AI-Enabled Microfluidic Device for Rapid CD4+ T Cell Counting in Whole Blood

CD4+ T cell counts are essential for diagnosing and monitoring diseases like HIV, cancers, and even COVID-19. The current gold standard, flow cytometry, is accurate but comes with drawbacks: it’s bulky, expensive, and time-consuming, making it unsuitable for decentralized or low-resource settings. Existing microfluidic platforms address some of these issues but often require complex sample processing or external pumps and specialized detection equipment. A recent microfluidic device reported in Microsystems and Nanoengineering journal aims to address these. The authors developed a pump-free, AI-enabled microfluidic cytometer that rapidly quantifies CD4+ T cells directly from whole blood. The system leverages gravity-driven slug flow to move the sample through the device and uses a convolutional neural network (CNN) to identify bead-labeled CD4+ T cells in flow videos. This approach allows for fast, low-cost operation using minimal hardware.

“Here, we report an artificial intelligence-enabled microfluidic cytometer for rapid CD4+ T cell quantification in whole blood requiring minimal sample preparation and instrumentation. CD4+ T cells in blood are labeled with anti-CD4 antibody-coated microbeads, which are driven through a microfluidic chip via gravity-driven slug flow, enabling pump-free operation.“, the authors explained.

The microfabrication of the microfluidic device was done using PDMS. The microfluidic device comprises a channel with a narrow constriction that confines blood cells to a single focal plane for imaging. CD4+ T cells are labeled with anti-CD4 antibody-coated microbeads using a quick, one-step, wash-free process. The diluted blood sample forms a “slug” that flows through the channel via gravity. As the sample moves, a video is recorded using a standard microscope and camera. The recorded footage is then analyzed by a trained CNN model to detect and count the bead-tagged cells.

The chip design enables the flow to remain stable and laminar, eliminating the need for pumps. Surface treatments ensure consistent slug formation and reliable flow profiles, while optimal channel dimensions (25 mm length, 1 mm width, 30 μm height at the constriction) maintain focus and minimize shear stress.

The platform demonstrated high accuracy in quantifying CD4+ T cells, with results deviating less than 10% from those obtained using standard flow cytometry. This level of agreement was achieved despite the system’s significantly simpler setup and operation. Moreover, the entire assay, from fingerprick blood collection to result, was completed in under 15 minutes, making it at least four times faster than traditional methods. The bead-based labeling process, which is crucial for identifying CD4+ T cells, proved to be highly effective; over 95% of target cells were labeled within just five minutes when using the optimized bead concentration at room temperature. Once the sample was introduced into the microfluidic chip, the gravity-driven flow ensured a stable and controlled motion through the constriction zone, enabling clear imaging of the cells. The convolutional neural network trained to detect bead-labeled cells was able to distinguish them accurately even in whole blood, which contains a complex mixture of other cell types. These combined outcomes confirm the platform’s reliability, speed, and suitability for use in clinical and field settings.

This gravity-driven microfluidic cytometer provides a practical alternative to flow cytometry for CD4+ T cell counting. Its compact, cost-effective design and simplified workflow position it well for point-of-care and resource-limited settings. Furthermore, the platform can be adapted to detect other immune cell types by modifying the bead surface chemistry, expanding its utility in diagnostic applications beyond HIV.

“This AI-enabled microfluidic cytometer can be readily modified to quantify other cell subpopulations by replacing the anti-CD4 antibody-coated beads with beads that are coated with antibodies targeting other proteins expressed on different cell types. Furthermore, we envision that this platform can be modified for multiplexed cell quantification by using different colored antibody-coated beads. “, the authors concluded

Figures are reproduced from Dixit, D.D., Graf, T.P., McHugh, K.J. et al. Artificial intelligence-enabled microfluidic cytometer using gravity-driven slug flow for rapid CD4+ T cell quantification in whole blood. Microsyst Nanoeng 11, 36 (2025). https://doi.org/10.1038/s41378-025-00881-y under a CC BY 4.0 Attribution 4.0 International license.

Read the original article: Artificial intelligence-enabled microfluidic cytometer using gravity-driven slug flow for rapid CD4+ T cell quantification in whole blood

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