Development of a method for biomarkers characterization by mass spectrometry techniques

The purpose of this study is to define an extractive approach for the detection of the low-molecule peptide fraction from human plasma or serum and the subsequent analysis and interpretation of the obtained data, with the ultimate aim of developing a standardised protocol for the identification of potential biomarkers. The extraction of the low molecular protein fraction was developed thanks to a series of standard peptides solutions and using silica magnetic beads techniques differently functionalised with the purpose to bind target molecules with a different type of intermolecular force. The treatment of the samples, plasma or serum, took place without the use of proteases, as trypsin, to generate digested lysates, or electrophoresis and gel separation techniques, to avoid creating additional complexity in subsequent steps of data interpretation and to use the lower quantity of sample as possible. Both the peptides contained in the standard solution and those in the low molecular weight fraction of the pre-treated biological sample were separated and characterized through high performance liquid chromatography (HPLC) coupled to full scan and tandem mass spectrometry equipped with an electrospray ion source (ESI-MS/MS). Samples from biological sources were subsequently analysed using the mass spectrometry MALDI-TOF technique. In this project the development of the extraction method was followed by its application to real samples. The presence of low-molecular-weight peptides in plasma samples, from dialysis nephrotic patients at various stages of Sars-COV2 infection, and in plasma from healthy donors was evaluated with the aim to find significant differences between groups, especially in terms of qualitative/quantitative differences in the m/z ratios present in MS spectra. A bioinformatics approach to data processing has also been implemented, either by using statistical tools such as the Venn diagram or the Meaning Analysis of Microarrays (SAM) or by developing a series of codes in Python, for processing spectral data combined with algorithms with silico fragmentation rules. Outputs were compared with information from peptide databases to obtain significant correspondences between the theoretical and experimental spectrum.