Single-cell RNA sequencing is a powerful tool for studying cellular diversity, for example in cancer where varied tumor cell types determine diagnosis, prognosis and response to therapy. Single-cell technologies generate hundreds to thousands of data points per sample, generating a need for new methods to define cell populations across different single-cell landscapes.
Qi Liu, PhD, Ken Lau, PhD, and colleagues have developed a new tool, sc-UniFrac, to quantify diverse cell types in single-cell studies. The tool compares hierarchical trees that represent single-cell landscapes and allows cells that drive differences to be identified as unbalanced branches on the trees.
Reporting in PLOS Biology, the investigators demonstrated the utility of sc-UniFrac in multiple applications, including regional specification of brain cells and identification of altered cells in tumor samples. The authors expect that sc-UniFrac will facilitate single-cell studies, in particular studies aimed at tracking how tumor cell populations evolve during disease progression and respond to drug treatments.
This research was supported by the National Institutes of Health (grants CA215798, DK103831, CA095103, HD007502, GM120940, CA197570, CA068485, DK065949, LM012412, CA233291).