Label-Free Classification of Cancer Cells in Blood Sample during Flow based on Interferometric Phase Microscopy

Acute diseases, such as cancer, often depend on the physiology of a small number of highly specialized cells. These cells can be obtained from liquid biopsies, such as blood and urine, in routine lab tests.

Various strategies have been developed for the detection of specific cell types in a heterogeneous cell population. The most widely used methods rely on antibody-based capturing of the cells like fluorescence-activated cell sorting (FACS). However, for many cell types, antigen combinations are missing. Furthermore, the inherent modification of the cell surface chemistry makes label-based approaches incompatible with noninvasive cell processing, disqualifying them for cell therapeutic application.

The cellular refractive index is related to the optical interaction of the light field with cellular organelles and their chemical composition. It is 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.

We propose an application of IPM for noninvasive and automated cell processing during flow. By acquiring holograms of each cell using IPM, we extract features that highly differentiate cancerous cells from a heterogeneous blood sample and build an automatic cell classifier. We expect this technique to yield a future diagnosis, prognosis, and therapeutic tools for cancer-based on routine lab tests.