Application of the NanoString nCounter System as an Alternative Method to Investigate Molecular Mechanisms Involved in Host Plant Responses to Plasmodiophora brassicae

Abstract

Clubroot, caused by the soilborne pathogen Plasmodiophora brassicae, is an important disease of canola (Brassica napus) and other crucifers. The recent application of RNA sequencing (RNA-seq) technologies to study P. brassicae−host interactions has generated large amounts of gene expression data, improving knowledge of the molecular mechanisms of pathogenesis and host resistance. Quantitative PCR (qPCR) analysis has been widely applied to examine the expression of a limited number of genes and to validate the results of RNA-seq studies, but may not be ideal for analyzing larger suites of target genes or increased sample numbers. Moreover, the need for intermediate steps such as cDNA synthesis may introduce variability that could affect the accuracy of the data generated by qPCR. Here, we report the validation of gene expression data from a previous RNA-seq study of clubroot using the NanoString nCounter System, which achieves efficient gene expression quantification in a fast and simple manner. We first confirm the robustness of the NanoString system by comparing the results with those generated by qPCR and RNA-seq and then discuss the importance of some candidate genes for resistance or susceptibility to P. brassicae in the host. The results show that the expression of genes measured using NanoString have a high correlation with the values obtained using the other two technologies, with R > 0.90 and p < 0.01, and the same expression patterns for most genes. The three methods (qPCR, RNA-seq, and NanoString) were also compared in terms of laboratory procedures, time, and cost. We propose that the NanoString nCounter System is a robust, sensitive, highly reproducible, and simple technology for gene expression analysis. NanoString could become a common alternative to qPCR to validate RNA-seq data or to create panels of genes for use as markers of resistance/susceptibility when plants are challenged with different P. brassicae pathotypes.

Keywords: NanoString nCounter system; Plasmodiophora brassicae; RNA-seq; clubroot; gene expression; qPCR.

Figure 1

Correlation of expression of 29 genes based on log2 fold change changes, as determined using the NanoString nCounter System vs. RNA-seq analysis. The R-values indicate the correlation coefficient between the two methods in each host and time-point, and the p-values indicate the significance level of the t-test.

Figure 2

Heatmaps of 29 selected genes in a resistant rutabaga “Wilhelmsburger” (Wil) and a susceptible rutabaga “Laurentian” (Lau) in response to Plasmodiophora brassicae from 7 to 21 days after inoculation (dai). Results are based on log₂ fold changes, as determined using the NanoString nCounter System and RNA-seq analysis.

Figure 3

A comparison of the workflows for the NanoString nCounter System, qPCR, and RNA-seq in gene expression analysis of P. brassicae/host interactions. Overall, four steps are required to obtain gene expression levels: (1) sample preparation, (2) sample processing, (3) data acquisition, and (4) data analysis. During sample preparation, RNA, cDNA, and cDNA libraries are obtained from clubroot-resistant (CR) and clubroot-susceptible (CS) plants, which are used as input for the NanoString nCounter System, qPCR, and RNA-seq, respectively. During sample processing, three methods are used to generate signals that can be read by corresponding equipment. Amplification of genes, which is required when using qPCR and RNA-seq to increase signals, is not required for the NanoString nCounter System. Instead, RNA is directly tagged with a capture probe and reporter probe (hybridization), creating a unique target–probe complex. Following hybridization, excess probes are removed (purification), leaving only the purified target–probe complexes which are immobilized and aligned on an imaging surface for counting (https://nanostring.com, accessed on 31 October 2022). During data acquisition and analysis, counts of barcodes measured with the NanoString nCounter System are analyzed using nSolver software developed by the company, Ct values obtained from qPCR are analyzed based on the 2^−∆∆Ct method to achieve relative quantification of gene expression, and reads generated from RNA-seq are analyzed using diverse bioinformatics pipelines generating semi-quantitative data.