The NIH Common Fund Human BioMolecular Atlas Program (HuBMAP) brings together molecular and cellular biologists, pathologists, and bioinformaticians to create a framework for mapping the human body at cellular resolution.  These scientists not only need to develop the tools necessary to study cells and tissues, but also must be able to integrate those data together into a comprehensive atlas.

Rahul Satija, PhD, and colleagues at the New York Genome Center, developed a process that connects DNA, RNA, chromatin, and protein data from separate experiments.  It takes data from different types of experiments and looks for information that the data were generated from the same kind of cell.  Once a match is identified, the algorithm ‘anchors’ the data together, generating links between two datasets.  This anchoring allows the researchers to identify known or unexpected types of cells in a tissue.

Using this method on data from mouse brain tissue, as well as human blood cells, researchers were able to 1) separate out four different types of neurons in one area of a mouse’s brain and find a region on a specific chromosome that instructs cells to become neurons, 2) find blood cells in different of developmental stages, and 3) identify different immune cells in a population by their cell surface proteins. 

By joining these data together, this new computational method has given researchers a novel tool to help build more complete biological atlases, leading the way to more discoveries about the intricacies of human cells and tissues.

Comprehensive Integration of Single-Cell Data.

Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R.  Cell. 2019 Jun 13;177(7):1888-1902.e21. doi: 10.1016/j.cell.2019.05.031. Epub 2019 Jun 6.  PMID: 31178118