A pillar-based microfluidic chip for T-cells and B-cells isolation and detection with machine learning algorithm

Abstract

Abstract Absolute counting of total leukocytes and specific subset (such as T-cells and B-cells) within small amounts of whole blood is difficult due to the lack of techniques that enables separation of leukocytes from limited volume of whole blood. In this study, a microfluidic chip equipped with a size controlled micropillar array for highly separation of T-cells and B-cells from sub-microliter of whole blood was studied. Based on the difference in size and deformability, leukocytes were separated from other blood cells by micropillar arrays. However, the variability of cells in size, morphology and color intensity along with the spectrum crosstalk between fluorescence dyes make cell detection among pillars extremely difficult. In this paper, an support vector machine supervised machine learning classifier based on both Histogram of Oriented Gradients (HOG) and color distribution features was proposed to distinguish T-cells and B-cells fast and robustly. HOG features were utilized to detect cells from background and noise; color distribution features were employed to alleviate the effect of fluorescence spectrum crosstalk. Experiment showed we achieved average detection accuracy of 94% for detecting T-cells and B-cells from the background. Furthermore, we also got 96% accuracy with cross validation to detect T-cells from B-cells. Both theoretical analysis and experiments demonstrated the proposed method and system has high performance in T-cells and B-cells counting. And our microfluidic cell counting system has great potential as a tool for point-of-care leukocyte analysis system.

Keywords

• Histogram of Oriented Gradients features
• Microfluidic chip
• Machine learning
• Support vector machine
• T-cells
• B-cells