In recent decades the study of extracellular vesicles (EV) has gained attention due to their function and role in intercellular communication and cargo transfer. Advancements in liquid chromatography and mass spectrometry (LC-MS/MS) has demonstrated the ability to comprehensively define EV proteomes. However, these studies are often limited by sample availability, requiring upscaled EV production from cell culture or biofluids, limiting its applicability to lower yield EV sources. Here, we establish a method which enables precise and comprehensive proteomic characterisation of small EVs (sEVs) from ultralow starting quantities. This pipeline is defined by its optimised sample preparation methods, short chromatography lengths, and data-independent acquisition (DIA).
This refined DIA-based MS approach on a Q-Exactive HF-X combines robust single-pot, solid-phase-enhanced sample preparation with temporally optimised enzymatic digestion and short chromatography gradients (15 to 44 min). Monitoring of protein identifications for 0.5 to 50 ng of peptide loads across different LC gradients revealed a linear increase with peptide input. For 50 ng loading, more than 3730 proteins for all gradient lengths were observed, with 4599 identifications in our 44 min workflow. The short gradient lengths favoured low peptide loads, with a 15 min gradient consistently quantifying >1100 protein groups from 500 pg of EV peptide, and >3800 protein groups from 50 ng, including the robust quantification of 22 core EV marker proteins. Furthermore, we optimised bead-based sample preparation for ultralow quantities of EV (0.5 to 1 µg) to obtain sufficient peptides for MS quantification. Our approach enables the generation of meaningful proteome insights from <1 µg starting EV protein, encompassing the identification of >1900 protein groups and capturing sufficient proteomic diversity of EV from different cell sources to determine known EV biology.
This innovative optimised workflow addresses the pressing need to capture precise and comprehensive proteomes of EVs from ultralow sample quantities, without compromising depth and accuracy. Finally, our workflow pipeline is straightforward and can be implemented to suit various laboratory conditions. This adaptability facilitates the characterisation of EVs, particularly where sample availability is constrained.