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Multiparameter mechanical and morphometric screening of cells.

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).

SUBMITTER: Masaeli M

PROVIDER: S-EPMC5133672 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Multiparameter mechanical and morphometric screening of cells.

Masaeli Mahdokht M Gupta Dewal D O'Byrne Sean S Tse Henry T K HT Gossett Daniel R DR Tseng Peter P Utada Andrew S AS Jung Hea-Jin HJ Young Stephen S Clark Amander T AT Di Carlo Dino D

Scientific reports 20161202

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 morph ...[more]

PMID: 27910869

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Project description:This paper describes two SlipChip-based approaches to protein crystallization: a SlipChip-based free interface diffusion (FID) method and a SlipChip-based composite method that simultaneously performs microbatch and FID crystallization methods in a single device. The FID SlipChip was designed to screen multiple reagents, each at multiple diffusion equilibration times, and was validated by screening conditions for crystallization of two proteins, enoyl-CoA hydratase from Mycobacterium tuberculosis and dihydrofolate reductase/thymidylate synthase from Babesia bovis, against 48 different reagents at five different equilibration times each, consuming 12 microL of each protein for a total of 480 experiments using three SlipChips. The composite SlipChip was designed to screen multiple reagents, each at multiple mixing ratios and multiple equilibration times, and was validated by screening conditions for crystallization of two proteins, enoyl-CoA hydratase from Mycobacterium tuberculosis and dihydrofolate reductase/thymidylate synthase from Babesia bovis. To prevent cross-contamination while keeping the solution in the neck channels for FID stable, the plates of the SlipChip were etched with a pattern of nanowells. This nanopattern was used to increase the contact angle of aqueous solutions on the surface of the silanized glass. The composite SlipChip increased the number of successful crystallization conditions and identified more conditions for crystallization than separate FID and microbatch screenings. Crystallization experiments were scaled up in well plates using conditions identified during the SlipChip screenings, and X-ray diffraction data were obtained to yield the protein structure of dihydrofolate reductase/thymidylate synthase at 1.95 A resolution. This free-interface diffusion approach provides a convenient and high-throughput method of setting up gradients in microfluidic devices and may find additional applications in cell-based assays.

Dataset Information

Multiparameter mechanical and morphometric screening of cells.

Publications

Multiparameter mechanical and morphometric screening of cells.

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