Article section

Article title
Contributor
Abstract
About article
DOI
Citation style
Copyright
Open access
References
Review Article

Phenotypic Analysis and Characterization Methods for Transgenic Setaria viridis: From Morphology to Molecular Markers: A Comprehensive Review

Authors

  • Chinechendo N. Eze Department of Biology, University of Louisiana at Lafayette, LA, USA

    chinexng@gmail.com

  • Richmore Chiamaka Ibeh Department of Biotechnology, Federal University of Technology, Akure, Nigeria https://orcid.org/0009-0002-6831-7285
  • Juwon I. Hassan Department of Biotechnology, Federal University of Technology, Akure, Nigeria https://orcid.org/0009-0008-7359-1784
  • Samuel Osabutey Department of Plant Pathology and Environmental Microbiology, State College, Pennsylvania State University, PA, 16801, USA

Abstract

Setaria viridis (green foxtail) has become a principal C4 model for functional genomics and translational crop research because its short life cycle and reliable transformation protocols enable rapid production of transgenic lines. Linking those genotypes to traits, however, demands a multilayered phenotypization strategy. This narrative review synthesizes current approaches that span traditional morphology, developmental staging, and precision measurement of vegetative and reproductive traits. It describes frameworks for assessing physiological performance, such as gas-exchange assays and chlorophyll-fluorescence imaging, that translate genetic changes into functional outcomes. Recent advances in automated, high-throughput phenotyping platforms are outlined, illustrating how time-series imaging accelerates large-scale trait discovery. The review also examines experimental designs for evaluating abiotic-stress responses, highlights biochemical assays and metabolite-profiling techniques that reveal underlying metabolic adjustments, and details molecular-marker systems that couple genotype with phenotype. By integrating these complementary methods, researchers can build comprehensive genotype–phenotype maps in S. viridis, thereby streamlining gene validation and informing next-generation plant-biotechnology applications.

Keywords:

Genotype High-Throughput Imaging Phenotype Mapping Physiological Assays Setaria Viridis Transgenic Phenotyping

Article information

Journal

Journal of Agriculture, Aquaculture, and Animal Science

Volume (Issue)

2(2), (2025)

Pages

53-65

Published

14-08-2025

How to Cite

Eze, C. N., Ibeh, R. C., Hassan, J. I., & Osabutey, S. (2025). Phenotypic Analysis and Characterization Methods for Transgenic Setaria viridis: From Morphology to Molecular Markers: A Comprehensive Review. Journal of Agriculture, Aquaculture, and Animal Science, 2(2), 53-65. https://doi.org/10.69739/jaaas.v2i2.809

References

Acharya, B. R., Roy Choudhury, S., Estelle, A. B., Vijayakumar, A., Zhu, C., Hovis, L., & Pandey, S. (2017). Optimization of Phenotyping Assays for the Model Monocot Setaria viridis. Frontiers in Plant Science, 8. https://doi.org/10.3389/fpls.2017.02172

Danforth Center. (n.d.). The Phenotyping Facility. The Donald Danforth Plant Science Center. Retrieved June 24, 2025, from https://www.danforthcenter.org/our-work/core-facilities/phenotyping/

de Souza, W. R., Martins, P. K., Freeman, J., Pellny, T. K., Michaelson, L. V., Sampaio, B. L., …, & Molinari, H. B. C. (2018). Suppression of a single BAHD gene in Setaria viridis causes large, stable decreases in cell wall feruloylation and increases biomass digestibility. New Phytologist, 218(1), 81–93. https://doi.org/10.1111/nph.14970

Fahlgren, N. (2015). A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria. Molecular Plant, . https://doi.org/10.1016/j.molp.2015.06.005

Gaillard, M., Miao, C., Schnable, J. C., & Benes, B. (2020). Voxel Carving Based 3D Reconstruction of Sorghum Identifies Genetic Determinants of Radiation Interception Efficiency (p. 2020.04.06.028605). bioRxiv. https://doi.org/10.1101/2020.04.06.028605

Gehan, M. A., Fahlgren, N., Abbasi, A., Berry, J. C., Callen, S. T., Chavez, L., Doust, A. N., Feldman, M. J. ...., & Sax, T. (2017). PlantCV v2: Image analysis software for high-throughput plant phenotyping. PeerJ, 5, e4088. https://doi.org/10.7717/peerj.4088

Guedes, F. A. D. F., Nascimento, L. B. D. S., Costa, M. P., Macrae, A., Alves-Ferreira, M., Caldana, C., & Reinert, F. (2023). Comparative Primary Metabolite Profiling of Setaria viridis Reveals Potential Markers to Water Limitation. Agriculture, 13(3), 660. https://doi.org/10.3390/agriculture13030660

Huang, P., Shyu, C., Coelho, C. P., Cao, Y., & Brutnell, T. P. (2016). Setaria viridis as a Model System to Advance Millet Genetics and Genomics. Frontiers in Plant Science, 7. https://doi.org/10.3389/fpls.2016.01781

Junqueira, N. E. G., Bezerra, A. C. M., Travassos-Lins, J., Esteves, J.-M. F., Gomes, J.-D. F., Ferreira, M. A., & Reinert, F. (2025). Phenological growth stages according to the BBCH-scale as a powerful tool for differentiating among CRISPR/Cas9 mutated lineages of the model plant Setaria viridis (L.) P. Beauv. (Poaceae). Botany, 103, 1–9. https://doi.org/10.1139/cjb-2024-0022

Kaggwa, R. J., Jiang, H., Ryan, R. A., Zahller, J. P., Kellogg, E. A., Woodford-Thomas, T., & Callis-Duehl, K. (2021). Exploring Grass Morphology & Mutant Phenotypes Using Setaria viridis. The American Biology Teacher, 83(5), 311–319. https://doi.org/10.1525/abt.2021.83.5.311

Kaplan, S. (1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15(3), 169–182. https://doi.org/10.1016/0272-4944(95)90001-2

Lefebvre, H. (1991). The production of space. Blackwell.

Li, A., Zhu, L., Xu, W., Liu, L., & Teng, G. (2022). Recent advances in methods for in situ root phenotyping. PeerJ, 10, e13638. https://doi.org/10.7717/peerj.13638

Li, W., Tang, S., Zhang, S., Shan, J., Tang, C., Chen, Q., Jia, G., Han, Y., Zhi, H., & Diao, X. (2016). Gene mapping and functional analysis of the novel leaf color gene SIYGL1 in foxtail millet [ Setaria italica (L.) P. Beauv]. Physiologia Plantarum, 157(1), 24–37. https://doi.org/10.1111/ppl.12405

Mauro-Herrera, M., & Doust, A. N. (2016). Development and Genetic Control of Plant Architecture and Biomass in the Panicoid Grass, Setaria. PLOS ONE, 11(3), e0151346. https://doi.org/10.1371/journal.pone.0151346

Mauro-Herrera, M., Wang, X., Barbier, H., Brutnell, T. P., Devos, K. M., & Doust, A. N. (2013). Genetic Control and Comparative Genomic Analysis of Flowering Time in Setaria (Poaceae). G3 Genes|Genomes|Genetics, 3(2), 283–295. https://doi.org/10.1534/g3.112.005207

Michelmore, R. W., Paran, I., & Kesseli, R. V. (1991). Identification of markers linked to disease-resistance genes by bulked segregant analysis: A rapid method to detect markers in specific genomic regions by using segregating populations. Proceedings of the National Academy of Sciences, 88(21), 9828–9832. https://doi.org/10.1073/pnas.88.21.9828

Passot, S., Gnacko, F., Moukouanga, D., Lucas, M., Guyomarc’h, S., Ortega, B. M., Atkinson, J. A., Belko, M. N., Bennett, M. J., Gantet, P., Wells, D. M., Guédon, Y., Vigouroux, Y., Verdeil, J.-L., Muller, B., & Laplaze, L. (2016). Characterization of Pearl Millet Root Architecture and Anatomy Reveals Three Types of Lateral Roots. Frontiers in Plant Science, 7. https://doi.org/10.3389/fpls.2016.00829

Rahaman, M. M., Chen, D., Gillani, Z., Klukas, C., & Chen, M. (2015). Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Frontiers in Plant Science, 6. https://doi.org/10.3389/fpls.2015.00619

Rajput, S. G., Plyler-Harveson, T., & Santra, D. K. (2014). Development and Characterization of SSR Markers in Proso Millet Based on Switchgrass Genomics. American Journal of Plant Sciences, 5(1), 175–186. https://doi.org/10.4236/ajps.2014.51023

Rellán-Álvarez, R., Lobet, G., Lindner, H., Pradier, P.-L., Sebastian, J., Yee, M.-C., Geng, Y., Trontin, C., LaRue, T., Schrager-Lavelle, A., Haney, C. H., Nieu, R., Maloof, J., Vogel, J. P., & Dinneny, J. R. (2015). GLO-Roots: An imaging platform enabling multidimensional characterization of soil-grown root systems. eLife, 4, e07597. https://doi.org/10.7554/eLife.07597

Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https://doi.org/10.1038/nmeth.2089

Sebastian, J., Yee, M.-C., Goudinho Viana, W., Rellán-Álvarez, R., Feldman, M., Priest, H. D., Trontin, C., Lee, T., Jiang, H., Baxter, I., Mockler, T. C., Hochholdinger, F., Brutnell, T. P., & Dinneny, J. R. (2016). Grasses suppress shoot-borne roots to conserve water during drought. Proceedings of the National Academy of Sciences, 113(31), 8861–8866. https://doi.org/10.1073/pnas.1604021113

Vadez, V., Kholová, J., Hummel, G., Zhokhavets, U., Gupta, S. K., & Hash, C. T. (2015). LeasyScan: A novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. Journal of Experimental Botany, 66(18), 5581–5593. https://doi.org/10.1093/jxb/erv251

Wei, W., Li, S., Li, P., Yu, K., Fan, G., Wang, Y., Zhao, F., Zhang, X., Feng, X., Shi, G., Zhang, W., Song, G., Dan, W., Wang, F., Zhang, Y., Li, X., Wang, D., Zhang, W., Pei, J., … Zhao, Z. (2023). QTL analysis of important agronomic traits and metabolites in foxtail millet (Setaria italica) by RIL population and widely targeted metabolome. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.1035906

Wilson, E. O. (1984). Biophilia. Harvard University Press.

Downloads

Views

66

Downloads

26