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Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment
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Researchers used Raman spectroscopy imaging to distinguish between healthy and cancerous human bladder tissue without the need for chemical stains or labels. The technique successfully identified cancer regions in tissue samples from ten patients, using advanced data analysis to map molecular differences. While not directly related to microplastics, this spectroscopy method is also used in microplastic research and demonstrates the power of label-free chemical imaging in medical applications.
Raman spectroscopy offers label-free assessment of bladder tissue for in vivo and ex vivo intraoperative applications. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates. Raman microspectroscopic images were collected from whole tissue samples in a closed chamber at 785 nm laser excitation using a 20× objective lens and 250 µm step size. Without further preprocessing, Raman images were decomposed by the hyperspectral unmixing algorithm vertex component analysis into endmember spectra and their abundancies. Hierarchical cluster analysis distinguished endmember Raman spectra that were assigned to normal bladder, bladder cancer, necrosis, epithelium and lipid inclusions. Interestingly, Raman spectra of microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. In conclusion, our Raman workflow and data processing protocol with minimal user interference offers advantages for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices.
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Raman spectroscopy as the quantum eye to reveal molecular dynamics in biology
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Researchers reviewed advances in Raman spectroscopy — a technique that identifies chemicals by how they scatter laser light — highlighting how recent innovations in surface-enhanced and stimulated Raman methods have expanded its applications in cell imaging, disease diagnosis, drug development, and microplastic detection.
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Researchers developed an advanced Raman imaging system that can identify and classify microplastics as small as 1 micrometer in diameter with 98% accuracy, working about 100 times faster than traditional methods. The system can also detect harmful chemical residues like phthalate plasticizers on microplastic surfaces. Faster and more accurate detection tools like this are essential for understanding the full scope of microplastic contamination in food and water and its potential impact on human health.
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