Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach
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Curated by eLife
eLife assessment
This paper presents a valuable automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system. The authors have developed a technique for analyzing cells that grow in suspension and used their method to look at different tumor cell lines that grow in suspension and determine the effect of drugs that directly affect the cell cycle. They show solid evidence that the method can be applied to both adherent and non-adherent cell lines. This paper will be of interest to cell biologists investigating cell cycle effects.
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Abstract
Understanding the details of the cell cycle at the level of individual cells is critical for both cellular biology and cancer research. While existing methods using specific fluorescent markers have advanced our ability to study the cell cycle in cells that adhere to surfaces, there is a clear gap when it comes to non-adherent cells. In this study, we combine a specialized surface to improve cell attachment, the genetically-encoded FUCCI(CA)2 sensor, an automated image processing and analysis pipeline, and a custom machine-learning algorithm. This combined approach allowed us to precisely measure the duration of different cell cycle phases in non-adherent cells.Our method provided detailed information from hundreds of cells under different experimental conditions in a fully automated manner. We validated this approach in two different Acute Myeloid Leukemia (AML) cell lines, NB4 and Kasumi-1, which have unique cell cycle characteristics. Additionally, we tested the impact of drugs affecting the cell cycle in NB4 cells. Importantly, our cell cycle analysis system is freely available and has also been validated for use with adherent cells.In summary, this report introduces a comprehensive, automated method for studying the cell cycle in both adherent and non-adherent cells, offering a valuable tool for cancer research and drug development.
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eLife assessment
This paper presents a valuable automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system. The authors have developed a technique for analyzing cells that grow in suspension and used their method to look at different tumor cell lines that grow in suspension and determine the effect of drugs that directly affect the cell cycle. They show solid evidence that the method can be applied to both adherent and non-adherent cell lines. This paper will be of interest to cell biologists investigating cell cycle effects.
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Reviewer #3 (Public review):
Summary:
This paper provides presents an automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system, and applies the method to look at different tumor cell lines that grow in suspension and determine their cell cycle profile and the effect of drugs that directly affect the cell cycles, on progression through the cell cycle for a 72 hour period.
Strengths:
This is a METHODS paper. The one potentially novel finding is that they can identify cells which are at the G1-S transition by the change in color as one protein starts to go up and the other one goes down, similar to change seen as cells enter G2/M. They have provided detailed data in the resubmission, demonstrating how this can be done in different cell lines and that the resolution of the brief …
Reviewer #3 (Public review):
Summary:
This paper provides presents an automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system, and applies the method to look at different tumor cell lines that grow in suspension and determine their cell cycle profile and the effect of drugs that directly affect the cell cycles, on progression through the cell cycle for a 72 hour period.
Strengths:
This is a METHODS paper. The one potentially novel finding is that they can identify cells which are at the G1-S transition by the change in color as one protein starts to go up and the other one goes down, similar to change seen as cells enter G2/M. They have provided detailed data in the resubmission, demonstrating how this can be done in different cell lines and that the resolution of the brief time is about (about 1 hr) when the cells are determined to be in the transition from G1 to S. They further showed how one can explore this period (using EDU labeling in conjunction with FUCCI how one can determine whether cells have entered S-phase. This nicely addressed a weakness identified in the previous review.
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Author response:
The following is the authors’ response to the original reviews.
Reviewer 1:
Comment 1 and 2: “The pipeline relies on a large number of hard-coded conditions: size of Gaussian blur (Gaussian should be written in uppercase), values of contrast, size of filters, levels of intensity, etc. Presumably, the authors followed a heuristic approach and tried values of these and concluded that the ones proposed were optimal. A proper sensitivity analysis should be performed. That is, select a range of values of the variables and measure the effect on the output.”
“Linked to the previous comments. Other researchers that want to follow the pipeline would have either to have exactly the same acquisition conditions as the manuscript or start playing with values and try to compensate for any difference in their data (cell diameter, …
Author response:
The following is the authors’ response to the original reviews.
Reviewer 1:
Comment 1 and 2: “The pipeline relies on a large number of hard-coded conditions: size of Gaussian blur (Gaussian should be written in uppercase), values of contrast, size of filters, levels of intensity, etc. Presumably, the authors followed a heuristic approach and tried values of these and concluded that the ones proposed were optimal. A proper sensitivity analysis should be performed. That is, select a range of values of the variables and measure the effect on the output.”
“Linked to the previous comments. Other researchers that want to follow the pipeline would have either to have exactly the same acquisition conditions as the manuscript or start playing with values and try to compensate for any difference in their data (cell diameter, fluorescent intensity, etc.) to see if they can match the results of the manuscript.”
We thank the Reviewer for his insightful comments. We have modified the “Usage” section of the GitHub page (https://github.com/ieoresearch/cellcycle-image-analysis) to include, for each step of the image processing, a paragraph explaining the significance of the operation and a paragraph named “Suggested Values Range” where tips for optimal parameter settings are given and examples with different parameter settings are shown. We believe that these new paragraphs help researchers easily customize the pipeline to their own data.
Reviewer 2:
Comment 1: “It would be useful to include frames from the movie showing a G1/S cell in Figures 1 and S1 with some indication of how long that cell is present. From Figure S4 it looks like it is substantially less than an hour.
It would definitely be nice to validate this observation. A brief pulse of EdU together with the FUCCI colors could allow you to do that in a culture of cycling cells. It appears that the green color as cells enter S-phase develops slowly (and maybe gets brighter continuously) as does the red color as cells progress through G1. It would be nice to validate what the color the cells are when they actually initiate DNA replication.”
We thank the Reviewer for the opportunity to further investigate our results and clarify points that were unclear in the first version of the manuscript. As suggested, we have included all acquired frames depicting the G1 to S transition/early S phase of three cells: the Kasumi-1 untreated cell and the PF-06873600 treated NB4 cell shown in Fig. 1A, and the MDA-MB-231 cell shown in Fig. S1; they are shown in panels D of Fig. 4 and S5, respectively.
For the Kasumi-1 and NB4 cells, the G1 to S transition/early S phase, defined in the pipeline refinement step as a yellow phase appearing before the S phase, is visible at the 12-hour frame. Conversely, the MDA-MB-231 cell shown in Fig. S5D does not exhibit the G1 to S or early S phase, yellow; it transitions abruptly from red to green within our acquisition timeframe (30 min in this case), producing a green early S phase. This observation supports the Reviewer's suggestion that the G1 to S yellow transition is often shorter than one hour and it is not identifiable in all cells.
To further investigate this point, we also conducted the EdU experiments kindly suggested by the Reviewer. Kasumi-1 and MDA-MB-231 cells expressing the FUCCI(CA)2 probes were exposed to a pulse of EdU, and subsequently analyzed using flow cytometry and confocal microscopy. A new paragraph titled “The workflow allows the identification of the G1 to S phase transition” has been added to the Results section, with the corresponding data presented in Fig. 4 and Fig. S5 for Kasumi-1 and MDA-MB-231 cells, respectively. The Methods section has also been updated describing the new experiments.
Additionally, in BOX1 under the 'Cell phase assignment' paragraph, point (III), we have removed point 'a. Re-assign the G2/M frames to G1'. Although theoretically possible according to the pipeline, this reassignment is incorrect in practice because mVenus fluorescence indicates that the cells are starting or have already initiated DNA replication.
All the modifications we made in the text and Figure captions are highlighted in red. We would be thankful if the co-first authorship of Kourosh Hayatigolkhatmi, Chiara Soriani and Emanuel Soda is acknowledged in the final published version of the article.
We believe that the revisions have strengthened our manuscript, and we hope that it now meets the reviewers' suggestions for greater clarity.
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Author Response:
We greatly appreciate the insightful feedback provided by the reviewers and the editor on our manuscript titled "Automated workflow for the cell cycle analysis of non-adherent and adherent cells using a machine learning approach". We will provide a revised version of the manuscript aiming to address the comments and recommendations provided by the reviewers to enhance the quality and clarity of our work. In detail:
Reviewer #1 (Public Review):
Summary:
The manuscript proposes a series of steps using the FIJI environment, the authors have created a plugin for the initial steps of the process, merging images into an RGB stack, conversion to HSV, and then using brightness for reference and hue to distinguish the phases of the cycle. Then, the well-known Trackmate plugin was used to identify single cells and extract …
Author Response:
We greatly appreciate the insightful feedback provided by the reviewers and the editor on our manuscript titled "Automated workflow for the cell cycle analysis of non-adherent and adherent cells using a machine learning approach". We will provide a revised version of the manuscript aiming to address the comments and recommendations provided by the reviewers to enhance the quality and clarity of our work. In detail:
Reviewer #1 (Public Review):
Summary:
The manuscript proposes a series of steps using the FIJI environment, the authors have created a plugin for the initial steps of the process, merging images into an RGB stack, conversion to HSV, and then using brightness for reference and hue to distinguish the phases of the cycle. Then, the well-known Trackmate plugin was used to identify single cells and extract intensities. The data was further post-processed in R, where a series of steps, smoothing, scaling, and addressing missing frames were used to train a random forest. Hard-coded values of hue were used to distinguish G1, S, and G2/M. The process was validated with a score comparing the quality of the tracks and the authors reported the successful measure of the cell cycles.
Strengths:
The implementation of the pipeline seems easy, although it requires two separate platforms: Fiji and R. A similar approach could be implemented in a single programming environment like Python or Matlab and there would not be any need to export from one to the other. However, many labs have similar setups and that is not necessarily a problem.
Weaknesses:
I found two important weaknesses in the proposal:
(1) The pipeline relies on a large number of hard-coded conditions: size of Gaussian blur (Gaussian should be written in uppercase), values of contrast, size of filters, levels of intensity, etc. Presumably, the authors followed a heuristic approach and tried values of these and concluded that the ones proposed were optimal. A proper sensitivity analysis should be performed. That is, select a range of values of the variables and measure the effect on the output.
(2) Linked to the previous comments. Other researchers that want to follow the pipeline would have either to have exactly the same acquisition conditions as the manuscript or start playing with values and try to compensate for any difference in their data (cell diameter, fluorescent intensity, etc.) to see if they can match the results of the manuscript.
We thank Reviewer #1 for the insightful comments. We acknowledge the importance of ensuring the reproducibility and robustness of our pipeline among different sample types, acquisition conditions and, consequently, image S/N ratio and resolution. To address the concerns regarding the reliance on hard-coded conditions and the impact of varying parameter values on the output, we will complete the Methods section of the manuscript and the “Usage” section of the README file in the Github repository (https://github.com/ieoresearch/cellcycle-image-analysis) providing a summary of best practices that should be applied in the pre-processing part of the analysis. As an example, the usable image filters types and their settings related to cells with different size, fluorescence intensities and acquisition conditions will be analysed in detail and general guidelines will be provided.
Moreover, we will provide detailed documentation on the acquisition conditions required for reproducibility in the README file and Methods section.
For the Tracking Analysis part, we will refer to the well documented TrackMate tutorial to adapt the tracking analysis to different cell types, image resolution and intensities.
Reviewer #2 (Public Review):
Summary:
This paper presents an automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system and applies the method to look at different tumor cell lines that grow in suspension and determine their cell cycle profile and the effect of drugs that directly affect the cell cycles, on progression through the cell cycle for a 72 hour period.
Strengths:
This is a METHODS paper. The one potentially novel finding is that they can identify cells that are at the G1-S transition by the change in color as one protein starts to go up and the other one goes down, similar to the change seen as cells enter G2/M.
Weaknesses:
They did not clearly indicate whether the G1/S cells are identified automatically or need to be identified by the person reviewing the data. In Figures 1 and S1, the movie shows cells with no color at a time corresponding to what is about the G1/S transition. Their assigned cell cycle phase is shown in Figure 1 but not in Figure S1. None of these pictures show the G1/S cells that they talk about being able to detect with a different color.
Thank you for your valuable feedback regarding the identification of G1/S cells in our pipeline. To clarify, the G1/S phase identification process is entirely automated within our pipeline. We apologize for any confusion caused by the lack of explicit indication in our manuscript. We will ensure to update the manuscript to clearly state that the identification of G1/S cells is performed automatically by our algorithm, eliminating the need for manual intervention.
Regarding the visualization of G1/S cells in Figures 1 and S1, we will revise the figures to include all the available frames referred to the G1/S transition. It's important to note that during this transition, fluorescence intensities for both the green and the red channels, are dimmer in comparison with their intensity levels during the G2/M transitions. This can result in frames that may seem visually darker, despite both colors coexisting at the same time point. In our revised figures, we will ensure to include all available frames relevant to the G1/S transition and provide a clearer representation of this phenomenon.
In response to Reviewer #2's recommendation, we plan to conduct additional experiments to further validate our observations. We will utilize the EdU technology to highlight the S-phase in FUCCI cells, allowing for better discrimination between the red and green fluorescence of the FUCCI reporter during the initial S-phase.
Additionally, we acknowledge that the link to the Docker container (https://hub.docker.com/r/emanuelsoda/rf_semi_sup) was not included in the manuscript. We apologize for this oversight, and it will be included in the revised version of the paper.
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eLife assessment
This paper presents a valuable automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system. The authors have developed a technique for analyzing cells that grow in suspension and used their method to look at different tumor cell lines that grow in suspension and determine the effect of drugs that directly affect the cell cycle. They show solid evidence that the method can be applied to both adherent and non-adherent cell lines. This paper will be of interest to cell biologists investigating cell cycle effects.
-
Reviewer #1 (Public Review):
Summary:
The manuscript proposes a series of steps using the FIJI environment, the authors have created a plugin for the initial steps of the process, merging images into an RGB stack, conversion to HSV, and then using brightness for reference and hue to distinguish the phases of the cycle. Then, the well-known Trackmate plugin was used to identify single cells and extract intensities. The data was further post-processed in R, where a series of steps, smoothing, scaling, and addressing missing frames were used to train a random forest. Hard-coded values of hue were used to distinguish G1, S, and G2/M. The process was validated with a score comparing the quality of the tracks and the authors reported the successful measure of the cell cycles.
Strengths:
The implementation of the pipeline seems easy, although …
Reviewer #1 (Public Review):
Summary:
The manuscript proposes a series of steps using the FIJI environment, the authors have created a plugin for the initial steps of the process, merging images into an RGB stack, conversion to HSV, and then using brightness for reference and hue to distinguish the phases of the cycle. Then, the well-known Trackmate plugin was used to identify single cells and extract intensities. The data was further post-processed in R, where a series of steps, smoothing, scaling, and addressing missing frames were used to train a random forest. Hard-coded values of hue were used to distinguish G1, S, and G2/M. The process was validated with a score comparing the quality of the tracks and the authors reported the successful measure of the cell cycles.
Strengths:
The implementation of the pipeline seems easy, although it requires two separate platforms: Fiji and R. A similar approach could be implemented in a single programming environment like Python or Matlab and there would not be any need to export from one to the other. However, many labs have similar setups and that is not necessarily a problem.
Weaknesses:
I found two important weaknesses in the proposal:
(1) The pipeline relies on a large number of hard-coded conditions: size of Gaussian blur (Gaussian should be written in uppercase), values of contrast, size of filters, levels of intensity, etc. Presumably, the authors followed a heuristic approach and tried values of these and concluded that the ones proposed were optimal. A proper sensitivity analysis should be performed. That is, select a range of values of the variables and measure the effect on the output.
(2) Linked to the previous comments. Other researchers that want to follow the pipeline would have either to have exactly the same acquisition conditions as the manuscript or start playing with values and try to compensate for any difference in their data (cell diameter, fluorescent intensity, etc.) to see if they can match the results of the manuscript.
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Reviewer #2 (Public Review):
Summary:
This paper presents an automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system and applies the method to look at different tumor cell lines that grow in suspension and determine their cell cycle profile and the effect of drugs that directly affect the cell cycles, on progression through the cell cycle for a 72 hour period.
Strengths:
This is a METHODS paper. The one potentially novel finding is that they can identify cells that are at the G1-S transition by the change in color as one protein starts to go up and the other one goes down, similar to the change seen as cells enter G2/M.
Weaknesses:
They did not clearly indicate whether the G1/S cells are identified automatically or need to be identified by the person reviewing the data. In …
Reviewer #2 (Public Review):
Summary:
This paper presents an automated method to track individual mammalian cells as they progress through the cell cycle using the FUCCI system and applies the method to look at different tumor cell lines that grow in suspension and determine their cell cycle profile and the effect of drugs that directly affect the cell cycles, on progression through the cell cycle for a 72 hour period.
Strengths:
This is a METHODS paper. The one potentially novel finding is that they can identify cells that are at the G1-S transition by the change in color as one protein starts to go up and the other one goes down, similar to the change seen as cells enter G2/M.
Weaknesses:
They did not clearly indicate whether the G1/S cells are identified automatically or need to be identified by the person reviewing the data. In Figures 1 and S1, the movie shows cells with no color at a time corresponding to what is about the G1/S transition. Their assigned cell cycle phase is shown in Figure 1 but not in Figure S1. None of these pictures show the G1/S cells that they talk about being able to detect with a different color.
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