Arabidopsis thaliana plants from germination to maturity . In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis, over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions’ respective origins.The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown  The diversity of shoot and root system forms within the plant kingdom mirrors the many functions required for plant survival in diverse habitats. However, these forms are not static, but dynamically change during the lifespan of a plant as it acclimates to a changing environment, and over evolutionary time scales as a consequence of natural selection and genetic drift. The visible above-ground shoot system is optimized for photosynthesis and reproduction while the below-ground root system is responsible for anchoring the plant and allowing it to reach essential nutrients and water, which is mediated, in part, through interactions with microbes in the soil . While sA. thaliana, the embryonically formed primary root is the first to emerge from the seed at germination and ultimately branches to give rise to secondary roots, which, in turn, branch and produce tertiary and higher order roots (Root system architecture (RSA) refers to the spatial arrangement of roots in soil, which encompasses the geometric nature of root connectivity and distribution . This arer roots . Each ster roots . Modulater roots . The horer roots . ABA lever roots . Hence, A. thaliana  yielded insight into the heritability and genetic architecture of distinct RSA traits, and significant associations with climates across the species’ geographic distribution highlighted the power of our approach and its relevance for understanding how root architectural traits contribute to local adaptation.Previously, we developed GLO-Roots (Growth and Luminescence Observatory for Roots), an imaging platform enabling visualization of soil-grown is suite . Here, wTo further develop the potential of GLO-Roots for time-lapse imaging of root development, from early stages to mature RSAs, we developed GLO-Bot, a Cartesian gantry robotic system consisting of an exterior frame and arm on a linear rail system built from T-Slotted aluminum extrusion, which houses the Growth and Luminescence Observatory 1 (GLO1) imaging system . In addiThe primary goal of developing the robotics platform was to automate plant imaging since the luciferin watering regime of each rhizotron and the long exposure time required to capture luminescence signal were the most time-consuming steps of the GLO-Roots system. An important consideration was the layout for the robot and range of motion for the arm . The perProgramed movements of the robotic arm bring the rhizotron to a barcode scanner, which allows the system to recall the specific treatment and imaging protocol to use . SubsequThe increased throughput of GLO-Bot enabled time-lapse imaging of root systems and created new opportunities for the measurement of dynamic root growth traits, but also revealed new challenges for root image analysis. Luciferase signal intensity varies between transgenic LUC-expressing lines and decreases in older root tissues, making it even more difficult to distinguish between roots and noise during the course of the experiment . While tWith luminescent root signal maximized throughout all of the images in a time-lapse series, the macro invert.ijm is used to invert the gray-scale values of the images, thus making the root system itself black and the background white for downstream whole root system analyses. In addition to determining architectural parameters of complete root systems, we also established methods to quantify the actively growing regions of root systems. For this, we use the macro tip_tracking.ijm to isolate the new growth between images by a series of dilating and subtracting of two consecutive images . The six accessions that were imaged displayed diverse RSAs , which plots the vectorized root systems for all of the accessions. The raw data is available through Zenodo at https://doi.org/10.5281/zenodo.5709009. Accessions and replicate numbers with excessive background noise or other anomalies are identified in Similar to the analysis of the first set of six accessions , PCA of Further analysis of the traits via pairwise comparison at all time points not only allowed us to visualize existing correlations within traits over time, but also among traits . LookingWe next wanted to investigate the genetic basis of RSA traits. Some traits, such as the angle measurements, had high broad-sense heritability while others, such as total length, had low broad-sense heritability . While sOut of the 29 identified significant SNPs, four synonymous SNPs can be found within the coding sequence of two genes, eight were in intergenic regions, whereas the majority of SNPs were found in upstream- and downstream regions of genes, potentially affecting expression patterns . It shou2=0.45) and average angle per day (H2=0.31), we found 12 and 8 significant SNPs, respectively  challenges our ability to discover SNPs significantly associated with the traits . Althougthaliana  allows wthaliana , it willthaliana . BesidesThe correlation of our nine extracted root traits to published bioclimatic data from the origin sites of the respective accessions reveals a significant correlation between average angle to mean diurnal temperature range. Thus, accessions with shallow RSAs tend to grow in climates that exhibit larger changes in the daily minimum and maximum temperatures reminiscent of desert-like climates. This fits with the known pattern that many plant species growing in the desert show rather shallow root systems with advantages including reduced energy input, increased ability to capture moisture from precipitation, and high nutrient availability in upper soil layers, which may improve survival in a more varied climate .Arabidopsis populations, where the majority of our characterized accessions originate (Arabidopsis root architecture compared to temperature. Indeed vapor pressure deficit (VPD), which is the driving force determining the rate of water loss during transpiration  and were water jet cut  into 15 cm × 30 cm rectangles with 14 small gaps running down each side (clear_sheets.dxf). Two spacers (spacers.eps) were laser cut  from 1/8” black cast acrylic . Two rubber U-channels were cut into 29 cm long strips from Neoprene Rubber Trim, 5/16” Wide × 23/32” High Inside . Additional adaptations for automation included: beveling the top of each polycarbonate sheet to a thin 45°̊ angle using a belt sander. Half of the polycarbonate sheets had 2.5 mm holes drilled in 5/8” down from the top edge and 3/16” in from each side. The small holes were used to screw a 1/16” aluminum water jet cut hook, outer dimensions 18 mm × 57 mm with a 13 mm diameter circle cutout (rhizotron-machine-hooks-13mm.pdf), to both sides of the sheet using 2×4 M3 stainless steel socket cap screws . Not only did the hooks provide a way to pick up the rhizotron, they also were threaded through a rhizotron top (rhizo-bin-top-enlarged.pdf) cut  from 1/8” black acrylic, which served as a light shield and a bearing surface for the hanging rhizotron. The other half of the polycarbonate sheets had 8 mm holes drilled in centered at the same location and were used as counterparts to the sheets with tops and hooks.Similar to https://doi.org/10.5281/zenodo.6694558.All build files for the rhizotrons and holders can be found at ProUBQ10::LUC2o reporter, which consists of the UBQ10 promoter region and first intron amplified from Col-0 genomic DNA, the plant-codon-optimized LUC2 coding sequence, and a plasma membrane-localized mCherry coding sequence driven by the 35S promoter . Positive transformants were selected under a fluorescent dissecting scope (Leica M165 FC) using the mCherry marker visible in the mature seed. Bay, Col-0, Est, Sha, and Tsu-0 seeds were carried through to the T3 generation. Tottarp was used in the T2 generation.Bay, Col-0, Est, Sha, Tottarp, and Tsu-0 were transformed using the floral dip method  with theA. thaliana accessions were ordered from the Arabidopsis Biological Resource Center , at which point all plants were photographed and moved to a 22°C greenhouse until seeds could be harvested. Ultimately, 148 lines yielded at least three independent lines with strong root luminescence. Before starting an experiment, seeds were again screened for positive transformants using the mCherry seed coat marker.Rhizotron preparation was done as described in Rellán-Álvarez et al., 2015, with minor modifications: a polycarbonate plastic sheet with 8 mm holes and a sheet with the black rhizotron top and metal hooks were each laid on the table. Since the acrylic rhizotron top extends from the top in every direction, those sheets were laid down on the building surface with the acrylic piece and hooks lying just-off the edge of the surface and the screws pointing up, which ensured that the sheet lay flat. To prevent excess buildup of soil, the spacers were inserted into the sheets with 8 mm holes, and ‘flags’  were inserted into the sheets with the attached tops. Using an electric paint sprayer (Wagner Spraytech Control Spray Double Duty HVLP Sprayer Model #0518050), a mist of water was applied to the sheets. Peat based Pro-Mix PGX soil  was gently sifted over the sheets using a 2 mm sieve (US Standard Sieve Series N ° 10) and excess soil was gently shaken off. The mist, sift, shake procedure was done for a second time, which creates a two-layer thick surface of soil thin enough to see a small amount of light through when held up to a lightbulb or window. To prevent soil from falling out, a folded and trimmed piece of paper towel was moistened then placed at the bottom of each rhizotron. The spacers and flags were removed and the flags were replaced with clean spacers. The two sheets are carefully placed together and rubber U-channels were slipped on to each side. A small handful of sifted soil was placed into the groove in the rhizotron top and gently pushed down. Completed rhizotrons were hung in the boxes and, due to the increased size of the boxes, 3 L of water were added to the bottom of each box.−1 s−1. For the six accessions, plants were grown until 9 or 21 DAS and then were imaged every day until 31 DAS. Five replicates of each accession were grown for each treatment and plants were watered with luciferin every 6 days. For the diversity panel, the population was grown up, as a whole, six times, thus having temporal population replicates rather than internal replicates in order to reduce variation effects of growth condition. Since we imaged the set of 93 accessions in two parts (parts 1 and 2), we mostly split the accessions alphabetically and added two Col-0 controls per imaging session. The rhizotrons of each imaging session were then swapped once between boxes to avoid rhizotron building effects. For the second imaging session (part 2), we made sure to include a Col-0 control to the bin that grew in the shelf outside of GLO-Bot  to examine position effects. After the first randomization, accessions were always planted in the same locations relative to each other (Two transfer pipettes (each ~2 mL) of Peters Professional 20-20-20 General Purpose fertilizer were added to each rhizotron immediately after building. Fertilized rhizotrons in filled boxes were covered and left to soak overnight. Seeds were stratified for 2 days at 4°C in distilled water. Three seeds were sown in the center of each rhizotron using a p200 pipette (Eppendorf) and a unique barcode was placed on the rubber U-channel. Rhizotrons were sprayed down with water and a clear acrylic sheet was placed on top of the box then sealed with packing tape to create a humid environment. Three days after sowing, clear sheets were unsealed and rhizotrons were watered with two transfer pipettes of water. The following day, the clear sheets were removed and rhizotrons were watered again. Rhizotrons were watered with two pipettes of water until 9 days after sowing. Plants were thinned to one central plant 5 days after sowing. Plants were grown under long day conditions  at 22/18°C (day/night) using LED lights  with a light intensity of about 130 μE mch other  so the pOn each imaging day (timing depending on experiment), GLO-Bot loaded each rhizotron into the imager then closed the door and triggered μManager to capture 5 min exposures on each side of the rhizotron. Inside the imaging system, there are two cameras on top of one another, with one taking an image of the top part of the rhizotron and the other, the bottom part of the rhizotron. After one side was imaged, the rhizotrons were rotated using a Lambda 10-3 Optical Filter Changer  and the other side of the rhizotron was imaged using the top and bottom camera, yielding four images per rhizotron. If it was the first imaging day or a designated luciferin day (every 6 days), GLO-Bot added 50 mL of 300 μM D-luciferin  to the top of each rhizotron immediately before loading the rhizotron into the imager. Before every replicate, a blank image was taken to account for background noise within the imager.Initial root image preparation to combine and align the four raw images was done as described in Rellán-Álvarez et al., 2015. Initial background noise removal was done using remove.background.ijm. By manually opening the blank image for the respective replicate and then running this macro, the open blank image will be subtracted from all of the files in the folder being processed by the macro. The ImageJ plugin Template Matching and Slice Alignment was run as instructed in the manual to register all root images. Images are then run through the macro denoise_overlay.ijm to remove additional noise and add images together, which maximizes signal intensity throughout the root by: (1) subtracting 1.5 from all pixels to remove pixels with low values; (2) multiplying all values by 3, which greatly amplifies pixels with high values; (3) subtracting 8 from all values, which again removes pixels with low values (i.e. those that did not get amplified); (4) running the ImageJ command Despeckle, which replaces every pixel with the median value in its 3×3 neighborhood ; (5) sethttps://doi.org/10.5281/zenodo.5574925). This macro works by: (1) opening two consecutive images; (2) using the ImageJ command Dilate twice on the image from the earlier time point; (3) using the ImageJ command Subtract Create to subtract the earlier day from the later day. By dilating the earlier root system, we are able to ensure complete subtraction from the later time point and, therefore, only analyze the new root growth. Again, the thresholding in the new growth isolation can be refined if needed. Images were then run through GLO-RIAv2 and downstream analysis was performed in R  stars in the upper right corner of some images were manually cropped out. Initially, these stars were placed in the images to aid image alignment, but instead generated too much noise. For the GLO-Roots diversity panel, a total of 34 images were removed . Those iRaw trait measurements were converted into single values for each genotype in the software R  using thgenoi), replicate (urep), and the time and genotype interaction (ugenot) were treated as random factors. The effects capture the average deviation for each genotype from the mean of the population. The script runs the data through a null model, which is a model run with no parameters and provides the baseline comparison for future models using the same input data, and then runs the previously described mixed model to estimate fixed effects by running 1,000,000 iterations in a Monte Carlo Markov chain (MCMC) and 200,000 burn-in using the MCMCglmm R package (where the trait (y) and time (t) were fitted as fixed effects, genotype  as well as the expected value at each time point for each genotype. Broad-sense heritability was calculated using standard approaches in MCMCglmm , which eIn addition, once we extracted genotype-specific values of each trait , we conducted GWAs using GEMMA and the AraGWAS database SNP matrix . GEMMA w Arabidopsis thaliana accessions provides a major resource for understanding the genetic basis for root system architecture variation under physiologically relevant conditions. The work will be of great interest to plant biologists and all those studying genetic variation in plants.The authors present an automated system for phenotyping root system architecture based on bioluminescent roots resulting from a constitutively expressed luciferase transgene (GLO-Roots). They have developed a robotics-assisted phenotyping platform and an automated image analysis pipeline for high throughput analysis. An impressive array of 93 luciferase expressing  public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jürgen Kleine-Vehn as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Niklas Schandry (Reviewer #1); Larry York (Reviewer #3).Thank you for submitting your article "Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.eLife. Please find the reviewer comments below. We have discussed the experiment with 6 accessions (mentioned by reviewer #3) and we agree that it should stay in the manuscript but that it also causes some distraction from the main point. We suggest to more strongly focus the text on these results to better integrate them with the next chapter describing results from the full panel. Otherwise, we consider all reviewer comments to be important to improve the manuscript.As you see from the reviewers' comments they (and myself) are excited about your manuscript, although it requires some revision and clarifications to become acceptable for Reviewer #1 (Recommendations for the authors):Comments on GWAS:– I noticed that sometimes, the data cleaning procedure appears to retain pixels which are not part of the root system .– The authors use “depth” to refer to the vertical distance covered by the overall root system, and “length” to refer to the sum of the length of all vectors that represent the root system. In the extreme case of a single main root growing straight down depth would be equal to cumulative length and depth should never be larger than cumulative length, yet there are cases where depth is larger than the total length of the root . The length measurement in several accessions suffers from unconnected vectors, while the overall shape of the root system is probably correct . See for example accession Ge-0 GWA3, GWA5 or GWA6, where the root appears to be made up of many very short vectors with gaps. This raises the question if the length variable is suitable for quantitative genetic analysis. I also wonder how this issue affects the accuracy of the vector angles with respect to the true root system.– Breeding values were computed for the quantitative genetic analysis. It should be clarified whether “breeding value” refers to the estimated model coefficients (p. 8), or to the fitted values (p. 54). All traits were overall using the same formula. It is not really clear why the authors opted for this approach instead of using different models for the different traits. Some traits, such as width  do not display a linear relationship with time.Average angle per day appears to extremely dynamic and I am not sure if the breeding values for this trait are meaningful with regards to the original data, raising the question if the fitted values for this trait are a good input for GWAS. Is it possible that the overall observation that p-values of some SNPs decrease over time simply reflects the linear relationship with time specified in the model, as fitted values increase over time?What is the advantage of using fitted values at different timepoints over using the model coefficients for each accession as the input?Since the model assumes a linear relationship, which appears to not capture the dynamics of all traits, would it not be reasonable to simply compute the trait mean per day per accession and use these values as input for GWAS, instead of using fitted values?Reviewer #2 (Recommendations for the authors):1. While the GLO-Bot system is using potting mix, it will still be very different from soil grown roots. With GLO-roots, the roots are grown in a thin sheet of soil  and the roots will become constrained by the width of the system as well. This will most likely reduce differences between shallow rooting accessions and intermediate accessions. It will be important to discuss the constraints of the system and discuss whether any time point will be closer to a 3D soil growth environment. One way how this might be elucidated would be to look at broad sense heritability at different time points. Such an approach might also indicate an optimal timepoint for the GWAS and the correlations with environmental variables.2. Page 8: "For each of these traits, we used a generalized linear mixed model in the R package MCMCglmm to account for replicate and block effects noises and estimate the "breeding value" of each accession for a given trait ; (Hadfield 2010) . The denoised trait values for each genotype at 0, 48, 96, 144, 192, 240, 288, and 336 hours after the start of imaging  were used for further analyses."It remains a bit unclear what the process was  and what the rationale was to use "breeding value". Also, most readers will not know what a "breeding value" is. Are the "denoised" trait values breeding values? Why do the authors think that the raw trait values are not helpful for further analysis ?3. Page 9ff: The candidate gene follow up is very incomplete. For instance, the authors show in their Manhattan plots that some associations are linked to other SNPs in that region. It therefore would be good to also include genes in proximity of the linked SNPs in the table. Also, the SNPs identified with a GWAS are not necessarily the causal SNPs . The causal SNPs could be linked or partially linked to the GWAS SNPs . It would therefore be helpful if for the genes discussed in the text, the genomic region could be shown with haplotypes or just using the 1001 genome project SNPs in accessions with alternative GWAS SNPs.4. Page 5: Root Angle output is unclear . This is important as it is a major trait in the manuscript. Is this the average of all roots? Do all roots need to be detected for that? What are the two points that are used to measure the angles? What is a root segment? How many are considered?5. Page 6: How many root systems were compared with SmartRoot? Is each point in Figure 2B an entire root system? Is the comparison similarly accurate for very different RSAs? What is the advantage of GloRoot vs Smartroot ?6. The trait data is not deposited with the manuscript or on a public repository. This would be very important to allow the community to build on the work of the authors.Reviewer #3 (Recommendations for the authors):The comments below may be helpful to the authors.Since there weren’t line numbers, I give page numbers below and tried to ‘quote’ key words that should allow the authors to find what my comments pertain to.1: ‘above ground’ is not hyphenated here but is elsewhere.1: 'climate variables of the accessions respective origins' or something as to not be confused with growth conditions, etc.2: 'between' should be among.2: "Most RSA studies…" – can the most here really be substantiated? If not, "many" may suffice. I am not sure that most are in gel or in Arabidopsis.3: rhizotrons are often called rhizoboxes. If rhizotron is preferred, perhaps "(or rhizobox)" can be added at first mention to orient some readers.5: 'detection and translation of subsequent images' – I believe this describes what is commonly called registration so the word may be useful to add here for clarity.5: 'invert the images' may be expressed as 'invert the colors of the images' to be precise.5: 'root segment' needs defined.6: 'series of vectors' – seems like RSML and some would ask if RSML is supported.6: 'half of which' could be confused for accession or replicate (I know it's replicate) but consider saying "where five replicates" instead.6: for the 6 accessions, you can add a bit more quantification by using a calculation of heritability or repeatability. You could have also used the additional replication for power analysis to determine the need for replications numbers, for example boot strapping from 2 – 10 reps to see if replicates improve measures or heritability.7: in the last paragraph, what type of randomization used should be specified . On page 8 it is mentioned that an R package is used for block. I think this is leading me to conclude the results simply have to methods mixed in.8: So is it right that a subset of days are used? Why?8: PCA over time implies time dependent traits are just sized related.9: ‘fitted values’ could be clarified.10: ‘published bioclimatic variables’ could include for clarity ‘…from the origin sites of the respective accessions.’11: ‘progress in machine-learning’ – I remember I think one of the original rationales for this system was to image on both sides for a more complete root system? Was that in the original paper? Is that done here? With regards to bioluminescence – is it still clear that this method is advantageous to RGB imaging or other forms of imaging? For example, the Smith et al. 2020 paper shows many examples of identifying roots from complex backgrounds. The RootPainter software has been used for a rhizobox study:https://www.biorxiv.org/content/10.1101/2021.08.24.457510v1Can the possible advantages be discussed, especially if there are specific examples from images that show color imaging alone is not sufficient?11: sample sizes could be sample numbers I think.Figure 1 – scale needed in all panels.48: Not clear why Materials and methods are in supplemental?51: "Accessions were always planted in the same locations relative to each other so the population was treated identically in each replicate." Strictly speaking – this is a bad choice, somehow you decided to intentionally NOT randomize which goes against statistical training and a strict reviewer may say it compromises the experimental design where all the measurements could be artifacts of position. Is there a good reason to not randomize? It seems odd in what otherwise seems like such careful design.51: 'four raw images' – root and shoot from each side is four? Maybe make clear.51: what is the 'correct blank images' – is this process entirely automated? Sounds like a user walks the processing through several fairly automated steps but not quite automated from image to data it seems. As you see from the reviewers' comments they (and myself) are excited about your manuscript, although it requires some revision and clarifications to become acceptable for eLife. Please find the reviewer comments below. We have discussed the experiment with 6 accessions (mentioned by reviewer #3) and we agree that it should stay in the manuscript but that it also causes some distraction from the main point. We suggest to more strongly focus the text on these results to better integrate them with the next chapter describing results from the full panel. Otherwise, we consider all reviewer comments to be important to improve the manuscript.Reviewer #1 (Recommendations for the authors):Comments on GWAS:– I noticed that sometimes, the data cleaning procedure appears to retain pixels which are not part of the root system .We thank reviewer #1 for this comment. It indeed happens that the data cleaning procedure does not remove all background pixels. The algorithm cannot distinguish background pixels from root pixels and it needed the mentioned adjustments to retain as much root signal as possible and remove as much background noise as possible. Since we wanted all images to be denoised in the same way, we had to compromise with some images showing remaining background noise. A list of images with these problems has been curated and is included in Supplementary File 3. Certain traits, such as average angle, have been weighted by vector length so tiny measurements from the noise do not have a strong influence on the trait value. Further, we expect noise to not be systematically associated with certain genotypes, rather it will be randomly scattered across replicates within a genotype and across all the genotypes. This measurement error would therefore result in lower heritability estimates, making the conclusions we draw naturally more conservative.– The authors use “depth” to refer to the vertical distance covered by the overall root system, and “length” to refer to the sum of the length of all vectors that represent the root system. In the extreme case of a single main root growing straight down depth would be equal to cumulative length and depth should never be larger than cumulative length, yet there are cases where depth is larger than the total length of the root . The length measurement in several accessions suffers from unconnected vectors, while the overall shape of the root system is probably correct . See for example accession Ge-0 GWA3, GWA5 or GWA6, where the root appears to be made up of many very short vectors with gaps. This raises the question if the length variable is suitable for quantitative genetic analysis. I also wonder how this issue affects the accuracy of the vector angles with respect to the true root system.We thank reviewer #1 for addressing this point. They are correct, the total length measurements come with caveats due to the nature of the unconnected vectors and therefore the unaccounted length between detected root segments. This caveat about the length measurement and other potential caveats have been included in the text on page 6/7. For the vector angles, each vector angle is weighted by the length of the segment, so angles computed from very small short segments are weighted differently than longer connected segments. While it is true that the angles for fully connected roots could differ, this is the most objective and robust approach we could devise and, given the high heritability of the trait, we believe the signal is well captured.– Breeding values were computed for the quantitative genetic analysis. It should be clarified whether “breeding value” refers to the estimated model coefficients (p. 8), or to the fitted values (p. 54). All traits were overall using the same formula. It is not really clear why the authors opted for this approach instead of using different models for the different traits. Some traits, such as width  do not display a linear relationship with time.Average angle per day appears to extremely dynamic and I am not sure if the breeding values for this trait are meaningful with regards to the original data, raising the question if the fitted values for this trait are a good input for GWAS. Is it possible that the overall observation that p-values of some SNPs decrease over time simply reflects the linear relationship with time specified in the model, as fitted values increase over time?What is the advantage of using fitted values at different timepoints over using the model coefficients for each accession as the input?Since the model assumes a linear relationship, which appears to not capture the dynamics of all traits, would it not be reasonable to simply compute the trait mean per day per accession and use these values as input for GWAS, instead of using fitted values?We thank the reviewer for the point regarding breeding values. We realize this was not explained in enough detail. The reviewer is correct about the estimated per-genotype trait deviations and the terminology in the text was unclear. For the sake of clarity, the terminology has been changed to only use “fitted values''. Using our temporal model, the slope and intercept coefficients were used to calculate the expected genotype value at any timepoint. The text on page 8 (now page 9) was clarified.Both using the accessions’ coefficients as well as the predicted value at a given timepoint are fair and interesting approaches to study the differences in root traits among ecotypes, and each provides different conclusions. Simply using the coefficients for GWA will tell us about the genes responsible for the main trends in the data, but these are less intuitive. Conducting GWA on the predicted values at each timepoint gives us an understanding about when a trait is most relevant, which is an important conclusion of our efforts to create a longer time series of trait data than previously available.Another question from the reviewer regards the different possible functions used to model traits; this is a matter of statistical tractability and trade-offs between different models. While higher order polynomials or sigmoid curves may fit well or even better than a linear relationship of the trends, these models were not tractable in our experimental and analysis design. Let us give some background: To make the most of the data, the approach we took is that of time series analyses of Bayesian generalized linear mixed models where we can include experimental design factors to control for batch effects and at the same time fit genotype factors to estimate genotype-specific trends . This is advantageous because we can control for noise while using all the replicates and all the time points to infer genotypic effects . The limitation is that when temporal functions  become more complex, the model requires fitting extra parameters (number of genotypes * new function parameter). This makes it infeasible to fit very complex models, and indeed when we tried using MCMCglmm the models often did not converge to a solution. One may be tempted to fit a polynomial model for all the data points for each genotype separately, which would be feasible. However, this approach would not enable calculating breeding values for accessions, and would lack control for batch effects. Given these trade-offs of model selection, we decided to sacrifice complexity of the time series function in favor of better control for experimental design.Reviewer #2 (Recommendations for the authors):1. While the GLO-Bot system is using potting mix, it will still be very different from soil grown roots. With GLO-roots, the roots are grown in a thin sheet of soil  and the roots will become constrained by the width of the system as well. This will most likely reduce differences between shallow rooting accessions and intermediate accessions. It will be important to discuss the constraints of the system and discuss whether any time point will be closer to a 3D soil growth environment. One way how this might be elucidated would be to look at broad sense heritability at different time points. Such an approach might also indicate an optimal timepoint for the GWAS and the correlations with environmental variables.We thank reviewer #2 for this very important point. We agree with the reviewer that the dimensions of the growth vessel will impact the architecture of the root system, as will soil type. While measurements of root growth in large volumes of natural soil will improve the ecological significance of the trait measurements, we are not aware of data supporting the view that this will necessarily affect the heritability of these traits. Likely, heritability will be affected by the complexity of the environment that roots grow; conditions such as agar gel will likely exhibit tighter relationships between genotype and phenotype because there is less heterogeneity in the environment. This fundamentally differs from more natural soil conditions where heterogeneity in the environment is intrinsic to the growth condition. We believe that the relationship between genotype and phenotype has greater ecological significance when measured using the GLO-Roots system due to the more physiologically relevant ways in which light, water and nutrient availability interact with the root system and processes such as light, transpiration and gas exchange impact the plant as a whole. Certainly, as soon as the roots touch the edges of the rhizotron, traits like root depth and root width will lose significance, however traits like average angle or length per day will still show differences until the rhizotron is filled with roots.We conducted an analysis of heritability over time for our traits, although we had not included it in the text. One could conduct MCMCglmm for each timepoint. Another alternative is to study the proportion of variance explained (PVE) of each trait by timepoint by the genome in the GWA analyses. GEMMA provides such PVE, a parameter that is also often called Chip heritability or narrow sense heritability, as the kinship matrix, built to control for genotype relatedness, captures additive differences. Similarly as Figure 6A, we can see how the PVE changes over time, see 2. Page 8: "For each of these traits, we used a generalized linear mixed model in the R package MCMCglmm to account for replicate and block effects noises and estimate the "breeding value" of each accession for a given trait ; (Hadfield 2010) . The denoised trait values for each genotype at 0, 48, 96, 144, 192, 240, 288, and 336 hours after the start of imaging  were used for further analyses."It remains a bit unclear what the process was  and what the rationale was to use "breeding value". Also, most readers will not know what a "breeding value" is. Are the "denoised" trait values breeding values? Why do the authors think that the raw trait values are not helpful for further analysis ?We realize that the rationale of the MCMCglmm approach was not described in enough detail. We have now included a much more extended version in the trait processing section of the methods. Further, we clarified the description highlighted in this comment on page 9 to be more consistent with the rest of the text. We have removed the term “breeding value” and instead use the general term “fitted values” to represent all of the fitted genetic values for each genotype: the slope, intercept, and the expected values at each time point for each genotype.3. Page 9ff: The candidate gene follow up is very incomplete. For instance, the authors show in their Manhattan plots that some associations are linked to other SNPs in that region. It therefore would be good to also include genes in proximity of the linked SNPs in the table. Also, the SNPs identified with a GWAS are not necessarily the causal SNPs . The causal SNPs could be linked or partially linked to the GWAS SNPs . It would therefore be helpful if for the genes discussed in the text, the genomic region could be shown with haplotypes or just using the 1001 genome project SNPs in accessions with alternative GWAS SNPs.We thank the reviewer for this point. We know the SNPs associated with the trait are not necessarily the causal ones. We have included the list of all genes within a 20 Kb region of the significant SNPs (10 kb upstream and 10kb downstream from the SNP) in Supplementary File 4 also listing the reference and alternative alleles. This has been clarified in the text, as well.4. Page 5: Root Angle output is unclear . This is important as it is a major trait in the manuscript. Is this the average of all roots? Do all roots need to be detected for that? What are the two points that are used to measure the angles? What is a root segment? How many are considered?Root angles are computed on sections of roots visible in the image (aka “segments”). These segments do not always correspond to a biological structure, but rather the visible readout of the root system structure within the GLO-Bot platform. These root system structures visible on the GLO-Bot images are indeed disconnected in many locations, due to variability in the reporter’s intensity and the presence of physical obstructions such as soil particles. We actually used this disconnection to our advantage to compute more faithful root angle measurements within the image, as we use the “segments” as individual elements see . We have5. Page 6: How many root systems were compared with SmartRoot? Is each point in Figure 2B an entire root system? Is the comparison similarly accurate for very different RSAs? What is the advantage of GloRoot vs Smartroot ?The growing root systems of 5 plants were traced with SmartRoot. Since each root was imaged over a series of days and occasionally imaged multiple times within one day each plant has between 15 and 22 root images. In total, 95 root images were traced. Each point in the plot is a time point. Manually tracing a single GLO-bot RSA image with SmartRoot can take days, depending on the size of the root system. SmartRoot relies on continuous structures to follow the root. Due to the nature of the GLO-bot RSA images, this becomes a very tedious process of tracing each root system. We added more details to the main text and the figure legend of Figure 2B, stating: “Manual SmartRoot tracing of a single GLO-Roots image can take multiple days, thus making analysis of large-scale time course experiments unfeasible.”6. The trait data is not deposited with the manuscript or on a public repository. This would be very important to allow the community to build on the work of the authors.The trait data is available on Zenodo (https://doi.org/10.5281/zenodo.5709009) as stated at the end of the manuscript. A link has also been inserted into the text for clarity.Reviewer #3 (Recommendations for the authors):The comments below may be helpful to the authors.Since there weren’t line numbers, I give page numbers below and tried to ‘quote’ key words that should allow the authors to find what my comments pertain to.1: ‘above ground’ is not hyphenated here but is elsewhere.We thank reviewer #3 for this comment and corrected this mistake. It now reads; ”The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground.”1: 'climate variables of the accessions respective origins' or something as to not be confused with growth conditions, etc.We have added in ‘of the accessions’ respective origins’ for clarity.2: 'between' should be among.Thank you, we changed “between” to “among”.2: "Most RSA studies…" – can the most here really be substantiated? If not, "many" may suffice. I am not sure that most are in gel or in Arabidopsis.We thank the reviewer for this valuable input and agree that “most” should be replaced by “many”.3: rhizotrons are often called rhizoboxes. If rhizotron is preferred, perhaps "(or rhizobox)" can be added at first mention to orient some readers.Thank you for this suggestion. We prefer rhizotrons to avoid confusion with the growth boxes that hold the rhizotrons in our setup. We added “rhizoboxes” to the text. It now reads; “In addition to GLO1, the frame contains a growth area for seven black polyethylene growth boxes , each of which holds 12 rhizotrons  arranged in a two by six grid .5: 'detection and translation of subsequent images' – I believe this describes what is commonly called registration so the word may be useful to add here for clarity.We agree with the reviewer and adapted the text. It now reads: “This plugin works by user selection of a region present in all images followed by automated detection and translation of subsequent images, a method that is also known as image registration.”5: 'invert the images' may be expressed as 'invert the colors of the images' to be precise.For clarity, this has been revised to: “…invert the gray-scale values of the images, thus making the root system itself black and the background white for downstream whole root system analyses.”5: 'root segment' needs defined.Root angles are computed on sections of roots visible in the image (aka “segments”). These segments do not always correspond to a biological structure, but rather the visible readout of the root system structure within the GLO-Bot platform. These root system structures visible on the GLO-Bot images are indeed disconnected in many locations, due to variability in the reporter’s intensity. We actually used this disconnection to our advantage to compute the root angle distribution within the image, as we use the “segments” as individual elements (see figure in comment 2.4). This also has been clarified in the text on page 6.6: 'series of vectors' – seems like RSML and some would ask if RSML is supported.We choose not to, since in the analysis we lose most of the topological information that is central in the RSML structure.6: 'half of which' could be confused for accession or replicate (I know it's replicate) but consider saying "where five replicates" instead.Thank you for this valid concern, we changed the text accordingly. It now reads; ”Each accession was grown in ten replicates, where five replicates were imaged daily from 9 days after sowing (DAS) until 31 DAS and the other five replicates from 21 – 31 DAS.”6: for the 6 accessions, you can add a bit more quantification by using a calculation of heritability or repeatability. You could have also used the additional replication for power analysis to determine the need for replications numbers, for example boot strapping from 2 – 10 reps to see if replicates improve measures or heritability.We thank reviewer #3 for these comments. We calculated heritability for the 6 accessions but chose not to include it since the small number of genotypes precludes confident heritability determination. We recognize the point about determining replicate numbers, however this was not something we did at the time. While the 6 accessions provided insight into some of the methods , they did not inform factors such as replicate number in the experiment. Based on reviewer comments, we are choosing to not only emphasize the 6 accessions as a foundation for the diversity panel, but also highlighting them as an alternative example of how the GLO-bot system can be used .7: in the last paragraph, what type of randomization used should be specified . On page 8 it is mentioned that an R package is used for block. I think this is leading me to conclude the results simply have to methods mixed in.The 93 accessions of the GLO-Roots diversity panel were selected balancing genetic similarity and environmental variation along with reporter intensity, and the number of independent transformation events recovered. Since we imaged the set of 93 accessions in two parts (part 1 and 2), we mostly split the accessions alphabetically and added two Col-0 controls per imaging session. The rhizotrons of each imaging session were then swapped once between boxes to avoid rhizotron building effects. For the second imaging session (part 2), we made sure to include a Col-0 control to the bin that grew in the shelf outside of GLO-Bot  to examine position effects. We now list the location of every accession in Figure 4—figure supplement 2. We added this information to the methods section on page 55.We removed the comment about the block effects from the text since the final model did not take positional effects into account. It is true that there are more methods mixed into the results than usual. Given the “tools and resources” style of paper, we tried to include key pieces of methodology to help the reader understand the results.8: So is it right that a subset of days are used? Why?The six accessions demonstrate how GLO-bot can be used for continuous imaging of a small number of accessions with many replicates. With this in mind, the rhizotrons are imaged every day for a long period of time. Since the diversity panel had so many more accessions the plants were imaged for a subset of the days to account for possible hardware errors as they arose. We added this important information to the text on page 9.8: PCA over time implies time dependent traits are just sized related.We agree with this and added in “and plant size”.9: ‘fitted values’ could be clarified.We agree with the reviewer and have clarified the language regarding the fitted values. The description of fitted value calculation on page 9 has been revised to explain how the values were calculated and the manuscript has been reviewed to use consistent terminology. Figures have also been updated to indicate whether fitted values or raw values were used.10: ‘published bioclimatic variables’ could include for clarity ‘…from the origin sites of the respective accessions.’We appreciate this suggestion and have added the phrase in for clarity.11: ‘progress in machine-learning’ – I remember I think one of the original rationales for this system was to image on both sides for a more complete root system? Was that in the original paper? Is that done here? With regards to bioluminescence – is it still clear that this method is advantageous to RGB imaging or other forms of imaging? For example, the Smith et al. 2020 paper shows many examples of identifying roots from complex backgrounds. The RootPainter software has been used for a rhizobox study:https://www.biorxiv.org/content/10.1101/2021.08.24.457510v1The reviewer is correct that imaging from both sides of the rhizotron is an important aspect of the GLO-Roots/GLO-bot methodology. This imaging itself in the GLO-Bot system is performed in the same manner as the original paper, so imaging from both sides does indeed happen here and continues to be advantageous for extensively capturing the entire root system. Given the size of Arabidopsis roots, the bioluminescence continues to be advantageous. While the field of machine-learning for identifying roots is rapidly progressing, advances seem to be primarily focused on plant systems with much thicker roots. To our knowledge, RootPainter is still not sufficient for observing Arabidopsis roots in RGB images. We have added the comparison to RGB imaging to the discussion on page 13.Can the possible advantages be discussed, especially if there are specific examples from images that show color imaging alone is not sufficient?In the original GLO-Roots paper , we scanned the rhizotrons in order to measure the moisture level of the soil. Since Arabidopsis has very thin roots, RGB imaging will not reveal the whole root system 30684-1.pdf). Furthermore, manual tracing is required since the contrast between the roots and the soil is not strong enough. This approach would not allow the throughput and time-lapse aspect that we were aiming for. The advantage of luminescence is that it makes fine roots appear thicker, that it shines through thin layers of soil and that it improves the contrast between root and soil signal. We used Arabidopsis because of its big collection of natural variation, genetic resources, plant size and generation time in order to possibly identify genes that are involved in root system architecture growth. We aimed at visualizing development from young root systems to mature root systems to observe if root traits like average angle change over time. For species with bigger roots, however, RootPainter would be a very interesting pipeline to use and would save the step of plant transformation, reduce imaging time and image processing. We added RGB imaging as a possibility for bigger root systems to the discussion (page 13).11: sample sizes could be sample numbers I think.We agree with the reviewer and changed the text accordingly.Figure 1 – scale needed in all panels.We have added scale bars to all relevant parts of this figure.48: Not clear why Materials and methods are in supplemental?We thank the reviewer for this comment. Since this manuscript is not layouted yet, we decided that the figures and supplemental figures and tables should follow the main text. In the layouted version, however, the figures, the figure supplements and supplementary files will be included in the text so that the Materials and methods would follow the discussion. We agree that this is important since the methods section is essential, especially in an article that describes new methods.51: "Accessions were always planted in the same locations relative to each other so the population was treated identically in each replicate." Strictly speaking – this is a bad choice, somehow you decided to intentionally NOT randomize which goes against statistical training and a strict reviewer may say it compromises the experimental design where all the measurements could be artifacts of position. Is there a good reason to not randomize? It seems odd in what otherwise seems like such careful design.We thank reviewer #3 for this very valid criticism. Before we started imaging the GLO-Roots diversity panel, we carefully discussed possible strategies. We discussed if all 6 replicates should be imaged within one experiment (imaging run) or if all 93 accessions should be included. Furthermore, we discussed if we should randomize the positions of rhizotrons or if we should keep the same order. We decided for the latter to be able to examine positional effects. Since we included 4 Col-0 controls distributed among the 8 rhizotron boxes, we believed that we could examine positional effects more efficiently. Rhizotron positions for the diversity panel are mapped out in the new Figure 4—figure supplement 2.51: 'four raw images' – root and shoot from each side is four? Maybe make clear.Within the imager, there are two cameras on top of one another, taking an image of the top part of the rhizotron and one of the bottom part of the rhizotron. After those images were taken, the stage turns the rhizotron and the cameras take another top and bottom image, yielding 4 images per rhizotron. This was clarified in the text on page 55.51: what is the 'correct blank images' – is this process entirely automated? Sounds like a user walks the processing through several fairly automated steps but not quite automated from image to data it seems.This assumption is correct. One blank image was taken for each replicate. The blank image for the respective replicate is manually selected and then the automated script runs and subtracts the inherent background noise. This was clarified in the text on page 55.