Multiparameter mechanical and morphometric screening of cells.

ABSTRACT: We introduce a label-free method to rapidly phenotype and classify cells purely based on physical properties. We extract 15 biophysical parameters from cells as they deform in a microfluidic stretching flow field via high-speed microscopy and apply machine-learning approaches to discriminate different cell types and states. When employing the full 15 dimensional dataset, the technique robustly classifies individual cells based on their pluripotency, with accuracy above 95%. Rheological and morphological properties of cells while deforming were critical for this classification. We also show the application of this method in accurate classifying cells based on their viability, drug screening and detecting populations of malignant cells in mixed samples. We show that some of the extracted par

SUBMITTER: Masaeli M 

PROVIDER: S-EPMC5133672 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature