBR, however some tech- nical or process-related challenges still exist and need to be addressed. First, depending on the assay complexity and the imager chosen to per- form the screening, the throughput figures can vary substantially (Table 1). One prominent factor impacting the throughput is the imaging time. Optimising the number of exposures, exposure time, magnification and number of images acquired per well can clearly influence the plate processing time and therefore the duration of the screening campaign.
Second, the throughput discrepancy between the plate preparation process and imaging can result in delayed quality control to detect errors in cell plating, antibody or compound distribution. Furthermore, in case the delay between plate preparation and imaging extends to several days, 1536-well plates with low volume bear the risk of evaporation. The use of specifically-designed
Table 1: Examples of high-throughput HCI assays with different imaging requirements and their effect on the plate reading time
24 Drug Discovery World Winter 2011/12
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