Causes and consequences of experimental variation in Nicotiana benthamiana transient expression
Sophia N. Tang, Matthew J. Szarzanowicz, Amy Lanctot, Sasilada Sirirungruang, Liam D. Kirkpatrick, Krista Drako, Simon Alamos, Lyurui Cheng, Lucas M. Waldburger, Shuying Liu, Lena Huang, Sami Kazaz, Emine Akyuz Turumtay, Edward Baidoo, Aymerick Eudes, Mitchell G. Thompson

TL;DR
This paper investigates and models the variability in a common plant biology technique using Nicotiana benthamiana, offering guidelines to improve reproducibility.
Contribution
The study quantifies sources of variation in agroinfiltration and provides practical guidelines for minimizing it through experimental design.
Findings
Variability in agroinfiltration experiments is substantial and not consistently reduced by normalization methods.
A model using power analysis determines the number of plants needed for specific effect sizes in experiments.
Longitudinal data over three years informs strategies to minimize variability in transient expression studies.
Abstract
Infiltration of Agrobacterium tumefaciens into Nicotiana benthamiana has become a foundational technique in plant biology, enabling efficient delivery of transgenes in planta with technical ease, robust signal, and relatively high throughput. Despite transient expression’s prevalence in disciplines such as synthetic biology, little work has been done to describe and address the variability inherent in this system, a concern for experiments that rely on highly quantitative readouts. In a comprehensive analysis of N. benthamiana agroinfiltration experiments, we model sources of variability that affect transient expression. Our findings emphasize the need to validate normalization methods under the specific conditions of each study, as distinct normalization schemes do not always reduce variation either within or between experiments. Using a dataset of 1915 plants collected over three…
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Taxonomy
TopicsTransgenic Plants and Applications · Plant tissue culture and regeneration · CRISPR and Genetic Engineering
