What we do
Our group studies the application of diffuse light to fluorescently detect circulating cells directly in the bloodstream. Our goal is to help detect rare cancerous cells that help facilitate the spreading of cancer. We ultimately hope to help clinicians detect cancer faster.
Diffuse in vivo Flow Cytometry
Hematogenous metastasis is responsible for the majority of cancer related deaths. As such, enumeration and molecular profiling of circulating tumor cells (CTCs). CTCs are of great interest in pre-clinical and clinical cancer research. The goal of this research is to use diffuse light to detect and count extremely rare CTCs directly in the bloodstream without having to draw blood samples. We probe large, superficial blood vessels in mice and can detect a very low abundance of fluorescently labeled cells. Since 2012, we have developed and tested a number of DiFC designs, from a multi-laser, multi-detector ring array, to an advanced (2019) dual fiber-bundle design that operates in “diffuse reflectance” mode from the skin surface.
We use DiFC to study pre-clinical models of cancer development and treatment response, as well as dynamics of CTCs and CTC clusters. Ongoing work includes development of improved DiFC instrument designs and signal processing algorithms.
In Vivo Flow Cytometry of Extremely Rare Circulating Cells
Fluorescently labeling and detecting cancer cells directly in vivo
DiFC is used to detect fluorescently expressing CTCs in the blood of small animals. To use DiFC to help humans, the cancer cells would need to be “tagged” with a fluorescent maker while they are in the body. Ongoing work involves using clinical stage fluorescent contrast agents with DiFC to label and detect CTCs directly in vivo
Near-infrared diffuse in vivo flow cytometry
DiFC Signal Processing Algorithm
Once fluoresecnt signals (or “peaks”) are detected, we use signal processing to determine true cells from false postives. We use “peak matching” between two DiFC fiber probes to determine true cells using the amplitude and speed. We use “coincidence detection” between the two DiFC fiber probes, which allows us to remove false detections due to motion or other artifacts. This allows us to get an accurate measurement of CTCs in circulation over time, and virtually eliminates false positives. Ongoing work involves further developing these* algorithms and combining machine learning models to help decern true cells from false positve algorithms and combining machine learning models to help decern true cells from false positives.
Research Paper/Protocol Example
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