Vivian Zhong

All Publications

• SeedSeg: image-based transgenic seed counting for segregation analysis of T-DNA loci. Plant methods Hernández, S., Zhong, V., Brophy, J. A. 2025; 21 (1): 87

  Abstract

  Transgenic plants are essential for both basic and applied plant biology. Recently, fluorescent and colorimetric markers were developed to enable nondestructive identification of transformed seeds and accelerate the generation of transgenic plant lines. Yet, transformation often results in the integration of multiple copies of transgenes in the plant genome. Multiple transgene copies can lead to transgene silencing and complicate the analysis of transgenic plants by requiring researcher to track multiple T-DNA loci in future generations. Thus, to simplify analysis of transgenic lines, plant researchers typically screen transformed plants for lines where the T-DNA inserted in a single locus - an analysis that involves laborious manual counting of fluorescent and non-fluorescent seeds for screenable markers.To expedite T-DNA segregation analysis, we developed SeedSeg, an image analysis tool that uses a segmentation algorithm to count the number of transformed and wild-type seeds in an image. SeedSeg runs a chi-squared test to determine the number of T-DNA loci. Parameters can be adjusted to optimize for different brightness intensities and seed sizes.By automating the seed counting process, SeedSeg reduces the manual labor associated with identifying transgenic lines containing a single T-DNA locus. SeedSeg is adaptable to different seed sizes and visual transgene markers, making it a versatile tool for accelerating plant research.

  View details for DOI 10.1186/s13007-025-01406-4

  View details for PubMedID 40551218

  View details for PubMedCentralID PMC12186423

• For Better Soil, Get Better Data ISSUES IN SCIENCE AND TECHNOLOGY Zhong, V., Mitchell, C. 2025; 41 (4)

  View details for Web of Science ID 001574964200016

• Policy makers, genetic engineers, and an engaged public can work together to create climate-resilient plants. PLoS biology Archibald, B. N., Zhong, V., Brophy, J. A. 2023; 21 (7): e3002208

  Abstract

  As climate change affects weather patterns and soil health, agricultural productivity could decrease substantially. Synthetic biology can be used to enhance climate resilience in plants and create the next generation of crops, if the public will accept it.

  View details for DOI 10.1371/journal.pbio.3002208

  View details for PubMedID 37440471

• Transcriptional and post-transcriptional controls for tuning gene expression in plants. Current opinion in plant biology Zhong, V., Archibald, B. N., Brophy, J. A. 2022; 71: 102315

  Abstract

  Plant biotechnologists seek to modify plants through genetic reprogramming, but our ability to precisely control gene expression in plants is still limited. Here, we review transcription and translation in the model plants Arabidopsis thaliana and Nicotiana benthamiana with an eye toward control points that may be used to predictably modify gene expression. We highlight differences in gene expression requirements between these plants and other species, and discuss the ways in which our understanding of gene expression has been used to engineer plants. This review is intended to serve as a resource for plant scientists looking to achieve precise control over gene expression.

  View details for DOI 10.1016/j.pbi.2022.102315

  View details for PubMedID 36462457

• Synthetic genetic circuits as a means of reprogramming plant roots. Science (New York, N.Y.) Brophy, J. A., Magallon, K. J., Duan, L., Zhong, V., Ramachandran, P., Kniazev, K., Dinneny, J. R. 2022; 377 (6607): 747-751

  Abstract

  The shape of a plant's root system influences its ability to reach essential nutrients in the soil and to acquire water during drought. Progress in engineering plant roots to optimize water and nutrient acquisition has been limited by our capacity to design and build genetic programs that alter root growth in a predictable manner. We developed a collection of synthetic transcriptional regulators for plants that can be compiled to create genetic circuits. These circuits control gene expression by performing Boolean logic operations and can be used to predictably alter root structure. This work demonstrates the potential of synthetic genetic circuits to control gene expression across tissues and reprogram plant growth.

  View details for DOI 10.1126/science.abo4326

  View details for PubMedID 35951698