In this virtual workshop, we will explore best practices for mapping single cell data to reference cell atlases. Mapping single cell data to reference cell atlases is complicated by batch effects between datasets, systematic differences in biological conditions of the samples, limited availability of computational resources, and sharing restrictions on raw data. While our focus is on transcriptomically-defined cell types of the brain, applications in other tissues and in the use of other data modalities will also be considered.

Defining and naming cell types is a key problem of contemporary neuroscience. While the problem has preoccupied neuroscientists for over a century, it now has significantly increased attention due to our ability to collect cellular level data in a high-throughput manner. International consortia involved in molecular brain cell classification, including the BRAIN Initiative Cell Census Network (BICCN), the Human Cell Atlas (HCA), and the Human Biomolecular Atlas Program (HuBMAP) are generating and classifying cell types in all organ systems in the human body at a rapid pace. Standards for classification, naming, and data mapping are essential to form a common language of cell types to make sense of these enormous datasets, and to promote understanding between multiple domains, projects, and scientists.

Essential to our understanding of cell types and their functions is to catalog measured transcriptomes of cells belonging to each cell type, and to present this information in the form of comprehensive reference single cell atlases. Much like how reference genomes provide a map for locating the origin of sequencing reads, reference cell atlases can be used to map query cells to potential cell types in the reference atlas in order to rapidly characterize and compare relevant cell phenotypes. Reference atlases ultimately help quantify transcriptional heterogeneity that arises as a result of natural variation, aging, environmental influences, and disease. Large single-cell atlases comprising millions of cells across tissues, organs, donors, developmental stages, and conditions are now being generated by consortia such as the Human Cell Atlas and BICCN to serve as references for smaller-scale studies.

The workshop will include presentations about key state-of-the-art methods and techniques, and a comparison of results obtained by different methods in a hands-on data mapping challenge. To explore these mapping techniques, workshop participants will be provided with data mapping “challenges” to be completed before the workshop. Creative solutions to these challenges will have the opportunity to present to a national audience at the June 6, 2022, BRAIN Initiative Cell Census Network (BICCN) consortium meeting and participate in a published article detailing the challenge results.