Optimizing experimental procedures for quantitative evaluation of crop plant performance in high throughput phenotyping systems

Frontiers in Plant Science

Volumn 5, Issue JAN, 2015, Pages 1-21

  Junker, Astrid ^a   Muraya, Moses M ^a   Weigelt Fischer, Kathleen ^a   Arana Ceballos, Fernando ^a   Klukas, Christian ^a   Melchinger, Albrecht E ^b   Meyer, Rhonda C ^a   Riewe, David ^a   Altmann, Thomas ^a  

^a LEIBNIZ INSTITUTE OF PLANT GENETICS AND CROP PLANT RESEARCH IPK   (Germany)

^b UNIVERSITY OF HOHENHEIM   (Germany)

Author keywords

Arabidopsis; Automated high throughput plant phenotyping; Image analysis; Maize; Plant growth protocol; REFERENCES

Indexed keywords

ARABIDOPSIS; ZEA MAYS;

EID: 84922232287     PISSN: None     EISSN: 1664462X     Source Type: Journal    
DOI: 10.3389/fpls.2014.00770     Document Type: Article

References (56)

1. Araus, J. L., and Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52-61. doi: 10.1016/j.tplants.2013.09.008
2. Arend, D., Lange, M., Chen, J., Colmsee, C., Flemming, S., Hecht, D., et al. (2014). e!DAL—a framework to store, share and publish research data. BMC Bioinformatics 15:214. doi: 10.1186/1471-2105-15-214
3. Biskup, B., Scharr, H., Fischbach, A., Wiese-Klinkenberg, A., Schurr, U., and Walter, (2009). Diel growth cycle of isolated leaf discs analyzed with a novel, high- throughput three-dimensional imaging method is identical to that of intact leaves. PlantPhysiol. 149, 1452-1461. doi: 10.1104/pp.108.134486
4. Bogena, H. R., Herbst, M., Huisman, J. A., Rosenbaum, U., Weuthen, A., and Vereecken, H. (2010). Potential of wireless sensor networks for measuring soil water content variability. Vadose Zone J. 9, 1002-1013. doi: 10.2136/.vzj2009.0173
5. Braam, J. (2005). In touch: plant responses to mechanical stimuli. New Phytol. 165, 373-389. doi: 10.1111/j.1469-8137.2004.01263.x
6. Brien, C. J., Berger, B., Rabie, H., and Tester, M. (2013). Accounting for variation in designing greenhouse experiments with special reference to greenhouses containing plants on conveyor systems. Plant Methods 9:5. doi: 10.1186/17464811-9-5
7. Bylesjo, M., Segura, V., Soolanayakanahally, R. Y., Rae, A. M., Trygg, J., Gustafsson, P., et al. (2008). LAMINA: a tool for rapid quantification of leaf size and shape parameters. BMCPlantBiol. 8:82. doi: 10.1186/1471-2229-8-82
8. Chehab, E. W., Eich, E., and Braam, J. (2009). Thigmomorphogenesis: a complex plant response to mechano-stimulation. J. Exp. Bot. 60, 43-56. doi: 10.1093/jxb/ern315
9. Chen, D., Neumann, K., Friedel, S., Kilian, B., Chen, M., Altmann, T., et al. (2014). Dissecting the phenotypic components of plant growth and drought responses based on high-throughput image analysis. Plant Cell. doi: 10.1105/tpc.114.129601. [Epub ahead of print].
10. Clewer, A. G., and Scarisbrick, D. H.(2001).Practical Statistics and Experimental DesignforPlantand Crop Science. Weinheim: John Wiley.
11. Cobb, J. N., Declerck, G., Greenberg, A., Clark, R., and 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. doi: 10.1007/s00122-013-2066-0
12. Crowe, M. (2011). The plant accelerator. Phytogen Newsl. ASPS 13, 35-38. Available online at: http://www.asps.org.au/wp-content/uploads/Phytogen13-2.pdf
13. Cuadros-Inostroza, A., Caldana, C., Redestig, H., Kusano, M., Lisec, J., Pena-Cortes, et al. (2009). TargetSearch—a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data. BMC Bioinformatics 10:428. doi: 10.1186/1471-2105-10-428
14. De Vylder, J., Vandenbussche, F., Hu, Y., Philips, W., and Van Der Straeten, (2012). Rosette tracker: an open source image analysis tool for automatic quantification of genotype effects. Plant Physiol. 160, 1149-1159. doi: 10.1104/pp.112.202762
15. Elwell, A. L., Gronwall, D. S., Miller, N. D., Spalding, E. P., and Brooks, T. L. Separating parental environment from seed size effects on next generation growth and development in Arabidopsis. Plant Cell Environ. 34, 291-301. doi: 10.1111/j.1365-3040.2010.02243.x
16. Fabre, J., Dauzat, M., Negre, V., Wuyts, N., Tireau, A., Gennari, E., et al. (2011). PHENOPSIS DB: an information system for Arabidopsis thaliana phenotypic data in an environmental context. BMC Plant Biol. 11:77. doi: 10.1186/14712229-11-77
17. Finkel, E. (2009). With ‘phenomics,’ plant scientists hope to shift breeding into overdrive. Science 325, 380-381. doi: 10.1126/science.325_380
18. Fiorani, F., and Schurr, U. (2013). Future scenarios for plant phenotyping. Annu. Rev. PlantBiol. 64, 267-291. doi: 10.1146/annurev-arplant-050312-120137
19. Furbank, R. T., and Tester, M. (2011). Phenomics—technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16, 635-644. doi: 10.1016/j.tplants.2011.09.005
20. Granier, C., Aguirrezabal, L., Chenu, K., Cookson, S. J., Dauzat, M., Hamard, P., et al. (2006). PHENOPSIS, an automated platform for reproducible phenotyp- ing of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytol. 169, 623-635. doi: 10.1111/j.1469-8137.2005.01609.x
21. Granier, C., and Vile, D. (2014). Phenotyping and beyond: modelling the relationships between traits. Curr. Opin. Plant Biol. 18, 96-102. doi: 10.1016/j.pbi.2014.02.009
22. Green, J. M., Appel, H., Rehrig, E. M., Harnsomburana, J., Chang, J. F., Balint- Kurti, P., et al. (2012). PhenoPhyte: a flexible affordable method to quantify 2D phenotypes from imagery. PlantMethods 8:45. doi: 10.1186/1746-4811-8-45
23. Grieder, C., Dhillon, B. S., Schipprack, W., and Melchinger, A. E. (2012). Breeding maize as biogas substrate in Central Europe: II. Quantitative-genetic parameters for inbred lines and correlations with testcross performance. Theor. Appl. Genet. 124, 981-988. doi: 10.1007/s00122-011-1762-x
24. Hannemann, J., Poorter, H., Usadel, B., Blasing, O. E., Finck, A., Tardieu, F., et al. (2009). Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy. Plant Cell Environ. 32, 1185-1200. doi: 10.1111/j.1365-3040.2009.01964.x
25. Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., and Schreiber, F. (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12:148. doi: 10.1186/1471-2105-12-148
26. Houle, D., Govindaraju, D. R., and Omholt, S. (2010). Phenomics: the next challenge. Nat.Rev. Genet. 11, 855-866. doi: 10.1038/nrg2897
27. Impa, S. M., Nadaradjan, S., Boominathan, P., Shashidhar, G., Bindumadhava and Sheshshayee, M. S. (2005). Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Sci. 45, 2517-2522. doi: 10.2135/cropsci2005.0119
28. Jansen, M., Gilmer, F., Biskup, B., Nagel, K. A., Rascher, U., Fischbach, A., et al. . 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. doi: 10.1071/FP09095
29. Johannson, W. (1909). Elemente der Exakten Erblichkeitslehre. Jena: Gustav Fischer Verlag.
30. Klukas, C., Chen, D., and Pape, J. M. (2014). Integrated analysis platform: an open-source information system for high-throughput plant phenotyping. Plant Physiol. 165, 506-518. doi: 10.1104/pp.113.233932
31. Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., and Li, C. (2010). Sensing technologies for precision specialty crop production. Comput. Electron. Agric. 74, 2-33. doi: 10.1016/j.compag.2010.08.005
32. Martin, B., Tauer, C. G., and Lin, R. K. (1999). Carbon isotope discrimination as a tool to improve water-use efficiency in tomato the research was supported in part by USDA-CSRS grant #94-37100-0693. Published with the approval of the Director, Oklahoma Agric. Exp. Stn. Crop Sci. 39, 1775-1783.
33. Massonnet, C., Vile, D., Fabre, J., Hannah, M. A., Caldana, C., Lisec, J., et al. (2010). Probing the reproducibility of leaf growth and molecular phenotypes: a comparison of three Arabidopsis accessions cultivated in ten laboratories. PlantPhysiol. 152, 2142-2157. doi: 10.1104/pp.109.148338
34. Meyer, R. C., Torjek, O., Becher, M., and Altmann, T. (2004). Heterosis of biomass production in Arabidopsis. Establishment during early development. Plant Physiol. 134, 1813-1823. doi: 10.1104/pp.103.033001
35. Nagel, K. A., Putz, A., Gilmer, F., Heinz, K., Fischbach, A., Pfeifer, J., et al. (2012). GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Funct. Plant Biol. 39, 891-904. doi: 10.1071/FP12023
36. Pereyra-Irujo, G. A., Gasco, E. D., Peirone, L. S., and Aguirrezabal, L. A. N. (2012). GlyPh: a low-cost platform for phenotyping plant growth and water use. Funct. PlantBiol. 39, 905-913. doi: 10.1071/FP12052
37. Petersen, R. G. (1994). Agricultural Field Experiments: Design and Analysis. Boca Raton, FL: CRC Press.
38. Poorter, H., Fiorani, F., Stitt, M., Schurr, U., Finck, A., Gibon, Y., et al. (2012). The art of growing plants for experimental purposes: a practical guide for the plant biologist. Funct. PlantBiol. 39, 821-838. doi: 10.1071/FP12028
39. Pound, M. P., French, A. P., Atkinson, J. A., Wells, D. M., Bennett, M. J., and Pridmore, T. (2013). RootNav: navigating images of complex root architectures. PlantPhysiol. 162, 1802-1814. doi: 10.1104/pp.113.221531
40. Rajjou, L., Duval, M., Gallardo, K., Catusse, J., Bally, J., Job, C., et al. (2012). Seed germination and vigor. Annu. Rev. Plant Biol. 63, 507-533. doi: 10.1146/annurev-arplant-042811-105550
41. Riewe, D., Koohi, M., Lisec, J., Pfeiffer, M., Lippmann, R., Schmeichel, J., et al. (2012). A tyrosine aminotransferase involved in tocopherol synthesis in Arabidopsis. Plant J. 71, 850-859. doi: 10.1111/j.1365-313X.2012.05035.x
42. Rocca-Serra, P., Brandizi, M., Maguire, E., Sklyar, N., Taylor, C., Begley, K., et al. (2010). ISA software suite: supporting standards-compliant experimental annotation and enabling curation at the community level. Bioinformatics 26, 2354-2356. doi: 10.1093/bioinformatics/btq415
43. Sadok, W., Naudin, P., Boussuge, B., Muller, B., Welcker, C., and Tardieu, F. (2007). Leaf growth rate per unit thermal time follows QTL-dependent daily patterns in hundreds of maize lines under naturally fluctuating conditions. Plant Cell Environ. 30, 135-146. doi: 10.1111/j.1365-3040.2006.01611.x
44. Skirycz, A., Vandenbroucke, K., Clauw, P., Maleux, K., De Meyer, B., Dhondt, S., et al. (2011). Survival and growth ofArabidopsis plants given limited water are not equal. Nat. Biotechnol. 29, 212-214. doi: 10.1038/nbt.1800
45. Steinfath, M., Groth, D., Lisec, J., and Selbig, J. (2008). Metabolite profile analysis: from raw data to regression and classification. Physiol. Plant. 132, 150-161. doi: 10.1111/j.1399-3054.2007.01006.x
46. Strigens, A, Grieder, C, Haussmann, B. I. G., and Melchinger, A. E. (2012). Genetic variation among inbred lines and testcrosses of maize for early growth parameters and their relationship to final dry matter yield. Crop Sci. 52, 1084-1092. doi: 10.2135/cropsci2011.08.0426
47. Suorsa, M., Jarvi, S., Grieco, M., Nurmi, M., Pietrzykowska, M., Rantala, M., et al. . PROTON GRADIENT REGULATION5 is essential for proper acclimation of Arabidopsis photosystem I to naturally and artificially fluctuating light conditions. PlantCell 24,2934-2948. doi: 10.1105/tpc.112.097162
48. Tanabata, T., Shibaya, T., Hori, K., Ebana, K., and Yano, M. (2012). SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis. PlantPhysiol. 160, 1871-1880. doi: 10.1104/pp.112.205120
49. Tisne, S., Serrand, Y., Bach, L., Gilbault, E., Ben Ameur, R., Balasse, H., et al.. Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J. 74, 534-544. doi: 10.1111/tpj.12131
50. Van Der Heijden, G., Song, Y., Horgan, G., Polder, G., Dieleman, A., Bink, M., et al. SPICY: towards automated phenotyping of large pepper plants in the greenhouse. Funct. Plant Biol. 39, 870-877. doi: 10.1071/FP12019
51. Wallihan, E. F., and Garber, M. J. (1971). Efficiency of glasshouse pot experiments rotating versus stationary benches. Plant Physiol. 48, 789-791. doi: 10.1104/pp.48.6.789
52. Walter, A., Scharr, H., Gilmer, F., Zierer, R., Nagel, K. A., Ernst, M., et al. (2007). Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species. New Phytol. 174, 447-455. doi: 10.1111/j.1469-8137.2007.02002.x
53. Wang, L., Uilecan, I. V., Assadi, A. H., Kozmik, C. A., and Spalding, E. P. (2009). HYPOTrace: image analysis software for measuring hypocotyl growth and shape demonstrated on Arabidopsis seedlings undergoing photomorphogenesis. Plant Physiol. 149, 1632-1637. doi: 10.1104/pp.108.134072
54. Wark, T., Corke, P., Sikka, P., Klingbeil, L., Ying, G., Crossman, C., et al. (2007). Transforming agriculture through pervasive wireless sensor networks. Pervasive Comput. IEEE 6, 50-57. doi: 10.1109/MPRV.2007.47
55. Weight, C., Parnham, D., and Waites, R. (2008). LeafAnalyser: a computational method for rapid and large-scale analyses of leaf shape variation. Plant J. 53, 578-586. doi: 10.1111/j.1365-313X.2007.03330.x
56. Zhu, J., Ingram, P. A., Benfey, P. N., and Elich, T. (2011). From lab to field, new approaches to phenotyping root system architecture. Curr. Opin. Plant Biol. 14, 310-317. doi: 10.1016/j.pbi.2011.03.020