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29 March 2017

 

A new tool will help identify which genes are expressed by patients with different versions of a rare blood cancer, it has been announced.

Scientists led by Dr Vladimir Yu Kiselev at the Wellcome Trust Sanger Institute, Hinxton, UK, developed the analysis tool based on single cell RNA sequencing. This approach can show different cell types via the proteins made by individual cells.

The team used an open source computer tool called Single Cell Consensus Clustering to analyse single-cell RNA-sequence data from two patients diagnosed with myeloproliferative neoplasm blood cancers.

They explained in Nature Methods on Monday (27 March) that single-cell RNA sequencing 'enables the quantitative characterisation of cell types based on global transcriptome profiles'. They found that it is user-friendly and achieves high accuracy by bringing together several clustering solutions.

They write: 'We demonstrate that Single Cell Consensus Clustering is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.'

The tool has been made freely available for all researchers to use.

Dr Kiselev said: 'We created the new Single Cell Consensus Clustering tool to analyse complex single-cell RNA-sequence data, and showed that it is more robust and accurate than existing methods at grouping cells. The tool contains added features that help interpret the biological function of the cells in that group, such as lists of marker genes for each group. We expect this will be used by many researchers around the world.'

Co-author Professor Tony Green added: 'The 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'.

Source: Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nature Methods 27 March 2017; doi: 10.1038/nmeth.4236

Link: http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4236.html

 

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