ODEP-Based Robotic System for Micromanipulation and In-Flow Analysis of Primary Cells.

The presence of cellular defects of multifactorial nature can be hard to characterize accurately and early due to the complex interplay of genetic, environmental, and lifestyle factors. With this study, by bridging optically-induced dielectrophoresis (ODEP), microfluidics, live-cell imaging, and machine learning, we provide the ground for devising a robotic micromanipulation and analysis system for single-cell phenotyping. Cells under the influence of nonuniform electric fields generated via ODEP can be recorded and measured. The induced responses obtained under time-variant ODEP stimulation reflect the cells' chemical, morphological, and structural characteristics in an automated, flexible, and label-free manner. By complementing the electrokinetic fingerprint of the cell centroid motion with data on the dynamics of electro-deformation and orientation, we show that subtle differences at the single-cell level can be elucidated. Specifically, here, we demonstrate, for the first time, the ability of the combined ODEP-based robotic and automatic analysis platform to discriminate between primary endometrial stromal cells obtained from fertile patients and patients with disrupted receptivity/selectivity equilibrium. When multiple cells were considered at the patient level, the performance achieved an average accuracy of 98%. Single-cell micro-operation and analysis systems may find a more general application in the clinical diagnosis and management of patients with pathological alterations at the cellular level.