Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

ABSTRACT: Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an a

SUBMITTER: Kim DNH 

PROVIDER: S-EPMC8484462 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature