Author response:
The following is the authors’ response to the original reviews
We thank the reviewers for their positive and constructive comments on the manuscript. In the revised manuscript we addressed these comments, which we believe have improved the quality of our work.
In summary:
(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, which is to provide an analytical framework for IVM data after segmentation and tracking. Developing open-source segmentation and tracking tools represents a substantial undertaking in its own right, which 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/s41592024-02295-6 - now cited in our revised manuscript).
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, recognizing the need for compatibility with tracking data from various pipelines, we have modified our tool to accept other data formats, such as those generated by open-source Fiji plugins like TrackMate, MTrackJ, ManualTracking (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input). These updates are available in our GitHub repository and are described in the revised manuscript.
(2) We appreciate the reviewer #3 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=readmeov-file#feature-selection ). In the revised manuscript, we highlighted this new functionality and provided 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 addressed 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 demos to our GitHub repository (https://github.com/imAIgene-
Dream3D/BEHAV3D_Tumor_Profiler/tree/BEHAV3D_TP-v2.0/demo_datasets). In the revised manuscript, we referenced this addition and present new figures with examples of these demo’s processing different IVM dataset (2D/3D, different tumors and healthy tissues). Additionally, we have provided processed DMG IVM movie samples in an imaging repository.
(5) Finally, we made some small changes to the manuscript based on the reviewers’ feedback.
Below we provide a point-by-point response to the reviewers’ comments
Reviewer #1 (Public review):
Comment #1: 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.
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. Several studies (e.g., Diego Ulisse Pizzagalli et al., J Immunol (2022); Aby Joseph et al., eLife (2020); Molina-Moreno et al., Medical Image Analysis (2022); Hidalgo-Cenalmor et al., Nat Methods (2024); Ershov et al., Nat Methods (2022)) have comprehensively addressed these topics, and we now reference them in the revised manuscript to provide readers with relevant background.
The objective of our manuscript is not to develop a complete segmentation or tracking pipeline but rather to introduce an analytical framework capable of extracting enhanced insights from the data generated by existing tools. This goal arises from our observations of the field: despite significant investment in image processing, researchers often rely on simplistic approaches, such as averaging single parameters across conditions, which can obscure tumor heterogeneity and spatial behavioral dynamics within the tumor microenvironment.
Our current tool focuses on providing this much-needed analytical capability. For our analysis we used Imaris, a widely utilized software in the intravital microscopy (IVM) community, known for its intuitive 3D visualization and analysis platform despite certain limitations.
In our own literature search of recent IVM studies published by leading laboratories in high-impact journals, we found that close to half used Imaris, while the remainder primarily relied on manual workflows with Fiji plugins. Thus, we consider it valuable to offer a pipeline compatible with such commonly used software, given its prevalence in the field.
However, following the suggestion of the reviewer, and to enhance the tool’s flexibility and compatibility, we have expanded the pipeline to accept data formats generated by open-source Fiji plugins, such as TrackMate, MTrackJ, and ManualTracking. These updates are detailed in the revised manuscript and are implemented in our GitHub repository (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ), where we also provide several demos using TrackMate and Imaris processed data. This addition demonstrates our tool's capability to integrate with segmented and tracked datasets from diverse platforms, increasing its applicability to a broader range of researchers using both commercial and open-source pipelines.
Comment #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.
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 have included detailed information in the figure legends and the number of independent mice represented in each figure legend to ensure transparency. Regarding the number
of cells, we have indicated the total number of processed cells in Figure 2b legend (953 cells). Additionally, we have now included figures (Sup Fig 4c, Sup Fig 5e-g, Fig 5c,e, Sup Fig 6 c,d) for each cluster, where individual dots represent the individual cell tracks with color indicating the position and the shape indicating individual mice.
Comment #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'.
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 (BEHAV3D_Tumor_Profiler/demo_datasets at main · imAIgeneDream3D/BEHAV3D_Tumor_Profiler · GitHub). In the revised manuscript we have referenced this addition in the Data availability section. Since we included now processing with Fiji as well, we provide 4 demo datasets (https://github.com/imAIgeneDream3D/BEHAV3D_Tumor_Profiler/tree/main/demo_datasets), one processed with Imaris in 3D; and one with CellPose2.0 and Trackmate in 2D; one processed with µSAM and Trackmate in 3D and one manually processed with MtrackJ in 2D . Moreover, we now provide Imaris-processed DMG IVM movie samples in an open-source repository.
Comment #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.
We appreciate the reviewer’s comment and in the revised manuscript we have now provided details in the methods section “Tumor large-scale spatial phenotyping with Cytomap” to clarify how the BEHAV3D-TP module maps LSR-3D and Cytomap data to 3D intravital imaging movies:
“To map the assigned regions onto IVM movies, a 3D image of the cluster distribution within the tumor was generated and exported for each sample (Figure Supplement 5a). Next, regions within the IVM movies were visually matched to the corresponding regions identified by the Large-Scale Phenotyping module of Cytomap (Figure 3c). For each mouse, at least one or two representative positions per matched region type were selected, cropped, and analyzed to assess tumor cell behavior, following the previously described cell tracking methodology (Imaris Cell tracking).”
Moreover, we updated Figure 3 c to further clarify these steps.
Comment #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.
We appreciate the reviewer’s comment and acknowledge that our conclusions should be tempered due to the preliminary nature of our evidence. In the revised version of the manuscript we have revised 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.
In discussion: “While our findings suggest that microenvironmental factors may influence tumor cell migration, further studies will be necessary to establish causal relationships. Additional experimental validation, such as macrophage ablation experiments, could help clarify the specific contributions of these factors.”
Reviewer #1 (Recommendations for the authors):
(1) To test the ability of the pipeline to identify relevant patterns of migratory behaviours additional 'control' experiments would be helpful e.g. comparing non-invasive vs invasive tumour cell lines, artificially controlling migratory behaviours of cells such as implanting beads soaked in factors that would attract/repel cells?
(2) Does the pipeline work well for a variety of cell types/contexts? e.g. can it identify and cluster more subtle migratory behaviours such as non-tumour cells during tissue development or regeneration conditions?
We appreciate the reviewer’s valuable suggestions. In the revised manuscript, we have included additional examples demonstrating the capability of our pipeline to investigate heterogeneous cell behavior across two additional experimental setups:
(1) We have now evaluated our BEHAV3D TP heterogeneity module using IVM data from breast cancer cell lines with varying migratory capacities (DOI: 10.1016/j.yexcr.2019.04.009). In these datasets, our pipeline extends beyond predefined characteristics based solely on speed, enabling the identification of distinct cell populations. Notably, our analysis reveals that the breast cancer lines exhibit different proportions of different migratory behaviors such as Fast, Intermediate, Very slow and Static (Supplementary Fig 1).
(2) We have now evaluated our BEHAV3D TP heterogeneity module using IVM data from healthy breast epithelial cells (DOI: 10.1016/j.celrep.2024.115073), where we identify distinct morhophynamic epithelial cell populations in the terminal end but of the mammary gland that have a distinct distribution among Hormone receptor (HR) + and HR- terminal end but cells.
(3) To support biological conclusions could the authors show that ablating tumourassociated macrophages or vasculature alters the migratory patterns of nearby tumour cells?
We appreciate the reviewer's suggestion regarding the potential effects of ablating tumor-associated macrophages or vasculature on the migratory patterns of nearby tumor cells. While these experiments would functionally validate the observations made by our method, we would like to clarify that the primary focus of our study was on the development and application of computational tools for behavioral analysis and thus we consider that delving deeper in understanding the biology behind our observation is out of the scope of the current study. However, as mentioned previously, we have carefully tempered our conclusions to acknowledge the limitations of our current study. In the revised manuscript, we explicitly highlight that experiments involving the ablation of tumor-associated macrophages or vasculature would be crucial for further understanding the biological relevance of our findings.
Minor corrections to text:
(4) Line 63 - are references formatted correctly?
Thank you for pointing out this error. We have corrected it in the revised manuscript.
(5) Lines 161 -162 - 'intravitally imaged' used twice in a sentence.
Thank you for pointing out the typo. We have corrected it in the revised manuscript.
Reviewer #2 (Public review):
Comment#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.
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 community due to its user-friendly interface. We conducted a literature review to evaluate this aspect and below we include references from leading laboratories in the IVM field that utilize Imaris. 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 updated our tool to support 2D and 3D data formats generated by open-source Fiji plugins like TrackMate, MTrackJ, and ManualTracking, improving compatibility with various segmentation and tracking pipelines (https://github.com/imAIgene-Dream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). In the revised manuscript, we describe the new functionality and demonstrate the operation of the BEHAV3D-TP heterogeneity module across various IVM datasets, processed in both 2D and 3D with different processing pipelines (Supplementary Fig 1-3). This includes CellPose 2.0 and the novel 'Segment Anything' model, followed by TrackMate tracking, applied to both tumor and healthy IVM data. Moreover we have developed a new web application that integrates morphological and tracking information from Segment Anything segmentation and Trackmate tracking, depicted in Supplementary Fig 3 a (https://morphotrack-merger.streamlit.app/ ). Additionally, we have updated the introduction to better clarify the scope of our study and include references to existing image processing solutions.
Comment#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.
We appreciate the reviewer’s insightful comments regarding the interpretation of the biological data in Figure 3.
To clarify, each imaged position is considered an independent biological replicate (n = 18 from a total of 6 mice). We acknowledge that the description of the statistical methods and the experimental units was not sufficiently clear in the previous version. In our original submission, we used an ANOVA to test whether the proportion of each behavioral cluster differed across the tumor microenvironment regions. Post hoc pairwise comparisons were performed using Tukey’s test, with the results shown in Supplementary Figure 2d (currently Fig 3d). However, we agree with the reviewer that this approach may be misleading when paired with stacked bar plots that lack error bars, as it can obscure individual variability and does not explicitly represent statistical uncertainty.
In the revised manuscript, we present the data as boxplots with individual data points, where each dot represents an imaged position, and the shape corresponds to a specific mouse. In Figure 3 d the y-axis displays the normalized percentage of each cluster across TME regions, expressed as z-scores. This normalization corrects for inter-mouse variability and facilitates a comparison of the relative distribution of clusters across TME regions, independent of the overall abundance differences between mice. We performed an ANOVA with Tukey's post hoc test for each individual behavioral cluster to assess differences across TME regions. Additionally, for transparency, in Supplementary Figure 5 d we provide the raw percentage values. The legends provide the number of positions and mice included in the analysis.
Comment#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.
We appreciate the reviewer’s comments and suggestions regarding Figure 4. In this case as we are comparing overall the behavioral clusters features, each individual cell is treated as a unit. In the revised manuscript, we have clarified this point in the figure legend and incorporated plots in Figure 4c and 4e, indicating the mouse and imaging position each data point originates from. This enhances the visualization of reproducibility and variability in our data, demonstrating that the results are consistent across multiple mice and positions and are not driven by a single mouse or imaging position.
Comment#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.
We appreciate the reviewer’s comment, although we are unclear about the specific concern being raised. To clarify, in our large-scale phenotyping analysis, each position is assigned to a TME niche based on the CytoMAP analysis and the workflow outlined in Figure 3c. Multiple positions are imaged per mouse. For each position, we measure the proportion of tumor cells exhibiting a specific behavioral phenotype, and these proportions are subsequently used for statistical analysis (Figure 3 d).
In contrast, in Supplementary Fig. 5e-g, we treat each cell track as an individual unit, grouping them by their assigned large-scale region. Here, we assess whether differences between regions can be detected using a conventional single-feature analysis—a more traditional approach. However, we find that this method loses important behavioral patterns and distinctions that BEHAV3D-TP captures.
We hope that this explanation, along with the modifications made to the figures and figure legends, provides greater clarity.
Reviewer #3 (Public review):
Comment #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.
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 (please refer to comment #1 to reviewer #1), our primary focus is to develop an analytical tool for comprehensive data analysis rather than developing tools for image processing. However 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/imAIgeneDream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). In Supplementary Figures 1, 2, and 3, we present IVM data from different sources, processed using three distinct methods: MTrackJ (Supplementary Fig. 1), Cellpose + TrackMate (Supplementary Fig. 2), and µSAM + TrackMate (Supplementary Fig. 3). The latter two represent state-of-the-art deep learning approaches.
On the other hand, 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 exists, we initially utilized Imaris for its ability to allow manual correction of faulty tracks, ensuring the reliability of our results. This approach, not only widely used (see above) but was the best available option when we began our analysis, allowing us to obtain accurate results efficiently.
In the revised manuscript, we clarify the scope of our study and provide information on both Imaris and alternative processing options to strengthen the reliability of our findings:
In introduction: “While significant efforts have been made to develop opensource segmentation and tracking tools for live imaging data, including IVM22–27 fewer tools exist for the unbiased analysis of tumor dynamics. One major barrier is that implementing such analytical methods often requires substantial computational expertise, limiting accessibility for many biomedical researchers conducting IVM experiments. To bridge this gap, we present BEHAV3D Tumor Profiler (BEHAV3D-TP) by providing a robust, user-friendly tool that allows researchers to extract meaningful insights from dynamic cellular behaviors without requiring advanced programming skills.”
In the Methods, we describe now describe not only Imaris processing pipeline, but also the µSAM segmentation pipelines and reference to CellPose IVM processing, which are combined with TrackMate for tracking. Additionally, to integrate morphological information from µSAM with tracking data from TrackMate, we developed a web tool to merge the outputs from both processing steps: https://morphotrack-merger.streamlit.app/
Comment #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.
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 (Figure 2 Invading and Retreating cells). 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 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.
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 (Dekkers, Alieva et al., Nat Biotech, 2023), immune cell types (Crainiciuc et al. (Nature, 2022)), tumor metastatic potential, and drug resistance states (Freckmann et al. (Nat Comm, 2022)). In the revised manuscript, we have referenced 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:
In discussion: “While our current study does not provide direct functional validation of the distinct motility clusters identified, existing literature strongly supports the notion that cell dynamics can serve as a proxy for functional states and phenotypic heterogeneity. Prior work, including studies by our group[19,66] as well as Crainiciuc et al.[35] and Freckmann et al.[20], has demonstrated that variations in cell motility patterns can reflect underlying functional characteristics. Specifically, cell morpho-dynamic features have been shown to correlate with differences in cell type identity, T-cell engagement, metastatic potential, and drug resistance states. This growing body of evidence suggests that tumor cell behavior, as captured by BEHAV3D-TP, may serve as a predictive tool for deciphering functional tumor heterogeneity. Future studies integrating transcriptomic or proteomic profiling of motility-defined subpopulations could further elucidate the biological significance of these behavioral phenotypes.”
Comment #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.
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, to finetune our analyses. To this end, we have adapted our pipeline to be compatible with any dynamic, morphologic or spatial features present in the data. In the revised manuscript we showcase this new addition with the analyses of two new dataset: 2D IVM data from healthy epithelial breast cells (Supplementary Fig 2) and 3D IVM data from adult gliomas (Supplementary Fig 3). These analyses identified cells with specific morphodynamic characteristics, which exhibited distinct kinetic behaviors or spatial distributions.
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 zaxis 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 clarify these considerations in the revised manuscript to better guide users in utilizing our computational tool effectively:
In discussion: “In addition to motility-based classification, features such as tumor cell morphology, proliferation state, and interactions with the tumor microenvironment can further refine tumor phenotyping. BEHAV3D-TP allows for the selection of diverse feature types, supporting datasets that include both dynamic, morphological and spatial parameters. However, we recognize that expanding the feature set may introduce biologically irrelevant noise, particularly in 3D microscopy data where limited z-axis resolution can lead to morphological artifacts. This highlights the potential need in the future to include unbiased feature selection strategies, such as bootstrapping methods67, to ensure the identification of meaningful and biologically relevant parameters. Careful consideration of these aspects is key to maximizing the interpretability and predictive value of analyses performed with BEHAV3D-TP.”
Comment #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.
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 ensure that these points are clearly articulated in the revised manuscript:
In introduction: “In line with this concept of characterizing cellular dynamic properties for cell classification, we have previously developed an analytical platform termed BEHAV3D 19,21 allowing to perform behavioral phenotyping of engineered T cells targeting cancer. While BEHAV3D was initially developed to analyze T cell migratory behavior under controlled in vitro conditions, we sought to expand its application to investigate tumor cell behaviors in IVM data, where the complexity of the TME presents distinct analytical challenges. This manuscript builds on our foundational work but represents a significant advancement by adapting the pipeline specifically for IVM datasets.”
Reviewer #3 (Recommendations for the authors):
(1) 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 thank the reviewer for this recommendation and as stated above we recognize that enhancing our pipeline's compatibility with open-source options is important. To this end, we have updated our tool to support data formats generated by open-source Fiji plugins like TrackMate, MTrackJ, and ManualTracking, improving compatibility with various segmentation and tracking pipelines (https://github.com/imAIgeneDream3D/BEHAV3D_Tumor_Profiler?tab=readme-ov-file#data-input ). In the revised manuscript, we detail this new functionality and demonstrate the operation of the BEHAV3D-TP heterogeneity module using an example dataset of glioma tumors.
Additionally, we have updated the introduction to better clarify the scope of our study (See comment #1 from Review #3) and include references to existing image processing solutions.
(2) 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.
As noted in the comment above, the revised manuscript now incorporates references to relevant literature that support our understanding that behavioral differences among cells are driven by their underlying functional differences (See comment #2 from Reviewer #3). Additionally, we would like to point to Figure 2d and Supplementary Fig 4 c that provide evidence of the functional distinctions between the identified clusters.
(3) 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 thank the reviewer for this valuable suggestion. In the revised manuscript, we have added the flexibility to incorporate a wide range of features, including morphological ones, and enabled users to select the specific features they wish to include in their analysis. To illustrate this functionality, we have included 2 example dataset analyzed using this approach (See comment #3 from Reviewer #3). Additionally, as indicated above we emphasize the importance of careful selection and interpretation of features, as improper choices may lead to biologically irrelevant results. This clarification is intended to ensure that users apply the tool thoughtfully and derive meaningful insights.
