Final Report Summary - SINGLE-CELL GENOMICS (Single-cell Gene Regulation in Differentiation and Pluripotency)
My research project aimed to better understand the molecular processes that regulate our genetic material through large-scale analyses of genetic activation patterns across cells. To this end, my lab proposed to develop new technologies that would allow us to record the activity of all genes in individual cells. At the time of proposing this project, it was standard to measure gene activities in cell populations of thousands to millions of cells, which therefore only gave cell population averages of gene activities. With a single-cell method, we would have unprecedented resolution to learn about which genes are expressed in each type of cell in our bodies and that would allow us to ask more precise scientific questions about the regulation of genes. Additionally, we wanted to use the single-cell resolution to study the genetic regulation of early embryonic development in mice, an important developmental period that has been hard to study due to early embryos are only containing few cells.
Within this project we introduced two novel technologies for single-cell gene expression analyses, and we comprehensive benchmarked these methods. Our methods have become highly used in the scientific community and been used to study various cell types in humans and other species. We also reached our objectives in our analyses of early mouse embryos in which we could demonstrate novel gene regulatory processes. In particular, we discovered that cells are more dynamic than previously envisioned in their activities of the gene copies inherited from both parents. In our study, we could show that cells often only have activities from one of the inherited genes at any given time and that these patterns are random and due to inherent stochastic molecular processes in gene expression. This finding is important for our basic understanding of how gene activation occurs in cells, but it also implicates that this randomness might lead to traits, e.g. it is often observed in human diseases that only a few individuals with a disease-gene develop disease – a phenomena known as incomplete penetrance. Further research is however needed to see whether stochastic transcription is indeed a mechanism to explain these random disease outcomes.
Within this project we introduced two novel technologies for single-cell gene expression analyses, and we comprehensive benchmarked these methods. Our methods have become highly used in the scientific community and been used to study various cell types in humans and other species. We also reached our objectives in our analyses of early mouse embryos in which we could demonstrate novel gene regulatory processes. In particular, we discovered that cells are more dynamic than previously envisioned in their activities of the gene copies inherited from both parents. In our study, we could show that cells often only have activities from one of the inherited genes at any given time and that these patterns are random and due to inherent stochastic molecular processes in gene expression. This finding is important for our basic understanding of how gene activation occurs in cells, but it also implicates that this randomness might lead to traits, e.g. it is often observed in human diseases that only a few individuals with a disease-gene develop disease – a phenomena known as incomplete penetrance. Further research is however needed to see whether stochastic transcription is indeed a mechanism to explain these random disease outcomes.