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32B-O-3
Classification of label-free cancerous cells in blood sample during flow based on interferometric phase microscopy (IPM)
N. Nissim1, N. Tzvi Shaked1
1Tel-Aviv University, Biomedical Engineering, Tel-Aviv, Israel
The initiation and progression of acute diseases such as cancer and immune deficiencies, as well as wound repair and other regenerative processes often depend on the physiology of a small number of highly specialized cells. These cells can be obtained from biopsies including body fluids, such as blood and urine in easily taken routine lab tests [1]. In cell biology and medical research, in targeted diagnostics and in personalized therapeutic interventions, the identification and isolation of intact, disease-associated cell subsets from complex tissues or heterogeneous cell populations is of utmost importance [2].
Various strategies have been developed for the detection of cell types, the most widely used rely on antibody-based capturing of the cells like fluorescence-activated cell sorting (FACS) [3]. However, for many cell types, antigen combinations are missing that would allow for their unambiguous identification. Moreover, the inherent modification of the cell surface chemistry makes label-based approaches incompatible with noninvasive cell processing and, therefore, disqualifies them for usage in many cell therapeutic applications [4].
The refractive index (RI) of the cell interior is related to the optical interaction of the light field with cellular organelles and their chemical composition and, therefore, is easy accessible by optical techniques without affecting the cell physiology. Label-free interferometric phase microscopy (IPM) is able to measure the cell optical thickness profile in sub-nanometer sensitivity [5-8].
We propose an application for the noninvasive and automated cell processing, with high discriminative power on the level of the individual cell. By acquiring holograms of each cell using IPM and achieving its OPD profile, we extract features that highly differentiate cancerous cells from heterogeneous blood sample.
References
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[4] Chen, C. L. et al. Deep Learning in Label-free Cell Classification. Sci. Rep. 6, 21471 (2016).
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[7] Balberg, M. et al. Localized measurements of physical parameters within human sperm cells obtained with wide-field interferometry. J. Biophotonics n/a-n/a (2017). doi:10.1002/jbio.201600186
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