EmbryoPhenomics: A New Technology For Capturing The Dynamic Process Of Embryonic Development
Genomics is the study of the molecular basis of life and has boomed as a result of new technologies. Genomics adopts a reductionist approach to understanding life – studying how the smallest components of an organism work or respond to the environment. There is no doubt that this approach is now central to every area of biological and medical research and has driven significant advances in our understanding.
In contrast, technologies for studying the whole-organism are less-well developed, particularly when it comes to the scale and quality of data that researchers can produce (Kültz 2013). Phenomics is the acquisition of high-dimensional data on an organism-wide scale – and is the natural complement to genomics. Put simply, it is the application of technology to quantify the phenotype — the observable characteristics of whole organisms — thoroughly and with high precision. Phenomics is already being used in plant science and medicine, but it remains a relatively unexplored approach in biology, perhaps due to a lack of appropriate technologies (Houle et al 2010).
Embryonic development is the most dynamic life stage with significant changes happening temporally, spatially, and functionally. Manual approaches to quantifying development will always be suboptimal, not surprising, given the mind-boggling complexity of the biological processes involved. So, scientists at the University of Plymouth have developed EmbryoPhenomics, new phenomics technology for studying aquatic embryos. This technology consists of custom bioimaging hardware (OpenVIM) and analytical software (EmbryoCV). OpenVIM records the entire development (days, weeks, or even months) of hundreds of microscopic embryos while simultaneously controlling the embryonic environment. EmbryoCV uses autonomous image analysis to analyze the hundreds of thousands of images of each developing embryo acquired by OpenVIM in order to quantify biological responses including changes in growth, behavior, cardiac activity, and overall health. The key strengths of this approach are that it captures biological responses that would not be noticed using traditional manual observation and it can analyze large numbers of embryos simultaneously.
Fig. 2. The 10-day development of a freshwater snail in just 30 seconds. Video courtesy Oliver Tills.
EmbryoPhenomics technology appears in a new study published in PLoS Biology. Here, it is applied to assessing the vulnerability of early life stages of a marine shrimp and a freshwater snail to climate change-related drivers. The researchers used the technology to measure the response of > 350 aquatic embryos of two quite different species, in a range of experiments that consisted of > 30 M images. They discovered that animals had fundamentally different developmental trajectories under increased temperatures, including those that embryos are already experiencing in their environments. Furthermore, a combination of temperature and salinity stress had even greater effects on growth and behavior, which is particularly worrying given that climate change is leading to changes in a broad range of environmental parameters.
Fig. 3. The developmental trajectories of 140 freshwater snails in different temperatures. Video courtesy Oliver Tills.
While providing important insights into the potential effects of climate change, the high-dimensional data produced by EmbryoPhenomics also helps us to understand better the complex process of development. The team are currently streamlining the technology and developing more advanced analytics with the help of artificial intelligence to further enhance the capability of this cutting-edge approach.
These findings are described in the article entitled A high-throughput and open-source platform for embryo phenomics, recently published in the journal PLOS Biology.
- Houle, D., Govindaraju, D.R. & Omholt, S., 2010. Phenomics: the next challenge. Nature Reviews Genetics, 11: 855–866.
- Kültz, D. et al., 2013. New Frontiers for Organismal Biology. BioScience, 63: 464–471.