Flow-through optical microscope detects rogue cancer cells
Knowing that the ability to distinguish and isolate rare cells from a large population of assorted cells is essential for early disease detection and disease monitoring, engineers at the University of California, Los Angeles (UCLA) have developed a new optical microscope that can detect circulating cancer tumor cells, which are precursors to metastasis (the spread of cancer).
Knowing that the ability to distinguish and isolate rare cells from a large population of assorted cells is essential for early disease detection and disease monitoring, engineers at the University of California, Los Angeles (UCLA) have developed a new optical microscope that can detect circulating cancer tumor cells, which are precursors to metastasis (the spread of cancer). Such "rogue" cells are not limited to cancer—they also include stem cells used for regenerative medicine and other cell types.
Detecting these cells is difficult, as achieving good statistical accuracy requires an automated, high-throughput instrument that can examine millions of cells in a reasonably short time. Microscopes equipped with digital cameras are currently the gold standard for analyzing cells, but they are too slow to be useful for this application.
"To catch these elusive cells, the camera must be able to capture and digitally process millions of images continuously at a very high frame rate," explains Bahram Jalali, who holds the Northrop Grumman Endowed Opto-Electronic Chair in Electrical Engineering at the UCLA Henry Samueli School of Engineering and Applied Science. "Conventional CCD and CMOS cameras are not fast and sensitive enough. It takes time to read the data from the array of pixels, and they become less sensitive to light at high speed."
The current flow cytometry method has high throughput, but since it relies on single-point light scattering, as opposed to taking a picture, it is not sensitive enough to detect very rare cell types, such as those present in early-stage or pre-metastasis cancer patients.
To overcome these limitations, the researchers, led by Jalali and Dino Di Carlo, a UCLA associate professor of bioengineering, have developed a high-throughput, flow-through optical microscope with the ability to detect rare cells with sensitivity of one part per million (ppm) in real time. The technology builds on the photonic time-stretch camera technology created by Jalali's team in 2009 to produce the world's fastest continuous-running camera.
Jalali, Di Carlo, and their colleagues integrated this camera with advanced microfluidics and real-time image processing in order to classify cells in blood samples. The new blood-screening technology boasts a throughput of 100,000 cells/second, approximately 100 times higher than conventional imaging-based blood analyzers.
Their research demonstrates real-time identification of rare breast cancer cells in blood with a record low false-positive rate of one cell in a million. Preliminary results indicate that the new technology has the potential to quickly enable the detection of rare circulating tumor cells from a large volume of blood, opening the way for statistically accurate, lower-cost early cancer detection and for monitoring the efficiency of drug and radiation therapy.
The results were obtained by mixing cancer cells grown in a laboratory with blood in various proportions to emulate real-life patient blood.
"To further validate the clinical utility of the technology, we are currently performing clinical tests in collaboration with clinicians," says lead study author Keisuke Goda, a UCLA program manager in electrical engineering and bioengineering and member of the California NanoSystems Institute (along with Jalali and Di Carlo). "The technology is also potentially useful for urine analysis, water quality monitoring, and related applications."
The work appears in the Proceedings of the National Academy of Sciences; for more information, please visit http://www.pnas.org/content/early/2012/06/25/1204718109.abstract?sid=276633b5-afa7-484f-8b17-91f321134bff.
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