Comprehensive quantification of phytoplankton abundance, sizes and other parameters, e.g. biomasses, has been an important, yet daunting task in aquatic sciences and biofuel research. It is primarily because of the lack of effective tool to image and thus accurately profile individual microalgae in a large population. The phytoplankton species are highly diversified and heterogeneous in terms of their sizes and the richness in morphological complexity. This fact makes time-stretch imaging, a new ultrafast real-time optical imaging technology, particularly suitable for ultralarge-scale taxonomic classification of phytoplankton together with quantitative image recognition and analysis. We here demonstrate quantitative imaging flow cytometry of single phytoplankton based on quantitative asymmetric-detection time-stretch optical microscopy (Q-ATOM) – a new time-stretch imaging modality for label-free quantitative phase imaging without interferometric implementations. Sharing the similar concept of Schlieren imaging, Q-ATOM accesses multiple phase-gradient contrasts of each single phytoplankton, from which the quantitative phase profile is computed. We employ such system to capture, at an imaging line-scan rate of 11.6 MHz, high-resolution images of two phytoplankton populations (scenedesmus and chlamydomonas) in ultrafast microfluidic flow (3 m/s). We further perform quantitative taxonomic screening analysis enabled by this technique. More importantly, the system can also generate quantitative phase images of single phytoplankton. This is especially useful for label-free quantification of biomasses (e.g. lipid droplets) of the particular species of interest – an important task adopted in biofuel applications. Combining machine learning for automated classification, Q-ATOM could be an attractive platform for continuous and real-time ultralarge-scale single-phytoplankton analysis.

Date Published: 7 March 2016
PDF: 6 pages
Proc. SPIE 9720, High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management, 972011 (7 March 2016); doi: 10.1117/12.2212246

Published in SPIE Proceedings Vol. 9720:
High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management
Kevin K. Tsia; Keisuke Goda, Editor(s)