Harnessing all-optical laser-scanning imaging for deep and large-scale image-based analysis

Abstract

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. State-of-the-art single-cell analysis approaches have overwhelmingly been biomolecularly-driven (e.g. analyzing cell-surface protein and gene expressions). Despite their exquisite specificity, they remain highly variable with regard to laborious and costly protocols. They also often lack the practical throughput in analyzing heterogeneous population, very often involving thousands to millions individual cells. What has been overlooked is biophysical properties of a cell, which influence and are influenced by its molecular signature. Defining biophysical markers, which are label-free in nature, could overcome the issues of scale and cost of analyzing numerous single cells. However, in-depth biophysical profiling of single-cell requires both high-throughput and high-content that are not achievable or affordable with current technologies. This speaks to their unappreciated level of adoption in practical single-cell analysis so far. To address these challenges, we recently developed two related imaging techniques, multi-ATOM imaging and FACED imaging, that generate multiple single-cell image contrasts from which not only a deep single-cell biophysical phenotypic profiles can be obtained (based on quantitative phase and other label-free contrasts), but also biochemical signatures of single-cells (based on fluorescence contrast). Based upon the concepts of all-optical laser-scanning through ultrafast spatiotemporal encoding of laser pulses, these techniques practically allows ultralarge-scale single-cell imaging (>millions of cells) with the unprecedented combination of imaging resolution and speed. This talk will introduce the technological developments of these imaging techniques. Combined with high-throughput computational methods (particularly machine learning), their utilities in large-scale cell-based assays using cell-lines, in-vivo mouse models, as well as primary human cells (e.g. for circulating tumor cell detection, routine blood analysis, drug screen). If time allows, I will also introduce how to extend the all-optical laser-scanning concept to high-speed parallelised volumetric imaging - visualising and understand the animate biological processes in 3D.