Representing particles’ motion patterns in microfluidic imaging platform using deep variational embeddings

Understanding the motion properties of cells or particles is important in microfluidic imaging applications. Motion-related analysis has proven to be a valuable tool for phenotyping particulates in biological samples. However, relying solely on trajectory features from individual cells may not always be sufficient to describe their overall motion patterns. This highlights the need for a more effective solution focusing on rotational components in movement. In this study, we developed a generalized motion pattern representation framework using deep variational embeddings to characterize biological samples with different morphology. First, we build a simplified optical setup with sufficient throughput to record sequential frames of cells containing orientational changes. Then, a self-supervised learning pipeline was developed to embed its motion pattern into a latent space. The latent variables are visualized as the generalized motion pattern to represent a sequence of consecutive frames. Finally, segment key frames of individual cells’ motion to divide a motion trajectory into consecutive sub-trajectories. Each sub-trajectory has a predefined specific meaning to be collected for downstream motion-related analysis. Our framework has been verified with two cell types with common shapes: plate-like erythrocytes and rod-like yeasts. The results demonstrate that the motion pattern representation is distinct and interpretable for these two samples. Utilized in a motion segmentation application, the represented motion achieved over 90% accuracy with unsupervised clustering, which has significantly enhanced relevant motion analysis. These promising findings underscore the practical value of our developed framework in extracting informative motion patterns for phenotyping.