In recent years, label-free microscopy has gained momentum over the well-established fluorescence microscopy, as it allows overcoming many important drawbacks related to the staining process. Among the label-free imaging techniques, Quantitative Phase Imaging (QPI) has emerged since biophysical properties of cells and tissues are measured. The latest development of QPI is Tomographic Phase Microscopy (TPM), which allows reconstructing the 3D volumetric distribution of the Refractive Indices (RIs) at the single-cell level by combining multiple phase-contrast maps recorded all around the sample. Very recently, the TPM paradigm has been even demonstrated working in Flow Cytometry (FC) modality, thus opening the route to the label-free, 3D, quantitative and high-throughput recording of living suspended cells. Nevertheless, the several advantages of QPI and TPM over fluorescence microscopy are counterbalanced by the lack of intracellular specificity due to the stain-free imaging modality. In fact, the inner cell contrast usually is not enough to properly recognize the several organelles, thus preventing intracellular studies. In QPI and in static TPM, virtual staining has been proposed as a solution, based on the training of deep learning strategies to numerically emulate the chemical staining process. However, the virtual staining approach cannot be replicated in the TPM-FC technique since a dataset of paired 3D RI and fluorescent tomograms of cells cannot be created. Here we show a computational method for the stain-free segmentation of the nucleus in 3D inside the TPM-FC tomograms of flowing cells based on an ad hoc clustering of the intracellular voxels according to their statistical similarities.
The actual gap of the label-free quantitative phase microscopy in respect to fluorescence microscopy, that allows the subcellular characterization by using exogenous markers, is the lack of intracellular specificity. Recently, computational methods based on artificial intelligence have been demonstrated, which allow a virtual staining of single cells in both 2D and 3D, but they require co-registration systems able to collect simultaneously both fluorescence and quantitative phase information. However, a real limitation exists, i.e. these approaches cannot be used in flow cytometry condition. In this paper, we discuss a new methodology for adding the intracellular specificity analysis to tomographic phase microscopy in flow cytometry. The proposed strategy is based on the statistical clustering of tomograms voxels, thus allowing the segmentation of cell’s organelles. Here we report the results of nuclear region identification for cancer cells.
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