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Label-free cell-cycle analysis by high-throughput quantitative phase time-stretch imaging flow cytometry
Author(s): Aaron T. Y. Mok; Kelvin C. M. Lee; Kenneth K. Y. Wong; Kevin K. Tsia
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Paper Abstract

Biophysical properties of cells could complement and correlate biochemical markers to characterize a multitude of cellular states. Changes in cell size, dry mass and subcellular morphology, for instance, are relevant to cell-cycle progression which is prevalently evaluated by DNA-targeted fluorescence measurements. Quantitative-phase microscopy (QPM) is among the effective biophysical phenotyping tools that can quantify cell sizes and sub-cellular dry mass density distribution of single cells at high spatial resolution. However, limited camera frame rate and thus imaging throughput makes QPM incompatible with high-throughput flow cytometry – a gold standard in multiparametric cell-based assay. Here we present a high-throughput approach for label-free analysis of cell cycle based on quantitative-phase time-stretch imaging flow cytometry at a throughput of > 10,000 cells/s. Our time-stretch QPM system enables sub-cellular resolution even at high speed, allowing us to extract a multitude (at least 24) of single-cell biophysical phenotypes (from both amplitude and phase images). Those phenotypes can be combined to track cell-cycle progression based on a t-distributed stochastic neighbor embedding (t-SNE) algorithm. Using multivariate analysis of variance (MANOVA) discriminant analysis, cell-cycle phases can also be predicted label-free with high accuracy at >90% in G1 and G2 phase, and >80% in S phase. We anticipate that high throughput label-free cell cycle characterization could open new approaches for large-scale single-cell analysis, bringing new mechanistic insights into complex biological processes including diseases pathogenesis.

Paper Details

Date Published: 20 February 2018
PDF: 5 pages
Proc. SPIE 10505, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, 105050J (20 February 2018); doi: 10.1117/12.2291864
Show Author Affiliations
Aaron T. Y. Mok, Cornell Univ. (United States)
Kelvin C. M. Lee, The Univ. of Hong Kong (Hong Kong, China)
Kenneth K. Y. Wong, The Univ. of Hong Kong (Hong Kong, China)
Kevin K. Tsia, The Univ. of Hong Kong (Hong Kong, China)


Published in SPIE Proceedings Vol. 10505:
High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)

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