muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Journal: Nature Communications

Published: 2020-11-30

DOI: 10.1038/s41467-020-19894-4

Affiliations: 6

Authors: 8

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Research Highlight

Making the most of single-cell sequencing

© Lucas Ninno/Moment/Getty Images

© Lucas Ninno/Moment/Getty Images

A new method for analysing single-cell RNA sequencing datasets allows researchers to measure changes in biological state across subpopulations of cells in different individuals and under multiple experimental conditions.

Scientists from Roche and elsewhere developed the platform using a simulation framework that mimics various characteristics of single-cell RNA sequencing data, including sample-to-sample variability.

The researchers adapted their models to analyse various subpopulation-specific or condition-specific changes in cell state. They then looked for concordances between the simulations and real-world data.

As a proof of principle, they considered sequencing information from mouse brain cells treated with an inflammation-inducing drug. They were able to identify genes and pathways affected by the treatment both in neuronal and non-neuronal cells.

Known as muscat (short for multi-sample multi-group scRNA-seq analysis tools), the technique should empower scientists to make the most of single-cell gene-expression assays, which are a powerful tool for studying cellular heterogeneity and hierarchies.

Supported content

  1. Nature Communications 11, 6077 (2020). doi: 10.1038/s41467-020-19894-4
Institutions Share
F. Hoffmann-La Roche Ltd., Switzerland 0.50
University of Zurich (UZH), Switzerland 0.22
Swiss Institute of Bioinformatics (SIB), Switzerland 0.19
Swiss Federal Institute of Technology Zurich (ETH Zurich), Switzerland 0.06
Friedrich Miescher Institute for Biomedical Research (FMI), Switzerland 0.03

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