Chemical Proteomics strategies explore drug-target-phenotype relationship, each with different angles and limits. Protein abundance analysis after treatment with a library of anticancer drugs show specific regulation of targets in late apoptosis. In contrast, TPP (also known as CETSA-MS) identifies targets by protein thermal stability changes, using one TMT batch per sample, and deriving differential Tm by curve fitting all temperature points of drug-treated and control samples. In TPP, the high sample amounts and costs complicate scalability and using more than two biological replicates; data analysis of multibatch TMT and suboptimal curve fitting damage proteome coverage; and straightforward thermodynamics interpretation obtained within complex intracellular environment as “protein melting” is questionable.

Our MS-based, proteome-wide PISA assay is the highest throughput method for target ID and MoA, with ~10 000 proteins identified and quantified in human cells, and optimized protocols for primary and iPSCs cells, organoids, and bacteria. Using up to 18 biological samples (cells or lysates) and up to 5 drugs in one TMT batch, PISA maximizes throughput, sustainability, proteome coverage, statistical power (n≥3), and confidence in results. In PISA, equal portions of each sample - thus of each soluble proteome - are exposed to many different temperatures before recomposition into one. Target and off-target interactions, early MoA, protein modifications, and early complex assembly variations occurred during a short treatment show reproducible soluble amount changes between drug-treated samples and controls. For fuller target deconvolution, PISA can use efficiently, also in one TMT-batch, many drugs, concentrations, cells and/or be integrated with the orthogonal, expression-based proteomics (“PISA-Express”). A third analysis dimension, RedOx proteomics, is in development and will allow multiplexing a 3D PISA profiling (“PISA-REX”) into a single TMT set.