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Since the launch of the Allen Cell Explorer in April, the Allen Institute for Cell Science team has added even more data and tools to provide a rich and valuable window into the human stem cell.
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With 19 cell lines targeting 16 key structures, the Allen Cell Collection now characterizes many of the organelles in human stem cells. Using CRISPR/Cas9 technology, Allen Institute scientists have been growing the portfolio of cell lines that illuminate individual structures in the cell with unprecedented precision.
Available through Coriell, these lines are already in use in labs around the world, enabling the study of both healthy cells and disease models.
In addition to cell lines, we have also made the plasmids and methods used to create them available through Addgene. If you would like to reproduce any of our fluorescently tagged structures in your own human cell line, our plasmids can make that possible. Read more about our methods and explore the plasmid options through the Cell Catalog on the Allen Cell Explorer.
Launched with images of over 7,000 human induced pluripotent stem cells, by December 1, the 3D Cell Viewer will include more than 20,000 cells visualized in three dimensions with an improved user interface. Users can explore the tremendous diversity of human stem cell organization by scrolling, filtering and visualizing the cells, and can download the image data for their own analyses.
The Allen Institute for Cell Science has collaborated with the Broad Institute to develop the 3D CellProfiler for the research community. Read more in our recent news story.
For more analysis on the data we’ve generated and the cell lines we create, view our most recent publications.
Systematic gene tagging using CRISPR/Cas9 in human stem cells to illuminate cell organization
Molecular Biology of the Cell (MBoC), August 16, 2017. Roberts B, Haupt A, Tucker A, Grancharova T, Arakaki J, Fuqua MA, Nelson A, Hookway C, Ludmann SA, Mueller IA, Yang R, Horwitz AR, Rafelski SM, Gunawardane RN.
Three dimensional cross-modal image inference: label-free methods for subcellular structure prediction
bioRxiv pre-print, November 2017. Ounkomol C, Fernandes DA, Seshamani S, Maleckar MM, Collman F, Johnson GR.