“…S7, S8, S10, S11, S13, S14, S16, and S17). Concerning inferential statistics, here for simplicity's sake we use the bootstrapped 95% confidence (95CI) interval (as calculated by the seaborn python library) as an estimation of difference rather than a test of significance [ 23 ], since more advanced hypothesis testing would require complex case-by-case modeling of the curve's trends to be correct [ 24 ], which is out of the scope of this work but could be performed if needed on the data generated by the workflow. Fig.…”
Background
Plants can perceive and respond to mechanical signals. For instance, cortical microtubule (CMT) arrays usually reorganize following the predicted maximal tensile stress orientation at the cell and tissue level. While research in the last few years has started to uncover some of the mechanisms mediating these responses, much remains to be discovered, including in most cases the actual nature of the mechanosensors. Such discovery is hampered by the absence of adequate quantification tools that allow the accurate and sensitive detection of phenotypes, along with high throughput and automated handling of large datasets that can be generated with recent imaging devices.
Results
Here we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets following an ablation in the epidermis — a simple and robust method to change mechanical stress pattern. Our Fiji-based workflow puts together several plugins and algorithms under the form of user-friendly macros that automate the analysis process and remove user bias in the quantification. One of the key aspects is also the implementation of a simple geometry-based proxy to estimate stress patterns around the ablation site and compare it with the actual CMT arrays orientation. Testing our workflow on well-established reporter lines and mutants revealed subtle differences in the response over time, as well as the possibility to uncouple the anisotropic and orientational response.
Conclusion
This new workflow opens the way to dissect with unprecedented detail the mechanisms controlling microtubule arrays re-organization, and potentially uncover the still largely elusive plant mechanosensors.
“…S7, S8, S10, S11, S13, S14, S16, and S17). Concerning inferential statistics, here for simplicity's sake we use the bootstrapped 95% confidence (95CI) interval (as calculated by the seaborn python library) as an estimation of difference rather than a test of significance [ 23 ], since more advanced hypothesis testing would require complex case-by-case modeling of the curve's trends to be correct [ 24 ], which is out of the scope of this work but could be performed if needed on the data generated by the workflow. Fig.…”
Background
Plants can perceive and respond to mechanical signals. For instance, cortical microtubule (CMT) arrays usually reorganize following the predicted maximal tensile stress orientation at the cell and tissue level. While research in the last few years has started to uncover some of the mechanisms mediating these responses, much remains to be discovered, including in most cases the actual nature of the mechanosensors. Such discovery is hampered by the absence of adequate quantification tools that allow the accurate and sensitive detection of phenotypes, along with high throughput and automated handling of large datasets that can be generated with recent imaging devices.
Results
Here we describe an image processing workflow specifically designed to quantify CMT arrays response to tensile stress in time-lapse datasets following an ablation in the epidermis — a simple and robust method to change mechanical stress pattern. Our Fiji-based workflow puts together several plugins and algorithms under the form of user-friendly macros that automate the analysis process and remove user bias in the quantification. One of the key aspects is also the implementation of a simple geometry-based proxy to estimate stress patterns around the ablation site and compare it with the actual CMT arrays orientation. Testing our workflow on well-established reporter lines and mutants revealed subtle differences in the response over time, as well as the possibility to uncouple the anisotropic and orientational response.
Conclusion
This new workflow opens the way to dissect with unprecedented detail the mechanisms controlling microtubule arrays re-organization, and potentially uncover the still largely elusive plant mechanosensors.
“…The resulting graphs are based on the means and their standard deviations. The statistical significance of the difference between genotypes and treatments was assessed by the mixed models' approach (Spyroglou et al, 2021). For the pairwise comparison of genotypes, the Benjamini-Hochberg Procedure correction method was used.…”
Seedling de-etiolation is one of the key stages of the plant life cycle, characterized by a strong rearrangement of the plant development and metabolism. The conversion of dark accumulated protochlorophyllide to chlorophyll in etioplasts of de-etiolating plants is taking place in order of ns to µs after seedlings illumination, leading to detectable increase of chlorophyll levels in order of minutes after de-etiolation initiation. The highly complex chlorophyll biosynthesis integrates number of regulatory events including light and hormonal signaling, thus making de-etiolation an ideal model to study the underlying molecular mechanisms. Here we introduce the iReenCAM, a novel tool designed for non-invasive fluorescence-based quantitation of early stages of chlorophyll biosynthesis during de-etiolation with high spatial and temporal resolution. iReenCAM comprises customized HW configuration and optimized SW packages, allowing synchronized automated measurement and analysis of the acquired fluorescence image data. Using the system and carefully optimized protocol, we show tight correlation between the iReenCAM monitored fluorescence and HPLC measured chlorophyll accumulation during first 4h of seedling de-etiolation in wild type Arabidopsis and mutants with disturbed chlorophyll biosynthesis. Using the approach, we demonstrate negative effect of exogenously applied cytokinins and ethylene on chlorophyll biosynthesis during early de-etiolation. Accordingly, we identify type-B response regulators, the cytokinin-responsive transcriptional activators ARR1 and ARR12 as negative regulators of early chlorophyll biosynthesis, while contrasting response was observed in case of EIN2 and EIN3, the components of canonical ethylene signaling cascade. Knowing that, we propose the use of iReenCAM as a new phenotyping tool, suitable for quantitative and robust characterization of the highly dynamic response of seedling de-etiolation.
“…The differences between the treatments were tested for significance using linear mixed models (LMMs), following the approach developed by Spyroglou et al (2021). We built a statistical model Using LMMs that predicted the response variable (i.e., the mean daily cycle of Fs) as a function of treatment and time as fixed factors (fixed for all data points), and each collar as a subject-specific factor (random effect).…”
Abstract. The mechanisms underlying the soil CO2 flux (Fs) in dry desert soils are not fully understood. To better understand these processes, we must accurately estimate these small fluxes. The most commonly used method, static chambers, inherently alter the conditions that affect the flux and may introduce errors of the same order of magnitude as the flux itself. Regional and global assessments of annual soil respiration rates are based on extrapolating point measurements conducted with flux chambers. Yet, studies conducted in desert ecosystems rarely discuss potential errors associated with using static chambers in dry and bare soils. We hypothesized that a main source of error is the collar protrusion above the soil surface. During the 2021 dry season, we deployed four automated chambers on collars with different configurations in the Negev Desert, Israel. Fs exhibited a repetitive diel cycle of nocturnal uptake and daytime efflux. CO2 uptake measured over the conventionally protruding collars was significantly lower than over the collars flushed with the soil surface. Using thermal imaging, we proved that the protruding collar walls distorted the ambient heating and cooling regime of the topsoil layer, increasing the mean surface temperatures. Higher soil temperatures during the night suppressed the flux driving forces, i.e., soil-atmosphere CO2 and temperature gradients, ultimately leading to an underestimation of up to 50 % of the actual Fs. Accordingly, the total daily CO2 uptake by the soil in the conventionally deployed collars was underestimated by 35 %. This suggests that desert soils are a larger carbon sink than previously reported and that drylands, which cover approximately 40 % of Earth’s terrestrial surface, may play a significant role in the global carbon balance.
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