Fostering Collaborative Tools for Science
Advancing the Global Human Cell Atlas Effort
At CZI and in our single-cell biology work, we believe it’s important to learn from the community. Over the years, we’ve heard from grantees by hosting workshops convening leaders in the field to talk about major challenges facing the scientific community, as well as connecting scientists to one another to iterate and learn from each other’s work.
Based on those learnings, our Pilot Projects RFA and Collaborative Computational Tools RFA awarded 123 grants to develop collaborative technology, tools, and data to support the global Human Cell Atlas — a shared, open reference atlas of all cells in the healthy human body for scientific studies of health and disease. These foundational single-cell tools, benchmark datasets, algorithms, and visualizations facilitated cooperation and helped researchers share results faster. Read more about how our grantees support the Human Cell Atlas.
Steve Henikoff, Fred Hutchinson Cancer Research Center
Steve’s lab focuses on chromatin dynamics. As a member of the Division of Basic Sciences at Fred Hutchinson, his project’s goal is to automate CUT&RUN, a novel high-resolution technology for profiling specific components of the chromatin landscape, and apply it to standard human cell lines and selected blood cell lineages. The tool his lab develops can be applied to all tissues and cells throughout the eukaryotic kingdom.
Steve’s lab adapted their CUT&RUN technology for high-throughput via automation, as described in a publicly available protocol. The high specificity and low background of CUT&RUN allows for high resolution mapping, and its robustness and low cost make it ideal for clinical application.
He has also worked to introduce a novel chromatin profiling technology, CUT&Tag, and demonstrate its single-cell application. When Covid-19 closed Steve’s lab in March, he fashioned a makeshift laboratory in his laundry room at home using a 10-year-old PCR machine and a microcentrifuge. This “remote” setup allowed him and his team to post a Cut&Tag@home protocol on April 17.
Nick Navin, The University of Texas MD Anderson Cancer Center
Nick works on the Human Breast Cell Atlas, which will establish a comprehensive reference of cell types and cell states in human breast tissue with single cell and spatial resolution. Specifically, Nick works on testing technical conditions for isolating and classifying cell types in normal breast tissue to recommend best practices for building the cell atlas.
The Human Breast Cell Atlas team tested technical parameters and developed optimized protocols for dissociating breast tissues, as detailed in their breast tissue dissociation and breast adipose scRNA-seq protocols. They developed an open-source software tool called SCOPIT to perform fast multinomial calculations and power estimations based on the frequencies of the rarest cell types and technical requirements to help researchers understand how many cells they need to sequence. Learn about their early findings and data from 25,790 single breast epithelial cells from seven individuals.
Sam Morris, Washington University in St. Louis
Sam developed a tool to facilitate single-cell transcriptome quality control, and to assign and assess cell identity in an unsupervised manner. Her lab at Washington University enhanced the precision of cell type classification using single-cell training data generated from the human small intestine. They developed a tool called Capybara to measure cell identity and fate transitions, as well as CellOracle, a python library for the analysis of Gene Regulatory Network with single-cell data. Learn more about Capybara and CellOracle.
“What energizes me is the data and pipeline sharing. Providing our experimental and computational tools has allowed us to receive rapid feedback on our work, and it’s also led to creative collaborations. I’m convinced that sharing our resources has accelerated the pace of our research.” -Sam Morris
Irene Papatheodorou, European Bioinformatics Institute of the European Molecular Biology Laboratory
Irene’s work implemented pipelines for tertiary analyses of Human Cell Atlas datasets and developed associated, community-driven standards for the supported tools. In collaboration with fellow grantee Kerstin Meyer’s group, they developed a Galaxy-based interactive computational platform for the tertiary analysis of single-cell RNA-seq data. The platform allows flexible construction of analysis workflows by linking computation steps, such as pre-processing, clustering, and differential expression analysis, in a drag-and-drop fashion.
“I enjoy the collaborative and interdisciplinary nature of our project within the wider Human Cell Atlas, involving computational biologists, engineers, biologists, and clinicians. It’s exciting and energizing to see different communities of developers across the world contribute to the project and work out problems together.” — Irene Papatheodorou
Gerald Quon, University of California, Davis
Cells within tissues often interact with neighboring cells, and can therefore change their behavior depending on where they are positioned. Measuring these systematic changes in behavior based on cell position is important to understand the spatial organization of tissues and organs. One of the barriers to measuring spatial patterns of individual cells is technology — several spatial technologies used today measure molecular profiles of small groups of cells together (“mini-bulk regions”) in a tissue rather than individual cells.
“Technologies for spatial mapping of molecular profiles have made it possible to answer long-standing questions about the function of cells within complex tissues, as well as opened up exciting computational challenges unlike previous problems we’ve tackled as a field.” -Gerald Quon
As part of the CZI Liver Seed Network, the QUON-titative biology lab, led by Gerald Quon, is developing computational deconvolution tools to extract molecular profiles of the individual cells from these mini-bulk regions. With these tools in hand, biologists can then study variation in the behavior of individual cells using these spatial technologies, and characterize variation within individual cell types across regions.