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Dear Author,
Congratulations! After your diligent work addressing the reviewers' comments, I am pleased to inform you that your manuscript has been accepted for publication in PeerJ. This version is more concise and formal, enhancing clarity and flow.
[# PeerJ Staff Note - this decision was reviewed and approved by Konstantinos Kormas, a PeerJ Section Editor covering this Section #]
Dear authors,
Manuscript titled "Different immunological characteristics of asymptomatic and symptomatic COVID-19 patients without vaccination in the acute and convalescence stages" that you submitted to PeerJ has been reviewed.
The reviewer(s) have suggested that some important points must be clarified and have requested substantial changes to be made in the manuscript. Therefore, I invite you to respond to the reviewer(s)' comments and revise your manuscript. The reviewer(s) comments are included at the end of this letter.
Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
I would like to commend the authors for their thorough and thoughtful revisions. The changes made to the manuscript have satisfactorily addressed the concerns I previously raised. The clarifications provided have strengthened the overall quality and rigor of the study. Given these improvements, I believe the manuscript is now in an excellent form and is suitable for publication in its current state.
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1. In line 32-33, there is a terminology mistake; the technique used in the manuscript is CyTOF. It provides single-cell resolution and involves mass spectrometry, but it is quite different from the concept of high-throughput single-cell mass spectrometry.
Reply from author:
Thank you for bringing this to our attention. We apologize for the terminology mistake. We have corrected it. The revised the sentence is as follows:
In this study, we performed high-throughput single-cell mass cytometry on peripheral blood samples from 10 COVID-19 patients and four healthy donors to analyze their immune status at acute and convalescence phases.
Reply from reviewer:
Please do not use the word ‘high-throughput single-cell mass spectrometry’ in any of the text as you did not use the technique.
4. In figures 1E, 2B, and S1C, it would be more understandable to group the marker genes of the same cell type together and label the cell type. Also, in figures 1E and 2B, I noticed many marker genes have 0 expression in all cell clusters or cell types. Were they not detected at all? If yes, why? Why do you still include them?
Reply from author:
Thank you for your insightful feedback. Regarding the expression levels in Figures 1E and 2B, the observed zero values are due to the use of median expression levels. For instance, if a marker in a cell type has a positive rate below 50%, the median expression level would mathematically be zero. However, this does not mean that these markers were not detected at all. This is why we present the t-SNE map, as it retains the information of marker expression levels across different cell types.
Reply from reviewer:
As a general reader, I would not consider the marker significantly expressed in the cell type if the median expression of a marker in a cell type is 0. If you believe t-SNE map is a better way to show the marker expression, you should only keep the t-SNE map. The more plots do not necessarily mean better explaining.
5. Similarly, in figures 1G and 2C, please group the plots of the marker genes of the same cell type together if you want to keep the two figures.
Reply from author:
Thank you for your valuable suggestion. We understand the importance of grouping the plots of the marker genes of the same cell type together in Figures 1G and 2C to enhance clarity. However, as we are not a professional bioinformatics group, these plots were automatically generated by the code we used, and we are currently unsure how to manually adjust the grouping of the plots.
We will try to seek assistance from bioinformatics experts to address this issue and improve the presentation of the figures if there would be further request.
Reply from reviewer:
Please group the marker genes if possible.
2. In lines 189-190, only CD45+ cells are mentioned. Did you sort CD45+ cells as the input for the CyTOF? Also, multiple important technical details and quality control information must be disclosed in the method or results section, such as the number of cells detected for each individual, the specificity of the antibodies used, whether there is any validation of the antibodies, and any filtering on the cells.
No we didn’t. We re-write that part as follows to answer your question:
Mass cytometry data acquisition and analysis
Mass cytometry data were randomized using the Fluidigm acquisition algorithm (V6.0.626). Individual samples were artificially gated using FlowJo to exclude normalizing beads, cell debris, dead cells, and duplexes for further analysis to identify cells. The removal of EQ beads and isolation of cells were achieved by setting the x-axis to Event_length, adjusting the range to 0-60, and the y-axis to the EQ beads channel, selecting regions where the x-values are below 40 and y-values are below 102. Isolate individual cells by setting both axes to DNA, with the brightest area in the graph indicating the target cells, and continue with further processing of these cells. Isolate live immune cells by setting the x-axis to CD45 and the y-axis to Pt platinum dye, typically selecting around 102 or below on the y-axis and generally above 101 on the x-axis for immune cells. Data were subsequently transformed using asinh (cofactor 5) and normalized to the 99.9th percentile for each channel in the R environment (V3.6.1). For dimensionality reduction analysis, the Barnes-Hut implementation of the t-SNE algorithm was applied using the Rtsne package. Then, they were clustered with the phenograph algorithm by the cytofkit package using k = 30.
Reply from reviewer:
Please address my concern “multiple important technical details and quality control information must be disclosed in the method or results section, such as the number of cells detected for each individual, the specificity of the antibodies used, whether there is any validation of the antibodies, and any filtering on the cells.”
No further comments on this.
No further comments on this.
Dear authors,
The study entitled “Different immunological characteristics of asymptomatic and symptomatic COVID-19 patients without vaccination in the acute and convalescence stages” demonstrated interesting findings using an appropriate methodological approach. However, some important points must be clarified in the manuscript. Your article has great potential for publication on PeerJ, but the reviewers have requested substantial changes to be made.
Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
This study investigates the immune status of COVID-19 patients at different stages of infection using high-throughput single-cell mass spectrometry on peripheral blood samples from 10 COVID-19 patients and four healthy donors. The findings highlight that the immune imbalance observed at the onset of COVID-19 can be corrected during recovery. The study uniquely tracks continuous immune changes in different disease stages within the same individuals, offering new insights into the immune status of COVID-19 patients and potential avenues for immunotherapy and immune intervention.
Method
1. Selection of Healthy Controls:
The credibility of the healthy control selection is questioned. Please provide information about whether there are matching criteria considered in choosing healthy controls, such as age, sex, and comorbidity.
2. Time Points for Healthy Controls:
Define the time points when the samples from the four healthy controls were collected (lines 102-104).
3. Antibody Samples:
Clarify how 42 antibody samples were obtained from 14 participants. Was it supposed to be two samples per person? (line 124).
4. Normalization and Transformation Methods:
The method mentions using asinh transformation (cofactor 5) and normalization to the 99.9th percentile for each channel (lines 139-140). While these practices are common, provide a rationale for these choices and explain why they were selected over other possible methods.
In addition, the description of the Student’s t-test used to determine differences between groups (lines 143-144) should be moved to the “Statistical Analysis” section for enhanced readability.
5. Gating Strategy:
The description of gating using Cytobank is brief (lines 138-139). Provide more details on the gating strategy, including specific markers used to identify and exclude unwanted events such as cell debris, dead cells, and doublets.
6. Statistical Analysis:
Include a description of normality tests (e.g., Shapiro-Wilk) to justify the use of non-parametric tests. If multiple comparisons were performed, mention any corrections applied to control for the risk of Type I errors (e.g., Bonferroni correction).
Laboratory Handling of Samples:
Several aspects of sample handling could affect the viability of the samples and the reliability of the results. These should be discussed in the limitations section:
The method states that PBMC cells were stored at -80°C until analysis (lines 121-122). Typically, PBMCs are stored in liquid nitrogen for long-term preservation to maintain cell viability. Storing at -80°C might affect cell viability and functionality, impacting downstream analysis.
The concentration of cisplatin used for viability staining is mentioned as 100 μM (line 130), which is quite high and may cause excessive cell death. Typically, lower concentrations (e.g., 5-20 μM) are used for viability staining. This potential limitation should be addressed or justified.
In the manuscript titled "Different immunological characteristics of asymptomatic and symptomatic COVID-19 patients without vaccination in the acute and convalescence stages," the authors performed CyTOF analysis on 24 samples from 10 COVID-19 patients and 4 healthy controls to examine changes in immune cell distribution at different disease stages.
Overall, the story is interesting and potentially beneficial to the field. The data appears to be analyzed correctly, and the main claims are supported by the data. Some specific comments are listed as follows:
1. In line 32-33, there is a terminology mistake; the technique used in the manuscript is CyTOF. It provides single-cell resolution and involves mass spectrometry, but it is quite different from the concept of high-throughput single-cell mass spectrometry.
2. The entire manuscript needs reorganization. Currently, the structure, especially the results section, is very confusing. I had to keep going back and forth to try to understand. Additionally, the organization of the figures is very confusing and does not align with the logical flow of the manuscript. See the following specific examples:
1) Figure 1B is referred to in the manuscript before Figure 2A, Figure S1E before S1D, Figure 3B before Figure 2.
2) Figure 1G and Figure 1E basically show the same expression of the markers, yet they are presented separately in figures and text.
3) From what I understand, Figures 1E and 1G both show marker gene expressions. Why include both in the main figures? Also, the genes shown in 1E and 1G are not exactly the same; how did you decide which marker gene to include or exclude in each figure? This is very confusing.
4) The same issue mentioned in the third point also applies to Figure 2B and 2C.
5) These are just examples; please reorganize the figures and text to make it logically smooth and presented in order.
3. The language used is not always scientific and accurate. For example, in lines 221-222, people typically use "fifteen T lymphocyte clusters" and do not use "differentiated." You actually merged the 15 clusters into 5, right?
4. In figures 1E, 2B, and S1C, it would be more understandable to group the marker genes of the same cell type together and label the cell type. Also, in figures 1E and 2B, I noticed many marker genes have 0 expression in all cell clusters or cell types. Were they not detected at all? If yes, why? Why do you still include them?
5. Similarly, in figures 1G and 2C, please group the plots of the marker genes of the same cell type together if you want to keep the two figures.
6. In lines 107-108, the sentence is unclear; please rephrase it.
7. In lines 175-176, since half of the patients have chronic diseases, it may be worthwhile to compare the immune cell distribution of these patients with other patients or healthy controls.
8. There are no legends for the supplementary figures.
9. The supplementary figures S2 and S3 contain too many panels, especially S2E. The plots and text are very small, making them barely readable.
1. In lines 105-107, "Patients in the acute stage were defined as individuals who tested positive for SARS-CoV-2 nucleic acid through quantitative real-time polymerase chain reaction and were within 7 days of symptom onset." How can you apply this definition to the 4 asymptomatic patients since they do not have symptoms?
2. In lines 189-190, only CD45+ cells are mentioned. Did you sort CD45+ cells as the input for the CyTOF? Also, multiple important technical details and quality control information must be disclosed in the method or results section, such as the number of cells detected for each individual, the specificity of the antibodies used, whether there is any validation of the antibodies, and any filtering on the cells.
1. In lines 207-212, the authors described changes in cell type distribution. I would suggest including box plots to demonstrate these changes in different groups of patients at different stages, similar to the plots in Figure 2D.
2. The authors discussed distribution changes in many T-cell and B-cell subtypes. Yet, in the discussion section, few are mentioned. It may be worthwhile to discuss these changes more.
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