Imaging ONEWORLD - Automatic whole cell organelle segmentation in volumetric electron microscopy - Aubrey Weigel
26 April 2021
This week will feature Aubrey Weigel from Janelia Research Campus.
Scientific Organisers: Stefanie Reichelt, Alex Sossick, Nick Barry, Alessandro Esposito and Kirti Prakash
The meeting will begin at 1pm BST.
As part of the 'Imaging ONEWORLD' series, the focus of these lectures is on microscopy and image analysis methods and how to apply these to your research. Almost all aspects of imaging such as sample preparation, labelling strategies, experimental workflows, ‘how-to’ image and analyse, as well as facilitating collaborations and inspiring new scientific ideas will be covered. Speakers will be available for questions and answers. The organisers, CRUK CI core facility staff, Gurdon Institute, MRC-LMB, MRC Cancer Unit and NPL will be able to continue the discussion and provide advice on your imaging projects.
Janelia Research Campus
Aubrey Weigel is currently a Project Scientist of the Cellular Organelle Segmentation in Electron Microscopy (COSEM) Project Team at HHMI – Janelia Research Campus. The team uses advanced imaging technologies to study the ultrastructure and dynamics of subcellular organelles under both healthy and pathological conditions. Aubrey’s previous research background is in biophysics, with an emphasis in microscopy. Her formal training is in physics and engineering. During her graduate work Aubrey was the co-discoverer of ergodicity breaking in cells along with Diego Krapf, driving a pivotal shift in the field of diffusion analysis in living systems. Throughout her postdoctoral career under the guidance of Jennifer Lippincott-Schwartz, she applied her training in physics and microscopy directly to answer biological questions. Here, Aubrey unraveled the underlying structure of the endoplasmic reticulum and revealed its complex dynamics. She also uncovered the nano-anatomy of early secretory compartments and discovered a new, dynamic organelle, responsible for delivering newly synthesized cargo from the endoplasmic reticulum to the Golgi. Aubrey’s recent work as the leader of the COSEM project includes developing an invaluable tool for cell biology - an analysis pipeline based on deep learning architectures for segmentation - allowing comprehensive reconstruction and analysis of organelles within entire cells imaged by volumetric electron microscopy. Aubrey is committed to integrating her multi-disciplinary training into facilitating large-effort, collaborative, team projects to take on challenging scientific problems and sharing these resources with the broader scientific community.
Cells contain hundreds of different organelle and macromolecular assemblies intricately organized relative to each other to meet any cellular demands. Obtaining a complete understanding of their organization is challenging and requires nanometer-level, three-dimensional reconstruction of whole cells. Even then, the immense size of datasets and large number of structures to be characterized requires robust automatic methods. To meet this challenge, we developed an analysis pipeline for comprehensively reconstructing and analyzing the cellular organelles in entire cells imaged by focused ion beam scanning electron microscopy (FIB-SEM) at a near-isotropic size of 4 or 8 nm per voxel. The pipeline involved deep learning architectures trained on diverse samples for automatic reconstruction of up to 35 different cellular organelle classes - ranging from endoplasmic reticulum to microtubules to ribosomes - from multiple cell types. Automatic reconstructions were used to directly quantify various previously inaccessible metrics about these structures, including their spatial interactions. We show that automatic organelle reconstructions can also be used to automatically register light and electron microscopy images for correlative studies. We created an open data and open source web repository, OpenOrganelle, to share the data, computer code, and trained models, enabling scientists everywhere to query and further reconstruct the datasets.