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Simple Trick Could Improve Accuracy of Plant Genetics Research

photo shows a green, leafy plant against a white backdrop
Photo credit: Igor Son.

For Immediate Release

Colleen Doherty

Researchers have published a simple trick that improves the accuracy of techniques that help us understand how external variables – such as temperature – affect gene activity in plants.

“There are really two contributions here,” says Colleen Doherty, corresponding author of a paper on the work and an associate professor of molecular and structural biochemistry at North Carolina State University. “First, we’re raising the visibility of a problem that many of us in the plant research community were unfamiliar with, as well as highlighting the solution. Second, we’ve demonstrated that addressing this problem can make a significant difference in our understanding of gene activity in plants.”

At issue is a technique called RNA-seq analysis, which is used to measure changes in gene activity – i.e., when genes are actively transcribing to produce proteins.

“We use RNA-seq analysis to assess how plants respond to various stimuli, or changes in their environment,” Doherty says. “It’s used widely because it’s a relatively easy and inexpensive way to monitor plant responses.”

For example, researchers can use RNA-seq analysis to see which genes are turned on when a plant is experiencing drought conditions, which then informs the development of new plant varieties that are drought resistant.

But there’s a specific challenge related to RNA-seq analysis, which Doherty and her collaborators ran into by accident.

“We were monitoring how plants respond to different temperatures at multiple times of day, and the results we got were wildly divergent,” Doherty says. “We initially thought we might be doing something wrong. But when we began looking into it, we learned that animals and yeasts are known to have global changes in transcription based on variables such as the time of day or nitrogen deprivation.”

In other words, researchers want to see how specific variables – such as increased temperature – affect transcription in specific genes. But there are some variables – like time of day – that can increase or decrease transcription in all the genes. This can throw off researchers’ ability to draw conclusions about the specific variables they want to study.

“Luckily, we found that this problem is sufficiently well-established among researchers who work on non-plant species that they have developed a method to account for it, called an artificial spike-in,” Doherty says. “These and similar techniques have been used in plant science in other contexts and when using older techniques and technologies. But for whatever reason, our field didn’t incorporate artificial spike-ins into our methodology when we adopted RNA-seq analysis.”

Artificial spike-ins make use of pieces of foreign RNA that are unlike anything in the plant’s genome, meaning that the foreign RNA will not be confused with anything the plant itself produces. Researchers introduce the foreign RNA into the analysis process at the beginning of the experiment. Because global changes in transcription will not affect the foreign RNA, it can be used as a fixed benchmark that allows researchers to determine the extent to which there is an overall increase or decrease in RNA that the plant itself is producing.

“When we used artificial spike-ins to account for global changes in transcription, we found that the differences in plants exposed to temperature changes at different times of day were actually even greater than we anticipated,” Doherty says.

“The artificial spike-in gave us more accurate information and greater insight into how plants are behaving at night – since we found that global transcription was higher at night. Before we adopted the use of artificial spike-ins, we were missing a lot of what was happening at night.

“Artificial spike-ins are an elegant solution to a challenge many of us in the plant research community didn’t even know was there,” Doherty says. “We’re optimistic this technique will improve the accuracy of transcriptional analysis in the wide variety of conditions that can affect global transcription in plant species. And that, in turn, may help our research community garner new insights into the species we study.

“We didn’t develop this solution – artificial spike-ins – but we really hope it garners more widespread use in plant science.”

The paper, “A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses,” is published open access in The Plant Journal. First author of the paper is Kanjana Laosuntisuk, a postdoctoral researcher at NC State. The paper was co-authored by Amaranatha Vennapusa of Delaware State University; Impa Somayanda of Texas Tech University; Adam Leman of the Good Food Institute; and SV Krishna Jagadish of Texas Tech University and Kansas State University.

The work was done with support from the Defense Advanced Research Projects Agency, under grant D19AP00026; the National Science Foundation, under grant 2210293; and the Development and Promotion of Science and Technology Talents Project, Thailand.


Note to Editors: The study abstract follows.

“A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses”

Authors: Kanjana Laosuntisuk and Colleen J. Doherty, North Carolina State University; Amaranatha Vennapusa, Delaware State University; Impa M. Somayanda, Texas Tech University; Adam R. Leman, the Good Food Institute; SV Krishna Jagadish, Texas Tech University and Kansas State University

Published: Jan. 30, The Plant Journal

DOI: 10.1111/tpj.16654

Abstract: RNA-Sequencing is widely used to investigate changes in gene expression at the transcription level in plants. Most plant RNA-Seq analysis pipelines base the normalization approaches on the assumption that total transcript levels do not vary between samples. However, this assumption has not been demonstrated. In fact, many common experimental treatments and genetic alterations affect transcription efficiency or RNA stability, resulting in unequal transcript abundance. The addition of synthetic RNA controls is a simple correction that controls for variation in total mRNA levels. However, adding spike-ins appropriately is challenging with complex plant tissue, and carefully considering how they are added is essential to their successful use. We demonstrate that adding external RNA spike-ins as a normalization control produces differences in RNA-Seq analysis compared to traditional normalization methods, even between two times of day in untreated plants. We illustrate the use of RNA spike-ins with 3′ RNA-Seq and present a normalization pipeline that accounts for differences in total transcriptional levels. We evaluate the effect of normalization methods on identifying differentially expressed genes in the context of identifying the effect of the time of day on gene expression and response to chilling stress in sorghum.