An Integrated Microscope for High Throughput Imaging of Circulating Tumour Cells on a Chip

Circulating tumor cells (CTCs) are cells that shed from primary tumors into the bloodstream, leading to the formation of metastases. Their presence and numbers reflect the tumor burden, making them valuable biomarkers. Consequently, a simple blood draw can be used to non-invasively monitor tumor progression and treatment efficacy, a rapidly growing approach known as liquid biopsy [1]. However, the extreme rarity and heterogeneity of CTCs pose significant challenges for their identification, requiring methods that are often time-consuming, complex, and expensive. Building on the concept that cytomorphological criteria can differentiate between healthy and unhealthy cells, we have developed an innovative microscope on a chip designed for automated, high-throughput imaging of samples flowing in a microfluidic channel. Utilizing advanced laser-based microfabrication techniques, namely femtosecond laser micromachining [2], we have integrated optical splitter, and a 3D optical remapper into a glass substrate assembled together with fiber-based optical delay lines (as in the scheme of Fig.1a). This system operates by splitting a single nanosecond laser pulse into a sequence of pulses that illuminate the specimen at different locations and times (Fig.1b illustrates device characterization). The signals are then efficiently collected using a fast photodetector and a high speed digitizer for subsequent intensity variation analysis, enabling the acquisition of a single cell image in just a few microseconds. This optical system is intended for detecting specimens flowing within a microfluidic channel. The fluidic layout has been optimized by employing 3D hydrodynamic focusing, in which sheath flows confine the sample within a narrow region precisely aligned with the detection area [3]. This optofluidic device has been successfully validated using calibration beads and various types of tumor cells, demonstrating its reliability and versatility (Fig.1c). This high level of integration ensures user-friendly operation and provides consistent image quality across different days and conditions, a fundamental aspects when processing rare cells as CTCs. The captured images are fully compatible with machine learning algorithms, enabling efficient and accurate classification of cells. This approach has the potential to transform the detection and analysis of CTCs, providing a scalable, cost-effective solution for advancing liquid biopsy technologies.