Analyzing single-cell landscapes

brain cells
Credit: CC0 Public Domain

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, Ph.D., Ken Lau, Ph.D., 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 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 evolve during and respond to drug treatments.

Explore further

Researchers use optimized single-cell multi-omics sequencing to better understand colon cancer tumor heterogeneity

More information: Qi Liu et al. Quantitative assessment of cell population diversity in single-cell landscapes, PLOS Biology (2018). DOI: 10.1371/journal.pbio.2006687
Journal information: PLoS Biology

Citation: Analyzing single-cell landscapes (2018, December 3) retrieved 21 January 2022 from
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Feedback to editors