Date Published: March 19, 2019
Publisher: Public Library of Science
Author(s): Matheus Thomas Kuska, Jan Behmann, Mahsa Namini, Erich-Christian Oerke, Ulrike Steiner, Anne-Katrin Mahlein, Sabrina Sarrocco.
Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley—powdery mildew interactions. Significant synergistic effects between the hyperspectral signal and the corresponding gene activities has been shown using the linear discriminant analysis (LDA). Combining the data sets of hyperspectral signatures and gene expression profiles allowed a more precise differentiation of the three investigated barley-Bgh interactions independent from the time after inoculation. This shows significant synergistic effects between the hyperspectral signal and the corresponding gene activities. To analyze this coherency between spectral reflectance and seven different gene expression profiles, relevant wavelength bands and reflectance intensities for each gene were computed using the Relief algorithm. Instancing, xylanase activity was indicated by relevant wavelengths around 710 nm, which are characterized by leaf and cell structures. HvRuBisCO activity underlines relevant wavebands in the green and red range, elucidating the coherency of RuBisCO to the photosynthesis apparatus and in the NIR range due to the influence of RuBisCO on barley leaf cell development. These findings provide the first insights to links between gene expression and spectral reflectance that can be used for an efficient non-invasive phenotyping of plant resistance and enables new insights into plant-pathogen interactions.
Molecular analysis entered as a rapid and advanced method for pre-selection and resistance screenings in plant breeding processes . However, it is necessary to test the function of the genome of breeding material in greenhouse and field trials to assess their stability in different environments . In addition, changes in gene expression and protein synthesis change the metabolism which influences the plant phenotype . Phenotyping by visual estimation is labor and cost-intensive . To overcome this bottleneck, many recent investigations deal with optical sensor approaches for a non-invasive and efficient evaluation of plant properties [5, 6]. Within this context, hyperspectral imaging (HSI) is a promising tool to assess different plant parameters with high accuracy . Compared to conventional red, green, blue (RGB) cameras, HSI includes high resolution optical techniques with increased spectral resolution. HSI assesses narrow wavebands in the visual light from 400–700 nm (VIS), in the near-infrared from 700–1000 nm (NIR), and in the shortwave infrared from 1000–2500 nm (SWIR). Different parameters of plant physiology, chemistry and health status can be derived from the electromagnetic spectrum with a range of 400–2500 nm [7, 8]. This enables a non-invasive detection and characterization of fungal plant pathogens as well as plant resistance reactions by hyperspectral imaging [9–11]. Hyperspectral imaging data are often analyzed and interpreted with histological and physiological observations as well as information from established sensors such as chlorophyll fluorescence [8, 9]. The correlation of genes or proteins to spectral reflectance patterns has not yet proven, despite the fact that many plant resistance reactions and the plant immunity pathways are known on the omic level .