Stain-free nucleus identification in holographic learning flow cyto-tomography

Pirone, D.; Lim, J.; Merola, F.; Miccio, L.; Mugnano, M.; Bianco, V.; Cimmino, F.; Visconte, F.; Montella, A.; Capasso, M.; Iolascon, A.; Memmolo, P.; Psaltis, D.; Ferraro, P.

doi:10.1101/2021.12.22.473826

bioRxiv · 10.1101/2021.12.22.473826

Stain-free nucleus identification in holographic learning flow cyto-tomography

Pirone, D.·Lim, J.·Merola, F.·Miccio, L.·Mugnano, M.·Bianco, V.·Cimmino, F.·Visconte, F.·Montella, A.·Capasso, M.·Iolascon, A.·Memmolo, P.·Psaltis, D.·Ferraro, P.

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Abstract

Quantitative Phase Imaging (QPI) has gained popularity because it can avoid the staining step, which in some cases is difficult or impossible. However, QPI does not provide the well-known specificity to various parts of the cell (e.g., organelles, membrane). Here we show a novel computational segmentation method based on statistical inference that bridges the gap between the specificity of Fluorescence Microscopy (FM) and the label-free property of QPI techniques to identify the cell nucleus. We demonstrate application to stain-free cells reconstructed through the holographic learning and in flow cyto-tomography modality. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus-like regions can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal FM data and microfluidic cytofluorimeter outputs. This is a significant step towards extracting the three-dimensional (3D) intracellular specificity directly from the phase-contrast data in a typical flow cytometry configuration.

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Pirone, D., Lim, J., Merola, F., Miccio, L., Mugnano, M., Bianco, V., Cimmino, F., Visconte, F., Montella, A., Capasso, M., Iolascon, A., Memmolo, P., Psaltis, D., Ferraro, P.. 2021-12-23. Stain-free nucleus identification in holographic learning flow cyto-tomography. https://doi.org/10.1101/2021.12.22.473826

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