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IBEC Seminar: Gopi Shah
Thursday, January 18, 2018 @ 3:00 pm–4:00 pm
Prospects of light sheet microscopy in developmental biology and cancer research
Gopi Shah (CRUK Cambridge Institute, University of Cambridge)Light sheet microscopy is one of the fastest fluorescence imaging technologies available today. In the last decade, it has emerged as an ideal technique for visualizing biological processes occurring at various time and length scales: rapid three-dimensional processes such as the beating zebrafish heart can be captured at >400 frames/sec, large samples such as the developing zebrafish embryo (~0.7-1mm) can be imaged in toto at high resolution through multi-view imaging and delicate samples such as in vitro cultured 3D organoids can be monitored over days owing to its non-invasive nature.
Nonetheless, most biological studies demand a higher imaging throughput in terms of sample size, which has been a challenge for light sheet microscopy both in terms of microscope design and the volume of data generated. To address this, we have developed customised light sheet microscopes with real-time image-processing engine that projects the 3D image volume onto a 2D map, drastically reducing the amount of data generated as well as providing a panoramic view of the sample for ease of downstream analyses. We also designed a fluidic sample delivery system to pump embryos through the microscope, enabling time-lapse imaging and screening of several samples simultaneously. Together, these tools harness the high-speed imaging capability of a light sheet system to obtain multi-dimensional data from many samples, essential for systematic population analysis. In my talk, I will discuss how this work (a) has enabled integration of whole-sample live imaging, genetic information and analysis of an ensemble of specimen to understand large-scale tissue movements during zebrafish embryogenesis and (b) facilitates my vision of establishing high-throughput imaging of organoids for understanding tumor cell dynamics and developing organoids as a model for image-based screening and therapeutics.
