Utilizing gravity to create low-cost microfluidic devices for rapid cell analysis

A team of researchers at George R. Brown School of Engineering and Computing at Rice University has developed an innovative, low-cost AI-enabled device for creating flow cytometry.

The prototypes identify and count cells from unpurified blood samples with similar accuracy to more expensive and bulky traditional flow cytometers, providing results within minutes, are rather cheap and compact, and are extremely attractive for point-of-care clinical applications, especially in low-resource and rural areas.

Peter Lillehodge, Associate Professor of Bioengineering at Leonard and Mary Elizabeth Shankle, and Kevin McHugh, Associate Professor of Bioengineering and Chemistry, led the development of the new device. This study was published in Microsystems & Nanoengineering.

First developed in the 1950s, flow cytometry is a powerful technique for sorting and analyzing single cells with applications in multiple medical fields, including immunology, molecular biology, and virology. It is a “gold standard” lab test for clinical diagnosis and care and is widely used in biomedical research. However, its use is currently limited to state-of-the-art diagnostic labs and medical centres. This is because you need large and expensive equipment to run specially trained staff, from dozens to hundreds of thousands to hundreds of thousands of dollars.

“Traditional flow cytometry is not practical in many resource-limited settings in the US and around the world,” says Lillehoj, the corresponding author of the study. “Our approach makes this technique easy to implement at a fraction of the cost. We envision innovative devices paving the way for many new points of care clinical and biomedical research applications.”

Utilizing gravity-based slug flow to build low-cost pump-free flow cytometers

Current flow cytometers rely on specialized pumps and valves for fluid flow and control, making equipment expensive and bulky. After trying out several alternative microfluidic flow options, the Rice Team came up with an innovative pump-free design solution. This has been key to reducing device costs and size.

Desh Deepak Dixit and Tyler Graf – Graduate students, led by Lillehoj and Mchugh, respectively, tweaked various parameters of microfluidic devices to achieve gravity-driven slug flow. Unlike hydrostatic gravitational flows, where fluid velocity changes depending on the hydrostatic pressure acting on a fluid, gravity-driven slug flow allows the sample to flow at a constant velocity through the microfluidic device.

Slug flow is a two-phase flow pattern observed when a fluid composed of one or two fluids of individual phases travels through a pipe or channel. It is mainly used to transport large quantities of liquids through industrial tools in oil and gas wells, chemical reactors, and fermenters, and is being studied by researchers interested in fluid dynamics.

“To our knowledge, this is the first time that gravity-driven slug flows have been adopted for biomedical applications,” Lilehodge said.

AI allows for rapid counting of specific immune cells from unpurified blood samples

The second important innovation in this study was the use of AI, which facilitated the rapid and accurate counting of a specialized group of immune cells called CD4+ T cells from unpurified blood samples.

CD4+ T cell count is a reliable marker of the body’s immune status and is used as a diagnostic and prognostic marker for cancer and infections such as HIV/AIDS and Covid-19.

The team incubated crude whole blood samples with anti-CD4+ antibody-coated beads and were able to specifically bind to CD4+ T cells within the samples. The samples were then passed through a microfluidic chip and the flow was recorded with an optical microscope and a video camera.

To speed up image analysis and quantification, researchers added AI capabilities by training complex neural networks, the type of machine learning algorithm used for image classification and object recognition, to detect only bead-labeled cells.

“Identifying and quantifying CD4+ T cells from unpurified blood samples is just one example of what this platform technology can achieve,” says McHugh, who is also the Cancer Prevention and Laboratory for Texas scholars.

“This technology can be easily adapted to sort and analyze different cell types from different biological samples using beads labeled with different antibodies. Based on the promising results obtained so far, we are very optimistic about the potential of this platform to transform future disease diagnosis, prognosis and biomedical research perspectives.”