FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis
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Abstract
Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.
For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide alternative cell segmentation method besides ClearMap’s threshold-based approach: Ilastik’s Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3D visualization of the annotated cells.
As a proof-of-principle, we use FriendlyClearMap to quantify the distribution of the three main GABAergic interneuron subclasses (Parvalbumin + , Somatostatin + , and VIP + ) in the mouse fore- and midbrain. For PV + neurons, we provide an additional dataset with adolescent vs. adult PV + neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
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Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide alternative cell segmentation method besides ClearMap’s threshold-based …
Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide alternative cell segmentation method besides ClearMap’s threshold-based approach: Ilastik’s Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3D visualization of the annotated cells.As a proof-of-principle, we use FriendlyClearMap to quantify the distribution of the three main GABAergic interneuron subclasses (Parvalbumin+, Somatostatin+, and VIP+) in the mouse fore- and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giad035 ), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
**Reviewer Yimin Wang **
This work (FriendlyClearMap) attempts to combine several tools such as ClearMap 1/2, BrainRender, etc., and integrate certain functions into a Docker image for the ease of use. The authors then demonstrated the use of FriendlyClearMap by analysing PV+, SST+ and VIP+ neurons. Some details comments are as below:
1/ P4, second paragraph, line 3, "vs." -> "versus".
2/ P9, third paragraph, line 8, conflict between "lastly" and "finally"
3/ P9, third paragraph, line 8, "our tool allows …".
4/ This work can be regarded as a reengineering effort based on several previous toolkits in order to facilitate the workflow of registration, segmentation, analysis, and visualization. Essentially, no new technology involved is involved in this work and no new application is enabled by FriendlyClearMap. Therefore, in order to emphasize the unique contribution of this work, the author could elaborate how this tool makes biologists' work easier.
5/ The results for Figure 2g are somewhat trivial. The authors might consider replace it with some more impressive analysis.
6/ The majority of the results are related to cell segmentation and counting. Quantitative plots/tables could be provided for more information. In addition, the accuracy of the results could also be discussed.
7/ Last but not least, as there is no substantial novelty in the software, the authors actually could consider change the focus of the manuscript from a tool paper to a resource/results paper, emphasizing new biological findings which is obtained by using FriendlyClearMap.
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Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide alternative cell segmentation method besides ClearMap’s threshold-based …
Tissue clearing is currently revolutionizing neuroanatomy by enabling organ-level imaging with cellular resolution. However, currently available tools for data analysis require a significant time investment for training and adaptation to each laboratory’s use case, which limits productivity. Here, we present FriendlyClearMap, an integrated toolset that makes ClearMap1 and ClearMap2’s CellMap pipeline easier to use, extends its functions, and provides Docker Images from which it can be run with minimal time investment. We also provide detailed tutorials for each step of the pipeline.For more precise alignment, we add a landmark-based atlas registration to ClearMap’s functions as well as include young mouse reference atlases for developmental studies. We provide alternative cell segmentation method besides ClearMap’s threshold-based approach: Ilastik’s Pixel Classification, importing segmentations from commercial image analysis packages and even manual annotations. Finally, we integrate BrainRender, a recently released visualization tool for advanced 3D visualization of the annotated cells.As a proof-of-principle, we use FriendlyClearMap to quantify the distribution of the three main GABAergic interneuron subclasses (Parvalbumin+, Somatostatin+, and VIP+) in the mouse fore- and midbrain. For PV+ neurons, we provide an additional dataset with adolescent vs. adult PV+ neuron density, showcasing the use for developmental studies. When combined with the analysis pipeline outlined above, our toolkit improves on the state-of-the-art packages by extending their function and making them easier to deploy at scale.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giad035 ), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:
Reviewer Chris Armit
This Technical Note paper describes "FriendlyClearMap: An optimized toolkit for mouse brain mapping and analysis".
Whereas the core concept of a data analysis tool to assist in spatial mapping of cleared mouse tissues is perfectly reasonable, there are multiple issues with the documentation that renders this toolkit very difficult to use. I detail below some of the issues I have encountered.
GitHub repositoryThe installation instructions are missing from the following GitHub repository:* https://github.com/MoritzNegwer/FriendlyClearMap-scriptsThe closest reference I could find to installation instructions is the following:* "Please see the Appendices 1-3 of our <X_upcoming> publication for detailed instructions on how to use the pipelines. <X_protocols.io goes here>"Step-bystep installation instructions should be included in the GitHub repository. In addition, the authors should add the protocols.io links to their GitHub repository.
Protocols.ioThe installation instructions are missing from the following protocols.io links:Run Clearmap 1 docker* dx.doi.org/10.17504/protocols.io.eq2lynnkrvx9/v1Run Clearmap 2 docker* dx.doi.org/10.17504/protocols.io.yxmvmn9pbg3p/v1Both of these protocols include the following instruction:* "Then, download the docker container from our repository: XXX docker container goes here"In the documentation, the authors need to unambiguously refer to the specific Docker container that a user needs to install for each software tool.
Test Data I could not find the test data in the form of image stacks that would be needed to test the FriendlyClearMap protocols. Figure 1 refers to 16-bit TIFF image stacks, and I presume these to be the input data that is needed for the image analysis pipelines described in the manuscript. The authors should provide details of the test imaging dataset, including links if necessary to where the image stacks data can be downloaded, in the 'Data Availability' section of the manuscript.
Platform / Operating SystemsIn the 'Data Availability' section of the manuscript, the authors specify that the Operating Systems are "platform-independent". However, the protocols.io documents lists a set of requirements for Windows and LINUX, but not for MacOS. The authors should provide installation instructions and system requirements for MacOS.I reject this manuscript on the grounds that, due to lack of appropriate documentation and installation instructions, the software tool is too difficult to use and therefore has extremely low reuse potential.
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