A new, affordable device developed by researchers at Rice University’s George R. Brown School of Engineering and Computing is set to revolutionize flow cytometry, a technique crucial for analyzing cells. This innovative artificial intelligence (AI)-enabled device is both cost-effective and accessible, potentially transforming clinical applications and biomedical research.
Making Flow Cytometry Accessible
Flow cytometry, vital for diagnosing and monitoring various medical conditions, has traditionally relied on expensive and cumbersome equipment. The new prototype, however, provides accurate cell analysis from unpurified blood samples, delivering results in a matter of minutes. Its compact size and reduced cost make it ideal for point-of-care applications, particularly beneficial 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 groundbreaking device. The findings of their study have been published in Microsystems and Nanoengineering.
“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,” said Lillehoj.
Flow cytometry, developed in the 1950s, is a powerful technique with wide-ranging applications in fields like immunology, molecular biology, cancer research, and virology. It is considered the gold standard for clinical diagnostics and biomedical research, but its high cost and the need for specialized staff have limited its availability to advanced medical centers.
Gravity-Driven Slug Flow: A Novel Approach
The device’s affordability and compactness are largely due to its innovative, pump-free design, which uses gravity-driven slug flow. Conventional flow cytometers depend on expensive pumps and valves for fluid control, adding to their overall cost and size. The Rice team’s approach addresses this by using gravity to achieve constant fluid velocity, crucial for precise cell analysis and sorting. This discovery was achieved by experimenting with various microfluidic flow options.
Graduate students Desh Deepak Dixit and Tyler Graf, mentored by Lillehoj and McHugh, respectively, fine-tuned the system to achieve gravity-driven slug flow. Unlike hydrostatic gravity flow, where fluid velocity changes based on pressure, slug flow allows for constant velocity, a key factor in accurate cell sorting.
Slug flow, a two-phase flow pattern, is typically observed when fluids in discrete phases move through a channel. It’s commonly used in the chemical and petroleum industries.
“To our knowledge, this is the first time gravity-driven slug flow has been employed for a biomedical application,” said Lillehoj.
AI-Powered Automation for Rapid Cell Counting
A key innovation is the use of AI to rapidly and accurately count specific immune cells known as CD4+ T cells from unpurified blood samples. CD4+ T cell counts are a vital indicator of the body’s immune health and are used in the diagnosis and prognosis of cancers and infectious diseases like HIV/AIDS and COVID-19.
The team incubated unpurified blood samples with beads coated with anti-CD4+ antibodies, which bind to CD4+ T cells. These samples were then passed through a microfluidic chip, and the flow was recorded using an optical microscope and video camera.
By training a convolutional neural network — a machine learning algorithm — to detect cells labeled with specific beads, the researchers expedited image analysis and quantification.
“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. He also noted that the technology could be adjusted to study a variety of cell types from biological samples by using 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 by the National Institutes of Health (R21CA283852) and Rice (U50807).
Source: Rice University Journal reference: Dixit, D. D., et al. (2025). Artificial intelligence-enabled microfluidic cytometer using gravity-driven slug flow for rapid CD4+ T cell quantification in whole blood. Microsystems & Nanoengineering. doi.org/10.1038/s41378-025-00881-y.
