Single cell projects

Cutting-edge single-cell sequencing technologies provide a view of tumour evolution at unprecedented resolution. These technologies enable us to explore the cancer genomes of individual cells, but they generally provide only a very limited amount of sequencing reads per cell. Thus, the potential of using such technologies for investigating the cancer evolutionary process is limited by the extreme sparsity of the resulting sequencing data.

Our lab focuses on the design and development of computational methods and algorithms to investigate tumour evolution and intra-tumour heterogeneity from single-cell sequencing data. Such computational methods leverage the features of single-cell sequencing technologies and integrate multiple sources of information to overcome the weaknesses and missing information or errors of these technologies. When applying such methods on single-cell sequencing data, we unveil key mechanisms of tumour evolution and insights into the cancer evolutionary processes.

Relevant publications

• Zaccaria S, Raphael BJ. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nature Biotechnology (2020).
• Myers MA, Zaccaria S, Raphael BJ. Identifying tumor clones in sparse single-cell mutation data. Bioinformatics 36: i186–i193 (2020).
• Satas G, Zaccaria S, Mon G & Raphael BJ. SCARLET: Single-Cell Tumor Phylogeny Inference with Copy-Number Constrained Mutation Losses. Cell Systems, 10(4): 323-332 (2020).

Sequencing the Single Cell - Adventures in Genomics

A single cell is the smallest building block in biology. Each and every cell contains an entire genome with all the information to create an entire organism, be it a bacterium or a buffalo cell. Recent advances in sequencing technology are making it possible to extract and sequence the genomes from individual cells. This is advancing our understanding of many biological processes.

Further useful videos

• CHISELing out Allele- and Haplotype-specific Copy Numbers in Single Cells from Dr Simone Zaccaria and Professor Ben Raphael.
• Inferring Cancer Evolution from Single-Cell DNA Sequencing Data from Professor Ben Raphael at CGSI 2019.