Abstract
Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase image based on off-axis interferometry of cells flowing through parallel microfluidic channels. Here, it is applied for characterizing morphological changes of red blood cells during storage under regular blood bank condition. The approach allows high quality phase imaging of a large number of cells greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology measurements show multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine learning algorithms. For the study here, logistic regression is used to classify the cells according to the storage time points. The results of the classifiers demonstrate the potential of holographic cytometry as a diagnostic tool.
Competing Interest Statement
The authors have declared no competing interest.
