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新一代植物表型组学的发展之路

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  • 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

  录用日期: 2019-08-21

  网络出版日期: 2019-08-21

A Path to Next Generation of Plant Phenomics

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  • 1. Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
    2. Crop Phenomics Joint Research Center, Wuhan 430074, China

Received date: 2019-07-29

  Accepted date: 2019-08-21

  Online published: 2019-08-21

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。

本文引用格式

胡伟娟,傅向东,陈凡,杨维才 . 新一代植物表型组学的发展之路[J]. 植物学报, 2019 , 54(5) : 558 -568 . DOI: 10.11983/CBB19141

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.

参考文献

1 周济, Tardieu F, Pridmore T, Doonan J, Reynolds D, Hall N, Griffiths S, 程涛, 朱艳, 王秀娥, 姜东, 丁艳锋 (2018). 植物表型组学: 发展、现状与挑战. 南京农业大学学报 41, 580-588.
2 Albetis J, Jacquin A, Goulard M, Poilvé H, Rousseau J, Clenet H, Dedieu G, Duthoit S (2019). On the potentiality of UAV multispectral imagery to detect Flavescence dorée and grapevine trunk diseases. Remote Sens 11, 23.
3 Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018). Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23, 451-466.
4 Araus JL, Serret MD, Edmeades GO (2012). Phenotyping maize for adaptation to drought. Front Physiol 3, 305.
5 Awada L, Phillips PWB, Smyth SJ (2018). The adoption of automated phenotyping by plant breeders. Euphytica 214, 148.
6 Bao Y, Tang L, Srinivasan S, Schnable PS (2019). Field- based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosyst Eng 178, 86-101.
7 Basnayake J, Lakshmanan P, Jackson P, Chapman S, Natarajan S (2017). Canopy temperature: a predictor of sugarcane yield for irrigated and rainfed conditions. Int Sugar J 29, 1-9.
8 Bauer SD, Kor? F, F?rstner W (2011). The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis Agric 12, 361-377.
9 Bowman BC, Chen J, Zhang J, Wheeler J, Wang Y, Zhao W, Nayak S, Heslot N, Bockelman H, Bonman JM (2015). Evaluating grain yield in spring wheat with canopy spectral reflectance. Crop Sci 55, 1881-1890.
10 Camino C, González-Dugo V, Hernández P, Sillero JC, Zarco-Tejada PJ (2018). Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture. Int J Appl Earth Obs Geoinform 70, 105-117.
11 Casanova JJ, O'Shaughnessy SA, Evett SR, Rush CM (2014). Development of a wireless computer vision instrument to detect biotic stress in wheat. Sensors 14, 17753-17769.
12 Chaerle L, Hulsen K, Hermans C, Strasser RJ, Valcke R, H?fte M, Van Der Straeten D (2003). Robotized time- lapse imaging to assess in-planta uptake of phenylurea herbicides and their microbial degradation. Physiol Plantarum 118, 613-619.
13 Chaerle L, Lenk S, Leinonen I, Jones HG, Van Der Straeten D, Buschmann C (2009). Multi-sensor plant imaging: towards the development of a stress-catalogue. Biotechnol J 4, 1152-1167.
14 Chen DJ, Neumann K, Friedel S, Kilian B, Chen M, Altmann T, Klukas C (2014). Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. Plant Cell 26, 4636-4655.
15 Cobb JN, DeClerck G, Greenberg A, Clark R, McCouch S (2013). Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype phenotype relationships and its relevance to crop improvement. Theor Appl Genet 126, 867-887.
16 Douarre C, Schielein R, Frindel C, Gerth S, Rousseau D (2018). Transfer learning from synthetic data applied to soil-root segmentation in X-ray tomography images. J Imaging 4, 65.
17 El-Hendawy S, Al-Suhaibani N, Dewir YH, Elsayed S, Alotaibi M, Hassan W, Refay Y, Tahir MU (2019). Ability of modified spectral reflectance indices for estimating growth and photosynthetic efficiency of wheat under saline field conditions. Agronomy 9, 35.
18 Elsayed S, Barmeier G, Schmidhalter U (2018). Passive reflectance sensing and digital image analysis allows for assessing the biomass and nitrogen status of wheat in early and late tillering stages. Front Plant Sci 9, 1478.
19 Elsayed S, Mistele B, Schmidhalter U (2011). Can changes in leaf water potential be assessed spectrally? Funct Plant Biol 38, 523-533.
20 Enders TA, St Dennis S, Oakland J, Callen ST, Gehan MA, Miller ND, Spalding EP, Springer NM, Hirsch CD (2019). Classifying cold-stress responses of inbred maize seedlings using RGB imaging. Plant Direct 3, 1-11.
21 Fabre J, Dauzat M, Negre V, Wuyts N, Tireau A, Gennari E, Neveu P, Tisne S, Massonnet C, Hummel I, Granier C (2011). PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol 11, 77.
22 Fahlgren N, Gehan MA, Baxter I (2015). Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24, 93-99.
23 Finkel E (2009). With 'Phenomics', plant scientists hope to shift breeding into overdrive. Science 325, 380-381.
24 Fiorani F, Schurr U (2013). Future scenarios for plant phenotyping. Annu Rev Plant Biol 64, 267-291.
25 Fischer RA, Rees D, Sayre KD, Lu ZM, Condon AG, Saavedra AL (1998). Wheat yield progress associated with higher stomatal conductance and photosynthetic rate, and cooler canopies. Crop Sci 38, 1467-1475.
26 Franceschini MHD, Bartholomeus H, van Apeldoorn DF, Suomalainen J, Kooistra L (2019). Feasibility of unmanned aerial vehicle optical imagery for early detection and severity assessment of late blight in potato. Remote Sens 11, 224.
27 Gomez FE, Carvalho Jr G, Shi FH, Muliana AH, Rooney WL (2018). High throughput phenotyping of morpho- anatomical stem properties using X-ray computed tomography in sorghum. Plant Methods 14, 59.
28 Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP (2019). On-the-go hyperspectral imaging for the in-field estimation of grape berry soluble solids and anthocyanin concentration. Aust J Grape Wine Res 25, 127-133.
29 Hartmann A, Czauderna T, Hoffmann R, Stein N, Schreiber F (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinform 12, 148.
30 Hatfield R, Fukushima RS (2005). Can lignin be accurately measured? Crop Sci 45, 832-839.
31 Jansen M, Gilmer F, Biskup B, Nagel KA, Rascher U, Fischbach A, Briem S, Dreissen G, Tittmann S, Braun S, De Jaeger I, Metzlaff M, Schurr U, Scharr H, Walter A (2009). Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Funct Plant Biol 36, 902-914.
32 Klukas C, Pape JM, Entzian A (2012). Analysis of high- throughput plant image data with the information system IAP. J Integr Bioinform 9, 191.
33 Li B, Xu XM, Han JW, Zhang L, Bian CS, Jin LP, Liu JG (2019). The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods 15, 15.
34 Lobet G (2017). Image analysis in plant sciences: publish then perish. Trends Plant Sci 22, 559-566.
35 Malambo L, Popescu SC, Horne DW, Pugh NA, Rooney WL (2019). Automated detection and measurement of individual sorghum panicles using density-based clustering of terrestrial lidar data. ISPRS J Photogramm 149, 1-13.
36 Merlot S, Mustilli AC, Genty B, North H, Lefebvre V, Sotta B, Vavasseur A, Giraudat J (2002). Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation. Plant J 30, 601-609.
37 Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA (2019). High-throughput phenotyping for crop improvement in the genomics era. Plant Sci 282, 60-72.
38 Moghimi A, Yang C, Miller ME, Kianian SF, Marchetto PM (2018). A novel approach to assess salt stress tolerance in wheat using hyperspectral imaging. Front Plant Sci 9, 1182.
39 Munns R, James RA, Sirault XRR, Furbank RT, Jones HG (2010). New phenotyping methods for screening wheat and barley for beneficial responses to water deficit. J Exp Bot 61, 3499-3507.
40 Murchie EH, Kefauver S, Araus JL, Muller O, Rascher U, Flood PJ, Lawson T (2018). Measuring the dynamic photosynthome. Ann Bot 122, 207-220.
41 Neilson EH, Edwards AM, Blomstedt CK, Berger B, M?ller BL, Gleadow RM (2015). Utilization of a high- throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time. J Exp Bot 66, 1817-1832.
42 Panjvani K, Dinh AV, Wahid KA (2019). LiDARPheno—a low-cost LiDAR-based 3D scanning system for leaf morphological trait extraction. Front Plant Sci 10, 147.
43 Patil JK, Kumar R (2017). Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features. Eng Agric, Environ Food 10, 69-78.
44 Poorter H, Bühler J, van Dusschoten D, Climent J, Postma JA (2012). Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Funct Plant Biol 39, 839-850.
45 Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2017). Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 6, gix083.
46 Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2018). Erratum to: deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 7, 042.
47 Pound MP, French AP, Murchie EH, Pridmore TP (2014). Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol 166, 1688-1698.
48 Rascher U, Blossfeld S, Fiorani F, Jahnke S, Jansen M, Kuhn AJ, Matsubara S, M?rtin LLA, Merchant A, Metzner R, Müller-Linow M, Nagel KA, Pieruschka R, Pinto F, Schreiber CM, Temperton VM, Thorpe MR, van Dusschoten D, van Volkenburgh E, Windt CW, Schurr U (2011). Non-invasive approaches for phenotyping of enhanced performance traits in bean. Funct Plant Biol 38, 968-983.
49 Raza SEA, Prince G, Clarkson JP, Rajpoot NM (2015). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS One 10, e0123262.
50 Raza SEA, Smith HK, Clarkson GJJ, Taylor G, Thompson AJ, Clarkson J, Rajpoot NM (2014). Automatic detection of regions in spinach canopies responding to soil moisture deficit using combined visible and thermal imagery. PLoS One 9, e97612.
51 Reynolds MP, Rajaram S, Sayre KD (1999). Physiological and genetic changes of irrigated wheat in the post-green revolution period and approaches for meeting projected global demand. Crop Sci 39, 1611-1621.
52 Ribaut JM, de Vicente MC, Delannay X (2010). Molecular breeding in developing countries: challenges and perspectives. Curr Opin Plant Biol 13, 213-218.
53 Roitsch T, Cabrera-Bosquet L, Fournier A, Ghamkhar K, Jiménez-Berni J, Pinto F, Ober ES (2019). Review: new sensors and data-driven approaches—a path to next generation phenomics. Plant Sci 282, 2-10.
54 Schreiber U (2004). Pulse-Amplitude-Modulation (PAM) fluorometry and saturation pulse method: an overview. In: Papageorgiou GC, Govindjee, eds. Chlorophyll Fluorescence: A Signature of Photosynthesis. Dordrecht: Springer. pp. 279-319.
55 Seelig HD, Hoehn A, Stodieck LS, Klaus DM, Adams III WW, Emery WJ (2008). The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. Int J Remote Sens 29, 3701-3713.
56 Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016). Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21, 110-124.
57 Subedi P, Walsh K, Purdy P (2013). Determination of optimum maturity stages of mangoes using fruit spectral signatures. Acta Hortic 992, 521-527.
58 Tardieu F, Cabrera-Bosquet L, Pridmore T, Bennett M (2017). Plant phenomics, from sensors to knowledge. Curr Biol 27, R770-R783.
59 Tester M, Langridge P (2010). Breeding technologies to increase crop production in a changing world. Science 327, 818-822.
60 Thorp KR, Thompson AL, Harders SJ, French AN, Ward RW (2018). High-throughput phenotyping of crop water use efficiency via multispectral drone imagery and a daily soil water balance model. Remote Sens 10, 1682.
61 Tripodi P, Massa D, Venezia A, Cardi T (2018). Sensing technologies for precision phenotyping in vegetable crops: current status and future challenges. Agronomy 8, 57.
62 Tuberosa R (2011). Phenotyping drought-stressed crops: key concepts, issues and approaches. In: Monneveux P, Ribaut JM, eds. Drought Phenotyping in Crops: From Theory to Practice. Texcoco: CGIAR Generation Challenge Programme. pp. 3-35.
63 van Veelen A, Tourell MC, Koebernick N, Pileio G, Roose T (2018). Correlative visualization of root mucilage degradation using X-ray CT and MRI. Front Environ Sci 6, 32.
64 Vasseur F, Wang G, Bresson J, Schwab R, Weigel D (2017). Image-based methods for phenotyping growth dynamics and fitness in large plant populations. BioRxiv doi: .
65 Veys C, Chatziavgerinos F, AlSuwaidi A, Hibbert J, Hansen M, Bernotas G, Smith M, Yin HJ, Rolfe S, Grieve B (2019). Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape. Plant Methods 15, 4.
66 Wang X, Xuan H, Evers B (2019). High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat. BioRxiv doi: .
67 Ward B, Brien C, Oakey H, Pearson A, Negr?o S, Schilling RK, Taylor J, Jarvis D, Timmins A, Roy SJ, Tester M, Berger B, van den Hengel A (2019). High-throughput 3D modelling to dissect the genetic control of leaf elongation in barley (Hordeum vulgare). Plant J 98, 555-570.
68 White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA, Kimball BA, Roth RL, Strand RJ, Thorp KR, Wall GW, Wang GY (2012). Field-based phenomics for plant genetics research. Field Crops Res 133, 101-112.
69 Wiley E, Casper BB, Helliker BR (2016). Recovery following defoliation involves shifts in allocation that favor storage and reproduction over radial growth in black oak. J Ecol 10, 1365-2745.
70 Woo NS, Badger MR, Pogson BJ (2008). A rapid, non- invasive procedure for quantitative assessment of drought survival using chlorophyll fluorescence. Plant Methods 4, 27.
71 Xu T, Su CL, Hu D, Li FF, Lu QQ, Zhang TT, Xu QS (2016). Molecular distribution and toxicity assessment of praseodymium by Spirodela polyrrhiza. J Hazard Mater 312, 132-140.
72 Xu Z, Valdes C, Clarke J (2018). Existing and potential statistical and computational approaches for the analysis of 3D CT images of plant roots. Agronomy 8, 71.
73 Yang WN, Guo ZL, Huang CL, Duan LF, Chen GX, Jiang N, Fang W, Feng H, Xie WB, Lian XM, Wang GW, Luo QM, Zhang QF, Liu Q, Xiong LZ (2014). Combining high- throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nat Commun 5, 5087.
74 Yao JN, Sun DW, Cen HY, Xu HX, Weng HY, Yuan F, He Y (2018). Phenotyping of Arabidopsis drought stress response using kinetic chlorophyll fluorescence and multicolor fluorescence imaging. Front Plant Sci 9, 603.
75 Zhang Y, Du JJ, Wang JL, Ma LM, Lu XJ, Pan XD, Guo XY, Zhao CJ (2018). High-throughput micro-phenotyping measurements applied to assess stalk lodging in maize (Zea mays L.). Biol Res 51, 40.
76 Zhao ZQ, Ma LH, Cheung YM, Wu XD, Tang YY, Chen CLP (2015). ApLeaf: an efficient android-based plant leaf identification system. Neurocomputing 151, 1112-1119.
77 Zhou W, Sui ZH, Wang JG, Hu YY, Kang KH, Hong HR, Niaz Z, Wei HH, Du QW, Peng C, Mi P, Que Z (2016). Effects of sodium bicarbonate concentration on growth, photosynthesis, and carbonic anhydrase activity of macroalgae Gracilariopsis lemaneiformis, Gracilaria vermiculophylla, and Gracilaria chouae( Gracilariales, Rhodophyta). Photosynth Res 128, 259-270.
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