The most recent breakthrough in genomics is the ability to measure within single cells. Data from hundred thousands, even millions of cells, is becoming available, measuring full genomes, transcriptomes and proteomic measurements for every single cell. This gives a wealth of data to capture heterogeneity in samples. For example, which cells do appear in blood, and how does their transcriptional profile look like. Or, which cells make up the hyppocampus of the brain, and what tasks do they perform. In cancer, this offers possibilities to capture the heterogeneity of different clones which are evolved over time, and that give cancer the possibility to escape targeted therapies. Clearly, the sheer amount of data stresses the limits of current analysis techniques. But, we also observe stochastic behavior between similar cells. For example, that in one cell there are a few RNA molecules of a specific gene, whereas in a similar other cell there are none. This call for new analysis methodologies that can deal with these stochasticity. The DBL group develops new algorithms in analysing single cell data.
Topics we address:
- Visualizing single cell data.
- Identifying cell populations using scRNA-seq.
- Integrating multi-panel mass spectrometry measurements (CyTOF) of single cells.