Sebastian Grossmann

Bio: Sebastian Grossmann is an academic researcher from Wellcome Trust Sanger Institute. The author has contributed to research in topics: Stem cell & Population. The author has an hindex of 6, co-authored 7 publications receiving 767 citations. Previous affiliations of Sebastian Grossmann include Heidelberg University & Wellcome Trust.

Intra-tumour diversification in colorectal cancer at the single-cell level

Abstract: Every cancer originates from a single cell. During expansion of the neoplastic cell population, individual cells acquire genetic and phenotypic differences from each other. Here, to investigate the nature and extent of intra-tumour diversification, we characterized organoids derived from multiple single cells from three colorectal cancers as well as from adjacent normal intestinal crypts. Colorectal cancer cells showed extensive mutational diversification and carried several times more somatic mutations than normal colorectal cells. Most mutations were acquired during the final dominant clonal expansion of the cancer and resulted from mutational processes that are absent from normal colorectal cells. Intra-tumour diversification of DNA methylation and transcriptome states also occurred; these alterations were cell-autonomous, stable, and followed the phylogenetic tree of each cancer. There were marked differences in responses to anticancer drugs between even closely related cells of the same tumour. The results indicate that colorectal cancer cells experience substantial increases in somatic mutation rate compared to normal colorectal cells, and that genetic diversification of each cancer is accompanied by pervasive, stable and inherited differences in the biological states of individual cancer cells.

Population dynamics of normal human blood inferred from somatic mutations.

Abstract: Haematopoietic stem cells drive blood production, but their population size and lifetime dynamics have not been quantified directly in humans Here we identified 129,582 spontaneous, genome-wide somatic mutations in 140 single-cell-derived haematopoietic stem and progenitor colonies from a healthy 59-year-old man and applied population-genetics approaches to reconstruct clonal dynamics Cell divisions from early embryogenesis were evident in the phylogenetic tree; all blood cells were derived from a common ancestor that preceded gastrulation The size of the stem cell population grew steadily in early life, reaching a stable plateau by adolescence We estimate the numbers of haematopoietic stem cells that are actively making white blood cells at any one time to be in the range of 50,000-200,000 We observed adult haematopoietic stem cell clones that generate multilineage outputs, including granulocytes and B lymphocytes Harnessing naturally occurring mutations to report the clonal architecture of an organ enables the high-resolution reconstruction of somatic cell dynamics in humans

Subtype-independent near full-length HIV-1 genome sequencing and assembly to be used in large molecular epidemiological studies and clinical management

Abstract: Introduction : HIV-1 near full-length genome (HIV-NFLG) sequencing from plasma is an attractive multidimensional tool to apply in large-scale population-based molecular epidemiological studies. It also enables genotypic resistance testing (GRT) for all drug target sites allowing effective intervention strategies for control and prevention in high-risk population groups. Thus, the main objective of this study was to develop a simplified subtype-independent, cost- and labour-efficient HIV-NFLG protocol that can be used in clinical management as well as in molecular epidemiological studies. Methods : Plasma samples ( n =30) were obtained from HIV-1B ( n =10), HIV-1C ( n =10), CRF01_AE ( n =5) and CRF01_AG ( n =5) infected individuals with minimum viral load >1120 copies/ml. The amplification was performed with two large amplicons of 5.5 kb and 3.7 kb, sequenced with 17 primers to obtain HIV-NFLG. GRT was validated against ViroSeq TM HIV-1 Genotyping System. Results : After excluding four plasma samples with low-quality RNA, a total of 26 samples were attempted. Among them, NFLG was obtained from 24 (92%) samples with the lowest viral load being 3000 copies/ml. High (>99%) concordance was observed between HIV-NFLG and ViroSeq TM when determining the drug resistance mutations (DRMs). The N384I connection mutation was additionally detected by NFLG in two samples. Conclusions : Our high efficiency subtype-independent HIV-NFLG is a simple and promising approach to be used in large-scale molecular epidemiological studies. It will facilitate the understanding of the HIV-1 pandemic population dynamics and outline effective intervention strategies. Furthermore, it can potentially be applicable in clinical management of drug resistance by evaluating DRMs against all available antiretrovirals in a single assay. Keywords: HIV-1 NFLG sequencing; subtype independent; molecular epidemiology. To access the supplementary material to this article please see Supplementary Files in the column to the right (under Article Tools). (Published: 25 June 2015) Citation: Grossmann S et al. Journal of the International AIDS Society 2015, 18 :20035 http://www.jiasociety.org/index.php/jias/article/view/20035 | http://dx.doi.org/10.7448/IAS.18.1.20035

Integrative Genomics Viewer

Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Organoids in cancer research

Abstract: The recent advances in in vitro 3D culture technologies, such as organoids, have opened new avenues for the development of novel, more physiological human cancer models. Such preclinical models are essential for more efficient translation of basic cancer research into novel treatment regimens for patients with cancer. Wild-type organoids can be grown from embryonic and adult stem cells and display self-organizing capacities, phenocopying essential aspects of the organs they are derived from. Genetic modification of organoids allows disease modelling in a setting that approaches the physiological environment. Additionally, organoids can be grown with high efficiency from patient-derived healthy and tumour tissues, potentially enabling patient-specific drug testing and the development of individualized treatment regimens. In this Review, we evaluate tumour organoid protocols and how they can be utilized as an alternative model for cancer research.

Construction of a human cell landscape at single-cell level.

Abstract: Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems1. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.

The evolutionary history of 2,658 cancers

Abstract: Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.