Time-resolved (TR) fluorescence resonance energy transfer (FRET) is a widely accepted technology for high throughput screening (HTS), being able to detect and quantify the interactions of specific biomolecules in a homogeneous format. TR-FRET has several advantages for HTS applications that reduce assay artifacts such as compound interference. However, in some cases artifacts due to compound autofluorescence, color quenching, or signal stability are still observed. This report presents strategies addressing these issues by several means. One recommendation is the recording and visualization of differences in the donor/acceptor fluorescence, which allows the identification of compound artifacts. Another suggestion is to adjust the time delay, between excitation and recording of the fluorescence, in order to reduce compound interference. Furthermore, configuring the assay to allow the TR-FRET measurement to be taken at different time points, creating a reaction time course, allows background correction for each sample. Finally, the optimization of the FRET pair, to ensure assay signal stability under screening conditions, can improve the assay quality. This report presents examples of how these simple steps can be applied to enhance the quality of TR-FRET screening campaigns.
The intramembrane protease signal peptide peptidase-like 2a (SPPL2a) is a potential drug target for the treatment of autoimmune diseases due to an essential role in B cells and dendritic cells. To screen a library of 1.4 million compounds for inhibitors of SPPL2a, we developed an imaging assay detecting nuclear translocation of the proteolytically released cytosolic substrate fragment. The state-of-the-art hit calling approach based on nuclear translocation resulted in numerous false-positive hits, mainly interrupting intracellular protein trafficking. To filter the false positives, we extracted 340 image-based readouts and developed a novel multiparametric analysis method that successfully triaged the primary hit list. The identified scaffolds were validated by demonstrating activity on endogenous SPPL2a and substrate CD74/p8 in B cells. The multiparametric analysis discovered diverse cellular phenotypes and provided profiles for the whole library. The principle of the presented imaging assay, the screening strategy, and multiparametric analysis are potentially applicable in future screening campaigns.
High-throughput screening, based on subcellular imaging, has become a powerful tool in lead discovery. Through the generation of high-quality images, not only the specific target signal can be analyzed but also phenotypic changes of the whole cell are recorded. Yet analysis strategies for the exploration of high-content screening results, in a manner that is independent from predefined control phenotypes, are largely missing. The approach presented here is based on a well-established modeling technique, self-organizing maps (SOMs), which uses multiparametric results to group treatments that create similar morphological effects. This report describes a novel visualization of the SOM clustering by using an image of the cells from each node, with the most representative cell highlighted to deploy the phenotype described by each node. The approach has the potential to identify both expected hits and novel cellular phenotypes. Moreover, different chemotypes, which cause the same phenotypic effects, are identified, thus facilitating “scaffold hopping.”
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