You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: A high-throughput and open-source platform for embryo phenomics}
Article:
Abstract:
Phenomics has the potential to facilitate significant advances in biology but requires the development of high-throughput technologies capable of generating and analysing high-dimensional data. There are significant challenges associated with building such technologies, not least those required for investigating dynamic processes such as embryonic development, during which high rates of temporal, spatial, and functional change are inherently difficult to capture. Here, we present EmbryoPhenomics, an accessible high-throughput platform for phenomics in aquatic embryos comprising an Open-source Video Microscope( OpenVIM) that produces high-resolution videos of multiple embryos under tightly controlled environmental conditions. These videos are then analysed by the Python package Embryo Computer Vision( EmbryoCV), which extracts phenomic data for morphological, physiological, behavioural, and proxy traits during the process of embryonic development. We demonstrate the broad-scale applicability of EmbryoPhenomics in a series of experiments assessing chronic, acute, and multistressor responses to environmental change( temperature and salinity) in >30 million images of >600 embryos of two species with markedly different patterns of development—the pond snail Radix balthica and the marine amphipod Orchestia gammarellus. The challenge of phenomics is significant but so too are the rewards, and it is particularly relevant to the urgent task of assessing complex organismal responses to current rates of environmental change. EmbryoPhenomics can acquire and process data capturing functional, temporal, and spatial responses in the earliest, most dynamic life stages and is potentially game changing for those interested in studying development and phenomics more widely.
Introduction:
Phenomics is the acquisition of high-dimensional phenotypic data on an organism-wide scale[1]. It is both analogous and complementary to genomics and has a similar capacity to facilitate advances in biology. However, it is now recognised that in many ways, the challenges of phenomics dwarf those faced at the advent of modern genomics[2, 3]. The information content of phenomes is far greater than genomes[1, 4], and whereas genomics has benefited from standardised tools and technologies that are readily applicable to different species and scales of study, phenomics requires the development and application of less generic approaches[5]. Consequently, a lack of transferrable technologies for high-dimensional acquisition of phenome-level data remains the greatest bottleneck to the advancement of phenomics and is recognised as a key challenge in biology[3, 5]. Technologies for acquiring high-resolution phenomic data enable scales of data collection[6] and combinatorial analyses that would never be possible using manual approaches[7]. Such analyses permit the relationships and interactions among multiple traits to be explored and integrated and, for phenomic data, have been demonstrated to have greater discriminatory and explanatory power than univariate approaches, thereby providing novel biological insight[8, 9]. High-dimensional phenotyping also allows for the identification and measurement of ‘proxy’ traits—phenome-level measures that are not detectable using manual observation[4]. In plant root phenomics, such proxy traits have been shown to possess strong discriminatory power and include automated traits with natural manual trait equivalents and novel measurements for which there was no manual trait equivalent[9]. Another key asset of data sets acquired using high-throughput phenomic technologies is the ability to incorporate biological complexity in the interrogation of a particular experimental response. For example, analysis of phenomic data for mice identified a combinatorial signal from >200 phenotypic traits that was predictive of a Huntington disease genotype[8]. Such studies point to the power of phenomics to transform our understanding of phenome-level response via the development and application of appropriate technologies. The rewards of phenomics will arguably be greatest when used to quantify aspects of the phenome for which current methods are most limiting. Embryonic development is a dynamic process with high levels of intraindividual temporal, spatial, and functional change, meaning that traditional approaches to its quantification at the whole-organism level can never be thorough. Furthermore, in addition to high levels of intraindividual change, embryonic development also exhibits high levels of interindividual variation[10], and therefore, an optimum approach to its study is to adopt longitudinal observation of large numbers of embryos[11]. This task is difficult to achieve and necessitates compromises in i) the number of embryos studied, ii) the number of phenomic measures quantified, iii) the precision with which measurements are made, and iv) the temporal resolution of measurements. Such compromises occur due to the highly dynamic nature of biological development but also because of the complexity and interconnectedness of biological responses through time. Therefore, automated technologies with the capability to make high-throughput longitudinal measurements of developing embryos offer huge potential to alleviate a major limitation in the application of phenomics. There are good examples of when automated longitudinal observation of developing embryos has been used effectively for commercial applications. Automated noninvasive imaging and analysis of cytokinetic patterns in early-stage cell cleavages in human embryos can predict survival to the blastocyst stage with >93% sensitivity[12]. Consequently, time-lapse imaging is now routinely offered as an option in in vitro fertilisation( IVF) treatments to select embryos for implantation on the basis of early cytokinetic parameters, and this approach significantly increases success rates[13]. Technologies used for nonhuman, high-throughput screening include the ImageXpress system for widefield cellular imaging[14, 15] and the EthoVision platform[16] for use with zebrafish embryos and larvae, with a focus on tracking[17]. A limitation of commercial platforms remains their transferability and applicability to different species, research questions, and study designs. Consequently, some laboratories are turning to self-built, open-access solutions, comprising open-source hardware[18, 19] and software[20, 21], and such technologies are becoming increasingly central to biology. They have advantages, including greater opportunities for educational engagement[22], accelerated innovation, reduced cost, reduced redundant problem solving in different laboratories, and more rapid advancement of scientific discovery via a greater return on investment[23]. Such accessible technologies for high-throughput phenomics during the dynamic and sensitive process of embryonic development are urgently required, particularly within the context of assessing the impacts of global change[4]. Here, we present EmbryoPhenomics, an open-source high-throughput platform for phenomics in aquatic embryos that consists of an Open-source Video Microscope( OpenVIM), with experimental control over the embryonic environment, and a Python package Embryo Computer Vision( EmbryoCV) for high-dimensional measurement of phenomic traits from video data sets. The EmbryoPhenomics platform enables the quantification and integration of interindividual and intraindividual temporal change in morphological, physiological, and behavioural traits with high resolution and to an unprecedented scale in a largely automated workflow. We demonstrate the application of EmbryoPhenomics for quantifying integrated organismal responses to environmental change, using experiments assessing i) chronic, ii) acute, and iii) interactive effects of environmental stressors. Furthermore, we show the efficacy and transferability of this platform using two ecologically important species with radically different patterns of development: Radix balthica, a freshwater gastropod mollusc, and Orchestia gammarellus, a marine amphipod crustacean.
Discussion:
Here, we demonstrate the ability of the open-source EmbryoPhenomics platform to quantify complex phenome-level responses of large numbers of developing aquatic embryos. EmbryoPhenomics is versatile and we demonstrate its application to a broad range of experimental designs, ranging from 2-week to 24-h experiments, and demonstrate its application to two species. These experiments include assessment of responses to acute and chronic thermal stress, arguably one of the greatest current threats to biodiversity and one to which early life stages can have heightened sensitivity[26]. EmbryoPhenomics was also used to quantify the response of 288 early developmental–stage R. balthica embryos to combined thermal and saline stress and revealed an inhibitory effect of salinity on growth and movement that contrasted with the effects of elevated temperature. The generation of high-dimensional data is a prerequisite to phenomics, and owing particularly to the dynamic nature of embryonic development, this capability will facilitate advances in our understanding of this pivotal life stage. EmbryoCV and OpenVIM are versatile, extendible, and open-source technologies applicable to a range of species and study designs. Our aim is that these resources will develop a community of users, in a similar model to that seen with the Open Selective Plane Illumination Microscopy( OpenSPIM) project[19]. In contrast to more restrictive and focused technologies for embryo phenotyping[27, 28], both OpenVIM and EmbryoCV are intentionally modular, versatile, high throughput, and transferrable to species with contrasting developmental patterns. EmbryoPhenomics can therefore underpin the generation of data describing whole-organism responses with a scale, diversity, and quality that is required to study embryonic development in a manner befitting phenomics, which compares with that produced using molecular-omic technologies. Concordance between manually and EmbryoCV-determined measures of embryo spatial and temporal characteristics was high( Fig 3). The blockwise signal and frequency workflow was effective at locating and quantifying both the two-chambered globular heart of R. balthica but also the elongated, tubular heart of O. gammarellus. The typical maximum cardiac rate of R. balthica at 20°C is 1. 5 Hz, whereas O. gammarellus has a higher cardiac rate of 3. 5 Hz with sustained periods of diapause, presenting challenges in the effective manual quantification of rate. Here, for O. gammarellus, to account for the skew introduced by diapause, beat-to-beat timings were recorded manually, and the rates calculated from the median timing were closely correlated with the frequency measurements produced by EmbryoCV. For R. balthica, direct comparisons of manually determined rate, i. e., counts of heartbeats, were closely correlated with EmbryoCV determined rates. Movements of the embryo meant that the heart and other features of interest were not always visible in the image. Consequently, EmbryoCV applies spectral frequency analysis across different areas of the embryo and attempts to identify biologically relevant frequencies for modelling heart rate. These data then inform the fitting of models to heart rate, and the resultant cardiac rates were closely correlated to manual measures for both species in Experiments 1 and 4. A similar workflow also underpins the automated identification of lethal end points across the three developmental stages of R. balthica studied in Experiment 2. Here, developmental stage–specific algorithms identifying rapid increases in size indicative of osmotic control, rapid reductions in energy within particular frequency bands, or a combination of the two and were highly effective at producing classifications of lethal end points closely aligned with those ascertained manually. Further optimisation and mining of the data produced by EmbryoPhenomics will allow tailored end points to be identified, including responses that, while present, are not immediately apparent to human observers in the multitemporal video produced by OpenVIM. Phenomics offers the potential to facilitate rapid advances in our understanding of biology via the generation of a quantity and quality of phenotype-level data that is more appropriate to addressing questions focused on understanding the complexities of organismal biology. The EmbryoPhenomics platform provides the technological capability for the study of organismal development in a way that captures temporal, spatial, and functional diversity at both interindividual and intraindividual levels—a task identified as a major challenge in biology[29, 11]. Here, we demonstrate the capability of OpenVIM to document the development of large numbers of aquatic embryos( N > 600) in experimental treatments ranging from long-( >240 h) to short-term( 24 h) exposures. Of the >30 million images acquired across the four experiments described here, EmbryoCV successfully extracted phenome-level data from 95%, producing high dimensional data describing morphological, physiological, and behavioural embryo responses.
Phenomics is the collection of high-dimensional phenotypic data on an organism-wide scale , and it requires high-throughput technologies . However , a lack of technologies for efficiently visualising and measuring whole-organism responses to different environments represents a serious challenge for biologists . This challenge is most apparent when studying complex responses , such as those occurring during the dynamic period of embryonic development , when the phenotype changes markedly through time . Here , we present EmbryoPhenomics ( www . embryophenomics . org ) , a new open-source technological platform comprising high-throughput bioimaging hardware that produces high-resolution video of multiple , developing embryos maintained under controlled environmental conditions and software for automatically quantifying embryo responses from these videos . We demonstrate the broad applicability of EmbryoPhenomics using four experiments assessing responses to global change ( elevated temperature and salinity ) in which we generate data for more than 600 embryos produced from video comprising more than 30 million images . EmbryoPhenomics was used to capture functional , temporal , and spatial change in morphological , physiological , and behavioural responses in the earliest , most dynamic life stages and addresses a serious bottleneck in biology . Such capabilities are urgently required , particularly within the context of assessing the response of embryos to the current unprecedented rates of global environmental change .