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Label-free nano- and microplastics detection in mammalian tissue by photothermal infrared spectroscopy
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Researchers applied optical photothermal infrared spectroscopy to detect and identify nano- and microplastics smaller than 1 µm in mammalian tissue sections without requiring labels or lengthy digestion protocols. The method successfully localized polystyrene particles in tissue samples with chemical specificity, offering a faster workflow for nanoplastic detection in biological matrices.
Nano- and microplastics (NMPs) are increasingly raising concerns about their potential health effects. Traditional methods for detection and characterization of NMPs in tissue require elaborate sample preparation, such as labelling or timeconsuming tissue digestion. In this contribution we report on the application of optical photothermal infrared spectroscopy (O-PTIR) for localization and identification of plastic particles smaller than 1 μm. We show that the method can be applied to routine clinical tissue samples, preserving tissue architecture while accurately locating NMPs. Since this technique is label-free, contactless and non-destructive it can be applied prior to routine histopathology. We present results on various types of clinical samples, among them 3D cell cultures, mouse kidney tissue and human colon tissue.
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Researchers developed a method using optical photothermal infrared spectroscopy to detect and map microplastics directly within tissue sections without requiring chemical or enzymatic digestion. The study suggests this approach preserves spatial information about where microplastics are located within tissue architecture, overcoming a key limitation of conventional digestion-based methods that can lose some particles.
Detection ofUnlabeled Polystyrene Micro- and Nanoplasticsin Mammalian Tissue by Optical Photothermal Infrared Spectroscopy
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Researchers evaluated optical photothermal infrared (O-PTIR) spectroscopy for detecting unlabeled polystyrene micro- and nanoplastics down to 200 nm in mammalian kidney tissues and 3D cell cultures. O-PTIR outperformed conventional FTIR in spatial resolution and signal-to-noise ratio, and machine learning accelerated particle detection with minimal human error.
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