Somatic variation in healthy organisms
Characterising the impact of somatic variation in healthy organisms on gene regulation and phenotype, providing a baseline for studies of disease.
Project Lead: Conrad Nieduszynski
Funding:
This research is supported by the UKRI Biotechnology and Biological Sciences Research Council (BBSRC).
Earlham Institute Strategic Programme Grant Cellular Genomics BBX011070/1 and its constituent work package BBS/E/ER/230001B.
Cells are a fundamental unit of biology and cellular heterogeneity is a hallmark of multicellular life.
Within an organism - plant, animal, or human - cells can display enormous functional diversity, fulfilling distinct roles that underpin the overall function of the organism.
Throughout the lifetime of an organism, individual cells acquire programmed or spontaneous changes in genome sequence or chromosome copy number as a result of replication errors or exposure to environmental stress (somatic variation).
Although variation between cells of an organism has often been associated with disorders and diseases - for example, in cancers - it is a phenomenon that occurs naturally in healthy cells and is fundamental to normal development and in reacting to the environment.
Most studies have so far dismissed the impact or function of somatic variation on the stability of an organism (homeostasis). Recent technical innovations offer the opportunity to explore random and programmed somatic mutations and determine the consequences for important traits.
As part of the Cellular Genomics research programme, we aim to characterise the impact of somatic variation in healthy organisms on gene regulation and phenotype, providing a baseline for disease studies.
We are applying our expertise in single-cell and long read sequencing to quantitatively assess somatic variation across the lifespan of organisms and determine how this influences traits.
This will allow us to:
This will result in a national and international hub of expertise in somatic variation in healthy organisms that spans advances in experimental protocols from data-generation through to analysis pipelines and advanced applications.