Protein aggregation capture assisted profiling of the thiol redox proteome

Oxidative damage is central to multiple diseases, ranging from cardiovascular to neurological pathologies. In this context, thiol oxidation-reduction (redox) reactions are known to be tightly regulated and can act as sensors of oxidative stress, with implications for protein structure and function. Redox proteomics using mass spectrometry aims to evaluate thiol redox status globally in cells and tissues. Thiol redox status can be quantified using differential alkylation, which involves labeling cysteines before and after reduction with cysteine-reactive chemicals.

Here, we present a novel strategy to perform differential alkylation, which is based on the protein aggregation capture (PAC) protocol[1]. In contrast to previous strategies, such as FASP-based (FASILOX)[2] or gel-based (GESILOX)[3] strategies, PAC-assisted redox profiling significantly reduces sample preparation time and costs without decreasing overall thiol or proteome coverage.

We tested our novel PAC-assisted redox profiling protocol using Data Independent Acquisition on primary cell lines (HUVEC) and tissues (pig heart). For that, 1ug of sample was acquired in an Evosep (15SPD) coupled to a Thermo orbitrap Eclipse. Data was analysed using DIANN (v1.8.1) with a previously generated experimental library. We were able to quantify 3,000 proteins in pig heart and >20,000 modified peptides, including ~4,000 cysteine-containing peptides, 25% of which corresponded to reversibly-oxidized cysteines. Interestingly, we observed a change in retention time between cysteines labelled with iodoacetamide and methyl-methanethiosulfonate that could hamper identification of the later when using library-free DIA in DIANN.

Finally, we benchmark PAC-assisted redox profiling against a previous study using FASILOX to evaluate oxidative damage in a pig myocardial infarction model[4]. Overall, we present a high-throughput and streamlined sample preparation PAC-based strategy for redox proteomics, that is compatible with isobaric labeling or can be combined with DIA analysis using label-free quantitative workflows.

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