利用植物表型組學(xué)挖掘基因組學(xué)的成果
到2050年，全球人口將達(dá)到97億，預(yù)計作物產(chǎn)量翻一番才能滿足全球人口的糧食需求。為了達(dá)到這一目標(biāo)，作物產(chǎn)量需每年增長2.4%，但目前作物產(chǎn)量平均增長率僅為1.3%。作物生產(chǎn)性能的遺傳改良仍然是提高作物生產(chǎn)力的關(guān)鍵因素，但當(dāng)前的改善速度無法滿足可持續(xù)性和糧食安全的需要。為了確保糧食安全、生態(tài)系統(tǒng)的可持續(xù)發(fā)展，必須培育高產(chǎn)、適應(yīng)新氣候和多變氣候的作物。
基因組學(xué)和表型組學(xué)的進(jìn)展正在提供對復(fù)雜的生物學(xué)機(jī)制的洞察，這些機(jī)制是植物對環(huán)境變化作出反應(yīng)的基礎(chǔ)。然而，將基因型與表型聯(lián)系起來培育氣候適應(yīng)性作物品種仍然是一個巨大的挑戰(zhàn)，阻礙了高通量基因組學(xué)和表型組學(xué)在育種中的最佳應(yīng)用。
本文綜述了植物表型系統(tǒng)化、快速化、微創(chuàng)化和低成本化的必要性，討論了其向現(xiàn)代高通量表型的演變、適應(yīng)高通量表型的性狀、高通量表型與基因組學(xué)的整合以及高通量表型在提高育種效率和加快作物品種培育中的意義。
根系表型（a，g為微根管法原位測量）
鷹嘴豆耐熱表型分析研究中的葉片葉綠素?zé)晒獬上?/em>
葉片上半部分沒有經(jīng)過熱處理，下半部分在46°C下熱處理1小時。深藍(lán)色為高光合活性（高fv/fm），而橙色、黃色和綠色或完全黑色代表低光合活性。
紅外熱成像表征冠層溫度
不同品種蘋果花粉的活性（微流控阻抗流式細(xì)胞法）
表1常用的作物高通量植物表型平臺（HTPPS）
|
Name |
Target Plant Organ |
Parameters |
Description |
|
PHENOPSIS |
Leaf |
Plant growth parameters |
An automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana |
|
WIWAM |
Leaf |
Growth parameters |
Used to impose stress early during leaf development |
|
PHENOSCOPE |
Shoots |
Vegetative growth and homogeneity |
An integrated device, allowing a simultaneous culture of individual Arabidopsis plants and high-throughput acquisition, storage, and analysis of quality phenotypes |
|
GROWSCREEN |
Leaf |
3D surface area of leaf discs |
Platform to study plant leaf growth fluorescence and root architecture from seedling under control conditions in Arabidopsis thaliana, barley and maize |
|
TraitMill |
Flowers, grains, etc. |
Growth and yield parameters |
Automated high resolution phenotypic platform, uniquely placed to identify genes that improve the yield of cereals |
|
PlantScan |
Whole plant |
Vegetative growth parameters |
Automated high-resolution phenomic center providing non-invasive analysis of plant structure, morphology and function in Gossypium, wheat and maize |
|
LemnaTec |
Leaf |
Growth and yield parameters |
Visualize and analyze 2D/3D non-destructive high-throughput imaging, monitor plant growth and behavior under fully controlled conditions |
|
LeasyScan |
Leaf, whole plant |
Canopy traits |
Phenotyping for traits controlling plant water use with precision in pearl millet |
|
HRPF |
Whole plant |
Growth and yield parameters |
High-throughput rice phenotyping facility |
|
GlyPh (self-construction) |
Whole plant |
Soil water content and growth estimation |
Low-cost platform for phenotyping plant growth and water use under a broad range of conditions |
|
BreedVision |
Whole plant |
Growth and physiological parameters |
Measures various agronomic traits and leads to non-destructive phenotyping for crop improvement and plant genetic studies |
|
PlantScreenTM |
Shoot |
Chlorophyll fluorescence imaging and non-imaging chlorophyll fluorescence, growth parameters |
Evaluates various parameters of chlorophyll fluorescence obtained from kinetic chlorophyll fluorescence imaging |
|
OloPhen |
Whole plant |
Rosette area, growth and survival rate |
Suitable for analysis of rosette growth in multi-well plates, suitable to evaluate plant stress tolerance. |
|
Color eye |
Leaf |
Leaf greenness, lesions |
Data can be overlayed over laser triangulation data obtained by plant eye |
|
LabVIEW |
Canopy |
Growth parameters |
Low-cost, accurate, and high-throughput phenotyping system with custom algorithms |
|
Shovelomics |
Root |
Root growth parameters |
Identification and selection of useful root architectural phenotypes for annual legume or dicotyledonous crops. |
|
Phenodyn/Phenoarch |
Leaf |
Leaf elongation rate |
Follows QTL-dependent daily patterns in maize lines under naturally fluctuating conditions, located in INRA, France |
|
LemnaGrid |
Root and leaf |
Plant and root growth parameters |
Compares growth behaviors of different genotypes, discriminates plant root zone water status |
|
Integrated Analysis Platform (IAP) |
Leaf |
Plant leaf orientation |
Provides user-friendly interfaces with highly adaptable core functions, supports image data transfer from different acquisition environments and large-scale image analysis |
|
LAMINA |
Leaf |
Leaf parameters |
Tool for automated analysis of images of leaves, designed to provide classical indicators of leaf structure |
|
Rosette Tracker |
Shoot |
Area, perimeter diameter stockiness |
Allows to simultaneously quantify plant growth, photosynthesis, and leaf temperature-related parameters |
|
Leaf Analyser |
Leaf |
Leaf architecture |
Provides a high-throughput method to evaluate leaf shape variation in higher-dimensional phenotypic space |
|
Self-construction |
Root |
Root growth parameters |
Algorithms allow the automatic extraction of many root traits in a high-throughput fashion |
|
Phenovator |
Leaf |
Photosynthesis |
High-throughput phenotyping facility for photosynthesis developed at Wageningen University and Research |
表2常用的高通量植物表型分析軟件包（節(jié)選）
|
Name of the Software |
Target Plant Organ |
Parameters |
Description |
|
MATLAB |
Leaf |
Leaf architecture |
Uses image processing algorithms for high-throughput analysis of images for estimating phenotypes/traits associated with tested plants |
|
HTPheno |
Shoot |
Height, width and shoot area |
Analyzes colour images of plants and different phenotypical parameters for each plant |
|
GiaRoots |
Root |
Morpho-geometric parameters |
Semi-automated software tool for high-throughput analysis of root system images |
|
RootReader 3D |
Roots |
Root types and phenotypic root traits |
Imaging and software platform for HTP of 3-D root traits during seedling development |
|
PhenoPhyte |
Leaf |
Leaf and plant growth parameters |
Tool to analyze the non-destructive imaging of plants can be used in suboptimal imaging conditions also |
|
RootNav |
Root |
Root system architecture |
Image analysis tool for semi-automated quantification of complex root system architecture in a range of plant species |
|
SmartGrain |
Seed |
Seed structure parameters |
Software for high-throughput measurement of seed shape, makes possible to distinguish between lines with small differences in seed shape |
|
SmartRoot |
Root |
Root system architecture |
Operating system-independent freeware and relies on cross-platform standards for communication with data-analysis software |
|
DART |
Root |
Root system architecture |
Uses human vision tracing to avoid analytical biases |
|
Tomato analyzer |
Fruit |
Fruit colour |
Analyzes tomato fruit colour |
圖5 高通量植物表型平臺（LemnaTec 3D Scanalyzer）
全文閱讀
Pratap A, Gupta S, Nair R M, et al. Using plant phenomics to exploit the gains of genomics. Agronomy, 2019, 9(3): 126.
- 植物功能結(jié)構(gòu)模型GreenLab最新研究進(jìn)展及未來發(fā)展的前景和方向探討
- 用于植物病害識別的局部與全局特征感知雙分支網(wǎng)絡(luò)-LGNet的研究
- 一種高效準(zhǔn)確測量未經(jīng)整形的水稻稻穗表型方法EOPT研究
- 基于范例數(shù)據(jù)生成和葉片結(jié)構(gòu)分析的干旱脅迫楊樹苗表型分析
- 植物表型成像技術(shù)應(yīng)用案例 - 優(yōu)化藥用植物人工培養(yǎng)方案
- 基于PDC-VLD的較強(qiáng)泛化性能番茄葉病多模態(tài)開放目標(biāo)檢測模型研究
- 多物種氣孔表型原位高效群體監(jiān)測設(shè)備的優(yōu)勢及應(yīng)用案例分享
- 基于AISOA-SSformer的有效水稻葉病圖像分割方法研究
- 博普特圓滿參展第二十一屆中國作物學(xué)會學(xué)術(shù)年會
- 博普特植物表型產(chǎn)品將參展第二十一屆中國作物學(xué)年會
- 博普特成功參展中國作物學(xué)會智慧農(nóng)業(yè)專業(yè)委員會年會
- 博普特攜植物表型組學(xué)產(chǎn)品參展中國園藝學(xué)會2024年會
- 博普特成功參展中國植物病理學(xué)會2024年學(xué)術(shù)年會
- 博普特將攜植物表型組學(xué)方案參展2024中國園藝學(xué)年會
- 易科泰邀您參加中國園藝學(xué)會 2024 年學(xué)術(shù)年會
- 易科泰邀您參加十九屆藥用植物及植物藥學(xué)術(shù)研討會
