Label Free Sorting of Cancer Cells from Heterogenous Blood Sample During Flow

The initiation and progression of acute diseases such as cancer 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. In cell biology and medical research, targeted diagnostics and personalized therapeutic interventions, the identification and isolation of intact, disease-associated cell subsets from complex tissues or heterogeneous cell populations is of utmost importance.

Various strategies have been developed for the detection of cell types, the most widely used ones rely on antibody-based capturing of the cells like fluorescence-activated cell sorting (FACS). However, for many cell types, antigen combinations that would allow for their unambiguous identification are missing. 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.

We propose a new label-free technique for noninvasive and automated cell processing, with high discriminative power on the level of the individual cell. By acquiring holograms of each cell and achieving its optical path delay (OPD) profile, we extract features that highly differentiate cancerous cells from heterogeneous blood sample. Using a dielectrophoresis flow chamber to control each cell, we can than create a separate flow of cancerous cells for further analysis and classification.