Optomicrofluidic detection of cancer cells in peripheral blood <i>via</i> metabolic glycoengineering

Abstract

The currently existing label-based techniques for the detection of circulating tumor cells (CTCs) target natural surface proteins of cells and are therefore applicable to only limited cancer cell types. We report optomicrofluidic detection of cancer cells in the pool of peripheral blood mononuclear cells (PBMCs) by exploiting the difference in their cell metabolism. We employ metabolic glycoengineering as a click chemistry tool for tagging cells that yields several fold-higher fluorescence signals from cancer cells compared to that from PBMCs. The effects of concentrations of the tagging compounds and cell incubation time on the fluorescence signal intensity are studied. The tagged cells were encapsulated in droplets ensuring that cells enter the detection region two-dimensionally focused in single-file and optically detected with a high detection efficiency and low coefficient of variation of the signals. The metabolic tagging approach showed a significantly higher tagging efficiency and average fluorescence signal compared to the well-established and widely adopted anti-EpCAM-FITC-based tagging. We demonstrated the detection of three different cancer cell lines - EpCAM-negative cervical cancer cell, HeLa, weakly EpCAM positive, and triple-negative breast cancer cell, MDA-MB-231, and strongly EpCAM positive breast cancer cell, MCF7, highlighting that the proposed technique is independent of naturally occurring cell surface proteins and widely applicable. The metabolically tagged and optically detected cells were successfully recultured, proving the compatibility of the proposed technique with downstream assays. The proposed technique is then utilised for the detection of CTCs in metastatic cancer patients' blood. The current work provides a new strategy for detecting cancer cells in the blood that can find potential applications in both fundamental research and clinical studies involving CTCs as well as in single-cell sequencing.