Author response:
Joint Public Review:
In the microglia research community, it is accepted that microglia change their shape both gradually and acutely along a continuum that is influenced by external factors both in their microenvironments and in circulation. Ideally, a given morphological state reflects a functional state that provides insight into a microglia's role in physiological and pathological conditions. The current manuscript introduces MorphoCellSorter, an open-source tool designed for automated morphometric analysis of microglia. This method adds to the many programs and platforms available to assess the characteristics of microglial morphology; however, MorphoCellSorter is unique in that it uses Andrew's plotting to rank populations of cells together (in control and experimental groups) and presents "big picture" views of how entire populations of microglia alter under different conditions. Notably, MorphoCellSorter is versatile, as it can be used across a wide array of imaging techniques and equipment. For example, the authors use MorphoCellSorter on images of fixed and live tissues representing different biological contexts such as embryonic stages, Alzheimer's disease models, stroke, and primary cell cultures.
This manuscript outlines a strategy for efficiently ranking microglia beyond the classical homeostatic vs. active morphological states. The outcome offers only a minor improvement over the already available strategies that have the same challenge: how to interpret the ranking functionally.
We would like to thank the reviewers for their careful reading and constructive comments and questions. While MorphoCellSorter currently does not rank cells functionally based on their morphology, its broad range of application, ease of use and capacity to handle large datasets provide a solid foundation. Combined with advances in single-cell transcriptomics, MorphoCellSorter could potentially enable the future prediction of cell functions based on morphology.
Strengths and Weaknesses:
(1) The authors offer an alternative perspective on microglia morphology, exploring the option to rank microglia instead of categorizing them with means of clusterings like k-means, which should better reflect the concept of a microglia morphology continuum. They demonstrate that these ranked representations of morphology can be illustrated using histograms across the entire population, allowing the identification of potential shifts between experimental groups. Although the idea of using Andrews curves is innovative, the distance between ranked morphologies is challenging to measure, raising the question of whether the authors oversimplify the problem.
We have access to the distance between cells through the Andrew’s score of each cell. However, the challenge is that these distances are relative values and specific to each dataset. While we believe that these distances could provide valuable information, we have not yet determined the most effective way to represent and utilize this data in a meaningful manner.
Also, the discussion about the pipeline's uniqueness does not go into the details of alternative models.The introduction remains weak in outlining the limitations of current methods (L90). Acknowledging this limitation will be necessary.
Thank you for these insightful comments. The discussion about alternative methods was already present in the discussion L586-598 but to answer the request of the reviewers, we have revised the introduction and discussion sections to more clearly address the limitations of current methods, as well as discussed the uniqueness of the pipeline. Additionally, we have reorganized Figure 1 to more effectively highlight the main caveats associated with clustering, the primary method currently in use.
(2) The manuscript suffers from several overstatements and simplifications, which need to be resolved. For example:
a) L40: The authors talk about "accurately ranked cells". Based on their results, the term "accuracy" is still unclear in this context.
Thank you for this comment. Our use of the term "accurately" was intended to convey that the ranking was correct based on comparison with human experts, though we agree that it may have been overstated. We have removed "accurately" and propose to replace it with "properly" to better reflect the intended meaning.
b) L50: Microglial processes are not necessarily evenly distributed in the healthy brain. Depending on their embedded environment, they can have longer process extensions (e.g., frontal cortex versus cerebellum).
Thank you for raising this point to our attention. We removed evenly to be more inclusive on the various morphologies of microglia cells in this introductory sentence
c) L69: The term "metabolic challenge" is very broad, ranging from glycolysis/FAO switches to ATP-mediated morphological adaptations, and it needs further clarification about the author's intended meaning.
Thank you for this comment, indeed we clarified to specify that we were talking about the metabolic challenge triggered by ischemia and added a reference as well.
d) L75: Is morphology truly "easy" to obtain?
Yes, it is in comparison to other parameters such as transcripts or metabolism, but we understand the point made by the reviewer and we found another way of writing it. As an alternative we propose: “morphology is an indicator accessible through…”
e) L80: The sentence structure implies that clustering or artificial intelligence (AI) are parameters, which is incorrect. Furthermore, the authors should clarify the term "AI" in their intended context of morphological analysis.
We apologize for this confusing writing, we reformulated the sentence as follows: “Artificial intelligence (AI) approaches such as machine learning have also been used to categorize morphologies (Leyh et al., 2021)”.
f) L390f: An assumption is made that the contralateral hemisphere is a non-pathological condition. How confident are the authors about this statement? The brain is still exposed to a pathological condition, which does not stop at one brain hemisphere.
We did not say that the contralateral is non-pathological but that the microglial cells have a non-pathological morphology which is slightly different. The contralateral side in ischemic experiments is classically used as a control (Rutkai et al 2022). Although It has been reported that differences in transcript levels can be found between sham operated animals and contralateral hemisphere in tMCAO mice (Filippenkov et al 2022) https://doi.org/10.3390/ijms23137308 showing that indeed the contralateral side is in a different state that sham controls, no report have been made on differences in term of morphology.
We have removed “non-pathological” to avoid misinterpretations
g) Methodological questions:
a) L299: An inversion operation was applied to specific parameters. The description needs to clarify the necessity of this since the PCA does not require it.
Indeed, we are sorry for this lack of explanation. Some morphological indexes rank cells from the least to the most ramified, while others rank them in the opposite order. By inverting certain parameters, we can standardize the ranking direction across all parameters, simplifying data interpretation. This clarification has been added to the revised manuscript as follows:
“Lacunarity, roundness factor, convex hull radii ratio, processes cell areas ratio and skeleton processes ratio were subjected to an inversion operation in order to homogenize the parameters before conducting the PCA: indeed, some parameters rank cells from the least to the most ramified, while others rank them in the opposite order. By inverting certain parameters, we can standardize the ranking direction across all parameters, thus simplifying data interpretation.”
b) Different biological samples have been collected across different species (rat, mouse) and disease conditions (stroke, Alzheimer's disease). Sex is a relevant component in microglia morphology. At first glance, information on sex is missing for several of the samples. The authors should always refer to Table 1 in their manuscript to avoid this confusion. Furthermore, how many biological animals have been analyzed? It would be beneficial for the study to compare different sexes and see how accurate Andrew's ranking would be in ranking differences between males and females. If they have a rationale for choosing one sex, this should be explained.
As reported in the literature, we acknowledge the presence of sex differences in microglial cell morphology. Due to ethical considerations and our commitment to reducing animal use, we did not conduct dedicated experiments specifically for developing MorphoCellSorter. Instead, we relied on existing brain sections provided by collaborators, which were already prepared and included tissue from only one sex—either female or male—except in the case of newborn pups, whose sex is not easily determined. Consequently, we were unable to evaluate whether MorphoCellSorter is sensitive enough to detect morphological differences in microglia attributable to sex. Although assessing this aspect is feasible, we are uncertain if it would yield additional insights relevant to MorphoCellSorter’s design and intended applications.
To address this, we have included additional references in Table 1 of the revised manuscript and clearly indicated the sex of the animals from which each dataset was obtained.
c) In the methodology, the slice thickness has been given in a range. Is there a particular reason for this variability?
We could not spot any range in the text, we usually used 30µm thick sections in order to have entire or close to entire microglia cells.
Although the thickness of the sections was identical for all the sections of a given dataset, only the plans containing the cells of interest were selected during the imaging for both of the ischemic stroke model. This explains why depending on how the cell is distributed in Z the range of the plans acquired vary.
Also, the slice thickness is inadequate to cover the entire microglia morphology. How do the authors include this limitation of their strategy? Did the authors define a cut-off for incomplete microglia?
We found that 30 µm sections provide an effective balance, capturing entire or nearly entire microglial cells (consistent with what we observe in vivo) while allowing sufficient antibody penetration to ensure strong signal quality, even at the section's center. In our segmentation process, we excluded microglia located near the section edges (i.e., cells with processes visible on the first or last plane of image acquisition, as well as those close to the field of view’s boundary). Although our analysis pipeline should also function with thicker sections (>30 µm), we confirmed that thinner sections (15 µm or less) are inadequate for detecting morphological differences, as tested initially on the AD model. Segmented, incomplete microglia lack the necessary structural information to accurately reflect morphological differences thus impairing the detection of existing morphological differences.
c) The manuscript outlines that the authors have used different preprocessing pipelines, which is great for being transparent about this process. Yet, it would be relevant to provide a rationale for the different imaging processing and segmentation pipelines and platform usages (Supplementary Figure 7). For example, it is not clear why the Z maximum projection is performed at the end for the Alzheimer's Disease model, while it's done at the beginning of the others.
The same holds through for cropping, filter values, etc. Would it be possible to analyze the images with the same pipelines and compare whether a specific pipeline should be preferable to others?
The pre-processing steps depend on the quality of the images in each dataset. For example, in the AD dataset, images acquired with a wide-field microscope were considerably noisier compared to those obtained via confocal microscopy. In this case, reducing noise plane-by-plane was more effective than applying noise reduction on a Z-projection, as we would typically do for confocal images. Given that accurate segmentation is essential for reliable analysis in MorphoCellSorter, we chose to tailor the segmentation approach for each dataset individually. We recommend future users of MorphoCellSorter take a similar approach. This clarification has been added to the discussion.
On a note, Matlab is not open-access,
This is correct. We are currently translating this Matlab script in Python, this will be available soon on Github.
https://github.com/Pascuallab/MorphCellSorter.
This also includes combining the different animals to see which insights could be gained using the proposed pipelines.
Because of what we have been explaining earlier, having a common segmentation process for very diverse types of acquisitions (magnification, resolution and type of images) is not optimal in terms of segmentation and accuracy in the analysis. Although we could feed MorphoCellSorter with all this data from a unique segmentation pipeline, the results might be very difficult to interprete.
d) L227: Performing manual thresholding isn't ideal because it implies the preprocessing could be improved. Additionally, it is important to consider that morphology may vary depending on the thresholding parameters. Comparing different acquisitions that have been binarized using different criteria could introduce biases.
As noted earlier, segmentation is not the main focus of this paper, and we leave it to users to select the segmentation method best suited to their datasets. Although, we acknowledge that automated thresholding would be in theory ideal, we were confronted toimage acquisitions that were notuniform, even within the same sample. For instance, in ischemic brain samples, lipofuscin from cell death introduces background noise that can artificially impact threshold levels. We tested global and local algorithms to automatically binarize the cells but these approaches resulted often on imperfect and not optimized segmentation for every cell. In our experience, manually adjusting the threshold provides a more accurate, reliable, and comparable selection of cellular elements, even though it introduces some subjectivity. To ensure consistency in segmentation, we recommend that the same person performs the analysis across all conditions. This clarification has been added to the discussion.
e) Parameter choices: L375: When using k-means clustering, it is good practice to determine the number of clusters (k) using silhouette or elbow scores. Simply selecting a value of k based on its previous usage in the literature is not rigorous, as the optimal number of clusters depends on the specific data structure. If they are seeking a more objective clustering approach, they could also consider employing other unsupervised techniques, (e.g. HDBSCAN) (L403f).
We do agree with the referee’s comment but the purpose of the k-mean we used was just to illustrate the fact that the clusters generated are artificial and do not correspond to the reality of the continuum of microglia morphology. In the course of the study we used the elbow score to determine the k means but this did not work well because no clear elbow was visible in some datasets (probably because of the continuum of microglia morphologies). Anyway, using whatever k value will not change the problem that those clusters are quite artificial and that the boundaries of those clusters are quite arbitrary whatever the way k is determined manually or mathematically.
L373: A rationale for the choice of the 20 non-dimensional parameters as well as a detailed explanation of their computation such as the skeleton process ratio is missing. Also, how strongly correlated are those parameters, and how might this correlation bias the data outcomes?
Thank you for raising this point. There is no specific rationale beyond our goal of being as exhaustive as possible, incorporating most of the parameters found in the literature, as well as some additional ones that we believed could provide a more thorough description of microglial morphology.
Indeed, some of these parameters are correlated. Initially, we considered this might be problematic, but we quickly found that these correlations essentially act as factors that help assign more weight to certain parameters, reflecting their likely greater importance in a given dataset. Rather than being a limitation, the correlated parameters actually enhance the ranking. We tested removing some of these parameters in earlier versions of MorphoCellSorter, and found that doing so reduced the accuracy of the tool.
Differences between circularity and roundness factors are not coming across and require further clarification.
These are two distinct ways of characterizing morphological complexity, and we borrowed these parameters and kept the name from the existing literature, not necessarily in the context of microglia. In our case, these parameters are used to describe the overall shape of the cell. The advantage of using different metrics to calculate similar parameters is that, depending on the dataset, one method may be better suited to capture specific morphological features of a given dataset. MorphoCellSorter selects the parameter that best explains the greatest dispersion in the data, allowing for a more accurate characterization of the morphology.
One is applied to the soma and the other to the cell, but why is neither circularity nor loudness factor applied to both?
None of the parameters concern the cell body by itself. The cell body is always relative to another metric(s). Because these parameters and what they represent does not seem to be very clear we will add a graphic representation of the type of measurements and measure they provide in the revised version of the manuscript.
f) PCA analysis:
The authors spend a lot of text to describe the basic principles of PCA. PCA is mathematically well-described and does not require such depth in the description and would be sufficient with references.
Thank you for this comment indeed the description of PCA may be too exhaustive, we will simplify the text.
Furthermore, there are the following points that require attention:
L321: PC1 is the most important part of the data could be an incorrect statement because the highest dispersion could be noise, which would not be the most relevant part of the data. Therefore, the term "important" has to be clarified.
We are not sure in the case of segmented images the noise would represent most of the data, as by doing segmentation we also remove most of the noise, but maybe the reviewer is concerned about another type of noise? Nonetheless, we thank the reviewer for his comment and we propose the following change, that should solve this potential issue.
“_PC_1 is the direction in which data is most dispersed.”
L323: As before, it's not given that the first two components hold all the information.
Thank you for this comment we modified this statement as follows: “The two first components represent most of the information (about 70%), hence we can consider the plan PC_1, PC_2 as the principal plan reducing the dataset to a two dimensional space”
L327 and L331 contain mistakes in the nomenclature: Mix up of "wi" should be "wn" because "i" does not refer to anything. The same for "phi i = arctan(yn/wn)" should be "phi n".
Thanks a lot for these comments. We have made the changes in the text as proposed by the reviewer.
L348: Spearman's correlation measures monotonic correlation, not linear correlation. Either the authors used Pearson Correlation for linearity or Spearman correlation for monotonic. This needs to be clarified to avoid misunderstandings.
Sorry for the misunderstanding, we did use Spearman correlation which is monotonic, we thus changed linear by monotonic in the text. Thanks a lot for the careful reading.
g) If the authors find no morphological alteration, how can they ensure that the algorithm is sensitive enough to detect them? When morphologies are similar, it's harder to spot differences. In cases where morphological differences are more apparent, like stroke, classification is more straightforward.
We are not entirely sure we fully understand the reviewer's comment. When data are similar or nearly identical, MorphoCellSorter performs comparably to human experts (see Table 1). However, the advantage of using MorphoCellSorter is that it ranks cells do.much faster while achieving accuracy similar to that of human experts AND gives them a value on an axis (andrews score), which a human expert certainly can't. For example, in the case of mouse embryos, MorphoCellSorter’s ranking was as accurate as that made by human experts. Based on this ranking, the distributions were similar, suggesting that the morphologies are generally consistent across samples.
The algorithm itself does not detect anything—it simply ranks cells according to the provided parameters. Therefore, it is unlikely that sensitivity is an issue; the algorithm ranks the cells based on existing data. The most critical factor in the analysis is the segmentation step, which is not the focus of our paper. However, the more accurate the segmentation, the more distinct the parameters will be if actual differences exist. Thus, sensitivity concerns are more related to the quality of image acquisition or the segmentation process rather than the ranking itself. Once MorphoCellSorter receives the parameters, it ranks the cells accordingly. When cells are very similar, the ranking process becomes more complex, as reflected in the correlation values comparing expert rankings to those from MorphoCellSorter (Table 1).
Moreover, MorphoCellSorter does not only provide a ranking: the morphological indexes automatically computed offer useful information to compare the cells’ morphology between groups.
h) Minor aspects:
% notation requires to include (weight/volume) annotation.
This has been done in the revised version of the manuscript
Citation/source of the different mouse lines should be included in the method sections (e.g. L117).
The reference of the mouse line has been added (RRID:IMSR_JAX:005582) to the revised version of the manuscript.
L125: The length of the single housing should be specified to ensure no variability in this context.
The mice were kept 24h00 individually, this is now stated in the text
L673: Typo to the reference to the figure.
This has been corrected, thank you for your thoughtful reading.
