Author:
X. Chen Cindy,B. Price Hillel,Wax Adam
Abstract
Holographic cytometry (HC) has been developed as an ultra-high throughput implementation of quantitative phase microscopy (QPM). While QPM has been well developed for studying cells based on endogenous contrast, few implementations have imaged cells in flow or provided high throughput measurements. Although QPI offers high resolution imaging, experiments are limited to examining a single cell at a time. The HC approach enables high throughput by imaging cells as they are flowed through microfluidic devices. Stroboscopic illumination is used in an off-axis interferometry configuration to produce holographic images of flowing cell samples without streaking artifact. The ability to profile large number of cells using individual images has been demonstrated in red blood cell and cancer cell samples. The large volume of data provides suitable training data for developing machine learning algorithms, producing excellent accuracy in classifying cell type. Analysis of the adherent cells to flow also produces diagnostically useful information in the form of biomechanical cell properties. Introduction of a new parameter, disorder strength, a measure of the variance of phase fluctuations across a cell, provides an additional window into the cell mechanical properties.