European Bioinformatics Institute

Results of new method for analysing RNA sequence data technique developed by scientists at The European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI)

Date:January 19, 2015

Source:

European Bioinformatics Institute EMBL-EBI

Abstract:

The method for analysing RNA sequence data allows researchers to identify new subtypes of cells, helping to create order out of chaos. The technique was developed by scientists at The European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI) .This will allow researchers to identify new subtypes of cells. It is a major step forward for single-cell genomics.

Body:

European Bioinformatics InstituteTill now there were a lot of confounding factors and challenges to describe the fundamental complexity of single-cell transcriptome profiles; since there weren’t enough robust methods to produce the exact picture of individual cells.

Now with a new technology; The Single-cell RNA-sequencing would help scientists to understand how genes are expressed in different types of healthy tissue and in cancers.

Under this Method, the Scientists are able to produce a detailed map of cortical cell types and the genes active within them.Using thisTechnology,the Team studied a complex tissue on a large scale and deducted their analysis with statistical tools.The team studied and investigated over three thousand cells, one at a time, and identified a number of hitherto unknown types.

The method involves a new single-cell latent variable model (scLVM) which allows the hidden sub-structure under most cell types; to be easily identified by detecting and controlling the relevant biological signals within the cells. This led to identify the different stages of the cell cycle. ; Old and New within the cell.

Thusthe Scientists were able to define factors such as cell-cycle stage, measurement noise or biological processes. The Project created a more accurate picture of gene expression in different cell types and subtypes bycombining single-cell with statistical methods.The new knowledge the project has generated can shed more light on diseases that affect the myelin, such as multiple sclerosis (MS).

This is a critical breakthrough;since the tool helped them to identify cell types that would otherwise remain undetected and the basic biology of the cell. The method derives the means tounderstand thegene expression data from single cells, leading to identify various factors that differentiate individual cells. The Model accounts for relatedness between single cells, for example whether they are at the same stage of the cell cycle, identifies potentially confounding variables and removes them. It also makes it easier to find new subtypes — variables which we never knew existed and corrected for them, all at one go giving better statistical data.

Conclusion:

The Model creates the possibilities of understanding the detailed cell profiles through statistical analysis of single cell types. This would open up new world of exploration which is critically essential for medical research. With this Model; Cancer cells, differentiation processes and the pathogenesis of various diseases can be better explored and can be understood.

Participants & Method Used:

Scientists: European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI).

Florian Buettner: Developed the method, performed the analysis and wrote the paper.

Kedar N Natarajan: Performed the mESC experiments and contributed to the analysis.

F Paolo Casaleand Antonio Scialdone : Contributed to method development and analysis.

Valentina Proserpio, Sarah A Teichmannand Fabian J Theis :  Helped to interpret the biological results.

Sarah A Teichmann and Valentina Proserpio: Designed the mouse TH2 differentiation experiment.

John C Marioni, Oliver Stegle: Designed and supervised this study, contributed to the method development and wrote the paper 

References

Buettner, F. (2015, January 19). Hidden cell types revealed: New method improves single-cell genomics analyses. Retrieved October 1, 2015, from http://www.sciencedaily.com/releases/2015/01/150119124553.htm

 

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