EmbryoPhenomics research team

Part of the Ecophysiology and Development Research Group
EmbryoPhenomics
Biology is complex, and early biological development is the most dynamic period of life. A time when animals must put themselves together while maintaining a functional phenotype with incredible temporal, spatial and functional change.
Measuring this period of life has always challenged biologists, but has also never been so important to address challenges ranging from protecting biodiversity, ensuring food security and sensing biological impacts of environmental degradation.
EmbryoPhenomics is a research team, led by Dr Oliver Tills , that is pushing the boundaries of measuring the process of embryonic development, complex and dynamic biological systems. The team applies bioimaging-based sensing approaches to measuring the development of form and function in developing animals – both in the laboratory and out in the environment.
They have a heavy reliance on innovation, supported by a rapid manufacture R&D workshop at Plymouth Science Park, integrating 3D printing, artificial intelligence and embedded electronics to measure biology in new ways.
The team use the concept of phenomics in their research – high-dimensional organismal phenotyping, an approach spearheaded in medicine and crop science, but which has remained largely inaccessible in biology more broadly. Phenomics is highlighted as being key for advancing biology in the 21st century. EmbryoPhenomics is a strong proponent for biological development as a model for phenomics – due to the high level of spatial and functional change occurring at timescales ranging from seconds to days, in small stages of life that can have extremely high sensitivity to the environment1,2.
EmbryoPhenomics laboratory
Embryo scan
Embryo scan
Embryo scan

Instrumentation

EmbryoPhenomics is applying opensource approaches to research – releasing software3, hardware4 and data as opensource. The team's rationale is clear – to maximise the impact of its research by making it as broadly accessible as possible. The benefits of opensource are well established, ranging from meeting global challenges, democratising science, through to improved return on investment to funders.
Despite being an opensource lab, the team’s innovations are available for sale through Phenomyx CIC – a not-for-profit Community Interest Company established by the team to support researchers. Many people lack the time, facilities or inclination to build their own hardware – Phenomyx prevents this from being a barrier.
EmbryoPhenomics technologies have broad relevance to research, education and industry – consequently, the team takes every effort to make its innovations and accompanying research versatile, user-friendly and reproducible.
Video is a central enabler of the EmbryoPhenomics team's research and provides a unique window onto the dynamic – and otherwise hidden – world of early development. The team uses these approaches for both teaching and research, including in schools; DiscoverOsmosis is a tool for aiding in the learning of osmosis at KS3 and higher.
Open Source Initiative logo
Render of EmbryoPhenomics equipment
LabVsField logo
Render of LabEmbryoCam

Key publications

  1. Ibbini, Z., Bruning, M., Allili, S., Holmes, L.A., Tully, E., McCoy, J., Larsen, B., Wilson, T., Ludford, G., Barrett-Kelly, J., Spicer, J.I., Tills, O., 2024. LabEmbryoCam: An opensource phenotyping system for developing aquatic animals. HardwareX 20, e00602. https://doi.org/10.1016/j.ohx.2024.e00602
  2. Tills, O., Ibbini, Z., Spicer., JI. 2024. Bioimaging and the future of whole-organismal developmental physiology. Comparative Biochemistry and Physiology, Part A, 111783. https://doi.org/10.1016/j.cbpa.2024.111783
  3. Tills, O, Spicer JI, Grimmer A, Marini S, Jie, VW. 2018 ‘A high-throughput and open-source platform for embryo phenomics’. PLoS Biology 16, e3000074 (2018). https://doi.org/10.1371/journal.pbio.3000074
  4. Ibbini Z, Truebano M, Spicer JI, McCoy JCS, Tills O. 2024. Dev-ResNet: Automated developmental event detection using deep learning. J. Exp. Biol. 227, 247046. https://doi.org/10.1242/jeb.247046

Funding