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WIREs Dev Biol
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Phenotyping the kinematics of leaf development in flowering plants: recommendations and pitfalls

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Leaves of flowering plants are produced from the shoot apical meristem at regular intervals and they grow according to a developmental program that is determined by both genetic and environmental factors. Detailed frameworks for multiscale dynamic analyses of leaf growth have been developed in order to identify and interpret phenotypic differences caused by either genetic or environmental variations. They revealed that leaf growth dynamics are non‐linearly and nonhomogeneously distributed over the lamina, in the leaf tissues and cells. The analysis of the variability in leaf growth, and its underlying processes, has recently gained momentum with the development of automated phenotyping platforms that use various technologies to record growth at different scales and at high throughput. These modern tools are likely to accelerate the characterization of gene function and the processes that underlie the control of shoot development. Combined with powerful statistical analyses, trends have emerged that may have been overlooked in low throughput analyses. However, in many examples, the increase in throughput allowed by automated platforms has led to a decrease in the spatial and/or temporal resolution of growth analyses. Concrete examples presented here indicate that simplification of the dynamic leaf system, without consideration of its spatial and temporal context, can lead to important misinterpretations of the growth phenotype. WIREs Dev Biol 2013, 2:809–821. doi: 10.1002/wdev.119

Conflict of interest: The authors have declared no conflicts of interest for this article.

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Responses of rosette area (a) and leaf number (b) of Arabidopsis Col‐0 to water deficit and bacterial inoculation at one date do not reflect the final responses. Plants were grown under four soil conditions: well watered (WW) and moderate water deficit (MWD), with (I) or without (NI) soil inoculation with a plant growth promoting rhizobacteria (PGPR), Phyllobacterium brassicacearum. In both watering conditions, bacterial inoculation has a negative effect on leaf number and rosette area during a first part of plant development. For instance, rosettes of inoculated plants are smaller and have a lower number of leaves than noninoculated plants 30 days after sowing. However, at the end of the vegetative period (i.e., bolting stage shown by arrows in (a)), the rhizobacteria has a significant promoting effect on both leaf number and rosette area. This promoting effect is more pronounced in the MWD condition than in the WW treatment. Inoculated plants have lower leaf emergence and rosette expansion rates from germination to the emergence of flower buds, but they delay flowering and finally produce a higher number of leaves and larger rosette areas than noninoculated plants (a, b). Error bars are standard errors of mean values (n = 11–13). Growth conditions and genotypes are described in Supplementary Table 1. Methods are given in Supplementary Information 1 and 2.
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Phenotyping cell density in a given tissue does not necessarily reflect the cellular phenotype of other tissues: example of the effects of the erecta mutation on cell number in different leaf tissues. Changes over time in leaf surface area (full lines) and thickness (dotted lines) (a), for the sixth leaf of Arabidopsis thaliana Col‐5 wild‐type (Col‐5ER, black) and Col‐5 harbouring the erecta mutation (Col‐5er, gray). Cell number increases during leaf blade expansion are shown for the adaxial epidermis (b) and the abaxial epidermis (c) for the two genotypes, revealing a strong effect of the y mutation on the dynamics of cell division in these tissues. Cell number increases during leaf blade expansion are also shown in the palisade (d, solid lines) and spongy mesophyll (d, dotted lines), revealing similar dynamics of cell number changes between the two genotypes in these two tissues. Growth conditions and genotypes are described in Supplementary Table 1. Methods are given in Supplementary Information 1 and 2.
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Differences in final individual leaf area can be due to differences in flowering date: example of ron2‐1 and Ler grown with or without synchronization of their flowering dates. Final number of rosette leaves of Ler wild‐type (light gray) and ron2‐1 (black) plants grown under a constant day length of 12 h (a) or transferred from 12 to 16 h after 15 days (c). Changes with time of leaf 6 area of Ler (light gray) and ron2‐1 (black) grown under the photoperiods of 12 h (b) or transferred from 12 to 16 h after 15 days (d). Data are means with 95% intervals of confidence (n = 5 plants per genotype and treatment). Growth conditions and genotypes are described in Supplementary Table 1. Methods are given in Supplementary Information 1 and 2.
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Differences in rosette area at a given time point can be due to differences in early development. Initial dynamics of whole rosette area over time expressed either as days after sowing (a) or days after stage 1.02 (b), i.e., when the second leaf emerged in the center of the rosette. Rosette area was determined on top view images automatically taken in the PHENOPSIS platform (see Figure (a)–(c)). Each point is the mean of three plants. Growth conditions and genotypes are described in Supplementary Table 1. Methods are given in Supplementary Information 1 and 2.
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Leaf growth phenotypes at one date do not necessarily reflect the ‘final’ phenotype. Dissected rosette area and leaf number measured 18 days after sowing are given in (a) and (c), respectively for 90 SALK T‐DNA lines and Col‐0. The same data at flowering are given in (b) and (d), respectively. Dotted lines correspond to Col‐0 values. Data are means with error bars for each genotype (n = 7). In all panels, genotypes are ranked according to their increasing rosette area at 18 days. The relationship between the time to bolting and the rosette area at 18 days is shown in (e). Growth conditions and genotypes are described in Supplementary Tables 1 and 2. Methods are given in Supplementary Information 1 and 2.
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Increasing the throughput of leaf growth phenotypic analyses as illustrated here by the PHENOPSIS platform. In a growth chamber, 504 plants are grown together where they are automatically imaged by top‐view and side‐view cameras (a, b, c, d). Total rosette area and leaf angles are measured over time on these images. In certain conditions, when leaves do not overlap, individual leaf areas can be measured on top view images (e, inset) and corrected by leaf angles (d). Changes in total rosette area and/or individual leaf area can be plotted over time and sigmoids can be fitted on these curves (e). In addition, at specific dates or stages, different growth traits can be measured at different scales on each plant by destructive measurements as shown for a nonexhaustive list of traits in (f).
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The same final leaf area can be reached via different dynamics and durations of developmental phases. Dynamics of leaf 6 area (a) and corresponding changes in absolute (b) and relative (c) leaf expansion rate for two Arabidopsis thaliana genotypes, Ler () and elo‐1 () (n = 5 or 6). The curves fitted in (a) are 3 parameter sigmoids for Ler y = 116.56/(1 + exp−((t−17.11)/2.25)) and elo‐1 y = 120.22/(1 + exp−((t−23.18)/2.58)). For each genotype, leaf development was characterized by three successive stages shown in (a): dates of initiation, emergence, and end of expansion. They are indicated from left to right by solid upward and dashed downward arrows for Ler and elo‐1, respectively. Note that the two genotypes reach the same final leaf 6, but with different dynamics of leaf expansion. The longer duration of expansion for elo‐1 is due to a longer phase between leaf initiation and leaf emergence (the hidden phase), whereas the duration of the phase between leaf emergence and end of expansion (the visible phase) did not significantly differ between the two genotypes. The figure is adapted from the dataset used in ref . Growth conditions and genotypes are described in Supplementary Table 1. Methods are given in Supplementary Information 1 and 2.
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