Multiparameter mechanical and morphometric screening of cells.

Journal: Scientific reports
PMID: 27910869

Abstract

We introduce a label-free method to rapidly phenotype and classify cells purely based on physical properties. We extract 15 biophysical parameters from cells as they deform in a microfluidic stretching flow field via high-speed microscopy and apply machine-learning approaches to discriminate different cell types and states. When employing the full 15 dimensional dataset, the technique robustly classifies individual cells based on their pluripotency, with accuracy above 95%. Rheological and morphological properties of cells while deforming were critical for this classification. We also show the application of this method in accurate classifying cells based on their viability, drug screening and detecting populations of malignant cells in mixed samples. We show that some of the extracted parameters are not linearly independent, and in fact we reach maximum classification accuracy by using only a subset of parameters. However, the informative subsets could vary depending on cell types in the sample. This work shows the utility of an assay purely based on intrinsic biophysical properties of cells to identify changes in cell state. In addition to a label-free alternative to flow cytometry in certain applications, this work, also can provide novel intracellular metrics that would not be feasible with labeled approaches (i.e. flow cytometry).

Authors

  • Mahdokht Masaeli
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Dewal Gupta
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Sean O'Byrne
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Henry T K Tse
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Daniel R Gossett
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Peter Tseng
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Andrew S Utada
    Department of Bioengineering, University of California, Los Angeles, CA, USA.
  • Hea-Jin Jung
    Molecular Biology Institute, University of California, Los Angeles, CA, USA.
  • Stephen Young
    Molecular Biology Institute, University of California, Los Angeles, CA, USA.
  • Amander T Clark
    Department of Molecular Cell and Developmental Biology, University of California, Los Angeles, CA, USA.
  • Dino Di Carlo
    2Department of Bioengineering, University of California, 420 Westwood Plaza, 5121 Engineering V, PO Box 951600, Los Angeles, CA 90095 USA.

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