Automation and High-Throughput Technologies
SLAS2018 Innovation Award Finalist: Ultrafast all-optical laser-scanning imaging - Enabling deep single-cell imaging and analysis
Monday, February 5
3:00 PM - 3:30 PM
Studying cell populations, their transition states and functions at the single cell level is critical for understanding in normal tissue development and pathogenesis of disease. However, current platforms for single-cell analysis (SCA) lack the practical combination of throughput and precision that is limited by the prohibitive costs and time in performing SCA, very often involving thousands to millions individual cells – largely explaining the limited applications of SCA to date. For creating new scientific insights and enriching the diagnostic toolsets, it is valuable to explore alternative biomarkers, notably biophysical markers, which maximizes the cost-effectiveness of SCA because of its label-free nature. Also, as it is closely tied with many cellular behaviours, biophysical markers can complement and correlate with the information retrieved by existing biochemical markers with high statistical precision – providing a comprehensive catalogue of single-cell properties and thus a new landscape of “Cell Altas”.
Optical microscopy is an effective tool to visualize cells with high spatiotemporal resolution. However, its full adoption for high-throughput SCA has been hampered by the intrinsic speed limit imposed by the prevalent image capture strategies, which involve the laser scanning technologies (e.g. galvanometric mirrors), and/or the image sensors (e.g. CCD and CMOS). The laser scanning speed is fundamentally limited by the mechanical inertia of the mirrors whereas the image capture rate of CCD/CMOS sensor is fundamentally limited by the required image sensitivity. Notably, this speed-versus-sensitivity trade-off of the image sensor explains why the throughput of flow cytometry has to be scaled down from 100,000 cells/sec to 1,000 cells/sec when the imaging capability is incorporated.
To address these challenges, we adopt two related techniques to enable imaging flow cytometry with the unprecedented combination of imaging resolution and speed. Sharing a common concept of all-optical laser-scanning by ultrafast spatiotemporal encoding of laser pulses, these techniques, time-stretch imaging and free-space angular-chirp-enhanced delay (FACED) imaging enable ultrahigh-throughput single-cell imaging with multiple image contrasts (e.g. quantitative phase and fluorescence imaging) at a line-scan rate beyond 10’s MHz (i.e. an imaging throughput up to ~100,000 cells/sec). Moreover, they also enable quantification of intrinsic biophysical markers of individual cells – a largely unexploited class of single-cell signatures that is known to be correlated with the overwhelmingly investigated biochemical markers. All in all, these ultrafast single-cell imaging platforms could find new potentials in deep machine learning complex biological processes from such an enormous size of image data (from molecular signatures to biophysical phenotypes), especially to unveil the unknown heterogeneity between different single cells and to detect (and even quantify) rare aberrant cells.