High-throughput sensing of single-cell properties using parallel multi-stage cell deformation

Abstract

Biophysical properties of single cells serve as label-free, noninvasive biomarkers for phenotyping. However, most techniques measure quantities dependent on multiple biophysical properties rather than individual ones, limiting their biological/clinical relevance. Here, we present a single-cell biophysical phenotyping technique that quantifies size (Dc) and elastic modulus (E) of cells deforming at different levels along constriction microchannels. A physical model is developed to resolve features of multiple deformation stages into Dc and E. Parallel-channel device design achieves a reasonably high throughput of ∼104 cells/min. The measurement employs lock-in amplification-assisted electrokinetic sensing via embedded electrodes across three channel sections with varying constriction widths, inducing distinct cell deformations. We demonstrate the technique by profiling normal and cancerous breast cell lines (MCF-10A, MCF-7, and MDA-MB-231), as well as drug-treated cancer cells (cytochalasin D, cetuximab, and lysophosphatidic acid). The biophysical phenotyping enables cell classification with high accuracy (>95.45% via principal component analysis; >97.32% via machine learning). This approach offers robust and high-throughput cell classification, with potential applications in basic research and clinical diagnostics.