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Washington State University News wrote a followup article to a webinar, Distance Learning for Cell Biology: A virtual laboratory for teaching mitosis, hosted on April 14th, 2020.
The article begins: Hundreds of Biology 107 students have taken part in a virtual laboratory exercise developed by two WSU professors [Eric Sheldon and Erika Offerdahl] in collaboration with the Allen Institute for Cell Science....
Amazon Web Services (AWS) interviewed research engineer at the Allen Institute for Cell Science, Jackson Brown about using a service called Quilt to provide efficient public access to the large datasets produced by the Allen Institute for Cell Science.
We present the Allen Cell Structure Segmenter, a python-based, open-source toolkit that combines classic 3D image segmentation with artificial intelligence to detect cellular structures.  Read more about this new toolkit and how it can help biologists analyze their 3D images of cells and be more quantitative with their results.
 The Allen Institute today released the Integrated Mitotic Stem Cell, a data-driven model and visualization tool that captures — for the first time — a holistic view of human cell division. By enabling a deeper understanding of how healthy human cells divide, a process known as mitosis, the model will further basic biology research as well as studies of cancer, a disease that often results from cell division gone awry. An open house showcasing Seattle-area cell biology research
Event highlights include:
A new online discussion forum offers a meeting spot for the gene-editing and stem cell research community to interact with one another and with the scientists that created the Allen Institute's fluorescently tagged human induced pluripotent stem cell (hiPSC) line collection.   Researchers at the Allen Institute for Cell Science were interviewed around the question "what don't we know about cells?" A full article summarizing their responses is available at alleninstitute.org.
Machine Learning Technique to Predict Human Cells' Organization Published in Nature Methods9/17/2018
Scientists at the Allen Institute have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish. Using 3D images of fluorescently labeled cells, the research team taught computers to find structures inside living cells without fluorescent labels, using only black and white images generated by an inexpensive technique known as brightfield microscopy. A study describing the new technique is published today in the journal Nature Methods.
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