2026-10-11T20:22:06Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34027400 2026-09-29T19:08:39Z category_12 category_21 category_734 category_61 category_64 category_128 portal_63 item_type_6 month_year_09_2026

Deep Learning of Position-Aligned Dynamic Cell Deformation Trajectories for Ovarian Cell Phenotyping in Hyperbolic Microchannels Yi-Bo Hu (12245512) Xi-Lin Gao (20454961) Hong-Fei Li (4761618) Zhuo Yang (314040) Shu-Song Huang (25138652) Yong-Jiang Li (3025092) Xu-Qu Hu (25138655) Cell Biology Biotechnology Biological Sciences not elsewhere classified Developmental Biology Cancer Hematology static morphological measurements predefined channel positions hyperbolic microchannels capture common spatial grid framework achieved 93 dynamic response analysis three flow rates isolated geometric states indexed deformation trajectories dynamic deformation trajectories uses deep learning ovarian cell phenotyping 1 </ sub geometric descriptors measured deep learning resolved framework f </ aligned analysis >< sub flow path flow conditions kinematic trajectories measured trajectory unknown cell test data speed imaging specific predictions specific evolution soft voting prediction errors macro recall macro precision macro -< inlet state hydrodynamic conditions direct comparison cell line cell differences axial velocity Cell deformation during microfluidic transport evolves continuously, whereas cell phenotyping commonly relies on geometric descriptors measured at one or a few predefined channel positions. Such fixed-position measurements capture selected deformation states but overlook how the cellular response progresses along the flow path. Combined with high-speed imaging, hyperbolic microchannels capture this progression as a continuous trajectory, enabling the shift from static morphological measurements to a dynamic response analysis. However, cell-to-cell differences in transit velocity cause equivalent image frames to represent different channel positions and hydrodynamic conditions, preventing a direct comparison of frame-indexed deformation trajectories. Here, we develop a trajectory-resolved framework that converts high-speed microfluidic image sequences into position-aligned dynamic cell deformation trajectories and uses deep learning to model their cell line-specific evolution for label-free ovarian cell phenotyping. Geometric and kinematic trajectories were extracted from three ovarian cancer cell lines (A2780, OVCAR-3, and SKOV-3) and one nonmalignant ovarian epithelial cell line (IOSE-80) at three flow rates. The trajectories were reparameterized by the axial position and resampled on a common spatial grid. Class-specific one-dimensional convolutional neural networks predicted downstream trajectories from the inlet state and flow conditions. An unknown cell was classified by comparing its measured trajectory with class-specific predictions, and prediction errors in the width, area, perimeter, and axial velocity were integrated by soft voting. The framework achieved 93.26% accuracy, 94.07% macro precision, 90.25% macro recall, and a 91.85% macro-<i>F</i>₁ score on the held-out test data. These results support extending microfluidic cell phenotyping from isolated geometric states to the position-aligned analysis of dynamic deformation trajectories. 2026-09-29T00:00:00Z Text Journal contribution 10.1021/acs.analchem.6c05067.s001 https://figshare.com/articles/journal_contribution/Deep_Learning_of_Position-Aligned_Dynamic_Cell_Deformation_Trajectories_for_Ovarian_Cell_Phenotyping_in_Hyperbolic_Microchannels/34027400 CC BY-NC 4.0