A parallel microfluidic flow cytometer for high-content screening

Journal name:
Nature Methods
Volume:
8,
Pages:
401–403
Year published:
DOI:
doi:10.1038/nmeth.1595
Received
Accepted
Published online

A parallel microfluidic cytometer (PMC) uses a high-speed scanning photomultiplier-based detector to combine low-pixel-count, one-dimensional imaging with flow cytometry. The 384 parallel flow channels of the PMC decouple count rate from signal-to-noise ratio. Using six-pixel one-dimensional images, we investigated protein localization in a yeast model for human protein misfolding diseases and demonstrated the feasibility of a nuclear-translocation assay in Chinese hamster ovary (CHO) cells expressing an NFκB-EGFP reporter.

At a glance

Figures

  1. High-content screening on a PMC.
    Figure 1: High-content screening on a PMC.

    (a) Schematic of the device. (b) Left, simulated 2D microscopy images. The dashed arrow shows the location of the single 1D scan with reporter fluorescence shown in green and a red fluorescent whole-cell marker in orange. Right, typical two-color 1D images that are produced by the PMC. (c,d) Simulation of phenotyping with 1D images of the 'positive' S. cerevisiae phenotype exhibiting induced α-Syn–GFP focal aggregates (c) and the negative phenotype with diffuse and membrane localized α-Syn–GFP (d). Scale bars, 5 μm. (e) Simulation model counts of 1D image classes (Sym, Asym and RO) from 400 scans, when a cell was scanned with the indicated round (1−7 μm diameter) and rectangular (8 μm × 1 μm to 8 μm × 5 μm) laser spots.

  2. Phenotyping [alpha]-Syn-GFP aggregation by PMC imaging.
    Figure 2: Phenotyping α-Syn–GFP aggregation by PMC imaging.

    (a) Kolmogorov-Smirnov test of 82 features for three positive (S12–S14) and three negative (S21–S23) samples displayed as a P-value heatmap with increasing probability from blue to red. (b) Plots of cumulative distribution functions (CDFs) for the Kolmogorov-Smirnov test (K-S), shown for two features for a positive and negative sample (left). Kolmogorov-Smirnov heat-map signatures (right) show the difference in CDF plots generated for six yeast samples across seven features. Control red features were total intensity ratio around signal peak (F60), intensity perimeter around signal peak (F64) and red perimeter ratio around calculated object center (F65). Discriminating features were green area around red peak (F63), green perimeter ratio around object center (F67), the ratio F67:F65 (F71) and green P2A:red P2A (P2A = perimeter2/2π × area) (F82). (c) Images showing aggregated (positive) S. cerevisiae α-Syn–GFP samples (S12 and S14) and nonaggregated (negative) samples (S21 and S23). Scale bar, 15 μm. (d) Single-cell event distribution across three 1D image classes for positive and negative samples.

References

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Author information

Affiliations

  1. Boston University, Departments of Biomedical Engineering, and Electrical and Computer Engineering, Boston, Massachusetts, USA.

    • Brian K McKenna,
    • James G Evans,
    • Man Ching Cheung &
    • Daniel J Ehrlich

Contributions

B.K.M., J.G.E., M.C.C. and D.J.E. designed the research; B.K.M., M.C.C., J.G.E. and D.J.E. performed the engineering and experiments; B.K.M. and M.C.C. wrote analytical software and performed the data analysis; and all authors contributed to writing the paper.

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

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  1. Supplementary Text and Figures (5M)

    Supplementary Figures 1–14, Supplementary Table 1

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