Researchers have developed a label-free and non-invasive Raman spectroscopy approach that can acquire microscopic images of biological samples and identify a wide range of biomolecules with unprecedented speed and sensitivity.
“Our work could lead to a non-invasive, label-free and user-friendly device for clinical use,” said research team leader Dario Polli from Politecnico di Milano in Italy. “This innovative microscope, coupled with deep learning-based algorithms, could eventually make it easier and faster to diagnose cancer by allowing the visualization of the chemical constituents of human tissues and cells.”
In the Optica Publishing Group journal Optics Express, the researchers describe their new technique, which is based on coherent anti-stokes Raman scattering (CARS) microscopy. CARS microscopy produces images based on the vibrational signatures of molecules by exploiting the interaction between ultrashort laser pulses and biological samples.
The new approach provides access to the hard-to-detect region of the vibrational spectrum known as the fingerprint region, which spans from 400 to 1800 cm?1. Although many individual compounds can be identified using their vibrational fingerprints in this region, it tends to produce weak signals that are difficult to detect.
“Commonly used techniques in biomedical sciences often require staining, which is not only cumbersome but can also introduce structural and chemical alterations that can lead to artifacts, or errors, in imaging and data processing,” said Polli. “Because our system can distinguish between many different chemical species in biological tissues without labels, it could be useful for live cell imaging and analyzing tissue biopsies.”
Lower repetition rate, faster imaging
This new work is part of the CRIMSON project funded by the European Commission, which aims to develop a turnkey imaging device that uses vibrational spectroscopy for fast cell and tissue classification. The project’s goal is to transform the study of the cellular origin of diseases to enable new approaches that could advance personalized therapy.
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