CSCI researchers and their collaborators at the Wellcome Trust Sanger Institute have developed a new analysis tool that was able to show, for the first time, which genes were expressed by individual cells in different genetic versions of a benign blood cancer.
Single cell RNA sequencing can define cell types by revealing differences in the proteins produced by individual cells, however analysing the data remains challenging. Reported in Nature Methods today, the new open source computer tool called Single Cell Consensus Clustering (SC3) was shown to be more accurate and robust than existing methods of analysing single-cell RNA sequence data, and is freely available for researchers to use*.
Recent advances in single-cell genomics technology has made it possible to separate individual cells from different tissues and organs, and measure the sets of RNA messages - called the transcriptome - which help give each cell its own identity. These individual transcriptomes can be used to define cell types and to understand the functions of healthy and diseased cells in the human body. This technology has enormous potential for biological research.
Prof Tony Green, CSCI and Cambridge University author, said: “The SC3 tool was able to use patterns of gene expression to distinguish, within an individual cancer, subclones that carried different mutations. This approach will help us define the cellular heterogeneity within each cancer, an important step towards improving cancer treatment”.
Read more: Wellcome Trust Sanger Institute
*SC3 is available from http://www.bioconductor.org/packages/SC3/ and the source code can be found at https://github.com/hemberg-lab/SC3
Publication details
Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg M. (2017)SC3: consensus clustering of single-cell RNA-seq data. Nature Methods. DOI: 10.1038/nmeth.4236