High throughput phenotyping of transgenic plants
Authors: 1Ekatpure Sachin Chandrakant, 2 Hegade Priti Manohar, 3 Wagh Yogesh Sahebrao, 1 Jadhav Pritam Ramesh
1 Department of Plant Biotechnology, College of Agriculture, Vellayani
2 Savitribai Phule Pune University, Pune
3 Department of Plant Physiology, College of Agriculture, Vellayani
*Corresponding Author: email@example.com
Process of predicting organismâ€™s phenotype using genetic information collected from genotyping. Current methods of in-house plant phenotyping provides powerful new tool for plant biology studies. Self-constructed and commercial platforms employ non-destructive methods for measurement of plants on high-throughput and large scale.
High throughput phenotyping
High-throughput integrative phenotyping facilities provide an opportunity to combine various methods.
The process of phenotyping is automated, simultaneous and non-destructive, which analyses the plant growth, morphology and physiology. It provides the complex picture of plant growth and vigour in one round, also it is possible to take readings multiple times during the life span of the plant.
Use of statistical approaches is possible to discover subtle but significant differences between studied genotypes and treatment variants. Phenotyping Facility is run by most advanced public plant research institutions around world, e.g. Australian Plant Phenomics Facility, European Plant Phenotyping Network and USDA. In India this facility is available in national research institutes like ICAR-NIASM (Baramati), IIHR (Bengaluru) and CRIDA (Hyderabad).
High throughput phenotyping methods
1. Visible RGB (Red Green Blue) imaging of plant shoots
Plant phenotyping facilities prefer to evaluate growth rate using imaging methods. It employs digital cameras with subsequent software image analysis. Also it enables faster and precise determination of parameters. Visible RGB imaging is non-invasive technique of shoot growth determination. It is very reliable and gives high correlations between digital area and shoot fresh or dry weights, respectively. Extensive research was done in the Arabidopsis, tobacco, cereals and pea.
a) Correct determination of digital plant growth area can be distorted by overlapping leaves, leaf twisting and curling and circadian movement.
b) RGB image is taken only from only one view
c) Due to use of only a top-view RGB imaging, this approach cannot be applied for analyses of most of agronomical important plants with vertical growth.
2. Chlorophyll fluorescence imaging (CFIM)
It is inexpensive, non-destructive technique of imaging, which provides great deal of information about photosynthetic function of plant sample. This method uses pulse amplitude modulation (PAM) techniques for measurement of CFIM together with application of saturation pulse (SP) method which enables separation of photochemical and non-photochemical events occurring in sample. This method is highly suitable for high-throughput plant phenotyping. It shows the difference between non-stressed and senescent leaves.
Generally plants are kept them self cool by transpiration. When stomata are closed, plant temperature increases. Thermal imaging was used for the first time to detect changes in temperature of sunflower leaves caused by water deficiency. This method of imaging uses camera to measure spatial heterogeneity of heat emissions, usually from leaves. Thermal imaging was successfully used in wide range of conditions and with diverse plant species. It can be applied to different scales, e.g., from single seedlings/leaves through whole trees or field crops to regions. Sometimes environmental variability affects accuracy of thermal imaging measurements
4. Hyperspectral imaging (VIS-NIR, SWIR)
Absorption of light by endogenous plant compounds is used for calculations of many indices. For estimation of Chlorophyll content normalized difference vegetation index (NDVI) is used. Photochemical reflectance index (PRI) is used for estimation of photosynthetic efficiency. Absorption of compound at given wavelength can also be used for direct estimation of compound contents in plant. Depending on measured wavelengths of reflected signal, various detectors are used, such as VIS-NIR (visible-near infrared region (400â€"750) - (750â€"1400 nm)) and SWIR (short wavelength infrared region; 1400â€"3000 nm). SWIR spectral region is mainly used for estimation of plantâ€™s water content.
5. Phenomobiles: Field-based high-throughput phenotyping platforms (HTPP)
Ground-based HTPPs include modified vehicles equipped with global positioning system (GPS) navigation device and sensors, referred to as phenomobiles. It carries set of sensors to measures canopy height, reflectance and temperature.
6. RootArray technology
RootArray technology is a real-time imaging system with micro-fluidics that allows non-invasive study of gene expression. One potential way of taking advantage is novel promoter discovery and evaluation. Tissue-specific promoters and/or promoters inducible to provided stimuli can be effectively identified and evaluated with this technique. Gene expression can be monitored based on marker gene expression driven by different types of promoters. Provides gene expression information, also eliminates potential artifacts of gene expression readouts due to degradation of mRNA or over-representation of certain species of mRNA during mRNA extraction and gene expression signal amplification processes. It allows in vivo monitoring of gene expression changes in responses to various stimuli provided into individual plants in micro-chambers using micro-fluidics.
7. RootXpose technology
One of key challenges to phenotype root traits is root excavation process where much of fine root traits are inevitably lost. RootXpose eliminates need for excavation by growing plant in transparent gel system where non-invasive phenotyping of fine root traits as well as major root traits is possible. Efficacy of transgenes of interest for modifying root traits can be readily and accurately evaluated in a non-invasive and high-throughput manner.
8. Whole plant phenotyping using automated greenhouse
Screening large number of transgenes in quantitative and high-throughput manner is a key challenge. Automated greenhouse at Research Triangle Park (RTP) site (North Carolina, USA) overcomes this challenge. By adopting industry-scale automated plant growth and proprietary imaging-based plant phenotyping. Key environmental and growth parameters such as temperature, photoperiods, light intensity, water level and fertigation are programmable and tightly controlled. Plants grown in automated greenhouse are moved through set of instrumentation by conveyor-belt system for image-based phenotyping and fertigation. Imaging station is equipped with set of visual and hyperspectral high resolution digital cameras in various angles. It collects images of individual plants on daily basis. It capture the information regarding,
• Morphological - biomass, plant height, canopy area.
• Hyperspectral traits - anthocyanin, chlorophyll, water content.
Imaging analysis software automatically extracts traits of interest from each image for phenotyping. It enables high precision phenotyping of GM plants in high-throughput manner. Key advantages of automated greenhouse phenotyping are controlling water levels to evaluate gene efficacy for drought tolerance and nitrogen use efficiency. These two environmental parameters are often very hard to control in field.
Use of phenotyping methods
• Studies of plantsâ€™ responses to various types of environmental stresses.
• Widely used for high-throughput phenotyping of drought-tolerant varieties.
• Able to automatically extract plant height and width from images.
• Also leaf colours to evaluate impact of drought on degradation of chlorophyll.
• Plant water management was automatically analysed by simple weighing of pots.
1. Li, L. , Zhang, Q. and Huang, D. 2014. A Review of Imaging Techniques for Plant Phenotyping. Sensors 14: 20078-20111.
2. Montes, J.M.; Melchinger, A.E. and Reif, J.C. 2007. Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci. 12: 433- 436.
About Author / Additional Info:
PhD research scholar (Plant biotechnology), interested in genetic engineering and molecular biology studies