Uncovering natural variation in root system architecture and growth dynamics using a robotics-assisted phenomics platform
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Evaluation Summary:
The authors present an automated system for phenotyping root system architecture based on bioluminescent roots resulting from a constitutively expressed luciferase transgene (GLO-Root). 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 Arabidopsis thaliana accessions provides a major resource for understanding the genetic basis for root system architecture variation in response to a range of environmental conditions. The work will be of interest to plant biologists and all those studying genetic variation in plants.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)
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Abstract
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 Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). 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.
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Author Response
Reviewer #1 (Public Review):
LaRue, Linder and colleagues present an automation (GLO-Bot) and analysis pipeline building on the previously developed GLO-Roots, which makes use of a constitutively expressed luciferase gene to image plant roots in thin soil containers (rhizotrons). After validation of the system using a set of 6 accessions, the authors then take advantage of the increased throughput to phenotype root system architecture (RSA) of 93 natural Arabidopsis accessions and perform genome-wide association to identify polymorphic genomic regions that are associated with specific RSA traits. I appreciate that the authors made all data available via zenodo.
The authors succeeded in automating the GLO-Root system. Overall, the GLO-Bot appears to be a nice platform to collect time-lapse images of root growth in …
Author Response
Reviewer #1 (Public Review):
LaRue, Linder and colleagues present an automation (GLO-Bot) and analysis pipeline building on the previously developed GLO-Roots, which makes use of a constitutively expressed luciferase gene to image plant roots in thin soil containers (rhizotrons). After validation of the system using a set of 6 accessions, the authors then take advantage of the increased throughput to phenotype root system architecture (RSA) of 93 natural Arabidopsis accessions and perform genome-wide association to identify polymorphic genomic regions that are associated with specific RSA traits. I appreciate that the authors made all data available via zenodo.
The authors succeeded in automating the GLO-Root system. Overall, the GLO-Bot appears to be a nice platform to collect time-lapse images of root growth in soil-substrate using rhizotrons. The automation of the GLO-Roots system using the GLO-Bot is well described, although not in sufficient detail to be rebuilt by interested researchers, e.g. the software controlling the robot is not described or made available, precluding wide adoption of the method. The image processing pipeline is clearly described in the methods and in Figure 2. The pipeline open source and available for use and appears to work well overall, although in some cases the vector representation of the root system appears to be incomplete.
We thank reviewer #1 for raising these concerns. We have now made the general code for the software available (GitHub: https://github.com/rhizolab/rhizo-server). In addition, we uploaded the rhizotron laser cutting files (Zenodo DOI: https://doi.org/10.5281/zenodo.6694558) that would facilitate rebuilding the robot.
We understand the concerns about the vector representations of the root system.
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 obstruction of the light path by soil particles. For traits like root angle, the disconnected nature of the root system is much less impactful as this method naturally uses “segments” of the root as individual elements for angle measurements.
The authors then present a quantitative analysis of RSA using a set of 93 accessions, with 6 replicates per accession, generating a large dataset on the diversity of RSA in Arabidopsis. Using average angle per day, the authors identify SNPs that significantly associated with angle at 28 days after sowing, and they describe a correlation between this trait and the mean diurnal temperature range at the site where the accession was originally collected. The main weakness of the manuscript in its current form are some details of the quantitative genetic analysis. In my opinion the quantitative genetic analysis would benefit from additional quality control as there are peculiarities in the dataset that was used as the basis for GWAS.
We understand the concerns from reviewer #1 about the quantitative genetic analysis. Ultimately, we performed the analyses in the way we explained in the paper with careful consideration. We have added in additional descriptions of the rationale for chosing certain methods that hopefully elucidate why we did the analyses in the way we did. We hope this paper serves as a resource for others to pursue additional studies on traits relevant to their research.
Reviewer #2 (Public Review):
Therese LaRue and colleagues have developed a second generation of the GLO-Roots system that had been developed in their lab and published in 2015. Importantly, the new system (GLO-Bot) and the analysis of the resulting images has now been largely automated and therefore provides a throughput allowing for genetic studies. In an impressive endeavor the authors have transformed more than 100 diverse accessions that had been selected using sensible criteria with the luciferase construct, which then allowed the RSA of these accessions to be measured using the GLO-Bot system. On a set of 6 diverse accessions, the authors carefully identify meaningful RSA traits that they then quantified in the accessions of a larger panel of almost 100 accessions. They also benchmarked the new imaging processing tools against gold-standard manual tools. Overall, they show that the data acquisition and analysis is reproducible and reasonably accurate. They then proceeded to conduct GWAS using the RSA traits and identified several significantly associated candidate SNPs. Finally, they correlated the RSA with environmental variables and found interesting correlations that are consistent with prior studies.
Strengths:
The manuscript presents interesting root phenotyping technology, a comprehensive atlas of RSA under rhizotron lab conditions in Arabidopsis, candidate genes potentially underlying RSA traits, and interesting associations of RSA and climate variables. This will be inspiring and useful to many other researchers and has the potential to be explored further in future studies.
We thank the reviewer for the encouraging feedback.
Weaknesses:
Some aspects of the data analyses are not well described and should be described more. The trait data is heavily processed to "breeding values" and it is a bit unclear when unprocessed and processed trait data is used and why. Also, limitations and caveats are not discussed sufficiently. For instance, presenting and discussing the issues and caveats of measuring RSA that was generated in thin and not very wide soil sheets using the GLO-Bot system when natural growth in soil is usually largely unconstrained. Moreover, the analysis of potential candidate genes from the GWAS is not very well developed. Finally, the trait data was not available with the manuscript and a major impact of a resource like this will come from the data being fully available to the community.
We appreciate the broad comments on the manuscript and have tried to address them through the specific responses below. Overall we believe the approaches we used are effective but with specific caveats and have used the revision as a means of better communicating the limitations of the approaches chosen.
Reviewer #3 (Public Review):
The authors provide a thorough description of a method to transform plants to be bioluminescent upon applications of the require substrate such that roots are visible on the windows of rhizoboxes. They have expanded on previous work by automatic the imaging process with a robot that moves rhizoboxes to an imager where images are captured. They have improved the image analysis pipeline to be mostly automated with a user presumably needed to run various scripts in batch mode on directories of images. One novel aspect of the image analysis pipeline is in using image subtraction to subtract the previous time root system from the current in order to identify new growth.
We thank the reviewer for highlighting the strengths of the manuscript.
Overall, I think the authors provide a great amount of detail in parts needed and the methods, but some recommendations to increase reproducibility are more information about actual root traits measured. For example, one concern would be if root length is only summing pixels without considering diagonal pixels having a length of square-root of two, sqrt(2).
This is a valid concern, rather than just summing the pixels, the length of the segments is actually calculated using the “Feret Diameter” (or caliper length) function in imageJ which does take diagonals into consideration
While the methodological aspects of the paper are compelling, the authors have furthered the significance through a biological application for genetic analysis among accessions of Arabidopsis and correlating root traits to climatic 'envirotypes' or data from the origin site of the respective accession. This genetic analysis would be furthered by greater consideration of time series analysis and multi-trait analysis, which is possible in GEMMA. The authors could consider genetic analysis of the PCA traits as well. Given the novelty of this type of time-series, multi-trait data - the authors can reach further here.
Absolutely, PCA approaches to disentangle the phenotype space would be highly interesting to further investigate, which we started in the Supplemental Figure 8. This figure decomposes all the data points including replicates and temporal values of the same replicate. The PC1 therefore mostly captures how plants change over time, while PC2 seems to capture the main trade-off of wide/horizontal vs deep/vertical root architectures that we describe throughout the text. We could make use of this PC space to quantify the average value per genotype in PC2 and utilize this value for GWA, although it is not obvious how replicated and temporal measurements behave in PCA and what would be its consequences when computing a genotype value. There will definitely be interesting work that we aim to pursue in this direction in the future.
Regarding the additional capabilities of GEMMA. We are not aware of a subtool that is able to analyze time series directly in GEMMA, but we will look into it. The multi-trait analysis in GEMMA is also interesting. We have utilized the multi-trait feature in the past, but this is limited to very few traits. We have 8 time points, thus 8 traits. For reference, when we have run multi-trait LMM with 2 traits, we have typically seen runtimes of ~9 days in large clusters. New tools continue to emerge in the field of quantitative genetics, such as the use of summary statistics of multiple GWAs to gain new insights, which we will pursue in the future. We have added possible future directions to the discussion section (page 14).
As far as the general structure of the manuscript, I struggled with the results mixing in the methods such that I was never sure if the lack of detail in methods there would be addressed later, along with the mixture of discussions. Perhaps these are personal choices, but the methods were also after supplemental. I simply ask the authors to consider the reader here by being honest with my own experience reading this manuscript.
We appreciate this comment of reviewer #3. Since this is a “Tools and Resources” article, we believe that a substantial part of the results section should include the methods that were applied. The methodology mentioned in the results section should always help the reader to understand the illustrated results in the figures. If readers would like to apply certain methods, however, more details can be found in the materials and methods section. We apologize if this was not always successful and led to confusion. In the final formatted version, all supplemental figures would be linked to the main figures so that the materials and methods section would follow the discussion.
Overall, I believe this manuscript advanced root phenotyping by providing relatively high-throughput (imaging is slow due to the long exposure times) data and doing the time-series, multi-trait genetic mapping. The authors mention imaging shoots but no data is presented - presumably, it would be interesting to tie that in but they may be reasons to not. The authors could also discuss more the advantages of this approach relative to color imaging that has also advanced significantly since the original GLO-Root paper was released. Last, I am not sure the description of the 6 accessions study adds much value to the paper, and probably many other preliminary studies were done to prototype. Overall, this is fantastic and substantial work presented in a compelling way.
Unfortunately, the shoot images that were taken did not have sufficient quality for further analysis and due to technical problems, the set of shoot images is not complete. We removed the part of shoot imaging from the text. It now reads:”Inside the imaging system, the rhizotrons were rotated using a Lambda 10-3 Optical Filter Changer (Sutter Instrument®, Novato, CA). If it was the first imaging day or a designated luciferin day (every six days), GLO-Bot added 50 mL of 300 μM D-luciferin (Biosynth International Inc., Itasca, IL) to the top of each rhizotron immediately before loading the rhizotron into the imager.”
The advantages of the GLO-Roots method over color imaging is clearly that the GLO-Roots method can capture a more complete image of root systems with finer roots (like Arabidopsis). We have added the possibility of using RGB imaging for bigger root systems to the discussion section (page 13).
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Evaluation Summary:
The authors present an automated system for phenotyping root system architecture based on bioluminescent roots resulting from a constitutively expressed luciferase transgene (GLO-Root). 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 Arabidopsis thaliana accessions provides a major resource for understanding the genetic basis for root system architecture variation in response to a range of environmental conditions. The work will be of interest to plant biologists and all those studying genetic variation in plants.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the …
Evaluation Summary:
The authors present an automated system for phenotyping root system architecture based on bioluminescent roots resulting from a constitutively expressed luciferase transgene (GLO-Root). 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 Arabidopsis thaliana accessions provides a major resource for understanding the genetic basis for root system architecture variation in response to a range of environmental conditions. The work will be of interest to plant biologists and all those studying genetic variation in plants.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 and Reviewer #3 agreed to share their names with the authors.)
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Reviewer #1 (Public Review):
LaRue, Linder and colleagues present an automation (GLO-Bot) and analysis pipeline building on the previously developed GLO-Roots, which makes use of a constitutively expressed luciferase gene to image plant roots in thin soil containers (rhizotrons). After validation of the system using a set of 6 accessions, the authors then take advantage of the increased throughput to phenotype root system architecture (RSA) of 93 natural Arabidopsis accessions and perform genome-wide association to identify polymorphic genomic regions that are associated with specific RSA traits. I appreciate that the authors made all data available via zenodo.
The authors succeeded in automating the GLO-Root system. Overall, the GLO-Bot appears to be a nice platform to collect time-lapse images of root growth in soil-substrate using …
Reviewer #1 (Public Review):
LaRue, Linder and colleagues present an automation (GLO-Bot) and analysis pipeline building on the previously developed GLO-Roots, which makes use of a constitutively expressed luciferase gene to image plant roots in thin soil containers (rhizotrons). After validation of the system using a set of 6 accessions, the authors then take advantage of the increased throughput to phenotype root system architecture (RSA) of 93 natural Arabidopsis accessions and perform genome-wide association to identify polymorphic genomic regions that are associated with specific RSA traits. I appreciate that the authors made all data available via zenodo.
The authors succeeded in automating the GLO-Root system. Overall, the GLO-Bot appears to be a nice platform to collect time-lapse images of root growth in soil-substrate using rhizotrons. The automation of the GLO-Roots system using the GLO-Bot is well described, although not in sufficient detail to be rebuilt by interested researchers, e.g. the software controlling the robot is not described or made available, precluding wide adoption of the method. The image processing pipeline is clearly described in the methods and in Figure 2. The pipeline open source and available for use and appears to work well overall, although in some cases the vector representation of the root system appears to be incomplete.
The authors then present a quantitative analysis of RSA using a set of 93 accessions, with 6 replicates per accession, generating a large dataset on the diversity of RSA in Arabidopsis. Using average angle per day, the authors identify SNPs that significantly associated with angle at 28 days after sowing, and they describe a correlation between this trait and the mean diurnal temperature range at the site where the accession was originally collected. The main weakness of the manuscript in its current form are some details of the quantitative genetic analysis. In my opinion the quantitative genetic analysis would benefit from additional quality control as there are peculiarities in the dataset that was used as the basis for GWAS.
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Reviewer #2 (Public Review):
Therese LaRue and colleagues have developed a second generation of the GLO-Roots system that had been developed in their lab and published in 2015. Importantly, the new system (GLO-Bot) and the analysis of the resulting images has now been largely automated and therefore provides a throughput allowing for genetic studies. In an impressive endeavor the authors have transformed more than 100 diverse accessions that had been selected using sensible criteria with the luciferase construct, which then allowed the RSA of these accessions to be measured using the GLO-Bot system. On a set of 6 diverse accessions, the authors carefully identify meaningful RSA traits that they then quantified in the accessions of a larger panel of almost 100 accessions. They also benchmarked the new imaging processing tools against …
Reviewer #2 (Public Review):
Therese LaRue and colleagues have developed a second generation of the GLO-Roots system that had been developed in their lab and published in 2015. Importantly, the new system (GLO-Bot) and the analysis of the resulting images has now been largely automated and therefore provides a throughput allowing for genetic studies. In an impressive endeavor the authors have transformed more than 100 diverse accessions that had been selected using sensible criteria with the luciferase construct, which then allowed the RSA of these accessions to be measured using the GLO-Bot system. On a set of 6 diverse accessions, the authors carefully identify meaningful RSA traits that they then quantified in the accessions of a larger panel of almost 100 accessions. They also benchmarked the new imaging processing tools against gold-standard manual tools. Overall, they show that the data acquisition and analysis is reproducible and reasonably accurate. They then proceeded to conduct GWAS using the RSA traits and identified several significantly associated candidate SNPs. Finally, they correlated the RSA with environmental variables and found interesting correlations that are consistent with prior studies.
Strengths:
The manuscript presents interesting root phenotyping technology, a comprehensive atlas of RSA under rhizotron lab conditions in Arabidopsis, candidate genes potentially underlying RSA traits, and interesting associations of RSA and climate variables. This will be inspiring and useful to many other researchers and has the potential to be explored further in future studies.
Weaknesses:
Some aspects of the data analyses are not well described and should be described more. The trait data is heavily processed to "breeding values" and it is a bit unclear when unprocessed and processed trait data is used and why. Also, limitations and caveats are not discussed sufficiently. For instance, presenting and discussing the issues and caveats of measuring RSA that was generated in thin and not very wide soil sheets using the GLO-Bot system when natural growth in soil is usually largely unconstrained. Moreover, the analysis of potential candidate genes from the GWAS is not very well developed. Finally, the trait data was not available with the manuscript and a major impact of a resource like this will come from the data being fully available to the community.
-
Reviewer #3 (Public Review):
The authors provide a thorough description of a method to transform plants to be bioluminescent upon applications of the require substrate such that roots are visible on the windows of rhizoboxes. They have expanded on previous work by automatic the imaging process with a robot that moves rhizoboxes to an imager where images are captured. They have improved the image analysis pipeline to be mostly automated with a user presumably needed to run various scripts in batch mode on directories of images. One novel aspect of the image analysis pipeline is in using image subtraction to subtract the previous time root system from the current in order to identify new growth.
Overall, I think the authors provide a great amount of detail in parts needed and the methods, but some recommendations to increase …
Reviewer #3 (Public Review):
The authors provide a thorough description of a method to transform plants to be bioluminescent upon applications of the require substrate such that roots are visible on the windows of rhizoboxes. They have expanded on previous work by automatic the imaging process with a robot that moves rhizoboxes to an imager where images are captured. They have improved the image analysis pipeline to be mostly automated with a user presumably needed to run various scripts in batch mode on directories of images. One novel aspect of the image analysis pipeline is in using image subtraction to subtract the previous time root system from the current in order to identify new growth.
Overall, I think the authors provide a great amount of detail in parts needed and the methods, but some recommendations to increase reproducibility are more information about actual root traits measured. For example, one concern would be if root length is only summing pixels without considering diagonal pixels having a length of square-root of two, sqrt(2).
While the methodological aspects of the paper are compelling, the authors have furthered the significance through a biological application for genetic analysis among accessions of Arabidopsis and correlating root traits to climatic 'envirotypes' or data from the origin site of the respective accession. This genetic analysis would be furthered by greater consideration of time series analysis and multi-trait analysis, which is possible in GEMMA. The authors could consider genetic analysis of the PCA traits as well. Given the novelty of this type of time-series, multi-trait data - the authors can reach further here.
As far as the general structure of the manuscript, I struggled with the results mixing in the methods such that I was never sure if the lack of detail in methods there would be addressed later, along with the mixture of discussions. Perhaps these are personal choices, but the methods were also after supplemental. I simply ask the authors to consider the reader here by being honest with my own experience reading this manuscript.
Overall, I believe this manuscript advanced root phenotyping by providing relatively high-throughput (imaging is slow due to the long exposure times) data and doing the time-series, multi-trait genetic mapping. The authors mention imaging shoots but no data is presented - presumably, it would be interesting to tie that in but they may be reasons to not. The authors could also discuss more the advantages of this approach relative to color imaging that has also advanced significantly since the original GLO-Root paper was released. Last, I am not sure the description of the 6 accessions study adds much value to the paper, and probably many other preliminary studies were done to prototype. Overall, this is fantastic and substantial work presented in a compelling way.
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