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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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Retrieval of reflectance data from hyperspectral images. Data in each plot are the mean ± SD (SD is shown by a vertical bar) derived from measurements in five different leaves of Arabidopsis plants from each subdata set or 25 different areas in the 50% reflectance standard. Details of each subdata set (#1–5) are summarized in Table 1. Plants grown under normal conditions (as defined in Table 1) were used. (A) Raw signal output from the hyperspectral imaging sensor. Values are in the range 0–255 (8-bit). Note that the signal detected in the areas of the standard is proportional to the irradiation intensity of the light source. (B) Reflectance spectra of leaves as determined by linear regression against the 50% reflectance standard. The means ± SD of the calculated (observed, abbreviated as obs.) and actual (expected, abbreviated as exp.) reflectance of five different standards (10, 25, 50, 70 and 99%) through the entire 72 wavebands (400–800 nm) are indicated on the right. Expected values are from the calibration certificate of the standards provided by the manufacturers. Except for the 50% standard, the observed and expected reflectance did not agree with each other at this step. (C) Reflectance spectra of leaves after second-order correction for the sensor’s non-linearity. The values of the coefficients in quadratic functions used to transform the reflectance at each waveband in B to that in C are shown in Supplementary Table S1. Here, the observed and expected reflectance of the five standards agree closely with each other as shown on the right.
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pcs043-F3: Retrieval of reflectance data from hyperspectral images. Data in each plot are the mean ± SD (SD is shown by a vertical bar) derived from measurements in five different leaves of Arabidopsis plants from each subdata set or 25 different areas in the 50% reflectance standard. Details of each subdata set (#1–5) are summarized in Table 1. Plants grown under normal conditions (as defined in Table 1) were used. (A) Raw signal output from the hyperspectral imaging sensor. Values are in the range 0–255 (8-bit). Note that the signal detected in the areas of the standard is proportional to the irradiation intensity of the light source. (B) Reflectance spectra of leaves as determined by linear regression against the 50% reflectance standard. The means ± SD of the calculated (observed, abbreviated as obs.) and actual (expected, abbreviated as exp.) reflectance of five different standards (10, 25, 50, 70 and 99%) through the entire 72 wavebands (400–800 nm) are indicated on the right. Expected values are from the calibration certificate of the standards provided by the manufacturers. Except for the 50% standard, the observed and expected reflectance did not agree with each other at this step. (C) Reflectance spectra of leaves after second-order correction for the sensor’s non-linearity. The values of the coefficients in quadratic functions used to transform the reflectance at each waveband in B to that in C are shown in Supplementary Table S1. Here, the observed and expected reflectance of the five standards agree closely with each other as shown on the right.

Mentions: Fig. 3A shows an example of raw reflective signals recorded in the areas of the standard and leaves of Arabidopsis plants from five different subdata sets (Table 1), which are described in detail in the following sections. As is evident from the figure, the irradiation spectrum from the light source was not flat over wavelengths (Fig. 3A, black line). Hence, the reflectance spectrum in each pixel of the images was calibrated by linear regression against the 50% reflectance standard (Fig. 3B). To semi-automate these calibration processes and thus facilitate retrieval of numerical spectral reflectance values from the hyperspectral images, we developed the software HSD Analyzer (Hyperspectral Data Analyzer; Fig. 2B), which is provided as Supplementary File S1 (see Supplementary Text S1 for legends and methods of operation). The subsequent development of RI-based equations for ‘targeted’ pigment estimation (Equations 2–12) and of PPM software (Fig. 1C) depended primarily on the analysis using HSD Analyzer.Fig. 3


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)

Retrieval of reflectance data from hyperspectral images. Data in each plot are the mean ± SD (SD is shown by a vertical bar) derived from measurements in five different leaves of Arabidopsis plants from each subdata set or 25 different areas in the 50% reflectance standard. Details of each subdata set (#1–5) are summarized in Table 1. Plants grown under normal conditions (as defined in Table 1) were used. (A) Raw signal output from the hyperspectral imaging sensor. Values are in the range 0–255 (8-bit). Note that the signal detected in the areas of the standard is proportional to the irradiation intensity of the light source. (B) Reflectance spectra of leaves as determined by linear regression against the 50% reflectance standard. The means ± SD of the calculated (observed, abbreviated as obs.) and actual (expected, abbreviated as exp.) reflectance of five different standards (10, 25, 50, 70 and 99%) through the entire 72 wavebands (400–800 nm) are indicated on the right. Expected values are from the calibration certificate of the standards provided by the manufacturers. Except for the 50% standard, the observed and expected reflectance did not agree with each other at this step. (C) Reflectance spectra of leaves after second-order correction for the sensor’s non-linearity. The values of the coefficients in quadratic functions used to transform the reflectance at each waveband in B to that in C are shown in Supplementary Table S1. Here, the observed and expected reflectance of the five standards agree closely with each other as shown on the right.
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Related In: Results  -  Collection

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pcs043-F3: Retrieval of reflectance data from hyperspectral images. Data in each plot are the mean ± SD (SD is shown by a vertical bar) derived from measurements in five different leaves of Arabidopsis plants from each subdata set or 25 different areas in the 50% reflectance standard. Details of each subdata set (#1–5) are summarized in Table 1. Plants grown under normal conditions (as defined in Table 1) were used. (A) Raw signal output from the hyperspectral imaging sensor. Values are in the range 0–255 (8-bit). Note that the signal detected in the areas of the standard is proportional to the irradiation intensity of the light source. (B) Reflectance spectra of leaves as determined by linear regression against the 50% reflectance standard. The means ± SD of the calculated (observed, abbreviated as obs.) and actual (expected, abbreviated as exp.) reflectance of five different standards (10, 25, 50, 70 and 99%) through the entire 72 wavebands (400–800 nm) are indicated on the right. Expected values are from the calibration certificate of the standards provided by the manufacturers. Except for the 50% standard, the observed and expected reflectance did not agree with each other at this step. (C) Reflectance spectra of leaves after second-order correction for the sensor’s non-linearity. The values of the coefficients in quadratic functions used to transform the reflectance at each waveband in B to that in C are shown in Supplementary Table S1. Here, the observed and expected reflectance of the five standards agree closely with each other as shown on the right.
Mentions: Fig. 3A shows an example of raw reflective signals recorded in the areas of the standard and leaves of Arabidopsis plants from five different subdata sets (Table 1), which are described in detail in the following sections. As is evident from the figure, the irradiation spectrum from the light source was not flat over wavelengths (Fig. 3A, black line). Hence, the reflectance spectrum in each pixel of the images was calibrated by linear regression against the 50% reflectance standard (Fig. 3B). To semi-automate these calibration processes and thus facilitate retrieval of numerical spectral reflectance values from the hyperspectral images, we developed the software HSD Analyzer (Hyperspectral Data Analyzer; Fig. 2B), which is provided as Supplementary File S1 (see Supplementary Text S1 for legends and methods of operation). The subsequent development of RI-based equations for ‘targeted’ pigment estimation (Equations 2–12) and of PPM software (Fig. 1C) depended primarily on the analysis using HSD Analyzer.Fig. 3

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