Sanger Institute-EBI Single-Cell Genomics Centre

We are exploring the DNA, RNA and epigenetic features of single cells in order to better understand normal biology and disease.

Single-cell genomics is the next frontier in molecular biology, offering unprecedented access to study how genetic variability and gene expression impacts on individual cells and cell types. Novel technologies and methods allow us to isolate and analyse the limited genetic material present within a single cell, enabling us to gain a whole-genome view of genetic variability and gene expression at single-cell level.

This field of research is opening up developmental and disease biology. For example, this approach can identify distinct sub-populations of cells within a tumour and reveal how they contribute to cancer development. However, there are significant technical and computational challenges to overcome before this type of research can be carried out in a routine, robust and high-throughput manner.

[Genome Research Limited]


Previously, we could only understand the impact of DNA, RNA and epigenetic changes by investigating large numbers, often millions, of cells. Because of this, we are limited in identifying functionally distinct subpopulations of cells and understand their contribution during development and in diseases such as cancer.

Now new techniques, including DNA-seq, epigenomic DNA-seq and RNA-seq, give us the opportunity to discover and study subpopulations and understand genomic, epigenomic and transcriptomic heterogeneity at the single-cell level.

Our project

The aim of the Sanger Institute-EBI Single-Cell Genomics Centre is to develop and apply methods that allow us to capture the complete genetic content of single cells in a high-throughput manner to explore the nature and role of cellular heterogeneity in normal development and disease. The Centre is an important part of the Cellular Genetics programme at the Wellcome Trust Sanger Institute, Hinxton and led by a consortium including:

  • Thierry Voet (Sanger Institute and KU Leuven)
  • Sarah Teichmann (Sanger Institute and the EMBL-European Bioinformatics Institute)
  • John Marioni (EMBL-European Bioinformatics Institute)
  • Chris Ponting (University of Oxford and Sanger Institute)
  • Wolf Reik (Babraham Institute and Sanger Institute)
  • Harold Swerdlow (Sanger Institute)

Genome sequencing:

Our knowledge of the nature and rates of genome mutation in a developing human being or any other organism is at best rudimentary. In addition, the degree of cellular selection acting on somatic variants during development, and these variants’ contributions to phenotype and disease etiology remain largely unknown.

We are developing and implementing methods that will enable the dissection of the genetic content of individual cells, providing insights into the operation of fundamental processes of genome maintenance and selection. In addition, we are investigating methods for simultaneous genome and transcriptome characterization of a cell.

Epigenome sequencing:

The human body contains many different epigenomes that are associated with differentiation of tissues and cells, but recent insights suggest the exciting possibility that epigenomes may fluctuate within cell populations and generate biological diversity.

Epigenetic heterogeneity occurring in regulatory DNA sequences (such as enhancers) may have the potential to create gene expression patterns that fluctuate in cell populations. Such a mechanism could be of fundamental importance for cell fate decisions during development, for cellular reprogramming strategies, and for variations in phenotype that are not predicted by genotype alone. We are profiling the epigenomes of small numbers of cells, down to single cells, to explore and understand this mechanism.

Transcriptome sequencing:

We are developing techniques to capture and explore the transcriptomes of single cells. This knowledge is vital in many biological contexts such as differentiation, development, cancer and immunity. Understanding cell-to-cell variation in gene expression is expected to provide insights into the heterogeneity of cell populations and their dynamic changes.

Computational approaches:

We are developing new computational approaches to store, analyse and interpret the genome-scale data that we are gathering from single cells. These seek to reveal biological phenomena that are obscured when studying large populations of cells. We are creating tools that will allow us to identify new candidate cell types by clustering cells and accurately identify genes that show highly variable patterns of expression or methylation across putatively homogeneous populations of cells. In all these contexts, we ensure that our statistical methods take account of biases inherent in experimental procedures.


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