New Rapid-Live Screening Microscopy Technique Enables Early Detection of Treatment-Resistant Cancer Cells
Posted on 27 Sep 2023
Chemotherapy serves as an effective tool in the fight against cancer, yet some cancer cells can evade treatment by going into a dormant state known as senescence. These so-called therapy-induced senescent (TIS) cells can become not just resistant to treatment but also potentially more harmful, even metastasizing. Early identification of TIS cells could be crucial in stopping their progression, but current detection techniques aren't quick or accurate enough. Now, new advanced microscopy techniques may offer a solution, allowing healthcare providers to identify these cells early on and adapt treatment plans accordingly.
A team of researchers at Johns Hopkins University (Baltimore, MD, USA) used a combination of three state-of-the-art, label-free microscopy methods—coherent Raman scattering, multi-photon absorption, and optical diffraction tomography—to examine TIS cells in their natural setting. This approach is unlike traditional methods and gave the scientists the ability to look at the cells' form, internal structure, and both physical and chemical properties during their entire life cycle.
The use of these advanced microscopy methods uncovered significant transformations within the TIS cells. For instance, within a day, the cells' mitochondria—the "energy factories" inside them—had repositioned themselves. By the 72-hour mark, the cells had started to excessively produce fatty molecules known as lipids and had become flatter and larger. This in-depth analysis helped the researchers establish a detailed timeline of these cellular changes. According to the team, their novel rapid-live screening microscopy methods offer great potential for advancing cancer research.
"Our work demonstrates the potential to transform anticancer treatment research," said Ishan Barman, an associate professor of mechanical engineering at Johns Hopkins Whiting School of Engineering. "Integrating these microscopy methods could help clinicians make more informed, timely treatment decisions."
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