Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.
Journal:
Lab on a chip
Published Date:
Oct 21, 2020
Abstract
The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical power and sensitivity that are unattainable with current technologies. To close this gap, we present a large-scale, integrative imaging flow cytometry platform and strategy that allows hierarchical analysis of intrinsic morphological descriptors of single-cell optical and mass density within a population of millions of cells. The optofluidic cytometry system also enables the synchronous single-cell acquisition of and correlation with fluorescently labeled biochemical markers. Combined with deep neural network and transfer learning, this massive single-cell profiling strategy demonstrates the label-free power to delineate the biophysical signatures of the cancer subtypes, to detect rare populations of cells in the heterogeneous samples (10-5), and to assess the efficacy of targeted therapeutics. This technique could spearhead the development of optofluidic imaging cell-based assays that stratify the underlying physiological and pathological processes based on the information-rich biophysical cellular phenotypes.