First Single Cell Expression resource at EMBL-EBI

Single Cell Expression Atlas launches

First Single Cell Expression resource at EMBL-EBI

30 May 2018 - 15:30


  • The Single Cell Expression Atlas, a part of EMBL-EBI’s Expression Atlas, is a new service that provides the research community with systematically reprocessed single-cell RNA sequencing data for a wide range of species
  • Although single-cell RNA sequencing is increasingly used in experiments, no central data resource existed until now
  • Single-cell sequencing allows us to observe how individual cells differ from each other, which could significantly increase our understanding of biology and disease

30 May, 2018, Cambridge – The European Bioinformatics Institute (EMBL-EBI) is launching the world’s first data resource specifically designed for hosting and processing single-cell RNA sequencing (scRNA-seq) data. As a part of EMBL-EBI’s Expression Atlas, the new Single Cell Expression Atlas takes raw data from published studies and processes the data in a systematic way. This enables scientists to search through numerous experiments to see how a gene is expressed in individual cells.

The Single Cell Expression Atlas launches today and contains human, mouse, zebrafish and common fruit fly data, with more species underway. The resource will also host some datasets from the Human Cell Atlas, thanks to support from the CZI computational tools funding.

“Laboratories across the world are already producing large amounts of scRNA-seq data, but there is currently no central resource to store and access all these datasets,” explains Irene Papatheodorou, Gene Expression Team Leader at EMBL-EBI. “The Single Cell Expression Atlas aims to fulfil this growing need, enabling easy access to single-cell datasets. The new resource differs from other similar platforms because it doesn’t just store raw data, but it also systematically processes every dataset, using the latest genome release. This means the data are consistent, up-to-date, and users can search across multiple datasets for the first time.

“Furthermore, by using annotation, ontologies and standardised analyses, we are making the data accessible to users other than the original data producers, which is essential in any scientific endeavour.”   

By making scRNA-seq data FAIR (Findable, Accessible, Interoperable and Reusable), EMBL-EBI hopes to aid scientists studying new biological questions, such as what cell types exist within a tissue or organism, the function of an individual cell in the context of its neighbours, heterogeneity of cell responses, etc.

What is single-cell sequencing and what can it tell us?   

Biologists use sequencing to determine the exact order of the bases A, C, G and T that are building blocks of the DNA molecule and spell out the genetic code. Traditional sequencing requires thousands of cells in order to have enough DNA for the sequencing instruments to detect. However, each cell in a colony has a slightly different DNA sequence, because of specific mutations or because of changes in the micro-environment, that make the cell slightly different to its neighbour cells. This means that sequencing DNA cell populations always results in an average.

While this information is useful to answer some questions – to find pathogens in samples, or to compare species’ sequences and understand how they are related – in other cases biologists need to know the sequence of a single cell. For example: if you treat a batch of cells with a drug, some cells react to the drug while others don’t. If you sequence all cells in the batch, the genetic code that results is an average of responsive and non-responsive cells. This average may not reveal a specific mutation that makes some cells respond. In this case, it makes sense to look at a single-cell level to learn what the mutation is.

Single-cell sequencing allows us to observe how individual cells differ from each other. Although the basic principle is the same, single-cell sequencing is more challenging to perform because a single cell only contains a tiny amount of DNA that is too small for the instruments to detect. To solve this problem, single-cell sequencing includes a DNA amplification step, in which the DNA from a single cell is copied and thousands to millions of exact copies of the cell’s DNA are generated. The copies are then used to determine the DNA sequence of that cell.

The Single Cell Expression Atlas is funded by the Wellcome Trust, and is a part of EMBL-EBI’s Expression Atlas. It was developed in a close collaboration with colleagues at the Wellcome Sanger Institute.

Contact the news team

Oana Stroe
Communications Officer
+44 (0)1223 494 369

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