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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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Related in: MedlinePlus

Relationship between observed and expected pigment concentrations in the validation data set. Details of each subdata set (#1–5) are summarized in Table 1. Horizontal and vertical axes, respectively, represent chemically determined (observed, abbreviated as obs.) and optically estimated (expected, abbreviated as exp.) concentrations of Chl a (A), Chl b (B), Car (C) or Anth (D). The background data points in light gray are from the calibration data set and provide a measure of the possible deviation range in the pigment estimation using Equations 3 (Chl a), 5 (Chl b), 7 (Anth) and 11 (Car). The statistical details are summarized in Table 2.
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pcs043-F6: Relationship between observed and expected pigment concentrations in the validation data set. Details of each subdata set (#1–5) are summarized in Table 1. Horizontal and vertical axes, respectively, represent chemically determined (observed, abbreviated as obs.) and optically estimated (expected, abbreviated as exp.) concentrations of Chl a (A), Chl b (B), Car (C) or Anth (D). The background data points in light gray are from the calibration data set and provide a measure of the possible deviation range in the pigment estimation using Equations 3 (Chl a), 5 (Chl b), 7 (Anth) and 11 (Car). The statistical details are summarized in Table 2.

Mentions: The correlation between (Chl b)RI and [Chl b]obs. in the whole calibration data set is shown by a scatter plot in Fig. 5B. A linear regression fitted to the wild-type subdata sets (#1 and 2) gives an equation:(5)where [Chl b]exp. denotes the expected Chl b concentration. The RMSE of the differences between [Chl b]obs. and [Chl b]exp., and related statistical parameters are summarized in Table 2. As is later confirmed in the validation section, the equation is quite successful in separating the Chl b-deficient ch1 mutants (subdata sets #3 and 4) from the wild type (subdata sets #1 and 2) (see Fig. 6B).Fig. 6


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)

Relationship between observed and expected pigment concentrations in the validation data set. Details of each subdata set (#1–5) are summarized in Table 1. Horizontal and vertical axes, respectively, represent chemically determined (observed, abbreviated as obs.) and optically estimated (expected, abbreviated as exp.) concentrations of Chl a (A), Chl b (B), Car (C) or Anth (D). The background data points in light gray are from the calibration data set and provide a measure of the possible deviation range in the pigment estimation using Equations 3 (Chl a), 5 (Chl b), 7 (Anth) and 11 (Car). The statistical details are summarized in Table 2.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

pcs043-F6: Relationship between observed and expected pigment concentrations in the validation data set. Details of each subdata set (#1–5) are summarized in Table 1. Horizontal and vertical axes, respectively, represent chemically determined (observed, abbreviated as obs.) and optically estimated (expected, abbreviated as exp.) concentrations of Chl a (A), Chl b (B), Car (C) or Anth (D). The background data points in light gray are from the calibration data set and provide a measure of the possible deviation range in the pigment estimation using Equations 3 (Chl a), 5 (Chl b), 7 (Anth) and 11 (Car). The statistical details are summarized in Table 2.
Mentions: The correlation between (Chl b)RI and [Chl b]obs. in the whole calibration data set is shown by a scatter plot in Fig. 5B. A linear regression fitted to the wild-type subdata sets (#1 and 2) gives an equation:(5)where [Chl b]exp. denotes the expected Chl b concentration. The RMSE of the differences between [Chl b]obs. and [Chl b]exp., and related statistical parameters are summarized in Table 2. As is later confirmed in the validation section, the equation is quite successful in separating the Chl b-deficient ch1 mutants (subdata sets #3 and 4) from the wild type (subdata sets #1 and 2) (see Fig. 6B).Fig. 6

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