The Chan Zuckerberg Initiative (CZI) seeks to support projects that will advance the fields of single-cell biology and data science. Grantees will be expected to interact with existing groups to build community and accelerate progress. Applications are encouraged from computational experts outside the field of single-cell biology, but with expertise relevant to overcoming current bottlenecks and driving discoveries in the single-cell biology field. Projects may include: dedicated efforts to democratize access and usability of existing datasets; demonstration of utility by leveraging existing datasets to address impactful and challenging biological questions; and developing methods that enable greater biological insight and other major challenges brought forward. This request for applications is the last of three currently planned cycles, with successful projects receiving 18 months of funding support.
Applications for two types of grants are welcome and will be reviewed independently. The maximum budgets for proposed projects are $400,000 total costs for Expanded Projects and $200,000 total costs for Focused Projects. All project awards will be for an 18-month duration. The goal of this opportunity is to create a network of projects that address broad computational challenges and needs within single-cell biology at a variety of scales. Applicants may highlight existing or prospective collaboration among projects, but should note that all applications will be reviewed for their individual merit and impact.
Single-cell biology has undergone rapid growth over the last five years, with a recent increase in the volume of available and openly accessible datasets and cell atlases, including the release of the 37+ million cells CZ CELLxGENE Discover Census. This funding opportunity is specifically intended to motivate and incentivize the development, refinement, and implementation of approaches that make it possible for greater insights to be gained from available single-cell data. With this in mind, any form of data generation is considered out of scope. Projects must propose and rely on existing data that is openly and freely available (count matrices at minimum) at the time of application via the inclusion of a link to specific datasets in the applications. Furthermore, we strongly encourage applications to use data generated outside of their labs to enable interoperability and advances that are extensible to a wider segment of interested researchers.
Addressing computational challenges and bottlenecks in single-cell biology will drive the field forward and make it possible for a greater number of scientists to benefit from emerging datasets and tissue atlases. This opportunity puts forward a broad scope that fundamentally aims to enable greater insight into specific biological questions to be gleaned from single-cell data. Successful proposals are likely to incorporate some or multiple of the following attributes:
- Meta-analysis of single-cell datasets or collections, such as available via CZ CELLxGENE Discover Census, that highlights its characteristics, usability, and utility and enables insight into more specific biological questions and unlock mysteries of the cell and how cells interact in systems.
- Address specific challenges, such as predicting cellular responses to perturbations, modeling regulatory networks, understanding evolutionary differences at the cellular level or cellular basis of traits and diseases, system-level delineation of lineage establishment, or deciphering cell-cell interactions and spatial organization.
- Democratize access and usability of existing datasets. For example, the development of subsampling approaches or scalable and accessible models for data representation (embeddings), querying, and downstream tasks of broad interest.
- Applications of AI and machine learning to open problems in single-cell biology with a possible focus on infusing domain knowledge in a model and experimental design and development of novel training paradigms or model evaluation strategies.
- Connecting data across scales and modalities.
- Using data from underrepresented ancestries or populations.
It is also an explicit goal of this effort to build a community among international participants that encourages collaboration and coordination, and we envision that the overlap between funding cycles will allow continuity and learning. CZI will support the coordination of this community and promote opportunities for training, documentation, and knowledge sharing across projects and cycles.