Tumour cell identification by means of Raman spectroscopy in combination with optical traps and microfluidic environments

TLDR

Raman spectroscopy is a powerful label‑free technique for discriminating cells, and sampling methods are being developed to identify cells in body fluids such as saliva, urine, or blood. The study applies optical traps combined with Raman spectroscopy to acquire spectra of single cells in microfluidic glass channels. The authors used two 1070 nm single‑mode fibre lasers to generate optical traps, controlled microflows with a syringe pump, and designed a novel microfluidic glass chip that injects single cells, adjusts flow speed, accommodates the laser fibres, and sorts cells after Raman‑based identification, with the integrated chip using 514 nm excitation and a quartz capillary setup using 785 nm. Linear discriminant analysis models distinguished erythrocytes, leukocytes, OCI‑AML3, BT‑20, and MCF‑7 cells with accuracies comparable to previous Raman studies, and integrating the system into microfluidic environments enables highly automated, high‑throughput Raman‑activated cell sorting.

Abstract

Raman spectroscopy has been recognized to be a powerful tool for label-free discrimination of cells. Sampling methods are under development to utilize the unique capabilities to identify cells in body fluids such as saliva, urine or blood. The current study applied optical traps in combination with Raman spectroscopy to acquire spectra of single cells in microfluidic glass channels. Optical traps were realized by two 1070 nm single mode fibre lasers. Microflows were controlled by a syringe pump system. A novel microfluidic glass chip was designed to inject single cells, modify the flow speed, accommodate the laser fibres and sort cells after Raman based identification. Whereas the integrated microchip setup used 514 nm for excitation of Raman spectra, a quartz capillary setup excited spectra with 785 nm laser wavelength. Classification models were trained using linear discriminant analysis to differentiate erythrocytes, leukocytes, acute myeloid leukaemia cells (OCI-AML3), and breast tumour cells BT-20 and MCF-7 with accuracies that are comparable with previous Raman experiments of dried cells and fixed cells in a Petri dish. Implementation into microfluidic environments enables a high degree of automation that is required to improve the throughput of the approach for Raman activated cell sorting.