BEHAV3D Tumor Profiler to map heterogeneous cancer cell behavior in the tumor microenvironment
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eLife Assessment
This study represents a potentially useful tool for extracting quantitative data from intravital microscopy directed at in vivo cancer models. In general, this is an area of interest as accessible non-proprietary tools are needed and some evidence of the tool's utility is provided. However, the work in its current form is incomplete as it is heavily reliant on proprietary software to segment, track, and correct the data. In addition, there are significant reservations regarding the methods used to produce statistics in the software, limiting its applicability and the potential advance over other approaches.
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Abstract
Intravital microscopy (IVM) enables live imaging of animals at single-cell level, offering essential insights into cancer progression. This technique allows for the observation of single-cell behaviors within their natural 3D tissue environments, shedding light on how genetic and microenvironmental changes influence the complex dynamics of tumors. The complexity of data generated by IVM often surpasses the capabilities of conventional analyses accessible to biomedical scientists, thereby neglecting single-cell heterogeneity and limiting the exploration of microenvironmental influences on cellular behavior without bias. To address this challenge, here we introduce BEHAV3D Tumor Profiler (BEHAV3D-TP), a user-friendly computational framework designed for the comprehensive analysis of single tumor cell behaviors and their interactions with the tumor microenvironment (TME). BEHAV3D-TP facilitates unbiased profiling of cancer cell dynamics without requiring advanced computational expertise. Here, we apply BEHAV3D-TP to study diffuse midline glioma (DMG), a highly aggressive pediatric brain tumor characterized by invasive growth. Our analysis reveals that distinct migratory behaviors of DMG cells correlate with specific TME components such as tumor-associated macrophages and vasculature. This approach, initially aimed at uncovering tumor invasive patterns and their interactions with the TME, holds promise for understanding additional cancer cell behaviors like intravasation and metastasis. BEHAV3D-TP represents a significant advancement in democratizing the analysis of heterogeneous cancer cell behaviors and their TME interactions, providing accessible computational insights into tumor dynamics.
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eLife Assessment
This study represents a potentially useful tool for extracting quantitative data from intravital microscopy directed at in vivo cancer models. In general, this is an area of interest as accessible non-proprietary tools are needed and some evidence of the tool's utility is provided. However, the work in its current form is incomplete as it is heavily reliant on proprietary software to segment, track, and correct the data. In addition, there are significant reservations regarding the methods used to produce statistics in the software, limiting its applicability and the potential advance over other approaches.
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Reviewer #1 (Public review):
Summary:
Intravital microscopy (IVM) is a powerful tool that facilitates live imaging of individual cells over time in vivo in their native 3D tissue environment. Extracting and analysing multi-parametric data from IVM images however is challenging, particularly for researchers with limited programming and image analysis skills. In this work, Rios-Jimenez and Zomer et al have developed a 'zero-code' accessible computational framework (BEHAV3D-Tumour Profiler) designed to facilitate unbiased analysis of IVM data to investigate tumour cell dynamics (via the tool's central 'heterogeneity module') and their interactions with the tumour microenvironment (via the 'large-scale phenotyping' and 'small-scale phenotyping' modules). It is designed as an open-source modular Jupyter Notebook with a user-friendly …
Reviewer #1 (Public review):
Summary:
Intravital microscopy (IVM) is a powerful tool that facilitates live imaging of individual cells over time in vivo in their native 3D tissue environment. Extracting and analysing multi-parametric data from IVM images however is challenging, particularly for researchers with limited programming and image analysis skills. In this work, Rios-Jimenez and Zomer et al have developed a 'zero-code' accessible computational framework (BEHAV3D-Tumour Profiler) designed to facilitate unbiased analysis of IVM data to investigate tumour cell dynamics (via the tool's central 'heterogeneity module') and their interactions with the tumour microenvironment (via the 'large-scale phenotyping' and 'small-scale phenotyping' modules). It is designed as an open-source modular Jupyter Notebook with a user-friendly graphical user interface and can be implemented with Google Colab, facilitating efficient, cloud-based computational analysis at no cost.
To demonstrate the utility of BEHAV3D-TP, they apply the pipeline to timelapse IVM imaging datasets to investigate the in vivo migratory behaviour of fluorescently labelled DMG cells in tumour-bearing mice. Using the tool's 'heterogeneity module' they were able to identify distinct single-cell behavioural patterns (based on multiple parameters such as directionality, speed, displacement, and distance from tumour edge) which was used to group cells into distinct categories (e.g. retreating, invasive, static, erratic). They next applied the framework's 'large-scale phenotyping' and 'small-scale phenotyping' modules to investigate whether the tumour microenvironment (TME) may influence the distinct migratory behaviours identified. To achieve this, they combine TME visualisation in vivo during IVM (using fluorescent probes to label distinct TME components) or ex vivo after IVM (by large-scale imaging of harvested, immunostained tumours) to correlate different tumour behavioural patterns with the composition of the TME. They conclude that this tool has helped reveal links between TME composition (e.g. degree of vascularisation, presence of tumour-associated macrophages) and the invasiveness and directionality of tumour cells, which would have been challenging to identify when analysing single kinetic parameters in isolation.
A key limitation of the pipeline is that it does not overcome the main challenges and bottlenecks associated with processing and extracting quantitative cellular data from timelapse and longitudinal intravital images. This includes correcting breathing-induced movement artifacts, automated registration of longitudinal images taken over days/weeks, and accurate, automated segmentation and tracking of individual cells over time. Indeed, there are currently no standardised computational methods available for IVM data processing and analysis, with most laboratories relying on custom-built solutions or manual methods. This isn't made explicit in the manuscript early on (described below), and the researchers rely on expensive software packages such as IMARIS for image processing and data extraction to feed the required parameters into their pipeline. This limitation unfortunately reduces the likely impact of BEHAV3D-TP on the IVM field.
Nonetheless, this computational framework appears to represent a useful and comparatively user-friendly tool to analyse dynamic multi-parametric data to help identify patterns in cell migratory behaviours, and to assess whether these behaviours might be influenced by neighbouring cells and structures in their microenvironment. When combined with other methods, it, therefore, has the potential to be a valuable addition to a researcher's IVM analysis 'tool-box'.
Strengths:
(1) The figures are clearly presented, and the manuscript is easy to follow.
(2) The pipeline appears to be intuitive and user-friendly for researchers with limited computational expertise. A detailed step-by-step video is also included to support its uptake.
(3) The different computational modules have been tested using a relevant dataset.
(4) All code is open source, and the pipeline can be implemented with Google Colab.
(5) The tool combines multiple dynamic parameters extracted from time-lapse IVM images to identify single-cell behavioural patterns and to cluster cells into distinct groups sharing similar behaviours, and provides avenues to map these onto in vivo or ex vivo imaging data of the tumour microenvironment.
Weaknesses:
(1) As highlighted above, the tool does not facilitate the extraction of quantitative kinetic cellular parameters (e.g. speed, directionality, persistence, and displacement) from intravital images. Indeed, to use the tool researchers must first extract dynamic cellular parameters from their IVM datasets, requiring access to expensive software (e.g. IMARIS as used here) and/or above-average computational expertise to develop and use custom-made open-source solutions. This limitation is not made explicit or discussed in the text.
(2) The number of cells (e.g. per behavioural cluster), and the number of independent mice, represented in each result figure, is not included in the figure legends and are difficult to ascertain from the methods.
(3) The data used to test the pipeline in this manuscript is currently not available, making it difficult to assess its usability. It would be important to include this for researchers to use as a 'training dataset'.
(4) Precisely how the BEHAV3D-TP large-scale phenotyping module can map large-scale spatial phenotyping data generated using LSR-3D imaging data and Cytomap to 3D intravital imaging movies is unclear. Further details in the text and methods would be beneficial to aid understanding.
(5) The analysis provides only preliminary evidence in support of the authors' conclusions on DMG cell migratory behaviours and their relationship with components of the tumour microenvironment. Conclusions should therefore be tempered in the absence of additional experiments and controls.
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Reviewer #2 (Public review):
Summary:
The authors produce a new tool, BEHAV3D to analyse tracking data and to integrate these analyses with large and small-scale architectural features of the tissue. This is similar to several other published methods to analyse spatiotemporal data, however, the connection to tissue features is a nice addition, as is the lack of requirement for coding. The tool is then used to analyse tracking data of tumour cells in diffuse midline glioma. They suggest that 7 clusters exist within these tracks and that they differ spatially. They ultimately suggest that these behaviours occur in distinct spatial areas as determined by CytoMAP.Strengths:
(1) The tool appears relatively user-friendly and is open source. The combination with CytoMAP represents a nice option for researchers.
- The identification of …
Reviewer #2 (Public review):
Summary:
The authors produce a new tool, BEHAV3D to analyse tracking data and to integrate these analyses with large and small-scale architectural features of the tissue. This is similar to several other published methods to analyse spatiotemporal data, however, the connection to tissue features is a nice addition, as is the lack of requirement for coding. The tool is then used to analyse tracking data of tumour cells in diffuse midline glioma. They suggest that 7 clusters exist within these tracks and that they differ spatially. They ultimately suggest that these behaviours occur in distinct spatial areas as determined by CytoMAP.Strengths:
(1) The tool appears relatively user-friendly and is open source. The combination with CytoMAP represents a nice option for researchers.
- The identification of associations between cell track phenotype and spatial features is exciting and the diffuse midline glioma data nicely demonstrates how this could be used.
Weaknesses:
(1) The strength of democratizing this kind of analysis is undercut by the reliance upon Imaris for segmentation, so it would be nice if this was changed to an open-source option for track generation.
(2) The main issue is with the interpretation of the biological data in Figure 3 where ANOVA was used to analyse the proportional distribution of different clusters. Firstly the n is not listed so it is unclear if this represents an n of 3 where each mouse is an individual or whether each track is being treated as a test unit. If the latter this is seriously flawed as these tracks can't be treated as independent. Also, a more appropriate test would be something like a Chi-squared test or Fisher's exact test. Also, no error bars are included on the stacked bar graphs making interpretation impossible. Ultimately this is severely flawed and also appears to show very small differences which may be statistically different but may not represent biologically important findings. This would need further study.
(3) Figure 4 has similar statistical issues in that the n is not listed and, again, it is unclear whether they are treating each cell track as independent which, again, would be inappropriate. The best practice for this type of data would be the use of super plots as outlined in Lord et al. (2020) JCI - SuperPlots: Communicating reproducibility and variability in cell biology.
(4) The main issue that this raises is that the large-scale phenotyping module and the heterogeneity module appear designed to produce these statistical analyses that are used in these figures and, if they are based on the assumption that each track is independent, then this will produce inappropriate analyses as a default.
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Reviewer #3 (Public review):
Summary:
The manuscript by Rios-Jimenez developed a computational tool, BEHAV3D Tumor Profiler, to analyze intravital imaging data and extract distinctive tumor cell migratory phenotypes based on the quantified 3D image data.
Weaknesses:
(1) The most challenging task of analyzing 3D time-lapse imaging data is to accurately segment and track the individual cells in 3D over a long time duration. BEHAV3D Tumor Profiler did not provide any new advancement in this regard, and instead relies on commercial software, Imaris, for this critical step. Imaris is known to have a very high error rate when used for analyzing 3D time-lapse data. In the Methods section, the authors themselves stated that "Tumor cell tracks were manually corrected to ensure accurate tracking". Based on our own experience of using Imaris, such …
Reviewer #3 (Public review):
Summary:
The manuscript by Rios-Jimenez developed a computational tool, BEHAV3D Tumor Profiler, to analyze intravital imaging data and extract distinctive tumor cell migratory phenotypes based on the quantified 3D image data.
Weaknesses:
(1) The most challenging task of analyzing 3D time-lapse imaging data is to accurately segment and track the individual cells in 3D over a long time duration. BEHAV3D Tumor Profiler did not provide any new advancement in this regard, and instead relies on commercial software, Imaris, for this critical step. Imaris is known to have a very high error rate when used for analyzing 3D time-lapse data. In the Methods section, the authors themselves stated that "Tumor cell tracks were manually corrected to ensure accurate tracking". Based on our own experience of using Imaris, such manual correction is tedious and often required for every time step of the movie. Therefore, Imaris is not a satisfactory tool for analyzing 3D time-lapse data. Moreover, Imaris is expensive and many research labs probably can't afford to buy it. The fact that BEHAV3D Tumor Profiler critically depends on the faulty ImarisTrack module makes it unclear whether the BEHAV3D tool or the results are reliable.
(2) The authors developed a "Heterogeneity module" to extract distinctive tumor migratory phenotypes from the cell tracks quantified by Imaris. The cell tracks of the individual tumor cells are all quite short, indicating relatively low motility of the tumor cells. It's unclear whether such short migratory tracks are sufficient to warrant the PCA analysis to identify the 7 distinctive migratory phenotypes shown in Figure 2d. It's also unclear whether these 7 migratory phenotypes correspond to unique functional phenotypes.
(3) Using only motility to classify tumor cell behaviours in the tumor microenvironment (TME) is probably not sufficient to capture the tumor cell difference. There are also other non-tumor cell types in the TME. If the authors aim to develop a computational tool that can elucidate tumor cell behaviors in the TME, they should consider other tumor cell features, e.g., morphology, proliferation state, and tumor cell interaction with other cell types, e.g., fibroblasts and distinct immune cells.
(4) The authors have already published two papers on BEHAV3D [Alieva M et al. Nat Protoc. 2024 Jul;19(7): 2052-2084; Dekkers JF, et al. Nat Biotechnol. 2023 Jan;41(1):60-69]. Although the previous two papers used BEHAV3D to analyze T cells, the basic pipeline and computational steps are similar, in particular regarding cell segmentation and tracking. The addition of a "Heterogeneity module" based on PCA analysis does not make a significant advancement in terms of image analysis and quantification.
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Author response:
We want to thank the reviewers for their positive and constructive comments on the manuscript. We already addressed some of their concerns and are planning the following revisions to both BEHAV3D-TP and the corresponding manuscript to address the reviewers’ comments. Below, we provide a response to the most significant comments, followed by a detailed, point-by-point response:
(1) We acknowledge the reviewer's suggestion to incorporate open-source segmentation and tracking functionalities, increasing its accessibility to a wider user base; however, these additions fall outside the primary scope of our current work and represent a substantial undertaking in their own right. This topic has been comprehensively explored in other studies (e.g. https://doi.org/10.4049/jimmunol.2100811 ; https://doi.org/10.7554/eLife.60547 ; h…
Author response:
We want to thank the reviewers for their positive and constructive comments on the manuscript. We already addressed some of their concerns and are planning the following revisions to both BEHAV3D-TP and the corresponding manuscript to address the reviewers’ comments. Below, we provide a response to the most significant comments, followed by a detailed, point-by-point response:
(1) We acknowledge the reviewer's suggestion to incorporate open-source segmentation and tracking functionalities, increasing its accessibility to a wider user base; however, these additions fall outside the primary scope of our current work and represent a substantial undertaking in their own right. This topic has been comprehensively explored in other studies (e.g. https://doi.org/10.4049/jimmunol.2100811 ; https://doi.org/10.7554/eLife.60547 ; https://doi.org/10.1016/j.media.2022.102358 ; https://doi.org/10.1038/s41592-024-02295-6), which we will cite in our revised manuscript as indicated in our responses to the reviewers’ comments. Instead, the goal of our manuscript is to provide an analytical framework for processing data generated by existing segmentation and tracking pipelines. In our analyses, we used data processed with Imaris, a commercial software that, despite its limitations, is widely used by the intravital microscopy community due to its user-friendly platform for 3D image visualization and analysis. Nevertheless, to enhance compatibility with tracking data from various pipelines, we have modified our tool to accept data formats, such as those generated by open-source Fiji plugins like TrackMate (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). These updates are available in our GitHub repository, and we will describe this feature in the revised manuscript to emphasize compatibility with segmented and tracked data from diverse open-source platforms.
(2) We appreciate the reviewer’s suggestion to incorporate additional features into our analytical pipeline. In response, we have already updated the GitHub repository to allow users to input and select which features (dynamic, morphological, or spatial) they wish to include in the analysis (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#feature-selection ) . In the revised manuscript, we will highlight this new functionality and provide examples using alternative datasets to demonstrate the application of these features.
(3) We appreciate the constructive feedback of reviewers #1 and #2 regarding the statistical analysis and interpretation of the data presented in Figures 3 and 4. We understand the importance of clarity and rigor in data analysis and presentation, and we are committed to addressing the concerns raised in the revised version of the manuscript.
(4) We appreciate Reviewer #1's suggestion regarding the inclusion of demo data, as we believe it would greatly enhance the usability of our pipeline. We acknowledge that this was an oversight on our part. To address this, we have now added demo data to our GitHub repository (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0/demo_datasets). In the upcoming revised manuscript, we will also ensure to reference this addition. Additionally, we will provide both original and processed IVM movie samples to support users in navigating the complete pipeline effectively.
(5) Finally, we agree with the reviewers to make some small changes to the manuscript based on their feedback.
Below we provide a point-by-point response to the reviewers’ comments, along with proposed revisions.
Reviewer #1:
Comment: A key limitation of the pipeline is that it does not overcome the main challenges and bottlenecks associated with processing and extracting quantitative cellular data from timelapse and longitudinal intravital images. This includes correcting breathing-induced movement artifacts, automated registration of longitudinal images taken over days/weeks, and accurate, automated segmentation and tracking of individual cells over time. Indeed, there are currently no standardised computational methods available for IVM data processing and analysis, with most laboratories relying on custom-built solutions or manual methods. This isn't made explicit in the manuscript early on (described below), and the researchers rely on expensive software packages such as IMARIS for image processing and data extraction to feed the required parameters into their pipeline. This limitation unfortunately reduces the likely impact of BEHAV3D-TP on the IVM field.
As highlighted above, the tool does not facilitate the extraction of quantitative kinetic cellular parameters (e.g. speed, directionality, persistence, and displacement) from intravital images. Indeed, to use the tool researchers must first extract dynamic cellular parameters from their IVM datasets, requiring access to expensive software (e.g. IMARIS as used here) and/or above-average computational expertise to develop and use custom-made open-source solutions. This limitation is not made explicit or discussed in the text.
As mentioned previously, we agree with the reviewer that image processing steps, such as segmentation, tracking, and motion correction, present significant challenges in intravital microscopy (IVM) data processing. While these aspects are being addressed by other researchers, our publication centers on the analysis of acquired data rather than on the image processing itself. Our motivation, as outlined in the manuscript, arises from our own experience: despite the substantial effort invested in image processing, researchers often rely on simplistic analytical approaches, such as averaging single parameters and comparing them across conditions. These approaches tend to overlook potential tumor heterogeneity.
Our work aimed to develop an analytical tool that provides a comprehensive framework for extracting more insights from processed IVM data, with a focus on two key aspects: capturing the heterogeneity of tumor behavior and examining the spatial distribution of these behaviors within the tumor microenvironment. In the revised manuscript, we will clarify the scope of our study, emphasizing its limitations as an analytical tool rather than an image-processing solution. Additionally, we will provide references to relevant literature on available (open-source) software options for image processing (e.g. Diego Ulisse Pizzagalli et al J Immunol (2022); Aby Joseph et al eLife (2020) ;Molina-Moreno M et al Medical Image Analysis (2022); Hidalgo-Cenalmor, I et al, Nat Methods (2024); Ershov. D et al Nat Methods (2022)).
Regarding the reviewer’s comment on our use of Imaris, we acknowledge that Imaris is a costly commercial software. However, based on our experience, it is widely used by the intravital microscopy community due to its user-friendly interface for 3D image visualization and analysis. Despite its limitations in accuracy and the fact that it is not open-source, we believe that including data processed with Imaris will be valuable to the IVM community.
However, to improve compatibility with data from other segmentation and tracking pipelines, we have already updated our tool to support formats generated by open-source Fiji plugins like TrackMate. These updates are available in our GitHub repository, and we will describe this functionality in detail in the revised manuscript to ensure compatibility with segmented and tracked data from various open-source platforms.
Comment: The number of cells (e.g. per behavioural cluster), and the number of independent mice, represented in each result figure, is not included in the figure legends and are difficult to ascertain from the methods.
We appreciate the reviewer's constructive feedback regarding the clarity of the number and type of replicates used in our analyses. In the revised manuscript, we will include detailed information in the figure legends regarding the number of cells (e.g., per behavioral cluster) and the number of independent mice represented in each result figure to ensure transparency.
Comment: The data used to test the pipeline in this manuscript is currently not available, making it difficult to assess its usability. It would be important to include this for researchers to use as a 'training dataset'.
As stated above we acknowledge that this was an oversight on our part and thank the reviewer for pointing this out. To address this, we have now added demo data to our GitHub repository (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0/demo_datasets). In the upcoming revised manuscript, we will also make sure to reference this addition. Additionally, we intend to provide both original and processed IVM movie samples to support users in navigating the complete pipeline effectively.
Comment: Precisely how the BEHAV3D-TP large-scale phenotyping module can map large-scale spatial phenotyping data generated using LSR-3D imaging data and Cytomap to 3D intravital imaging movies is unclear. Further details in the text and methods would be beneficial to aid understanding.
We appreciate the reviewer’s comment and will provide additional details in the text and methods of the revised manuscript to clarify how the BEHAV3D-TP module maps LSR-3D and Cytomap data to 3D intravital imaging movies.
Comment: The analysis provides only preliminary evidence in support of the authors' conclusions on DMG cell migratory behaviours and their relationship with components of the tumour microenvironment. Conclusions should therefore be tempered in the absence of additional experiments and controls.
We appreciate the reviewer’s comment and acknowledge that our conclusions should be tempered due to the preliminary nature of our evidence. To be able to directly analyze the impact of the brain tumor microenvironment on cancer cell behavior, we will include a new set of analyses in the revised manuscript. Specifically, we will utilize BEHAV3D-TP to analyze existing IVM data from adult gliomas with and without macrophage depletion (Alieva et al, Scientific Reports, 2017; https://doi.org/10.1038/s41598-017-07660-4 ) to evaluate the differences in heterogeneous cell populations under these conditions. Since this analysis pertains to a different tumor type, we will revise our conclusions accordingly and emphasize the necessity for additional experiments and controls to further validate our findings on DMG cell migratory behaviors and their relationship with the tumor microenvironment.
Reviewer #2:
Comment: The strength of democratizing this kind of analysis is undercut by the reliance upon Imaris for segmentation, so it would be nice if this was changed to an open-source option for track generation.
As noted in our previous response to Reviewer #1, we would like to point out that although Imaris is a commercial software, it is widely used in the intravital microscopy (IVM) community due to its user-friendly interface. One of its key advantages, which we also utilized, is semi-automated data tracking that allows for manual corrections in 3D—a process that can be more challenging in other open-source software with less effective data visualization.
However, we recognize that enhancing our pipeline's compatibility with open-source options is important. To this end, we have already updated our tool to support data formats generated by open-source Fiji plugins like TrackMate, improving compatibility with various segmentation and tracking pipelines (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). We will describe these updates in the revised manuscript to clarify our study's scope and the available image processing options.
Comment: The main issue is with the interpretation of the biological data in Figure 3 where ANOVA was used to analyse the proportional distribution of different clusters. Firstly the n is not listed so it is unclear if this represents an n of 3 where each mouse is an individual or whether each track is being treated as a test unit. If the latter this is seriously flawed as these tracks can't be treated as independent. Also, a more appropriate test would be something like a Chi-squared test or Fisher's exact test. Also, no error bars are included on the stacked bar graphs making interpretation impossible. Ultimately this is severely flawed and also appears to show very small differences which may be statistically different but may not represent biologically important findings. This would need further study.
We appreciate the reviewer’s insightful comments regarding the interpretation of the biological data in Figure 3. To clarify, each mouse serves as an independent unit in this analysis. We believe that ANOVA is the appropriate test for comparing the proportions of different behavioral signatures across the tumor microenvironment (TME) regions identified by large-scale phenotyping. However, we acknowledge that using a stacked bar plot may have been misleading. While a Chi-squared test could show differences in the distribution of behavioral signatures, it would not indicate which specific signatures are responsible for those differences. Therefore, in the revised manuscript, we will retain the ANOVA analysis but will represent the proportions using a bar chart that clearly illustrates multiple conditions for each behavioral cluster. We also appreciate the reviewer’s concern regarding the transparency of our data. In the revised manuscript, we will include the number of replicates for all figures to enhance clarity and understanding.
Comment: Figure 4 has similar statistical issues in that the n is not listed and, again, it is unclear whether they are treating each cell track as independent which, again, would be inappropriate. The best practice for this type of data would be the use of super plots as outlined in Lord et al. (2020) JCI - SuperPlots: Communicating reproducibility and variability in cell biology.
We appreciate the reviewer’s comments and suggestions regarding Figure 4. In the revised manuscript, we will clarify the number of replicates used and our approach to treating cell tracks as independent units. We will implement super-plots where appropriate, to enhance the communication of reproducibility and variability in our data.
Comment: The main issue that this raises is that the large-scale phenotyping module and the heterogeneity module appear designed to produce these statistical analyses that are used in these figures and, if they are based on the assumption that each track is independent, then this will produce inappropriate analyses as a default.
We appreciate the reviewer’s comment, though we find ourselves unsure about the specific concern being raised. To clarify, each mouse is treated as an independent unit in our analyses. For each large-scale phenotyping region, we measure the proportion of tumor cells displaying a specific behavioral phenotype independently for each mouse. These proportions are then used for statistical analysis. We hope this explanation provides clarity, and we will adjust the manuscript to better convey this methodology.
Reviewer #3:
Comment: The most challenging task of analyzing 3D time-lapse imaging data is to accurately segment and track the individual cells in 3D over a long time duration. BEHAV3D Tumor Profiler did not provide any new advancement in this regard, and instead relies on commercial software, Imaris, for this critical step. Imaris is known to have a very high error rate when used for analyzing 3D time-lapse data. In the Methods section, the authors themselves stated that "Tumor cell tracks were manually corrected to ensure accurate tracking". Based on our own experience of using Imaris, such manual correction is tedious and often required for every time step of the movie. Therefore, Imaris is not a satisfactory tool for analyzing 3D time-lapse data. Moreover, Imaris is expensive and many research labs probably can't afford to buy it. The fact that BEHAV3D Tumor Profiler critically depends on the faulty ImarisTrack module makes it unclear whether the BEHAV3D tool or the results are reliable.
If the authors want to "democratize the analysis of heterogeneous cancer cell behaviors", they should perform image segmentation and tracking using open-source codes (e.g., Cellpose, Stardisk & 3DCellTracker) and not rely on the expensive and inaccurate ImarisTrack Module for the image analysis step of BEHAV3D.
We appreciate the reviewer’s comments on the challenges of segmenting and tracking individual cells in 3D time-lapse imaging data. As mentioned previously, our primary focus is to develop an analytical tool for comprehensive data analysis rather than developing tools for image processing. To enhance accessibility, we have updated our tool to support data formats from open-source Fiji plugins, such as TrackMate, which will benefit users without access to commercial software (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ).
While we recognize the limitations of Imaris, it remains widely used in the intravital microscopy community due to its user-friendly interface for 3D visualization and semi-automated segmentation capabilities. Since no perfect tracking method currently exist, we utilized Imaris for its ability to allow manual corrections of faulty tracks, ensuring the reliability of our results. This approach was the best available option when we began our analysis, allowing us to obtain accurate results efficiently.
In the revised manuscript, we will clarify our methodology and provide information on both Imaris and alternative processing options to strengthen the reliability of our findings.
Comment: The authors developed a "Heterogeneity module" to extract distinctive tumor migratory phenotypes from the cell tracks quantified by Imaris. The cell tracks of the individual tumor cells are all quite short, indicating relatively low motility of the tumor cells. It's unclear whether such short migratory tracks are sufficient to warrant the PCA analysis to identify the 7 distinctive migratory phenotypes shown in Figure 2d. It's also unclear whether these 7 migratory phenotypes correspond to unique functional phenotypes.
For the 7 distinctive motility clusters, the authors should provide a more detailed analysis of the differences between them. It's unclear whether the difference in retreating, slow retreating, erratic, static, slow, slow invading, and invading correspond to functional difference of the tumor cells.
While some tumor cells exhibit limited motility, indicated by short tracks, others demonstrate significant migratory capabilities. This variability in tumor cell behavior is a central focus of our analysis, and our tool is specifically designed to identify and distinguish these differences. Our PCA analysis effectively captures this variability, as illustrated in Figure 2 d-f. It differentiates between cells exhibiting varying degrees of migratory behavior, including both highly migratory and less migratory phenotypes, as well as their directionality relative to the tumor core and the persistence of their movements. Thus, we believe that our approach provides valuable insights into the distinct migratory phenotypes within the tumor microenvironment. We will clarify these aspects further in the revised manuscript to enhance the reader's understanding of our findings.
While our current manuscript does not provide explicit evidence linking each motility cluster to functional differences among the tumor cells, it is important to note that the state of the field supports the idea that cell dynamics can predict cell states and phenotypes. Research conducted by ourselves (Dekkers, Alieva et al., Nat Biotech, 2023) and others, such as Craiciuc et al. (Nature, 2022) and Freckmann et al. (Nat Comm, 2022) has shown that variations in cell motility patterns are indicative of underlying functional characteristics. For instance, cell morphodynamic features have been shown to reflect differences in cell types, T cell targeting states, tumor metastatic potential, and drug resistance states. In the revised manuscript, we will reference relevant studies to underscore the biological significance of these behaviors. By doing so, we hope to clarify the potential implications of our findings and strengthen the overall narrative of our research.
Comment: Using only motility to classify tumor cell behaviours in the tumor microenvironment (TME) is probably not sufficient to capture the tumor cell difference. There are also other non-tumor cell types in the TME. If the authors aim to develop a computational tool that can elucidate tumor cell behaviors in the TME, they should consider other tumor cell features, e.g., morphology, proliferation state, and tumor cell interaction with other cell types, e.g., fibroblasts and distinct immune cells.
The authors should expand the scale of tumor behavior features to classify the tumor phenotype clusters, e.g., to include tumor morphology, proliferation state, and tumor cell interaction with other TME cell types.
We believe that using dynamic features alone is sufficient to capture differences in tumor behavior, as demonstrated by our results in Figure 2. However, we appreciate the reviewer’s suggestion to consider additional features, such as cell morphology and interactions with other cell types, to finetune our analyses. To this end, we have adapted our pipeline to be compatible with various features present in the data (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0?tab=readme-ov-file#feature-selection ). We will emphasize this in the revised manuscript. However, we would like to point out that not all features may provide informative insights and that a wide range of features can instead introduce biologically irrelevant noise, making interpretation more challenging. For instance, in 3D microscopy, the z-axis resolution is typically lower, which can lead to artifacts like elongation in that direction. Adding morphological features that capture this may skew the analysis. Therefore, we believe that incorporating additional features should be approached with caution. We will clarify these considerations in the revised manuscript to better guide users in utilizing our computational tool effectively. We will also reference the use of unbiased feature selection techniques, such as bootstrapping methods, to identify biologically relevant features based on the conditions provided (D.G. Aragones et al, Computers in Biology and Medicine (2024)).
Comment: The authors have already published two papers on BEHAV3D [Alieva M et al. Nat Protoc. 2024 Jul;19(7): 2052-2084; Dekkers JF, et al. Nat Biotechnol. 2023 Jan;41(1):60-69]. Although the previous two papers used BEHAV3D to analyze T cells, the basic pipeline and computational steps are similar, in particular regarding cell segmentation and tracking. The addition of a "Heterogeneity module" based on PCA analysis does not make a significant advancement in terms of image analysis and quantification.
We want to emphasize that we have no intention of duplicating our previous publications. In this manuscript, we have consistently cited our foundational papers, where BEHAV3D was first developed for T cell migratory analysis in in vitro settings. In the introduction, we clearly state that our earlier work inspired us to adopt a similar approach for analyzing cell behavior in intravital microscopy (IVM) data, addressing the specific needs and complexities of analyzing tumor cell behaviors in the tumor microenvironment.
Importantly, our new work provides several key advancements: 1) a pipeline specifically adapted for intravital microscopy (IVM) data; 2) integration of spatial characteristics from both large-scale and small-scale phenotyping; and 3) a zero-code approach designed to empower researchers without coding skills to effectively utilize the tool. We believe that these enhancements represent meaningful progress in the analysis of cell behaviors within the tumor microenvironment which will be valuable for the IVM community. We will ensure that these points are clearly articulated in the revised manuscript.
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