Single-cell omics research is currently dominated by RNA-sequencing methods. Thanks to recent technological advances, mass spectrometry-based single-cell proteomics (SCP) is emerging as an alternative strategy, uniquely allowing for the untargeted investigation of cellular heterogeneity at the proteome level. This technology has tremendous potential in settings where protein-level alterations underlying cellular heterogeneity need to be accurately mapped and understood, such as in studies of nongenetic drug resistance mechanisms. To enable SCP in a biomedically relevant setting, advances are required in both data generation and data analysis. As a part of this proposal, I will advance state-of-the-art SCP workflows by implementing a novel multiplexing strategy in data independent acquisition mode, allowing for a simultaneous increase in sample throughput and data quality. Moreover, I will contribute to the development of SCP-specific normalisation approaches, allowing for the valid analysis and comparison of cells of varying (sub)types and sizes. Finally, I will demonstrate the utility of the developed approach by applying it to investigate minimal residual disease (MRD) mediated by (heterogenous) drug-tolerant persister cells (DTPCs) in a patient-derived xenograft (PDX) model of melanoma.