植物学报 ›› 2019, Vol. 54 ›› Issue (5): 558-568.doi: 10.11983/CBB19141

• 特邀综述 • 上一篇    下一篇

新一代植物表型组学的发展之路

胡伟娟1,2,*,傅向东1,2,陈凡1,2,杨维才1,2   

  1. 1. 中国科学院遗传与发育生物学研究所, 北京 100101
    2. 作物表型组学联合研究中心, 武汉 430074
  • 收稿日期:2019-07-29 接受日期:2019-08-21 出版日期:2019-09-01 发布日期:2020-03-10
  • 通讯作者: 胡伟娟

A Path to Next Generation of Plant Phenomics

Hu Weijuan1,2,*,Fu Xiangdong1,2,Chen Fan1,2,Yang Weicai1,2   

  1. 1. Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
    2. Crop Phenomics Joint Research Center, Wuhan 430074, China
  • Received:2019-07-29 Accepted:2019-08-21 Online:2019-09-01 Published:2020-03-10
  • Contact: Hu Weijuan

摘要:

随着多种植物全基因组测序的完成, 科研人员越来越认识到植物表型研究的重要性, 并将其提升至“组学”的高度。植物表型组学是研究植物生长、表现和组成的科学, 能够有效追踪基因型、环境因素和表型之间的联系, 是突破未来作物学研究和应用的关键领域。该文介绍了植物表型采集分析经历的从手工测量计数的初始阶段到特定测量工具的辅助阶段再到高通量表型组学3个阶段; 提出了推动植物表型采集分析发展的3个要素: 表型组学研究设施、表型采集技术及图像数据分析方法; 进而详细阐述了表型组学设施的发展、国际上代表性的设施平台情况以及表型采集传感器和图像数据分析方法的发展, 并展望了植物表型组学未来的研究方向。

关键词: 植物表型组学, 表型研究设施, 采集技术, 图像分析

Abstract:

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant research, promising to alleviate the phenotypic bottleneck. Plant phenomics is a science that studies the growth, performance and composition of plants. It can effectively track the relationship among genotypes, environmental factors, and phenotypes. It is a key research field to break through the future crop research and application. In this paper, three stages of plant phenotypic analysis are discussed, that is, from the initial stage of manual measurement and counting and the assistant stage of specific measurement tools to the stage of high throughput phenomics. It is proposed that the development of plant phenotypic acquisition and analysis is driven by three important factors: phenotypic research facilities, phenotype acquisition technology and image analysis methods. Finally, the plant phenomic research is prospected.

Key words: plant phenomics, phenotypic research facilities, acquisition technology, image analysis

表1

高通量植物表型组学设施分类及代表性平台"

分类 采集方式 国际代表性平台
平台名称 国家 概况/特色
温室型表型平台(可控环境) 传送式 植物表型加速器 澳大利亚 主要有2套大型温室表型平台, 合计通量2400盆。主要应用于非生物胁迫和植物衰老等方面
德国Julich中心表型平台 德国 自主研发了温室表型系统, 包括根系MRI扫描系统和根系PET-CT扫描系统, 用于植物地上及地下部位的表型研究
英国亚伯大学国家植物表型中心 英国 一套通量800盆的温室表型系统。重点关注能源植物(草本)研究
法国农科院表型中心 法国 在蒙彼利埃(Montpelier)和第戎(Dijon)各有一套大型温室表型平台, 合计通量2800盆。用于多种农作物的育种
根特大学温室表型平台 比利时 主要集成可见光、热成像及高光谱成像等成像单元, 主要应用于玉米等农作物的非生物胁迫研究
轨道式 德国马普学会轨道型温室表型平台 德国 轨道上搭载了多光谱激光3D成像单元, 用于各种作物的三维结构采集及光谱成像
上海师范大学轨道型温室表型平台 中国 搭载了RGB、多光谱和多光谱激光3D成像传感器, 用于采集分析各种植物的颜色、三维结构、植物反射指数及光谱成像
田间表型平台 轨道式 英国洛桑实验站田间表型平台 英国 成像覆盖面积10 m×120 m, 包括可见光、红外、激光3D、叶绿素荧光、高光谱、NDVI和CO2等多个传感器。应用于油菜和小麦等作物不同营养处理下相关田间表型研究
英国JIC田间表型平台 英国 以植物激光三维扫描测量仪为核心, 通过3D顶部成像, 获取植物生长情况。对生长在自然土壤里的农作物进行高通量表型测量
绳索悬浮式 美国内布拉斯加林肯大学田间表型平台 美国 高吞吐量的表型机器人安装在一个30 t重的钢架上, 沿着200英尺高的钢轨移动, 在1.5英亩田间移动。主要应用于研究植物高度、叶表面积、生物量、耐热性和对当地条件的其它反应的众多变化
行走式(手动或自动) 澳大利亚昆士兰大学和CSIRO 澳大利亚 行走式田间表型, 三轮带电动驱动系统, 配置可见光和激光扫描测量器。对油菜等田间作物进行高通量表型测量
日本东京大学田间表型
平台
日本 在集成多个低成本传感器的基础上, 通过网络云服务实现对植物在不同环境下的长期观察
英国诺维奇科学研究院作物表型监测平台Crop Quant 英国 通过自主研发的软件系统动态控制, 根据不同光照条件自动调整成像模式对田间作物的连续拍摄, 完成初步的表型分析, 实现对作物全生育期关键性状的高通量、高频率表型分析
无人机(UAV) 德国波恩大学田间平台 德国 利用无人机搭载不同类型的高光谱传感器, 主要应用于监测田间大麦的表型参数
CSIRO昆士兰生物科学区 澳大利亚 改装后的载人直升机Pheno-Copter被应用于测量数以千计的田间小区的冠层温度和倒伏情况
国际玉米和小麦改良中心CIMMYT 意大利 结合全球定位系统和无人机影像信息来创建精确的正射影像图, 用于计算并分析植物覆盖率和光合作用

表2

表型采集技术及应用简介"

分类 成像
技术
元数据 波长范围 采集性状
(传统农艺性状)
新参数 应用实例
二维成像技术 可见光成像 灰度或彩色图像, RGB通道反射值 400-700 nm 株高, 叶面积, 物候学信息, 叶型, 根系结构, 产量性状, 穗型, 种子形态, 绝对生长率(GR)和相对生长率(RGR)等 投影面积, 紧密度, 叶片衰老指数、伸长速率、卷曲指数, 叶面积垂直分布, 绿度, 开花率等 玉米耐冷性(Enders et al., 2019), 小麦产量预测(Bowman et al., 2015), 马铃薯出苗量预测(Li et al., 2019), 小麦开花率(Wang et al., 2019), 大麦叶片伸长QTL (Ward et al., 2019), 拟南芥群体生长(Vasseur et al., 2017), 春小麦产量性状(Neilson et al., 2015), 大麦抗旱性状(Chen et al., 2014), 水稻QTL (Yang et al., 2013), C4作物地上部生物量(Fiorani and Schurr, 2013), 种子形态(Fahlgren et al., 2015)等
近红外成像 灰度图像 900-1700 nm NIR反射值, 组织含水量 含水量垂直分布, 辐射分布等 小麦叶片含水量监测(Elsayed et al., 2011), 玉米木质素(Hatfield and Fukushima, 2005), 叶片含水量(Seelig et al., 2008), 大麦抗旱性状等
热成像 灰度图像,
IR反射值
8000-14000 nm IR反射值, 叶片或冠层温度 冠层温度下降差, 温度分布 甘蔗产量(Basnayake et al., 2017), 大麦及小麦叶片水分状态(Munns et al., 2010), 干旱耐受性评价(Fischer et al., 1998, Tuberosa, 2011, Araus et al., 2012), 产量贡献(Reynolds et al., 1999), 拟南芥突变体筛选(Merlot et al., 2002)等
荧光
成像
颜色图像,
荧光反
射值
400-700 nm 荧光反射强度 衰老指数, 胁迫指数等 小麦及大麦干旱胁迫(Munns et al., 2010), 拟南芥干旱胁迫(Woo et al., 2008), 叶片生长及胁迫(Jansen et al., 2009), 除草剂应用(Chaerle et al., 2003), 作物生长发育及胁迫(Chaerle et al., 2009)等
叶绿素荧光成像 颜色图像 400-700 nm 光合效率, 光系统II产生荧光强
叶绿素指数, 花青素指数, Fv/Fm, Fo 光合效率检测(van Veelen et al., 2018), 小麦光合动力学变化(Murchie et al., 2018), 拟南芥干旱胁迫(Yao et al., 2018)等
多光谱成像 灰度或彩色图像, 光谱吸收曲线 400-2500 nm 可溶性固形物, 花青素, 叶绿素含量, 叶片N、P元素含量, 组织含水量等 归一化植被指数(NDVI), 叶黄素, 叶绿素等色素的反射峰值, 生化组分光谱值, 植物光谱反射指数 马铃薯晚疫病分级评价(Franceschini et al., 2019), 油菜光叶斑病(Veys et al., 2019), 葡萄病害分辨(Albetis et al., 2019), 棉花水分利用效率(Thorp et al., 2018)等
高光谱成像 灰度或彩色图像, 光谱吸收曲线 400-2500 nm连续波长 可溶性固形物, 花青素, 叶绿素含量, 叶片N、P元素含量, 组织含水量等 归一化植被指数(NDVI), 叶黄素, 叶绿素等色素的反射峰值, 叶片组织反射率, 叶片生化组分光谱值, 植物光谱反射指数(NDVI、RVI和GVI等) 小麦光合效率评估(EI-Hendawy et al., 2019), 卷心莴苣评分及分类(Bauer et al., 2011), 葡萄浆果品质(Gutiérrez et al., 2019), 小麦N素评估(Elsayed et al., 2018), 小麦含氮量监测(Camino et al., 2018), 小麦耐盐(Moghimi et al., 2018)等
三维成像技术 激光雷达成像 点阵云图 532 nm 株高, 叶面积, 物候学信息, 叶型, 根系结构, 产量性状, 穗型, 种子形态等 大小(高度、宽度、长度), 倾角如叶倾角(点云倾角), 基本体积测量等 高粱田间穗数及尺寸(Malambo et al., 2019), 粗肋草属叶片性状分析(Panjvani et al., 2019), 玉米田间结构特征(Bao et al., 2019)等
计算机断层扫描成像 连续灰度图像 100 µm或更低 生物量, 分蘖数、分蘖角度, 穗粒数, 内部结构信息等 各部位密度分布, 茎秆强度等 玉米茎秆抗倒伏(Zhang et al., 2018), 高粱茎解剖学特征(Gomez et al., 2018), 小麦根系研究(Douarre et al., 2018, Xu et al., 2018), 根系分泌物(Valdes et al., 2018)等
磁共振成像 连续灰度图像 200-500 µm 根系长度, 体内可动水的分布图, 内部结构等 组织体内电磁分布, 根系结构等 根系分泌物(van Veelen et al., 2018), 根系生长(Poorter et al., 2012), 鹰嘴豆表型(Rascher et al., 2011)等
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