Why Computational Biologists are Essential to Advancing Science

The field of single-cell biology is growing rapidly, generating large amounts of data from a variety of tissues and organs — to the tune of 20 million cellular data points alone in cellxgene (​​a CZI-developed tool that allows scientists to visualize datasets from single cells). The growth in the number and size of available single-cell datasets provides exciting opportunities to push the boundaries of current computational tools.

However, as the data volume has grown, so too have the analytical bottlenecks for the field. With access to a large amount of data and rapid development of corresponding tools and methodologies, the field is now primed to begin compiling and querying the initial drafts of tissue atlases, such as the Human Cell Atlas (HCA), to drive new science, technologies, and discoveries into health and disease.

CZI grantees Kim-Anh Le Cao (left) and Angela Pisco (right) at the CZI Collaborative Computational Tools for the Human Cell Atlas meeting in April 2018.

At the beginning of the Covid-19 pandemic, the value of these emerging tissue atlases was made tangible. In work conducted by the HCA Lung Biological Network — a consortium of scientists focused on the developing single-cell references of the lung — early data intended to contribute to the HCA was brought together and shared via cellxgene to identify cells in the nose that had a potential role in spreading the virus. Not only did this study provide crucial information to the scientific community, it also highlighted the utility and real life application of a reference atlas of human tissue. Since this initial finding, the publication has been cited by more than 1,000 other studies.

The intersection of single-cell biology and data science will enable scientists to clarify cellular mechanisms of disease — an essential step on the path to curing, managing, or preventing all diseases. That’s why we’re launching a new funding opportunity to support computational biologists in advancing approaches capable of deriving greater insights and added value to the single-cell biology field, such as integrating datasets, scaling to higher dimensionalities, mapping new datasets to reference atlases, and more.

Advancements like these come with a new set of challenges. Researchers currently report struggling to extract additional knowledge from existing single-cell datasets, and additionally, because of the lack of data integration, the potential value of individual datasets is significantly limited. Although progress within single-cell biology has been rapid, there remains important needs for computational methods development, benchmarking, collaboration, and dissemination of generalizable and robust tools to help strengthen the foundation of the field for future growth.

In addition, progress across the field has resulted in many new datasets that stimulated a large number of bespoke tools for their analysis and exploration. The time has come to incentivize and build community around generalizing these tools that are necessary to develop larger atlases. Solving the analytical challenges of analyzing data at scale and utilizing data from multiple labs is a critical step toward assembling and using single-cell reference atlases, and will require coordinated effort to improve and evolve computational tools. 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.

“As a computational biologist, I’m dedicated to building the bridge between data collection and data science. Building novel computational resources and tools that embed biological mechanisms will continue the momentum toward uncovering knowledge from the wealth of valuable atlas datasets,” says CZI Computational Tools for the Human Cell Atlas (HCA) grantee Elana Fertig, Associate Professor of Johns Hopkins University.

Single-Cell Biology Data Insights RFA

Our goal is that these projects will provide meaningful insights into biology (e.g. meta-analysis to explore cell type variation across organs and disease conditions) and technical progress in improving and standardizing technology that benefits the scientific community. Central to these efforts is the development of benchmarking frameworks, tasks, and tools that enable comparison of a class of tools to stimulate future development that increases scale, efficiency, and reproducibility.

This funding opportunity builds on learnings from the Computational Tools for the HCA grantees and extends our efforts to support a community of experts to make progress on computational challenges using available single-cell data sets. This opportunity will deliberately lay the groundwork for a data analysis-centered computational community that will continue to overlap with subsequent cycles of grantees, expanding the grantee network and empowering collaboration. Providing bridges between people and fields is a long-term priority and an important feature of this grant program.

The CZI Single-Cell Biology Program works to support the generation, assembly, and utilization of tissue atlases that will be broadly useful to clarify cellular mechanisms of disease. Our strategy to accomplish this involves support for data generation; building technology, like cellxgene, to help aggregate and make data accessible; and support for computational research and development to fill gaps and improve analytical capabilities in the field.

CZI’s Single-Cell Biology Data Insights Requests for Applications (RFA) aims to support technologists and computational experts to carry out projects focused on advancing the intersection of single-cell biology and data science. This new funding opportunity invites researchers, both individually and as part of collaborative teams, to advance tools and resources that make it possible to gain greater insights into health and disease from single-cell biology datasets.

Applications for two types of grants for these 18-month projects are welcome and will be reviewed independently:

  • $200,000 total costs for projects directed towards benchmarking tools, extension of existing toolchains, or curating/integrating existing datasets to boost their utility for the field; and
  • $400,000 for projects to improve standards, improve toolchain interoperability, or undertake more extensive integration or benchmarking tasks.

All projects will document and disseminate information via open source tools. Learn more and apply.

This RFA is the first of three cycles, and will serve as an ongoing model for the Single-Cell program to help us continue to understand and learn how to best meet the needs of the computational community as a larger number of groups seek to assemble and use comprehensive single-cell datasets.

We hope this opportunity is poised to capture the current momentum in the single-cell computational biology field. Increasing the robustness and performance of tools, better integrating disparate data, and openly sharing datasets will allow deeper inquiry and produce new scientific insights into cellular mechanisms of disease, further demonstrating the promise of single-cell biology and collaboration.

Learn more about CZI’s work in single-cell biology.

Ivana Williams, Science Program Manager, Chan Zuckerberg Initiative
Ivana Williams is a Science Program Manager on the Single-Cell Biology team at the Chan Zuckerberg Initiative, responsible for computational biology strategy. With an extensive background and experience in mathematics, statistics, data science, and machine learning, she is passionate about building bridges between the machine learning, data science, and CZI’s Single-Cell Biology communities. Her work engages with the community on open computational challenges such as data integration, label propagation, standardization, benchmarking, and more. Ivana’s previous research focused on natural language processing and the implementation of state-of-the-art machine learning and data science solutions in support of accelerating scientific discovery and unlocking insights from scientific publications.

Jonah Cool, Science Program Officer, Chan Zuckerberg Initiative
Jonah Cool is a cell biologist and geneticist by training, and is currently a program officer at the Chan Zuckerberg Initiative, where he leads the organization’s Single-Cell Biology Program. He was an American Heart Association fellow while completing his PhD at Duke Medical Center, with a focus on the role of vascularization during cell differentiation and organ morphogenesis, and was subsequently a Ruth Kirchstein Fellow at the Salk Institute studying nuclear organization during stem cell differentiation. Dr. Cool previously worked in intellectual property litigation, as well as ran an industry research group working toward therapeutic application of 3D bioprinted human tissue. He has a deep love of cell biology and, in particular, the origins of cellular heterogeneity and how diverse cells assemble into complex tissues.




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