A new optical instrument has proven able to detect circulating "rogue" cells that are precursors to tumor metastasis, among other applications. Detecting such cells is difficult because achieving good statistical accuracy requires high-throughput instrumentation that can quickly and continuously capture and digitally process millions of images.
Microscopes equipped with digital cameras are currently the gold standard for analyzing cells, but they fall short for this application. "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," according to Bahram Jalali of the the University of California, Los Angeles (UCLA) Henry Samueli School of Engineering and Applied Science. Traditional flow cytometry offers high throughput but relies on single-point light scattering, and thus is not sensitive enough to detect very rare cell types.
UCLA researchers, led by Jalali and Dino Di Carlo, associate professor of bioengineering, have developed a high-throughput, flow-through optical microscope capable of detecting rare cells with sensitivity of one part per million (ppm) in real time.1 The work builds on the photonic time-stretch camera created by Jalali's team in 2009 to produce the "world's fastest continuous-running camera."
The research team integrated this camera with self-focusing microfluidics and real-time image processing in order to classify cells in blood samples. The new technology claims per-second throughput of 100,000 cells (about 100 times that of conventional imaging-based blood analyzers).
The work has demonstrated 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.
To further validate the approach's clinical utility, the team is now running tests in collaboration with clinicians. The technology may also prove useful for urine analysis, water quality monitoring, and more.
1. K.Goda et al., PNAS, doi:10.1073/pnas.1204718109 (2012).