Recent technological advances, both experimental and computational, make it possible to address key questions that will bring a quantitative, mechanistic, and molecular understanding of environmental effects on human biology. The Human Ecosystems research theme will generate multilayered data at the individual level, including single-cell, spatial, multi-omic, and imaging data.
These will be combined with cutting-edge model systems, such as organoids, and state-of-the art molecular techniques, for example in genome editing, to obtain insight into gene–environment interactions. Research in this theme will also leverage the unprecedented depth and breadth of existing human cohort data, and will integrate environmental, molecular, and genomic data using sophisticated computational and statistical approaches, to dissect the gene–environment interplay and its effects on phenotypes.
In Human Ecosystems, EMBL researchers aim to understand how the environment impacts human biology, both at the individual and population level. Researchers will do this by combining sophisticated experimental and powerful computational approaches. A central question is how environmental factors mediate phenotypes and, more generally, how genotype and environment interact and influence molecular, cellular, or organism phenotypes in areas such as health, disease, development, and ageing. With close collaboration of scientists from a range of disciplines, this research theme will study the environment through various lenses focusing on the physical and biological, as well as social, components of the human environment. It will consider the organismal environment as well as the local (micro)environment of human cells and tissues.
Current frontiers at the molecular and intercellular level include the development of experimental techniques for coupled single-cell, spatial, multi-omic, and cellular imaging, which make it possible to uncover microenvironmental effects mediated by human cells or commensals on cellular interactions. Leading-edge techniques and model systems can also be applied to address causally how environmental factors may affect molecular phenotypes in normal tissues or disease states. Another frontier involves understanding the influence of environmental factors on the spatial and cellular heterogeneity of tissues, including somatic mosaicism, and the interplay of this heterogeneity with cellular and organismal phenotypes, including those associated with disease and ageing. Experimental projects are supported by advanced facilities in genomics, single-cell and spatial techniques, flow sorting, and advanced imaging technologies.
This theme will also require sophisticated computational methods for data integration, and for hypothesis generation and testing. At the organismal level, challenges include the development of sophisticated and innovative computational tools and methods to integrate data from large national and international population-level cohorts with associated omics data (such as UK Biobank and Danish Health Data). By integrating such data, it will be possible to uncover the influences of the physical, biological, and social environments on phenotypes and human health. Research in this thematic area will be facilitated by institutional access to state-of-the-art data science infrastructure (cloud-based and HPC computing), data integration expertise, and access to large datasets from national and global initiatives relevant to human health research.