Biomedical engineers at Duke University have engineered a holographic system capable of imaging and analyzing tens of thousands of cells per minute to discover and recognize signs of disease.

In the proof-of-concept demonstration, the technique distinguished between healthy samples and either cancerous or carcinogen-exposed, pre-cancerous cells with nearly 100 percent accuracy, using just four basic cellular physical parameters out of a holographic panel of 25.

The results point toward a promising screening or diagnostic technology that is simpler and cheaper to use than current standard practices, making it a potential target for use in remote, low-resource settings.

The research appears online in the journal Frontiers in Physics.

“The cells are flying through the scanner so fast that if the computer didn’t slow them down on the screen, you wouldn’t even be able to see them,” says Adam Wax, PhD, professor of biomedical engineering at Duke. “We were very excited to be able to image this many cells at once because it points toward this technology’s potential for point-of-care diagnostics.”

With the new holographic imaging approach, sample cells are rinsed off of the collection instrument into a biocompatible solution and inserted into a microfluidic chip. The small device diverts the sample into a series of parallel channels that pass beneath a line camera.

When slowed down, the march of cell images is reminiscent of green characters cascading down a computer screen in The Matrix, wherein a camera shines a light on them to determine their topography in an effort to spot disease.

“It takes about 30 seconds to process a 1 milliliter sample that could contain more than a half-million cells,” says Cindy Chen, a doctoral student working in Wax’s lab and first author of the paper. “And once it’s finished, a pathologist could pull up individual data for any of the cells that were imaged for closer inspection.”

In a demonstration of the approach using 8,500 cells and a machine learning algorithm, the device was able to successfully categorize each cell into three different cell lines with 98% to 99% accuracy using just the 2D area it occupies, the 3D space it takes up, its shape and its average height.

“This is a simple, label-free imaging technique that is getting data similar to that of flow cytometry, which looks at cells one at a time and typically requires fluorescent markers to be introduced,” says Chen. “The idea is that, by having this wealth of quantitative data, you can separate the cells better than if you were using one single metric.”

This research was supported by the National Institutes of Health.

“We’re now trying to figure out how to put this technology at the point-of-care to figure out if people have been exposed to these carcinogens in the developing world, like maybe arsenic in the ground water,” says Wax. “For example, you might brush some cells off of the top of someone’s mouth to see if they’re being exposed to carcinogens in their drinking water.”

Featured Image: A new fast cell imaging device can analyze tens of thousands of cells per minute to spot physical signs of disease. The bottom line contains whole cells as they are identified. The middle line shows the area parameters for each cell’s scan. The top line shows the final scan of each cell, color coded to indicate thickness. Photo: Cindy Chen, Duke University