Aplicação de métodos de precipitação e imunoafinidade para depleção de proteínas plasmáticas de alta abundância e análise proteômica: implicações para pesquisas sobre Covid longa

Abstract

The main challenge of mass spectrometry-based plasma proteomic analysis is the broad concentration range of proteins. High-abundance proteins, such as albumin, can hinder the detection of low-abundance ones, limiting the identification of protein biomarkers. Therefore, the choice of the best protein preparation method is crucial for blood plasma studies because, although numerous studies use this type of sample, the methodologies employed are quite variable and can determine the possible findings. This study aimed to compare and evaluate three distinct plasma protein preparation methods for high-resolution shotgun proteomics studies acquired in DDA mode on a Q Exactive Plus mass spectrometer coupled to a UHPLC Ultimate 3000 (Thermo Scientific). The goal was to identify the most effective method, enable data acquisition by DIA, and achieve a higher number of identifications. Additionally, this work aimed to make this optimized method applicable in future analyses, especially for a cohort of patients with long COVID, classified by clinical data and APOE genotype, to explore potential associations with the proteome. To achieve this, plasma samples from ten healthy participants were collected and subjected to three different preparation methods: 1) Precipitation with acetonitrile (ACN); 2) Chromatographic depletion with a Hitrap® Albumin IgG Depletion immunoaffinity column (COL); and 3) Precipitation with isopropanol-1% trichloroacetic acid (TCA). All samples underwent the same conditions for denaturation, reduction, alkylation, trypsinization, and desalting, and were analyzed by LC-MS/MS. The generated m/z spectra were processed using Pattern Lab, Raw Vegetable, MaxQuant, Perseus, and Metaboloanalyst software. A total of 338 proteins were identified. The depletion methods resulted in distinct proteomic profiles. In terms of high-abundance protein depletion efficiency, the COL method was the most effective, followed by TCA and ACN. Our results demonstrate that the COL and TCA depletion methods were more efficient at removing high-abundance proteins and identifying a greater number of proteins with less variability compared to the ACN method. The superiority of these methods was confirmed by the higher recovery of clinically relevant proteins, such as APOE, APOA4, PON1, and haptoglobins, which are implicated in biological pathways associated with long COVID and Alzheimer's Disease. The proteins recovered by the COL and TCA methods, in turn, formed more robust interaction networks, suggesting a more significant enrichment of functional pathways. This emphasizes the importance of choosing the analysis method based on the study's focus. These findings validate the use of more efficient depletion protocols and provide a solid basis for future longitudinal proteomic studies, using more advanced data acquisition techniques like DIA, to investigate the pathophysiology of long COVID.