In JoVE (1)
Other Publications (1)
Articles by Queenie T. K. Lai in JoVE
Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM) Anson H. L. Tang1, Queenie T. K. Lai1, Bob M. F. Chung2, Kelvin C. M. Lee1, Aaron T. Y. Mok1, G. K. Yip1, Anderson H. C. Shum2, Kenneth K. Y. Wong1, Kevin K. Tsia1 1Department of Electrical and Electronic Engineering, The University of Hong Kong, 2Department of Mechanical Engineering, The University of Hong Kong This protocol describes the implementation of an asymmetric-detection time-stretch optical microscopy system for single-cell imaging in ultrafast microfluidic flow and its applications in imaging flow cytometry.
Other articles by Queenie T. K. Lai on PubMed
High-throughput Time-stretch Imaging Flow Cytometry for Multi-class Classification of Phytoplankton Optics Express. Dec, 2016 | Pubmed ID: 27958529 Time-stretch imaging has been regarded as an attractive technique for high-throughput imaging flow cytometry primarily owing to its real-time, continuous ultrafast operation. Nevertheless, two key challenges remain: (1) sufficiently high time-stretch image resolution and contrast is needed for visualizing sub-cellular complexity of single cells, and (2) the ability to unravel the heterogeneity and complexity of the highly diverse population of cells - a central problem of single-cell analysis in life sciences - is required. We here demonstrate an optofluidic time-stretch imaging flow cytometer that enables these two features, in the context of high-throughput multi-class (up to 14 classes) phytoplantkton screening and classification. Based on the comprehensive feature extraction and selection procedures, we show that the intracellular texture/morphology, which is revealed by high-resolution time-stretch imaging, plays a critical role of improving the accuracy of phytoplankton classification, as high as 94.7%, based on multi-class support vector machine (SVM). We also demonstrate that high-resolution time-stretch images, which allows exploitation of various feature domains, e.g. Fourier space, enables further sub-population identification - paving the way toward deeper learning and classification based on large-scale single-cell images. Not only applicable to biomedical diagnostic, this work is anticipated to find immediate applications in marine and biofuel research.