The researchers of Beckman Institute of the University of Illinois at Urbana-Champaign have invented a method that deploys ultrashort laser pulses to visualize extracellular vesicles in the tissue samples without using stains or labeling compounds.
Fremont, CA: The growth in the number of these tiny vesicles is known to be linked with cancer wherein cancer cells use them to communicate with each other. Henceforth, microscopically visualizing them could be useful in detecting cancer progression. In the current scenario, the researchers are leveraging labels and stains so that they can see particular structures in tissue samples, like extracellular vesicles. This leads to the creation of extra work and expenses and continuously means that the tissue sample is not usable for any other type of analysis in the future. The researchers have come up with a new imaging technique, which does not require staining or labels.
The imaging method comprises directing ultrashort laser pulses at the imagined tissue sample that helps the researchers to detect the optical signature of the structures. They can even acquire the information about the structure and metabolism of the living tissue. The imaging systems enable them to see many intricate details about the tissue, cells, and functions compared with the current ways of imaging.
Various structures within the tissue offer different signals which help the researchers to distinguish between them. Two mechanisms are involved in the imaging. The tissue components emit multiple types of fluorescence that come in different colors, and the other mechanism involves the molecular structures that, when aligned in a certain way, will provide you with a completely different set of colors.
The technique leads to creating some striking images, but it additionally has the potential to help out with the diagnostics. Until now, the researchers have used it to analyze extracellular vesicles, which a mark of tumor progression.
Another application comprises using a technique to discern between cancerous and healthy tissue in tissue samples from the tumor border that is obtained during the surgery to help the surgeons determine if the entire tumor is successfully removed. The researchers are trying to use the label-free technique to see the tissue in the surgical room itself, and after they get the images, they use deep learning, which can act as a differentiator between cancerous and normal breast tissue.