Summary Over a lifetime, all cells in the body accumulate somatic mutations. The catalogue of mutations from a single cell constitutes a “fossil record” of its developmental history and the mutational processes to which the cell and its ancestors have been exposed during the lifetime of the individual. The aim of the proposed studies is to detect somatic mutations in hundreds to thousands of normal somatic cells of mice and humans in order to generate a fundamentally new perspective on developmental biology and to elucidate the mutational processes, and thus biological states, operative throughout the life of healthy human beings. Background There are ~1014 cells in the human body. Each cell is at the end of a lineage of mitotic cell divisions starting at the fertilised egg from which the individual developed. Thus the whole of the human body can be seen as a single tree of branching cellular lineages that originate from the fertilised egg. During a lifetime all normal cells acquire somatic mutations. The numbers and patterns of somatic mutations in adult cells will depend on the operative mutational processes, the numbers of mitoses in the lineage from the fertilised egg and the mutation rate at each cell division. For example, a facial skin epidermal cell will accumulate more and different types of mutation compared to a brain neuron because the lineage from fertilised egg to adult epidermal cell includes more cell divisions and because facial epidermal cells are exposed to ultraviolet (UV) light, which is a powerful mutagen. The somatic mutations present in two adult cells also provide information concerning their relative position in the tree of branching divergent cell lineages emanating from the fertilised egg. For example, two neighbouring epidermal cells in the skin derived from the same local skin stem cell will share most somatic mutations. Conversely, if a neuron and a skin epidermal cell are compared, they likely have shared a cell lineage for only a few cell divisions following the fertilised egg, at which point their lineages diverged. As a result, they will share only a few somatic mutations, which occurred during early embryogenesis. Although little work has been conducted on somatic mutation profiles in normal cells, a substantial body of information on somatic mutations in cancers, which are derivatives of single cells, illustrates the power of this approach. Thousands of cancer genomes have now been sequenced. Collectively, these have shown tremendous variation in the numbers and patterns of somatic mutations between individual cancers due to differences in mutation rates, mutational processes and cell divisions to the fertilised egg. Comparison of different cancer samples from the same patient has illustrated that they sometimes share many somatic mutations, and are thus part of the same neoplastic clone, while sometimes they do not and thus are distinct neoplasms far apart on the lineage tree of the person. Pilot experiments of genome sequencing of normal cells We have piloted in mice the strategy of normal single cell sequencing to track developmental origins and mutational processes. Using organoid technology, single cell clones were derived from 25 normal cells of the large bowel, small bowel, Figure 1: Reconstructed phylogenetic trees of cells from early mouse stomach and prostate from two mice. Catalogues of somatic embryos: A: Mouse 1; B: Mouse 2. Each white-­‐filled large circle represents a base substitution mutations for each of the 25 lines were putative early embryo cell that is defined by a unique combination of mutations. Each mutation is represented by a number inside the white circles. generated by whole genome sequencing. Figure 1 shows Yellow highlighted numbers are mutations acquired during the most recent cell phylogenetic trees for each mouse reconstructed using division. Letters next to white circles are identifiers of each putative embryonic precursor cell. Roman numerals indicate each reconstructed cell generation of somatic mutations. The results show that cells from early the embryos. C and D: colour filled circles represent individual organoids that stages of embryonic development are pluripotent, are derived from adult cells from various anatomical regions of each mouse.
contributing to all tissue classes (endoderm, ectoderm and mesoderm). Insights into the asymmetric nature of early embryonic cell divisions were observed. Unambiguous differences in mutation number between different tissues, correlating with differences in stem cell division rates, and differences in mutation pattern indicating different levels of exposure to mutational processes in different tissues were found. Thus, this small pilot showed all elements of insight expected of the strategy. 1
Project Outline Detection of somatic mutations in normal cells In this project we propose to use next generation sequencing (NGS) to extend the pilot study outlined above in order to generate a comprehensive cell lineage for both humans and mice. The ESPOD fellow will focus on the development of computational and statistical approaches necessary to generate these lineages. From an experimental perspective, NGS will be used to detect somatic mutations present in hundreds to thousands of normal cells. Because the mutation rate in normal cells is relatively low we will need to apply whole genome sequencing. We will exploit three different strategies: whole genome sequencing of nuclei from individual cells; ultra high-­‐throughput sequencing of pieces of tissues obtained from multiple organs; targeted genome sequencing (“genotyping”) of single cells for mutations found using the two previous approaches. Mouse and human studies Studies will initially be undertaken in mice to develop technical and analytic approaches. Concurrently, we will begin pilot studies in humans. Analytic and statistical approaches Collectively, these studies will present a series of analytical and statistical challenges ranging from error modelling of mutation calling in single cells, detection of low read count mutations, transcriptome analysis from single cells and phylogenetic analysis incorporating these considerations. The first challenge will be to accurately call somatic mutations in individual cells – to do this, the ESPOD fellow will explore computational approaches for calling variants simultaneously across cells – since most somatic mutations will be present in a fraction of the cells present (but not in a 50:50 ratio like heterozygous sites in bulk analyses) this will require development of novel statistical approaches. Moreover, it will be critical to account for allelic dropout, which arises frequently in single-­‐cell DNA sequencing experiments. Once an accurate set of somatic mutations are called across cells, the next challenge will be to use these (in conjunction with the genotyping data) to construct a phylogeny. Based upon previous experience, Maximum Parsimony (MP) based approaches will first be exploited; if these are inadequate, alternative strategies will be explored. Finally, the ESPOD fellow will also gain experience in handling single-­‐cell transcriptomics data, when available. This will build upon statistical approaches for identifying cell types that are being developed by the Marioni and Stegle labs at the EBI that account for potentially confounding variables such as the cell cycle. References 1. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-­‐cell sequencing-­‐based technologies will revolutionize whole-­‐
organism science. Nat Rev Genet 14, 618-­‐30 (2013). 2. Salipante, S.J. & Horwitz, M.S. A phylogenetic approach to mapping cell fate. Curr Top Dev Biol 79, 157-­‐84 (2007). 3. Lasken, R.S. Single-­‐cell sequencing in its prime. Nat Biotechnol 31, 211-­‐2 (2013). 4. Sato, T. et al. Long-­‐term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium. Gastroenterology 141, 1762-­‐72 (2011). 2