In the past, there has been a significant demand to develop rapid, label-free, and physiologically relevant drug discovery assays; anticipating growing need for high-throughput (HT) compatible cellular assays. MALDI-TOF MS has played a key role in expanding the label-free HT screening environment of biochemical assays and it is routinely used for rapid phenotyping of prokaryotic cells. I will present a novel workflow that distinguishes human iPSC-derived macrophage phenotypes by MALDI-TOF MS based on cell-specific fingerprints. The workflow is utilised to characterise ~200 small molecule compounds and to analyse macrophage response to different bacterial stimuli. Accompanying proteomics analysis was carried out with HT compatible gradients on a timsTOF instrument.

We utilised iPSC-derived macrophages from multiple human donors and polarised them into a pro-inflammatory phenotype with bacterial (LPS, M.tb, E.coli, Staph) and cytokine (IFNγ) stimuli in the presence or absence of compounds. The supernatant was collected to measure secreted cytokine levels and the frozen cell pellets were utilised for MS analysis. Samples were prepared with S-Trap and analysed on a Evosep coupled to the timsTOF HT; acquired with diaPASEF and a 60 sample per day (60SPD) method. For MALDI-TOF MS, the lysed cell pellet and matrix were spotted onto the target plate and acquired with a speed of one second per sample on the rapifleX MALDI PharmaPulse. We implemented workflow automation at different stages which enabled cell culture in 96 well plates and data acquisition from as little as 5000 cells.

We can show robust and reproducible separation between non-inflammatory and pro-inflammatory macrophage phenotypes across multiple human donors in a PCA, and with "biomarker" peaks from the lipid and metabolite region of the mass spectrum. As a first proof of concept screen, the Broad Institute JUMP compound set was utilised; demonstrating assay robustness and identification of common phenotypic screen hitters. The second set contained ~100 well-annotated compounds from a Chemogenomics library, some with macrophage inflammatory pathway targets. The MALDI-TOF MS results were compared against the conventional label-based approach: secreted pro- and anti-inflammatory cytokine levels. A large overlap between the identified hit compounds in the screens, and a correlation between MALDI-TOF MS "biomarker" peaks and the cytokines were observed. The proteomics workflow was optimised to allow HT measures of all compounds to investigate the underlying signalling pathways. Further, macrophage clustering was observed according to the bacterial stimuli by MALDI-TOF MS and investigated by proteomics, indicating M.tb infection unique targets.

We describe a novel MALDI-TOF MS based HT compatible cell phenotyping workflow and its combination with proteomics to address needs in the drug discovery field.