The Cellular Thermal Shift Assay (CETSA) is a powerful tool for monitoring target engagement by measuring the changes in the thermal stability of proteins across a temperature gradient. The technique relies on generating a melt curve based on relative quantification data from Tandem Mass Tag (TMT) isobaric labelling. A similar approach, termed Proteome Integral Solubility Alteration (PISA), aims to overcome issues of missing potential targets whose thermal profile does not fit into a melt curve during CETSA analysis. PISA reduces sample consumption, TMT requirement and analysis time. During PISA, samples from a single experimental condition are pooled before isobaric labelling. These techniques require a complex data analysis pipeline to calculate protein melt temperatures, plot the melt curves and identify thermal stability shifts from protein abundance data.
To overcome the analysis time and effort, cost of isobaric tag and requirement of a high-resolution instrument to deconvolute isobaric masses in CETSA and PISA analysis, we designed a label-free PISA approach analysing each PISA pool by data-independent acquisition (DIA-PISA). A comparative experiment was performed between CETSA, PISA and DIA-PISA to benchmark proteome-wide thermal profiling techniques for target interaction studies. For the purpose, samples were analysed as follows i) CETSA using TMT-16 plex (8 temperatures between 38 °C – 63 °C, 2 replicates) ii) PISA assay (8 temperatures across the following ranges: 37 °C – 49 °C, 44 °C – 56 °C and 51 °C – 63 °C, 3 replicates). Selected two replicates from the PISA assay (from all 3 temperature ranges) were further TMT labelled with TMT-16 plex. 20 μg protein from U266B1 cell lysate was taken per sample for the thermal challenge upon lysate treatment. Cell lysates were either treated with a specific Bcl-xL inhibitor or DMSO in duplicates. TMT data were analysed on Thermo Orbitrap Eclipse, whereas DIA data was obtained from the Buker timsTOF Pro instrument. We are now assessing the results to confirm the identification of known protein targets and off-target effects. The comparative analysis will provide deeper insights into the suitability, ease of analysis and cost-effectiveness of these methods in target-interaction studies.