Intelligent 3D imaging cytometer for scalable and hierarchical single-cell spatial profiling
Grant Data
Project Title
Intelligent 3D imaging cytometer for scalable and hierarchical single-cell spatial profiling
Principal Investigator
Professor Tsia, Kevin Kin Man
(Principal Investigator (PI))
Duration
60
Start Date
2021-01-01
Amount
5155380
Conference Title
Intelligent 3D imaging cytometer for scalable and hierarchical single-cell spatial profiling
Keywords
High-throughput flow cytometry, Single-cell imaging, Ultrafast microscopy
Discipline
Photonics
Panel
Engineering (E)
HKU Project Code
RFS2021-7S06
Grant Type
RGC Research Fellow Scheme
Funding Year
2020
Status
On-going
Objectives
1. Develop an intelligent 3D imaging cytometer - We will establish a microfluidic imaging cytometer that enables synchronized deep-learning-assisted 3D single-cell refractive index (RI) and fluorescence imaging at a high imaging throughput up to 104 cells/sec with sub-cellular resolution. Using the physicomechanical and biochemical phenotypes extracted from the images, the system will be integrated with a deep-learning-powered image-activated microfluidic cell sorter, which allows ""smart"" isolation of subpopulations for downstream molecular analysis (e.g. gene expression profiling). 2. Formulate a hierarchical single-cell spatial phenotyping strategy - We aim to establish a multifaceted image-based single-cell profiling strategy, based upon extensive image feature extraction from (1) physicomechanical analysis using 3D refractive index (RI) images and (2) correlative analysis in which the physicomechancial phenotypes will be spatially mapped to different subcellular components based on both physical and in-silico fluorescence labelling. The selected phenotypic information could be fed to trigger cell sorting in real-time (103-104 cells/sec). 3. Explore pilot experiments of the proposed cytometry - We will employ the integrative, intelligent imaging cytometry strategy to explore and investigate the impact of the single-cell physicomechancial phenotypic profiles (also their correlations with other molecular/biochemical phenotypes) on the real-world biological inquiries (e.g. assessment of stem cell differentiation) that could create new clinical applications in the context of intelligent label-free diagnosis.