Automated and Dynamic Control of Chemical Content in Droplets for Scalable Screens of Small Animals

Screening functional phenotypes in small animals is important for genetics and drug discovery. Multiphase microfluidics has great potential for enhancing throughput but has been hampered by inefficient animal encapsulation and limited control over the animal's environment in droplets. Here, a highly efficient single-animal encapsulation unit, a liquid exchanger system for controlling the droplet chemical environment dynamically, and an automation scheme for the programming and robust execution of complex protocols are demonstrated. By careful use of interfacial forces, the liquid exchanger unit allows for adding and removing chemicals from a droplet and, therefore, generating chemical gradients inaccessible in previous multiphase systems. Using Caenorhabditis elegans as an example, it is demonstrated that these advances can serve to analyze dynamic phenotyping, such as behavior and neuronal activity, perform forward genetic screen, and are scalable to manipulate animals of different sizes. This platform paves the way for large-scale screens of complex dynamic phenotypes in small animals.

Detection of Potassium Deficiency and Momentary Transpiration Rate Estimation at Early Growth Stages Using Proximal Hyperspectral Imaging and Extreme Gradient Boosting

Potassium is a macro element in plants that is typically supplied to crops in excess throughout the season to avoid a deficit leading to reduced crop yield. Transpiration rate is a momentary physiological attribute that is indicative of soil water content, the plant’s water requirements, and abiotic stress factors. In this study, two systems were combined to create a hyperspectral–physiological plant database for classification of potassium treatments (low, medium, and high) and estimation of momentary transpiration rate from hyperspectral images. PlantArray 3.0 was used to control fertigation, log ambient conditions, and calculate transpiration rates. In addition, a semi-automated platform carrying a hyperspectral camera was triggered every hour to capture images of a large array of pepper plants. The combined attributes and spectral information on an hourly basis were used to classify plants into their given potassium treatments (average accuracy = 80%) and to estimate transpiration rate (RMSE = 0.025 g/min, R² = 0.75) using the advanced ensemble learning algorithm XGBoost (extreme gradient boosting algorithm). Although potassium has no direct spectral absorption features, the classification results demonstrated the ability to label plants according to potassium treatments based on a remotely measured hyperspectral signal. The ability to estimate transpiration rates for different potassium applications using spectral information can aid in irrigation management and crop yield optimization. These combined results are important for decision-making during the growing season, and particularly at the early stages when potassium levels can still be corrected to prevent yield loss.

High-throughput physiological phenotyping and screening system for the characterization of plant-environment interactions

We present a simple and effective high-throughput experimental platform for simultaneous and continuous monitoring of water relations in the soil-plant-atmosphere continuum of numerous plants under dynamic environmental conditions. This system provides a simultaneously measured, detailed physiological response profile for each plant in the array, over time periods ranging from a few minutes to the entire growing season, under normal, stress and recovery conditions and at any phenological stage. Three probes for each pot in the array and a specially designed algorithm enable detailed water-relations characterization of whole-plant transpiration, biomass gain, stomatal conductance and root flux. They also enable quantitative calculation of the whole plant water-use efficiency and relative water content at high resolution under dynamic soil and atmospheric conditions. The system has no moving parts and can fit into many growing environments. A screening of 65 introgression lines of a wild tomato species (Solanum pennellii) crossed with cultivated tomato (S. lycopersicum), using our system and conventional gas-exchange tools, confirmed the accuracy of the system as well as its diagnostic capabilities. The use of this high-throughput diagnostic screening method is discussed in light of the gaps in our understanding of the genetic regulation of whole-plant performance, particularly under abiotic stress.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.