4.3 Article Proceedings Paper

Quantitative Phase Imaging Flow Cytometry for Ultra-Large-Scale Single-Cell Biophysical Phenotyping

Journal

CYTOMETRY PART A
Volume 95A, Issue 5, Pages 510-520

Publisher

WILEY
DOI: 10.1002/cyto.a.23765

Keywords

imaging flow cytometry; quantitative phase imaging; ultrafast single cell imaging; label-free biophysical phenotyping

Funding

  1. Innovation and Technology Support Programme [GHP/024/16GD, ITS/204/18]
  2. Research Grants Council of the Hong Kong Special Administrative Region of China [17207714, 17207715, C7047-16G, HKU 17208918, HKU 719813E, T12-708/12-N]
  3. University Development Funds of the University of Hong Kong

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Cellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of sub-cellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s-a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (similar to 92-97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. (C) 2019 International Society for Advancement of Cytometry

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