We present cytometric classification of live healthy and cancerous cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cells and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted and then used to calculate 15 parameters derived from the cellular 3D morphology and texture. Upon analyzing tens of cells in each group, we found high statistical significance in the difference between the groups in most of the parameters calculated, with the same trends for all statistically significant parameters. Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81-93% sensitivity and 81-99% specificity. The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers. © 2017 International Society for Advancement of Cytometry.

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We present a portable, off-axis interferometric module for quantitative phase microscopy of live cells, positioned at the exit port of a coherently illuminated inverted microscope. The module creates on the digital camera an interference pattern between the image of the sample and its flipped version. The proposed simplified module is based on a retro-reflector modification in an external Michelson interferometer. The module does not contain any lenses, pinholes, or gratings and its alignment is straightforward. Still, it allows full control of the off-axis angle and does not suffer from ghost images. As experimentally demonstrated, the module is useful for quantitative phase microscopy of live cells rapidly flowing in a micro-channel.

We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoderdecoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

We present highly dynamic photothermal interferometric phase microscopy for quantitative, selective contrast imaging of live cells during flow. Gold nanoparticles can be biofunctionalized to bind to specific cells, and stimulated for local temperature increase due to plasmon resonance, causing a rapid change of the optical phase. These phase changes can be recorded by interferometric phase microscopy and analyzed to form an image of the binding sites of the nanoparticles in the cells, gaining molecular specificity. Since the nanoparticle excitation frequency might overlap with the sample dynamics frequencies, photothermal phase imaging was performed on stationary or slowly dynamic samples. Furthermore, the computational analysis of the photothermal signals is time consuming. This makes photothermal imaging unsuitable for applications requiring dynamic imaging or real-time analysis, such as analyzing and sorting cells during fast flow. To overcome these drawbacks, we utilized an external interferometric module and developed new algorithms, based on discrete Fourier transform variants, enabling fast analysis of photothermal signals in highly dynamic live cells. Due to the self-interference module, the cells are imaged with and without excitation in video-rate, effectively increasing signal-to-noise ratio. Our approach holds potential for using photothermal cell imaging and depletion in flow cytometry.