Hyperspectral imaging for chloroplast movement detection

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Abstract

We employed hyperspectral imaging to detect chloroplast positioning in Nicotiana benthamiana and Arabidopsis thaliana leaves and assess its influence on commonly used vegetation indices. In low light, chloroplasts move to cell walls perpendicular to the direction of the incident light. In high light, they move to cell walls parallel to the light direction. Chloroplast movements result in significant changes in leaf transmittance and reflectance. The changes in leaf reflectance offer a way to examine chloroplast positioning in a non-contact way. At the same time, they may confound remote sensing of other physiological traits. The shape of reflectance spectra recorded on irradiated and non-irradiated parts of N. benthamiana and A. thaliana leaves indicated the specific position of chloroplasts. Low blue light resulted in a decrease in leaf reflectance in the green-yellow region of the spectrum. High blue light irradiation caused an increase in leaf reflectance in the visible range. The differential spectra, showing the effect of high light on leaf reflectance, exhibited a characteristic saddle in the green-yellow region and a peak at around 695 nm. Results obtained for A. thaliana mutants with disrupted chloroplast movements suggest that the observed spectral changes are mostly due to the chloroplast relocations. The reflectance spectra were used to train machine learning methods in the classification of leaves according to the chloroplast positioning. The convolutional network showed low levels of misclassification of leaves irradiated with high light even when different species were used for training and testing. This suggests that reflectance spectra may be used to detect the chloroplast avoidance response in heterogeneous patches of vegetation. We also examined the correlation between chloroplast positioning and values of indices of normalized-difference type for various combinations of wavelengths and proposed a chloroplast movement index for validation of chloroplast positions in leaves. The analysis of commonly used vegetation indices showed that their values may be altered due to chloroplast rearrangements. Our work indicates that changes in leaf reflectance due to chloroplast movements may be substantial and should be taken into account in remote sensing studies.

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  1. It serves as a proof of concept for a high throughput, remote technique applicable to field conditions.

    This seems great for surveying plant physiology for ecological field work. I didn't see anywhere an estimation of the throughput though, can any numbers be assigned to this to help a researcher see how the technique would increase teh efficiency of their work?

  2. especially NDVI (Fig. 5 A, Fig. S5 A), SR (Fig. 5 B, Fig. S5 B), RENDVI (Fig. 5 F, Fig. S5 F), mRENDEVI (Fig. 5 G, Fig. S5 G), mRESR (Fig. 5 H, Fig. S5 H0, VOG1 (Fig. 5 I, Fig. S5 I), PRI (Fig. 5 L, Fig. S5 L), SIPI (Fig. 5 M, Fig. S5 M), RGRI (Fig. 5 N, Fig. S5 N), PSRI (Fig. 5 O, Fig. S5 O) CAR1 (Fig. 5 P, Fig. S5 P), and CAR2 (Fig. 5 Q, Fig. S5 Q).

    Can you spell out these acronyms here so the reader can refer to this section when interpreting the y-axes on Fig. 5 and 6? I see that they're listed in the abbreviations, but it would handy to have them written out in this section of the results.

  3. Welch t-test.

    It would be worth reporting the sample sizes for each treatment, as that would help the reader understand why you chose different tests (like Welch's t-test). Also for D, I am assuming you actually performed an ANOVA and then a Tukey's post-hoc test, so you should report the ANOVA statistics (F value, df, p value).

  4. Fig. 2.

    Same comment as for Fig. 1 but also it would help to make the label "Dark" more parallel. Is that a "No light" treatment as control? Consider new labels to make it clearer as to whether Dark means a condition or an observation as in Fig 1, "Darkened" seems like an observation.

  5. ig. 1.

    small note - but consider labeling the panels "low" and "high" blue light and keep the info on the values for each treatment in the caption. This would improve readability.