Ivan Nabi lab

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This week we profile a recent publication in Scientific Reports from the laboratory
of Dr. Ivan Nabi (pictured, top left) at the UBC Life Sciences Institute.

Can you provide a brief overview of your lab’s current research focus?

Dr. Nabi is a Professor of cancer cell biology at UBC’s Life Sciences Institute (LSI) and director of LSI IMAGING, a super-resolution microscope facility located in the LSI. Dr. Nabi uses super-resolution microscopy to study the nano-scale biology of cell surface domains and the endoplasmic reticulum to define their role in tumour cell migration and cancer progression. With the Hamarneh group (Computing Science, SFU), they have applied machine and deep learning to super-resolution microscopy to develop analytical tools and software enabling novel insight into subcellular biology. These include development of a new software package, SuperNet, to analyze 3D point clouds produced by single molecule localization microscopy as well as novel approaches to study the endoplasmic reticulum (ER) and specifically contact sites between the ER and mitochondria. In collaboration with the Jean lab (LSI, UBC), they used interpretable deep learning analysis of super-resolution microscopy to define Zika virus-induced reorganization of the ER of neural cells and organoids, and are now applying these approaches to SARS-CoV-2 infected cells at UBC FINDER, one of the largest university-based containment level 3 facilities in the world, founded by Dr. Jean.

What is the significance of the findings in this publication?

The endoplasmic reticulum is an expansive organelle composed of tubules and sheets whose width (30-100 nm) is below that of the diffraction limit of light. Therefore, while diffraction limited confocal microscopy shows segregation of the ER into peripheral and central ER domains, better definition of ER structure by florescence microscopy has proven difficult. Improved resolution of super-resolution microscopy has better characterized the structure of peripheral ER tubules and sheets and identified peripheral convoluted networks of ER tubules, or tubular matrices. However, application of super-resolution microscopy to the denser central ER has yet to be performed. In our manuscript “Super Resolution Microscopy and Deep Learning Identify Zika Virus Reorganization of the Endoplasmic Reticulum”, 3D STED super-resolution microscopy has defined for the first time the formation of a dense central ER tubular matrix associated with ZIKV viral replication factories. We further show that a deep convolutional neural network (CNN) can identify ZIKV infected cells based on central ER reorganization, representing the first application of deep learning to super-resolution microscopy images and to the ER.

What are the next steps for this research?

Our ability to detect ZIKV reorganization of the central ER is important, as the ability to detect viral infection through alteration of host cell properties is key to screening for viral inhibitors. This has become even more imperative in light of the ongoing COVID-19 crisis. Importantly, like flaviviruses, coronavirus infection, including SARS-CoV-2 responsible for COVID-19, is associated with ER reorganization. We are actively applying super-resolution microscopy and AI analysis to define ER morphological changes associated with SARS-CoV-2, including defining ER contacts with other organelles. Further, deep learning detection of ZIKV infected cells represents proof-of-principle of a sensitive detection approach to identify SARS-CoV-2-infected cells based on host cell reorganization of the ER. In light of the urgency of detecting inhibitors of SARS-CoV-2 infection, this manuscript therefore represents not only the novel detection of a ZIKV-induced central ER tubular matrix by super resolution microscopy and deep learning but also a highly topical manuscript of relevance and importance to ongoing efforts to combat and develop novel therapeutics for treatment of COVID-19.

If you’d like us to mention your funding sources, please list them.

Canadian Institutes for Health Research (CIHR; Nabi: PJT-148698; Jean: PJT-153434); Natural Sciences and Engineering Research Council of Canada (NSERC; IRN: RGPIN-2019-05179; GH: RGPIN-2015-06795, RGPIN-2020-06752; FJ: CRDPJ 531024-18); Infrastructure: Canadian Foundation of Innovation/BC Knowledge Development Fund; Strategic Investment Fund (Faculty of Medicine, UBC). COVID-19 research in the Jean, Nabi and Hamarneh labs is supported by CIHR COVID-19 May 2020 Rapid Research (VR3-172639) and NSERC Alliance COVID-19 grants.

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