Technical Notes

2 TECHNICAL NOTE ©2019 NanoTemper Technologies, Inc. South San Francisco, CA, USA. All Rights Reserved. requires choosing the optimum sample matrix. This is a process that is crucial for sensitivity, stability, and reproducibility of the screening assay being o en complex, time-consuming and expensive. For this technical note, we applied a novel, automated, and plate-based assay development procedure utilizing NanoTemper's Buffer Exploration Kit. We use it for the systematic testing of 96 different buffer conditions using the Dianthus NT.23PicoDuo system and theTemperature Related Intensity Change (TRIC) technology. TRIC allows to quantitate the strength of molecular interactions involving a fluorescently-labelled target molecule and is based on the temperature-dependent change in fluorescence of a fluorescence-labelled target molecule measured a er a very rapid and precise IR-laser induced temperature increase (Gupta et al., 2018). The goal of this project was twofold: to identify assay conditions that result in the highest possible binding signal amplitude for the positive control — or highest possible assay sensitivity — and that also ensure reproducibility, which is o en related to target stability (Bartoschik et al., 2017). Results G9a was chosen as the target molecule and the buffer screening experiments were carried out in duplicate under 96 different conditions from the Buffer Exploration Kit. S-adenosyl methionine (SAM) was used as a positive control ligand at a final concentration of 100 µM. G9a was used at a final concentration of 5 nM. For each of the 96 buffer conditions screened, a duplicate of reference and positive control, i.e. four data points in total, were measured (Figure 2, le panel). Data were recorded and analyzed with NanoTemper's DI.Control v1.0.1 so ware, which provides a pre-defined protocol for setting up an experiment with the Buffer Exploration Kit as well as an integrated, tailor-made analysis function. The suitability of the 96 different assay buffers was evaluated by calculating and then comparing the Signal Quality (SQ) values for each condition. SQ is calculated by computing the means of the areas between two TRIC traces (Figure 1). Three distinct areas are used to calculate SQ for each condition: Ligand Area: Area contained between two positive control traces (Figure 1 A) Reference Area: Area contained between two reference traces (Figure 1 B) Signal Area: Area contained between a reference trace and a positive control trace (Figure 1 C). Note that the Reference Areas and Ligand Areas can be correlated with the reproducibility of the experiment. The smaller the Reference Area or Ligand Area, the better the repetitions of the respective duplicate match, so their reproducibility is improved. On the other hand, Signal Areas correlate with the assay sensitivity, as they represent signal amplitude between reference

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