Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s−1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.


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Timing Data exclusions Non-participation Randomization A goal of the experiments is to demonstrate that our method is capable of obtaining microscopy-grade images of a large number of cells, which cannot be done by conventional microscopes. Therefore, the sample size was determined such that the number of cells is too large to capture all cell images using conventional microscopes and is rationalized by the goal.
Data points with a quantification error (value of 0) were excluded from the populations of the histograms shown in Fig. 4b and Fig. 5b. A data point of the maximum value (46) in the population of E. gracilis -N was excluded for better visibility of the histogram shown in Fig. 5c. Defocused images of particles were excluded from the populations of the histograms shown in Supplementary Fig. 9. Data points with a value more than 120 were excluded from the histogram shown in Supplementary Fig. 11a for better visibility of the histogram. Data points outside the range of 25 um^2 < x < 40 um^2 were excluded from the population of the histogram shown in Supplementary Fig. 11b as quantification errors. Data points outside the range of 0.65 um^2 < x < 1.6 um^2 were excluded from the population of the histogram shown in Supplementary Fig. 13 as quantification errors. Data points outside the range of 0.65 x 10^-6 um < x < 1.95 um were excluded from the population of the histograms shown in Supplementary Fig. 15 as quantification errors. All the exclusion criteria were determined after reviewing images of particles to confirm that the excluded data points were obvious errors.
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nature research | reporting summary
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nature research | reporting summary
October 2018

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The femur bones from C57BL/6 female mice (8 weeks old) were collected by cutting above and below the joints. Bone-marrow cells were washed out of each bone by inserting a needle (26 gauge) with a sterile syringe filled with PBS/2% FBS into one side of the bone. After removing red blood cells by lysis buffer (Sigma-Aldrich), white blood cells were stained with antibodies described above.

FlowJo (BD Biosciences)
Neutrophils and lymphocytes were sorted based on the gating strategy below and both cells were 100% purity by resorting analysis.
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