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. 2015 Jun 10:5:11108.
doi: 10.1038/srep11108.

Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging

Wen-Juan Pan  1 , Xia Wang  2 , Yong-Ren Deng  3 , Jia-Hang Li  3 , Wei Chen  1 , John Y Chiang  4 , Jian-Bo Yang  5 , Lei Zheng  6
Affiliations

Nondestructive and intuitive determination of circadian chlorophyll rhythms in soybean leaves using multispectral imaging

Wen-Juan Pan et al. Sci Rep. .

Abstract

The circadian clock, synchronized by daily cyclic environmental cues, regulates diverse aspects of plant growth and development and increases plant fitness. Even though much is known regarding the molecular mechanism of circadian clock, it remains challenging to quantify the temporal variation of major photosynthesis products as well as their metabolic output in higher plants in a real-time, nondestructive and intuitive manner. In order to reveal the spatial-temporal scenarios of photosynthesis and yield formation regulated by circadian clock, multispectral imaging technique has been employed for nondestructive determination of circadian chlorophyll rhythms in soybean leaves. By utilizing partial least square regression analysis, the determination coefficients R(2), 0.9483 for chlorophyll a and 0.8906 for chlorophyll b, were reached, respectively. The predicted chlorophyll contents extracted from multispectral data showed an approximately 24-h rhythm which could be entrained by external light conditions, consistent with the chlorophyll contents measured by chemical analyses. Visualization of chlorophyll map in each pixel offers an effective way to analyse spatial-temporal distribution of chlorophyll. Our results revealed the potentiality of multispectral imaging as a feasible nondestructive universal assay for examining clock function and robustness, as well as monitoring chlorophyll a and b and other biochemical components in plants.

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Figures

Figure 1
Figure 1. Measured chlorophyll a (a) and b (c) concentrations showed rhythmic activities under LD, LL, and DD conditions.
Amplitude for chlorophyll a (b) and b (d) in three light conditions was calculated by FFT-NLLS analysis according to data from ZT24 to ZT72. Data are means ± SEM of n = 15 soybean leaves from three independent experiments.
Figure 2
Figure 2. Measured and predicted values of chlorophyll a (a) and b (c) concentration for the PLSR models under the full range spectra.
90 samples (orange triangle) were used for training set and 60 samples (black dot) were used for test set. Regression coefficients of the PLSR model for chlorophyll a and b parameter are shown in (b) and (d), respectively. Three independent experiments were performed with similar results.
Figure 3
Figure 3. Circadian rhythms of predicted chlorophyll a (a) and b (b) concentrations based on PLSR functions when they were exposed to LD, LL, and DD conditions.
Amplitude for chlorophyll a (b) and b (d) in three light conditions was calculated by FFT-NLLS analysis according to data from ZT24 to ZT72. Data are means ± SEM of n = 15 soybean leaves from three independent experiments.
Figure 4
Figure 4. Circadian rhythms of the reflectance value under the optimal wavelengths selected for chlorophyll a and b by SPA.
Plot of ten wavelength variables for chlorophyll a (a) and b (b) selected by SPA. Columns represent selected optimal wavelength variables. Black curve shows original spectrum. Circadian rhythms of reflectance value at 470 nm (c) and 660 nm (d) under LL conditions. Data are means ± SEM from three independent experiments.
Figure 5
Figure 5. Reflectance differential images (color) exhibit the rhythm of heterogeneity at 780 nm during the recordings of Fig. 4 for soybean leaves in LL condition (a) at different time in addition to DD (b).
Figure 6
Figure 6. Visualization maps for chlorophyll a (a) and b (b) content distribution of soybean under LL condition.
The parallel color bar represents the chlorophyll content of the images.
Figure 7
Figure 7. Circadian rhythms can be measured by MSI under stress conditions and with a range of plant species.
(a) Soybean plants under drought stress condition. (b) Wheat plants.
See this image and copyright information in PMC

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References

    1. Bell-Pedersen D. et al. Circadian rhythms from multiple oscillators: Lessons from diverse organisms. Nat. Rev. Genet. 6, 544–556 (2005). - PMC - PubMed
    1. Nozue K. et al. Rhythmic growth explained by coincidence between internal and external cues. Nature 448, 358–361 (2007). - PubMed
    1. Dong G. G. et al. Elevated ATPase activity of KaiC applies a circadian checkpoint on cell division in synechococcus elongates. Cell 140, 529–539 (2010). - PMC - PubMed
    1. Moreno-Risueno M. A. et al. Oscillating gene expression determines competence for periodic Arabidopsis root branching. Science 329, 1306–1311 (2010). - PMC - PubMed
    1. Ruts T., Matsubara S., Wiese-Klinkenberg A. & Walter A. Aberrant temporal growth pattern and morphology of root and shoot caused by a defective circadian clock in Arabidopsis thaliana. Plant J. 72, 154–161 (2012). - PubMed

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