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Hyperspectral imaging techniques for rapid identification of Arabidopsis mutants with altered leaf pigment status.

Matsuda O, Tanaka A, Fujita T, Iba K - Plant Cell Physiol. (2012)

Bottom Line: The 'non-targeted' mode highlights differences in reflectance spectra of leaf samples relative to reference spectra from the wild-type leaves.Analysis of these and other mutants revealed that the RI-based targeted pigment estimation was robust at least against changes in trichome density, but was confounded by genetic defects in chloroplast photorelocation movement.Notwithstanding such a limitation, the techniques presented here provide rapid and high-sensitive means to identify genetic mechanisms that coordinate leaf pigment status with developmental stages and/or environmental stress conditions.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581 Japan. matsuda.osamu.084@m.kyushu-u.ac.jp

ABSTRACT
The spectral reflectance signature of living organisms provides information that closely reflects their physiological status. Because of its high potential for the estimation of geomorphic biological parameters, particularly of gross photosynthesis of plants, two-dimensional spectroscopy, via the use of hyperspectral instruments, has been widely used in remote sensing applications. In genetics research, in contrast, the reflectance phenotype has rarely been the subject of quantitative analysis; its potential for illuminating the pathway leading from the gene to phenotype remains largely unexplored. In this study, we employed hyperspectral imaging techniques to identify Arabidopsis mutants with altered leaf pigment status. The techniques are comprised of two modes; the first is referred to as the 'targeted mode' and the second as the 'non-targeted mode'. The 'targeted' mode is aimed at visualizing individual concentrations and compositional parameters of leaf pigments based on reflectance indices (RIs) developed for Chls a and b, carotenoids and anthocyanins. The 'non-targeted' mode highlights differences in reflectance spectra of leaf samples relative to reference spectra from the wild-type leaves. Through the latter approach, three mutant lines with weak irregular reflectance phenotypes, that are hardly identifiable by simple observation, were isolated. Analysis of these and other mutants revealed that the RI-based targeted pigment estimation was robust at least against changes in trichome density, but was confounded by genetic defects in chloroplast photorelocation movement. Notwithstanding such a limitation, the techniques presented here provide rapid and high-sensitive means to identify genetic mechanisms that coordinate leaf pigment status with developmental stages and/or environmental stress conditions.

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Equipment for hyperspectral reflectance imaging and data analysis. The programs shown in B–D are provided as Supplementary File S1 of this article (see Supplementary Text S1 for legends and methods of operation). (A) Hyperspectral image acquisition system. The system is basically composed of a hyperspectral camera directed downward to the sample stage and epi-illumination halogen lights. The inset shows the VIS/NIR hyperspectral camera HSC1700 (early 2007 model). (B) Screenshot of HSD Analyzer software. The software facilitates calibration and extraction of numerical reflectance data from the areas of interest in hyperspectral images. (C) Screenshot of PPM software. The software allows visualization of individual concentrations and compositional parameters of major pigments in Arabidopsis leaves. (D) Screenshot of HSD Visualizer software. The software displays the deviation of spectral reflectance relative to the reference spectra (usually from the wild type) as a pseudocolor image.
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pcs043-F2: Equipment for hyperspectral reflectance imaging and data analysis. The programs shown in B–D are provided as Supplementary File S1 of this article (see Supplementary Text S1 for legends and methods of operation). (A) Hyperspectral image acquisition system. The system is basically composed of a hyperspectral camera directed downward to the sample stage and epi-illumination halogen lights. The inset shows the VIS/NIR hyperspectral camera HSC1700 (early 2007 model). (B) Screenshot of HSD Analyzer software. The software facilitates calibration and extraction of numerical reflectance data from the areas of interest in hyperspectral images. (C) Screenshot of PPM software. The software allows visualization of individual concentrations and compositional parameters of major pigments in Arabidopsis leaves. (D) Screenshot of HSD Visualizer software. The software displays the deviation of spectral reflectance relative to the reference spectra (usually from the wild type) as a pseudocolor image.

Mentions: The configuration of the hyperspectral imaging set-up used in this study is basically identical to the one proposed previously (Lenk et al. 2007) (Fig. 2A). This configuration was originally applied to multispectral rather than hyperspectral imaging. Both of these spectral imaging techniques allow extraction of information that cannot be retained in the standard RGB images, due to the higher spectral resolution of the techniques. Multispectral imaging, however, produces discrete, not contiguous, spectral information. Hence, the reflectance images were captured using a line-scanning hyperspectral camera (HSC1700, the earliest commercial model manifactured in 2007; Hokkaido Satellite). This camera is capable of aquiring 8-bit VGA (640×480 pixel) images for 72 contiguous wavebands from 400 to 800 nm (5.6 nm bandwidth). In our set-up, the scanning area was approximately 140×120 mm; the resulting spatial resolution of the images was 116 and 100 dots per inch in the horizontal and vertical dimensions, respectively. As the epi-illumination light source, two double-ended 250 W halogen lamps were used. When capturing Arabidopsis images, a 50% reflectance standard (SRS-050-010; Labsphere) was placed in the same visual field. A hyperspectral image of a sheet of homogenous white paper (No. 526; ADVANTEC) was captured under the same conditions, and was used to calibrate the Arabidopsis images for spatial non-uniformity of illumination.Fig. 2


Hyperspectral imaging techniques for rapid identification of Arabidopsis mutants with altered leaf pigment status.

Matsuda O, Tanaka A, Fujita T, Iba K - Plant Cell Physiol. (2012)

Equipment for hyperspectral reflectance imaging and data analysis. The programs shown in B–D are provided as Supplementary File S1 of this article (see Supplementary Text S1 for legends and methods of operation). (A) Hyperspectral image acquisition system. The system is basically composed of a hyperspectral camera directed downward to the sample stage and epi-illumination halogen lights. The inset shows the VIS/NIR hyperspectral camera HSC1700 (early 2007 model). (B) Screenshot of HSD Analyzer software. The software facilitates calibration and extraction of numerical reflectance data from the areas of interest in hyperspectral images. (C) Screenshot of PPM software. The software allows visualization of individual concentrations and compositional parameters of major pigments in Arabidopsis leaves. (D) Screenshot of HSD Visualizer software. The software displays the deviation of spectral reflectance relative to the reference spectra (usually from the wild type) as a pseudocolor image.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3367163&req=5

pcs043-F2: Equipment for hyperspectral reflectance imaging and data analysis. The programs shown in B–D are provided as Supplementary File S1 of this article (see Supplementary Text S1 for legends and methods of operation). (A) Hyperspectral image acquisition system. The system is basically composed of a hyperspectral camera directed downward to the sample stage and epi-illumination halogen lights. The inset shows the VIS/NIR hyperspectral camera HSC1700 (early 2007 model). (B) Screenshot of HSD Analyzer software. The software facilitates calibration and extraction of numerical reflectance data from the areas of interest in hyperspectral images. (C) Screenshot of PPM software. The software allows visualization of individual concentrations and compositional parameters of major pigments in Arabidopsis leaves. (D) Screenshot of HSD Visualizer software. The software displays the deviation of spectral reflectance relative to the reference spectra (usually from the wild type) as a pseudocolor image.
Mentions: The configuration of the hyperspectral imaging set-up used in this study is basically identical to the one proposed previously (Lenk et al. 2007) (Fig. 2A). This configuration was originally applied to multispectral rather than hyperspectral imaging. Both of these spectral imaging techniques allow extraction of information that cannot be retained in the standard RGB images, due to the higher spectral resolution of the techniques. Multispectral imaging, however, produces discrete, not contiguous, spectral information. Hence, the reflectance images were captured using a line-scanning hyperspectral camera (HSC1700, the earliest commercial model manifactured in 2007; Hokkaido Satellite). This camera is capable of aquiring 8-bit VGA (640×480 pixel) images for 72 contiguous wavebands from 400 to 800 nm (5.6 nm bandwidth). In our set-up, the scanning area was approximately 140×120 mm; the resulting spatial resolution of the images was 116 and 100 dots per inch in the horizontal and vertical dimensions, respectively. As the epi-illumination light source, two double-ended 250 W halogen lamps were used. When capturing Arabidopsis images, a 50% reflectance standard (SRS-050-010; Labsphere) was placed in the same visual field. A hyperspectral image of a sheet of homogenous white paper (No. 526; ADVANTEC) was captured under the same conditions, and was used to calibrate the Arabidopsis images for spatial non-uniformity of illumination.Fig. 2

Bottom Line: The 'non-targeted' mode highlights differences in reflectance spectra of leaf samples relative to reference spectra from the wild-type leaves.Analysis of these and other mutants revealed that the RI-based targeted pigment estimation was robust at least against changes in trichome density, but was confounded by genetic defects in chloroplast photorelocation movement.Notwithstanding such a limitation, the techniques presented here provide rapid and high-sensitive means to identify genetic mechanisms that coordinate leaf pigment status with developmental stages and/or environmental stress conditions.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Faculty of Sciences, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka, 812-8581 Japan. matsuda.osamu.084@m.kyushu-u.ac.jp

ABSTRACT
The spectral reflectance signature of living organisms provides information that closely reflects their physiological status. Because of its high potential for the estimation of geomorphic biological parameters, particularly of gross photosynthesis of plants, two-dimensional spectroscopy, via the use of hyperspectral instruments, has been widely used in remote sensing applications. In genetics research, in contrast, the reflectance phenotype has rarely been the subject of quantitative analysis; its potential for illuminating the pathway leading from the gene to phenotype remains largely unexplored. In this study, we employed hyperspectral imaging techniques to identify Arabidopsis mutants with altered leaf pigment status. The techniques are comprised of two modes; the first is referred to as the 'targeted mode' and the second as the 'non-targeted mode'. The 'targeted' mode is aimed at visualizing individual concentrations and compositional parameters of leaf pigments based on reflectance indices (RIs) developed for Chls a and b, carotenoids and anthocyanins. The 'non-targeted' mode highlights differences in reflectance spectra of leaf samples relative to reference spectra from the wild-type leaves. Through the latter approach, three mutant lines with weak irregular reflectance phenotypes, that are hardly identifiable by simple observation, were isolated. Analysis of these and other mutants revealed that the RI-based targeted pigment estimation was robust at least against changes in trichome density, but was confounded by genetic defects in chloroplast photorelocation movement. Notwithstanding such a limitation, the techniques presented here provide rapid and high-sensitive means to identify genetic mechanisms that coordinate leaf pigment status with developmental stages and/or environmental stress conditions.

Show MeSH
Related in: MedlinePlus