HOUSTON – (Feb. 28, 2025) – A team of researchers at the George R. Brown School of Engineering and Computing at Rice University has developed an innovative artificial intelligence (AI)-enabled, low-cost device that will make flow cytometry ⎯ a technique used to analyze cells or particles in a fluid using a laser beam ⎯ affordable and accessible.
The prototype identifies and counts cells from unpurified blood samples with similar accuracy as the more expensive and bulky conventional flow cytometers, provides results within minutes and is significantly cheaper and compact, making it highly attractive for point-of-care clinical applications, particularly in low-resource and rural areas.
Peter Lillehoj , the Leonard and Mary Elizabeth Shankle Associate Professor of Bioengineering, and Kevin McHugh , assistant professor of bioengineering and chemistry, led the development of this new device. The study was published in Microsystems and Nanoengineering.
First developed in the 1950s, flow cytometry is a powerful technique for sorting and analyzing single cells that has applications in multiple medical fields including immunology, molecular and cancer biology and virology. It is the "gold standard" lab test for clinical diagnosis and care and is used extensively in biomedical research. However, its use is currently limited to state-of-the-art diagnostic labs and medical centers since it requires large, expensive equipment ranging from tens to hundreds of thousands of dollars and specially trained staff to operate it.
"Conventional flow cytometry is not practical for many resource-limited settings in the U.S. and around the globe," said Lillehoj, the study's corresponding author. "With our approach, this technique can be performed with ease for a fraction of the cost. We envision our innovative device will pave the way for many new point-of-care clinical and biomedical research applications."
Leveraging gravity-based slug flow to build a low-cost, pump-free flow cytometer
Current flow cytometers rely on specialized pumps and valves for fluid flow and control, making the equipment expensive and bulky. After experimenting with several alternate microfluidic flow options, the Rice team devised an innovative pump-free design solution, which was key to reducing the device's cost and size.
Desh Deepak Dixit and Tyler Graf — graduate students mentored by Lillehoj and McHugh respectively — fine-tuned various parameters of the microfluidic device to achieve gravity-driven slug flow. Unlike hydrostatic gravity flow where the fluid velocity changes depending on the hydrostatic pressure acting on the fluid, gravity-driven slug flow allows the sample to flow at a constant velocity through the microfluidic device, which is crucial for accurate cell sorting and analysis.
Slug flow is a two-phase flow pattern observed when a fluid composed of one or two fluids in discrete phases moves through a pipe or channel. It is used primarily for transporting large volumes of liquids through industrial equipment in oil and gas wells, chemical reactors and fermenters and is studied by researchers interested in fluid dynamics. "To our knowledge, this is the first time gravity-driven slug flow has been employed for a biomedical application," said Lillehoj.
AI enables rapid counting of specific immune cells from unpurified blood samples
The study's second important innovation was the use of AI, which facilitated rapid yet 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 cancers and infectious diseases such as HIV/AIDS and COVID-19.
The team incubated unpurified whole blood samples with beads coated with anti-CD4+ antibodies, which allowed them to bind specifically to CD4+ T cells in the sample. The sample was then passed through the microfluidic chip, and the flow was recorded with an optical microscope and video camera. To speed up image analysis and quantification, the researchers added AI capabilities by training a convoluted neural network — a type of machine learning algorithm used for image classification and object recognition — to only detect cells labeled with beads.
"Identifying and quantifying CD4+ T cells from unpurified blood samples is just one example of what one can achieve with this platform technology," said McHugh, who is also a Cancer Prevention and Research Institute of Texas Scholar. "This technology can be easily adapted to sort and analyze a variety of cell types from various biological samples by using beads labeled with different antibodies. Based on the promising results we've obtained so far, we are very optimistic about this platform's potential to transform disease diagnosis, prognosis and the biomedical research landscape in the future."
The research was supported in part by the National Institutes of Health (R21CA283852) and Rice (U50807). The content herein is solely the responsibility of the authors and does not necessarily represent the official views of the funders.
⎯ by Raji Natarajan, science writer, George R. Brown School of Engineering and Computing
