In the era of single-cell analysis, the application of mass spectrometry (MS)-based proteomics to paucicellular samples remains limited. While new approaches have emerged for single-cell studies, most have not been standardized and/or often rely on specialized, custom-built equipment, hindering widespread implementation, particularly in non-specialized settings. Hence, it is necessary to select an optimal MS-oriented proteomics approach suitable for clinical settings.

To this aim, we assessed ten different sample preparation procedures in paucicellular samples of closely-related cell types, using monocytic cells as a model system. Five cell lysis protocols with varying chemistries and mechanical forces were combined with two sample clean-up techniques (C18 filter- and SP3-based), followed by tandem mass tag (TMT)-based protein quantification. The assessment was performed in three phases: i) selection of the sample preparation approach, employing hematopoietic and non-hematopoietic cell lines. This analysis identified the combination of a urea-based lysis buffer with the SP3 bead-based clean-up system as the best performer (allowing the reliable characterization of proteomes using only 2,500 cells), considering parameters such as reproducibility, accessibility, spatial distribution, ease of use, processing time and cost. ii) Evaluation of the method on maturation-related cell populations: classical, intermediate and non-classical monocytes from peripheral blood and macrophages/microglia (MAC) from glioblastoma samples. Closer protein expression profiles were observed between non-classical monocytes and MAC, with the latter showing the co-expression of M1 and M2 markers, although pro-tumoral and anti-inflammatory proteins were more represented. iii) Validation of results by high-end spectral flow cytometry on paired monocyte/MAC samples to determine the sensitivity of the selected MS approach. The feasibility of the urea-based lysis and SP3 bead-based clean-up method was proven in 194 additional samples corresponding to 38 different cell types, including e.g. distinct tissue origins, lineages, phenotypes and stimulus conditions.

In summary, we selected a reproducible, easy-to-implement sample preparation method for MS-based proteomic characterization of paucicellular samples, applicable to functionally closely-related cell populations. This approach addresses the current limitations in single-cell proteomics and enables broader implementation in clinical settings.