Quantitative asymmetric-detection time-stretch optical microscopy (Q-ATOM) for ultrafast quantitative phase imaging flow cytometry

Abstract

Based on the interferometric or holographic approaches, recent QPM techniques provide quantitative-phase information, e.g cell volume, dry mass and optical scattering properties for label-free cellular physical phenotyping. These approaches generally rely on iterative phase-retrieval algorithms to obtain quantitative-phase information, which are computationally intensive. Moreover, current QPM techniques can only offer limited image acquisition rate by using CMOS/CCD image sensors, these two limitations hinder QPM for high-throughput quantitative image-based single-cell analysis in real-time. To this end, we demonstrate an interferometry-free quantitative phase microscopy developed on a new generation of time-stretch microscopy, asymmetric-detection time-stretch optical microscopy (ATOM), which is coined quantitative ATOM (Q-ATOM) - featuring an unprecedented cell measurement throughput together with the assorted intrinsic optical phenotypes (e.g. angular light scattering profile) and the derived physical properties of the cells (e.g. cell size, dry mass density etc.). Based on a similar concept to Schlieren imaging, Q-ATOM retrieves quantitative-phase information through multiple off-axis light-beam detection at a line-scan rate of >10 MHz - a speed unachievable by any existing QPM techniques. Phase retrieval in Q-ATOM relies on a non-iterative method, significantly reducing the computational complexity of the technique. It is a particularly important feature which facilitates real-time continuous label-free single-cell analysis in Q-ATOM. With the use of a non-interferometric configuration, we demonstrate ultrafast Q-ATOM of mouse chondrocytes and hypertrophic chondrocytes in ultrafast microfluidic flow with sub-cellular resolution at an imaging throughput equivalent to ~100,000 cells/sec without image blur. This technique shows a great potential for ultrahigh throughput label-free image-based single-cell biophysical phentotyping.