Build, Fund, Do: A Dynamic Approach to Advancing Single-Cell Biology


*** This is a hybrid event - with the speaker attending in-person and viewable on Teams (via the link below) *** *** After the talk there is a 1 hour meet-and-greet session with the speaker - all students and postdocs are welcome *** https://teams.microsoft.com/l/meetup-join/19%3ameeting_NDAwMGI2MzAtMDY0My00MTNiLWExMTctYjU3ZGUwMTA1MWI4%40thread.v2/0?context=%7b%22Tid%22%3a%22cc95de1b-97f5-4f93-b4ba-fe68b852cf91%22%2c%22Oid%22%3a%2266944c60-3be0-4237-b221-0db3b4194fd2%22%7d

The Chan Zuckerberg Initiative (CZI) is an organization that seeks to dynamically address high value bottlenecks that lay at the intersection of science and technology. One of our dedicated programs focused on the acceleration of single-cell biology with a goal of advancing a suite of technologies that robustly analyze diverse features of single cells in an unbiased and scalable manner. CZI is unique given that it brings an interdisciplinary approach to supporting progress that includes funding research around the world, active software development, and collaborations that rest at the intersection. In this talk, an overview of the last 5 years of progress will be provided with an eye toward key progress and learnings. Focus will be on programs that have funded the development of reference data sets that contribute to inital drafts of the international Human Cell Atlas community. In parallel, CZI has been developing CZ CELLxGENE, which is a suite of tools that support annotation, dissemination and reuse of single-cell data. Cellxgene (cellxgene.cziscience.com) is a free-to-use online data portal hosting a growing corpus of more than 350 single-cell datasets with over 30 million unique cells from human and mouse. The portal hosts single-cell data from modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics. All data are standardized to include raw and normalized counts, and annotated using an ontological shared vocabulary for cell and gene metadata.