Microplastics (MPs) and nanoplastics (NPs) are emerging environmental contaminants increasingly detected in human tissues and fluids, highlighting the need for reliable analytical methods capable of isolating and characterizing these particles in complex biological matrices while reducing contamination risks. This work presents a systematic, integrative, and reproducible protocol for detecting MPs in human blood using confocal Raman microscopy. The method incorporates strict contamination-control measures, includes negative and positive controls to ensure analytical reliability, and provides reference Raman spectra from commonly used clinical and laboratory materials to identify potential sources of cross-contamination. Spectral data are compared using the open-source platform Open Specy, enabling similarity matching with an extensive polymer database and improving the confidence of particle identification. Application of the protocol enabled the detection and characterization of MPs in human blood samples, identifying polymers such as polystyrene (PS), ethylene-vinyl acetate (EVA), and polyethylene (PE). Overall, this protocol demonstrates high specificity for detecting MPs in human blood and provides a robust framework for future exposure studies. • Provides a contamination-controlled analytical framework for MP detection in human blood. • Integrates reference materials and controls to ensure data reliability and trace contamination sources. • Uses open-source spectral comparison to support confident polymer identification. Schematic workflow of the proposed three-step protocol for the isolation and characterization of microplastics (MPs) in human blood. The procedure integrates: (1) MPs decontamination and quality control, including the use of negative controls (NC), plastic-free materials, and contamination-minimization measures; (2) sample processing, involving blood collection, digestion, and filtration to retain MPs (<5 mm); and (3) MPs identification by confocal Raman microscopy, enabling reliable detection and classification through comparison with spectral libraries. This approach highlights the simplicity, reproducibility, and contamination-minimized nature of the method. Figure created with BioRender.com.
Sarabia et al. (Sun,) studied this question.