Detection of released free heme by erythrocytes by metabolomics

<b>Heme analysis by metabolomics</b><br><b>Sample Extraction</b>: metabolomics´ analyzes were performed by combining supernatants from two different solvent precipitation process: (i) 400 µL of sodium-phosphate buffer (pH 7.0) was added to 400 µL of whole blood and mixed. Then, 1200 µL of acetonitrile was added, vortexed for 10 seconds and centrifuged at 13,000 g. The same process was repeated (ii) in another 400 µL of sodium-phosphate buffer (pH 7.0) previously added to 400 µL of whole blood and mixed, but replacing the acetonitrile by 1200 µL of methanol, vortexed for 10 seconds, and centrifuged at 13,000 g. Both supernatants were transferred to a new test tube and mixed. The organic solvents were evaporated at nitrogen flow, and the extracted resuspended in 1 mL of 2% acetic acid water solution for the analysis by LC-HRMS. <b>LC-HRMS analysis:</b> The analytical platform used was an AccelaLC (ThermoFisher Scientific, Bremen, Germany) coupled to a QExactive Orbitrap Plus (MS) mass spectrometer (ThermoFisher Scientific, Bremen, Germany). Non-Target statistical analyzes were performed using a differential analysis platform, the SIEVE® software version 2.1 (ThermoFisher Scientific, Rockford, USA). The masses selected from the analysis of SIEVE were searched for in two different databases of human metabolites: Human Metabolome Database (HMDB; The metabolomics Innovation Center, Canada, USA) and MetabolomicsDatabase (METLIN ™; California, USA). <b>Chemometrics: </b>The raw data acquired with LC–HRMS were directly processed with Sieve applying the experiment type defined as “Small Molecule”, “Chromatographic Alignment and Framing”, and “Two Sample Differential Analysis” options. Sieve® software operated in two steps, comprising a first alignment of the chromatogram employing a ChromAlign™ algorithm, and second, a recursive base-peak framing, where spectral data from samples in the experiment were grouped by relative intensity. The data matrix was constructed by Sieve with the samples as observations, the m/z values and retention time pairs as the response variables, identifying statistically significant abundance differences in the signal intensity of m/z values among different samples.

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