Single-cell RNA-seq analysis reveals penaeid shrimp hemocyte subpopulations and cell differentiation process
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Curated by eLife
Evaluation Summary:
This study provides identification of different subpopulations of blood cells and gives new insights in putative hemocyte lineage relationships by single cell RNA sequencing. The main conclusions are fairly well supported by the data and this manuscript will be of high interest to crustacean immunologists and readers in the field of aquaculture.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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Abstract
Crustacean aquaculture is expected to be a major source of fishery commodities in the near future. Hemocytes are key players of the immune system in shrimps; however, their classification, maturation, and differentiation are still under debate. To date, only discrete and inconsistent information on the classification of shrimp hemocytes has been reported, showing that the morphological characteristics are not sufficient to resolve their actual roles. Our present study using single-cell RNA sequencing revealed six types of hemocytes of Marsupenaeus japonicus based on their transcriptional profiles. We identified markers of each subpopulation and predicted the differentiation pathways involved in their maturation. We also predicted cell growth factors that might play crucial roles in hemocyte differentiation. Different immune roles among these subpopulations were suggested from the analysis of differentially expressed immune-related genes. These results provide a unified classification of shrimp hemocytes, which improves the understanding of its immune system.
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Author Response:
Reviewer #1:
Summary and Strength:
Single-cell RNA sequencing is the most appropriate technique to profile unknown cell types and Koiwai et al. made good use of the suitable tool to understand the heterogeneity of shrimp hemocyte populations. The authors profiled single-cell transcriptomes of shrimp hemocytes and revealed nine subtypes of hemocytes. Each cluster recognizes several markers, and the authors found that Hem1 and Hem2 are likely immature hemocytes while Hem5 to Hem9 would play a role in immune responses. Moreover, pseudotime trajectory analysis discovered that hemocytes differentiate from a single subpopulation to four hemocyte populations, indicating active hematopoiesis in the crustacean. The authors explored cell growth- and immune-related genes in each cluster and suggested putative functions of …
Author Response:
Reviewer #1:
Summary and Strength:
Single-cell RNA sequencing is the most appropriate technique to profile unknown cell types and Koiwai et al. made good use of the suitable tool to understand the heterogeneity of shrimp hemocyte populations. The authors profiled single-cell transcriptomes of shrimp hemocytes and revealed nine subtypes of hemocytes. Each cluster recognizes several markers, and the authors found that Hem1 and Hem2 are likely immature hemocytes while Hem5 to Hem9 would play a role in immune responses. Moreover, pseudotime trajectory analysis discovered that hemocytes differentiate from a single subpopulation to four hemocyte populations, indicating active hematopoiesis in the crustacean. The authors explored cell growth- and immune-related genes in each cluster and suggested putative functions of each hemocyte subtype. Lastly, scRNA-seq results were further validated by in vivo analysis and identified biological differences between agranulocytes and granulocytes. Overall, conclusions are well-supported by data and hemocyte classifications were carefully performed. Given the importance of aquaculture in both biology and industry, this study will be an extremely useful reference for crustacean hematopoiesis and immunity. Moreover, it will be a good example and prototype for cell-type analysis in non-model organisms.
Thank you very much for your kind review. We hope that this paper will lead to a better understanding of the immune system of shrimp and further development of aquaculture.
Weaknesses:
The conclusions of this paper are mostly well supported by data, but some aspects of data analysis QC and in vivo lineage validation need to be clarified.
- It is not a trivial task to perform genome-wide analyses of gene expression on species without sufficient reference genome/transcriptome maps. With this respect, the authors should have de novo assembled a transcriptome map with a careful curation of the resulting transfrags. One of the weaknesses of this study is the lack of proper evaluation for the assembly results. To reassure the results, the authors would need to first assess their de novo transcripts in detail and additional data QC analysis would help substantiate the validity.
The genome sequence of the kuruma shrimp M. japonicus has only been registered, and the high-quality data has not been published yet. Therefore, we could not perform validation using the genome sequence. However, by applying the BUSCO tool to the assembled sequences, we verified the quality of the assembly genes. Line 80-82 and 634-636.
- The authors applied SCTransform to adjust batch effects and to integrate independent sequencing libraries. SCTransform performs well in general; however, the authors would need to present results on how batch effects were corrected along with before and after analysis. In addition, the authors would need to check if any cluster was primarily originated from a single library, which could be indicative of library-specific bias (or batch effects).
Thank you for your suggestion. The triplicate distribution after batch correction is shown in the Figure 2-figure supplement 1 and Figure 5-figure supplement 1. Line 123 (Figure 2-figure supplement 1), 244 ( Figure 5-figure supplement 1) and 686-689.
- Hem6 cells lack specific markers and some cells in this cluster are scattered throughout the other clusters (Fig. 1 & 2). Based on the pattern, it is possible that these cells are continuous subsets of other clusters. It would be good if the authors could group these cells with Hem7 or other clusters based on transcriptomic similarities or by changing clustering resolution. Additionally, they may also be a result of doublets, and it is unclear whether doublets were removed. Hem6 cells require additional measures to fully categorize as a unique subset.
Based on the new UMI counts, we re-did in silico clustering and pseudotime analysis with new parameters. For Doublets, we assumed UMI less than 4000 this time because none of them had prominent UMI. Line 118 (Figure 2), 237 (Figure 5), 686-689 and 710-712.
- The authors took advantage of FACS sorting, qRT-PCR, and microscopic observation to verify in silico analyses and defined R1 and R2 populations. While the experiments are appropriate to delineate differences between the two populations, it is not sufficient to determine agranulocytes as a premature population (Hem1-4) and granulocytes as differentiated subsets (Hem5-9). To better understand the two groups (ideally nine subtypes), additional in vivo experiments would be essential. For example, proliferation markers (BrdU or EdU) could be examined after FACS sorting R1 and R2 cells to show R1 cells (immature hemocytes) are indeed proliferating as indicated in the analyses.
Since stable culture of shrimp hemocytes is still difficult, it is difficult to implement BrdU assay now. We believe the advantages of our study are that single-cell analysis can be used in shrimp, that we explored marker candidates, and that we were able to provide guidelines for cell classification in the future. Of course, we are going to adapt BrdU or EdU assay on hemocytes in the feature.
- FACS-sorted R1 or R2 population does not look homogeneous based on the morphology and having two subgroups under nine hemocyte subtypes may not be the most appropriate way to validate the data. The better way to prove each subtype is to use in situ hybridization to validate marker gene expressions and match with morphology.
What we want to show here is that it is very difficult to classify hemocytes by morphologically, and even if we could, it is likely to be divided into two rough groups (FACS result). As in the answer to the question above, we believe the advantage of this project is that we were able to search for marker candidates and provide guidelines for cell classification in the future. Of course, in the future, we hope to look at the function and expression of each gene. Since it is difficult to perform the in-situ assay or BrdU assay in shrimp hemocytes immediately, we have removed the Figure 7.
Reviewer #2:
In this manuscript Koiwai et al. used single cell RNA sequencing of hemocytes from the shrimp Marsupenaeus japonicus. Due to lack of complete genome information for this species, they first did a de novo assembly of transcript data from shrimp hemocytes, and then used this as reference to map the scRNA results. Based on expression of the 3000 most variable genes, and a subsequent cluster analysis, nine different subpopulations of hemocytes were identified, named as Hem1-Hem9. They used the Seurat marker tool to find in total 40 cluster specific marker transcripts for all cluster except for Hem6. Based upon the predicted markers the authors suggested Hem1 and Hem2 to be immature hemocytes. In order to determine differentiation lineages they then used known cell-cycle markers from Drosophila melanogaster and could confirm Hem1 as hemocyte precursors. While genes involved in the cell cycle could be used to identify hemocyte precursors, the authors concluded that immune related genes from the fly was not possible to use to determine functions or different lineages of hemocytes in the shrimp. This is an important (and known) fact, since it is often taught that the fruit fly can be used as a general model organism for invertebrate immunologists which obviously is not the case. Even among arthropods, animals are different. The authors suggest four lineages based upon a pseudo temporal analysis using the Drosophila cell-cycle genes and other proliferation-related genes. Further, they used growth factor genes and immune related genes and could nicely map these into different clusters and thereby in a way validating the nine subpopulations. This paper will provide a good framework to detect and analyze immune responses in shrimp and other crustaceans in a more detailed way.
Strengths:
The determination of nine classes of hemocytes will enable much more detailed studies in the future about immune responses, which so far have been performed using expression analysis in mixed cell populations. This paper will give scientists a tool to understand differential cell response upon an injury or pathogen infection. The subdivision into nine hemocyte populations is carefully done using several sets of markers and the conclusions are on the whole well supported by the data.
Thank you for taking the time to review our paper. We hope that this paper will serve as a guideline for crustacean hemocyte research.
Weaknesses:
One obvious drawback of the paper is first the low number of UMIs. A total number of 2704 cells gave a median UMI as low as 718 which is very low. Especially shrimp no. 2 has an average far below 500 and should perhaps be omitted. Therefore, one question is about cell viability prior to the drop-seq analysis. The fact of this low number of UMIs should be discussed more thoroughly.
By confirming the mitochondrial-derived sequences, we cleared up the suspicion that large numbers of dead cells were contaminating. We have also succeeded in increasing the number of UMIs by changing mapping software and adjusting the parameters. The value of UMIs is still lower than that of other model organisms, but we think that will improve as the reference genome is published in the future. I have discussed this in the manuscript. Line 87-89, 118 (Figure 2) and 716-717.
Details about how quality control (QC) was performed would be needed, for example the cutoff values for number of UMI per cell, and also one important information showing the quality is the proportion of mitochondrial genes.
As we answered in the above section, we checked and figured the results of mitochondrial contents. Since there are no set rules here, we set the parameters for one cell based on the initial distribution diagram. Line 87-89, 118 (Figure 2) and 686-689
The clustering into nine subpopulations seems solid, however the determination of lineages based upon the pseudo time analysis with cell-cycle related genes is not that strong. The authors identify four lineages, all starting from hem1 via hem2-Hem3- Hem4 and then one to Hem5, another through part of Hem 6 to Hem 7, next through part of Hem 6 to Hem 8 and finally through part of Hem 6 to Hem 9. Referring to Figure 3 - supplement 3, it seems as if Hem6 could be subdivided into two clusters, one visible in B and C, while another part of Hem & is added in D.
Based on the new UMI counts, we re-did in silico Clustering and pseudotime analysis with new parameters. It made more clear result. Line 118 (Figure 2), 237 (Figure 5), 686-689 and 710-712.
Also, the data in figure 3 - supplement 1 showing expression of cell cycle markers do not convincingly show the lineages. Cluster Hem 3 and 4 seems to express much fewer and lower amount of these markers compared to cluster Hem6 - Hem9.
As a result of the new clustering and other analyses, we can now see more clearly how growth-related genes vary along the clusters (Figure 7). Line 366 (Figure 7).
It is also clear (from figure 5 - supplement 1) that there are more than one TGase gene and the authors would need to discuss that fact related to differentiation.
Thank you for your suggestion. We discussed about different type of TGase in revised paper. Line 386-399, 457 (Figure 8-figure supplement 2).
While the part to determine subpopulations is very strong, the part about FACS analysis and qRT-PCR is weaker than the other sections, and doesn't add so much information. Validation of marker genes and the relationship between clusters and morphology shown in figure 6 is not totally convincing. It seems clear that both R1 and R2 contains a mixture of different cell types even if TGase expression is a bit higher in R1. A better way to confirm the results could be to do in situ hybridization (or antibody staining) and show the cell morphology of some selected marker proteins in a mixed hemocyte population. FACS sorting is very crude and does not really separate the shrimp hemocytes in clear groups based on granularity and size. This may be because the size of hemocytes without granules vary a lot. You need cell surface markers to do a good sorting by FACS.
We agree your comments that in situ hybridization or antibody staining are powerful tools to support our new findings. However, it is difficult to perform in-situ assay or preparation of antibody for shrimp hemocytes immediately. What we want to show here is that it is very difficult to classify hemocytes by morphologically, and even if we could, it is likely to be divided into two rough groups (FACS result). As in the answer to the question above, we believe the advantage of this project is that we were able to search for marker candidates and provide guidelines for cell classification in the future. Of course, in the future, we hope to look at the function and expression of each gene.
Another minor issue is the discussion about KPI. There are a huge number of Kazal-type proteinase inhibitors in crustaceans and it is not clear from this data if the authors discuss a specific KPI-gene, and there is a mistake in referring to reference 65 which is about a Kunitz-type inhibitor.
Thank you for your important pointing. In case of kuruma shrimp, de novo assembled genes and blast results showed low (around 60%) identity against L. vannamei’s Kazal-type proteinase inhibitor, not against kuruma shrimp. Therefore, we could not discuss about which type of KPI in this study. We consider it important that further research on KPIs for kuruma shrimp be conducted in the future. Also, as you pointed out, reference 65 was wrong, so we removed it. Line 474 (Figure 8-figure supplement 5).
In summary, this paper is a very important contribution to crustacean immunology, and although a bit weak in lineage determination it will be of extremely high value.
Thank you for giving us a good feedback. We understand that the evaluation of the gene as a marker and the expression of the marker gene in each cell is poor in not being able to confirm. However, we believe that our research will hopefully serve as a basis for future research.
Reviewer #3:
This manuscript by Koiwai et al. described the single-cell RNA-seq analysis of shrimp hemocytes and was submitted as a Resource Paper in eLife. In this study, they identified 9 cell types in shrimp hemocytes based on their transcriptional profiles and identified markers for each subpopulation. They predicted different immune roles among these subpopulations from differentially expressed immune-related genes. They also identified cell growth factors that might play important roles in hemocyte differentiation. This study helps to understand the immune system of shrimp and maybe useful for improving the control of the pathogen infections. The analysis of the data and interpretation is overall good but there are also some concerns:
Thank you for your careful peer review. We hope that this paper will be useful to other researchers in the future. We have made a revise based on your comments, please review it again.
- The number of UMI and genes detected per cell after mapping to the in-house reference genome does not appear to be presented, and the similarities or differences between the three replicated samples are not discussed, as well as the low number of genes detected per cell (~300 in this study) .
By confirming the mitochondrial-derived sequences, we cleared up the suspicion that large numbers of dead cells were contaminating. We have also succeeded in increasing the number of UMIs by changing mapping software and adjusting the parameters. The value of UMIs is still lower than that of other model organisms, but we think that will improve as the reference genome is published in the future. I have discussed this in the manuscript. Line 87-89, 118 (Figure 2) and 686-689.
- The correlation between the morphology and the expression of marker genes demonstrated in Figure 6 is questionable. Cells of the same size could express totally different genes. On the other hand, cells that are different in size can express nearly identical genes. The evidence presented in this manuscript is not enough to support a correlation between cell size and gene expression. Therefore, the author would either need to provide more evidence to support this correlation, or not make such correlation.
Yes, we agree your comments. What we want to show here is that it is very difficult to classify hemocytes by morphologically, and even if we could, it is likely to be divided into two rough groups (FACS result). So, it is not surprising that similar cells may or may not express similar genes. However, some of genes can be used as markers for cell (may refer to cell size too), such as TGase or proPO genes.
- There are many spindle-shaped cells in Figure 6B, but none of them appeared in Figure 6C and D after sorting, and the reason for this is unclear.
We don't have any idea why the cells were deformed either, and we think this is exactly why it is so difficult to classify hemocytes by morphologically. This reason is unknown as cell culture is also not currently possible.
- The hemocyte differentiation model in Figure 7 is not supported by any experimental data.
We understood your comment. Since we could not conduct any functional research about marker genes, we have removed figure 7.
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Reviewer #3 (Public Review):
This manuscript by Koiwai et al. described the single-cell RNA-seq analysis of shrimp hemocytes and was submitted as a Resource Paper in eLife. In this study, they identified 9 cell types in shrimp hemocytes based on their transcriptional profiles and identified markers for each subpopulation. They predicted different immune roles among these subpopulations from differentially expressed immune-related genes. They also identified cell growth factors that might play important roles in hemocyte differentiation. This study helps to understand the immune system of shrimp and maybe useful for improving the control of the pathogen infections. The analysis of the data and interpretation is overall good but there are also some concerns:
The number of UMI and genes detected per cell after mapping to the in-house …
Reviewer #3 (Public Review):
This manuscript by Koiwai et al. described the single-cell RNA-seq analysis of shrimp hemocytes and was submitted as a Resource Paper in eLife. In this study, they identified 9 cell types in shrimp hemocytes based on their transcriptional profiles and identified markers for each subpopulation. They predicted different immune roles among these subpopulations from differentially expressed immune-related genes. They also identified cell growth factors that might play important roles in hemocyte differentiation. This study helps to understand the immune system of shrimp and maybe useful for improving the control of the pathogen infections. The analysis of the data and interpretation is overall good but there are also some concerns:
The number of UMI and genes detected per cell after mapping to the in-house reference genome does not appear to be presented, and the similarities or differences between the three replicated samples are not discussed, as well as the low number of genes detected per cell (~300 in this study) .
The correlation between the morphology and the expression of marker genes demonstrated in Figure 6 is questionable. Cells of the same size could express totally different genes. On the other hand, cells that are different in size can express nearly identical genes. The evidence presented in this manuscript is not enough to support a correlation between cell size and gene expression. Therefore, the author would either need to provide more evidence to support this correlation, or not make such correlation.
There are many spindle-shaped cells in Figure 6B, but none of them appeared in Figure 6C and D after sorting, and the reason for this is unclear.
The hemocyte differentiation model in Figure 7 is not supported by any experimental data.
-
Reviewer #2 (Public Review):
In this manuscript Koiwai et al. used single cell RNA sequencing of hemocytes from the shrimp Marsupenaeus japonicus. Due to lack of complete genome information for this species, they first did a de novo assembly of transcript data from shrimp hemocytes, and then used this as reference to map the scRNA results. Based on expression of the 3000 most variable genes, and a subsequent cluster analysis, nine different subpopulations of hemocytes were identified, named as Hem1-Hem9. They used the Seurat marker tool to find in total 40 cluster specific marker transcripts for all cluster except for Hem6. Based upon the predicted markers the authors suggested Hem1 and Hem2 to be immature hemocytes. In order to determine differentiation lineages they then used known cell-cycle markers from Drosophila melanogaster and …
Reviewer #2 (Public Review):
In this manuscript Koiwai et al. used single cell RNA sequencing of hemocytes from the shrimp Marsupenaeus japonicus. Due to lack of complete genome information for this species, they first did a de novo assembly of transcript data from shrimp hemocytes, and then used this as reference to map the scRNA results. Based on expression of the 3000 most variable genes, and a subsequent cluster analysis, nine different subpopulations of hemocytes were identified, named as Hem1-Hem9. They used the Seurat marker tool to find in total 40 cluster specific marker transcripts for all cluster except for Hem6. Based upon the predicted markers the authors suggested Hem1 and Hem2 to be immature hemocytes. In order to determine differentiation lineages they then used known cell-cycle markers from Drosophila melanogaster and could confirm Hem1 as hemocyte precursors. While genes involved in the cell cycle could be used to identify hemocyte precursors, the authors concluded that immune related genes from the fly was not possible to use to determine functions or different lineages of hemocytes in the shrimp. This is an important (and known) fact, since it is often taught that the fruit fly can be used as a general model organism for invertebrate immunologists which obviously is not the case. Even among arthropods, animals are different. The authors suggest four lineages based upon a pseudo temporal analysis using the Drosophila cell-cycle genes and other proliferation-related genes. Further, they used growth factor genes and immune related genes and could nicely map these into different clusters and thereby in a way validating the nine subpopulations. This paper will provide a good framework to detect and analyze immune responses in shrimp and other crustaceans in a more detailed way.
Strengths:
The determination of nine classes of hemocytes will enable much more detailed studies in the future about immune responses, which so far have been performed using expression analysis in mixed cell populations. This paper will give scientists a tool to understand differential cell response upon an injury or pathogen infection. The subdivision into nine hemocyte populations is carefully done using several sets of markers and the conclusions are on the whole well supported by the data.
Weaknesses:
One obvious drawback of the paper is first the low number of UMIs. A total number of 2704 cells gave a median UMI as low as 718 which is very low. Especially shrimp no. 2 has an average far below 500 and should perhaps be omitted. Therefore, one question is about cell viability prior to the drop-seq analysis. The fact of this low number of UMIs should be discussed more thoroughly.
Details about how quality control (QC) was performed would be needed, for example the cutoff values for number of UMI per cell, and also one important information showing the quality is the proportion of mitochondrial genes. The clustering into nine subpopulations seems solid, however the determination of lineages based upon the pseudo time analysis with cell-cycle related genes is not that strong. The authors identify four lineages, all starting from hem1 via hem2-Hem3- Hem4 and then one to Hem5, another through part of Hem 6 to Hem 7, next through part of Hem 6 to Hem 8 and finally through part of Hem 6 to Hem 9. Referring to Figure 3 - supplement 3, it seems as if Hem6 could be subdivided into two clusters, one visible in B and C, while another part of Hem & is added in D. Also, the data in figure 3 - supplement 1 showing expression of cell cycle markers do not convincingly show the lineages. Cluster Hem 3 and 4 seems to express much fewer and lower amount of these markers compared to cluster Hem6 - Hem9.
It is also clear (from figure 5 - supplement 1) that there are more than one TGase gene and the authors would need to discuss that fact related to differentiation.
While the part to determine subpopulations is very strong, the part about FACS analysis and qRT-PCR is weaker than the other sections, and doesn't add so much information. Validation of marker genes and the relationship between clusters and morphology shown in figure 6 is not totally convincing. It seems clear that both R1 and R2 contains a mixture of different cell types even if TGase expression is a bit higher in R1. A better way to confirm the results could be to do in situ hybridization (or antibody staining) and show the cell morphology of some selected marker proteins in a mixed hemocyte population. FACS sorting is very crude and does not really separate the shrimp hemocytes in clear groups based on granularity and size. This may be because the size of hemocytes without granules vary a lot. You need cell surface markers to do a good sorting by FACS. Another minor issue is the discussion about KPI. There are a huge number of Kazal-type proteinase inhibitors in crustaceans and it is not clear from this data if the authors discuss a specific KPI-gene, and there is a mistake in referring to reference 65 which is about a Kunitz-type inhibitor.
In summary, this paper is a very important contribution to crustacean immunology, and although a bit weak in lineage determination it will be of extremely high value.
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Reviewer #1 (Public Review):
Summary and Strength:
Single-cell RNA sequencing is the most appropriate technique to profile unknown cell types and Koiwai et al. made good use of the suitable tool to understand the heterogeneity of shrimp hemocyte populations. The authors profiled single-cell transcriptomes of shrimp hemocytes and revealed nine subtypes of hemocytes. Each cluster recognizes several markers, and the authors found that Hem1 and Hem2 are likely immature hemocytes while Hem5 to Hem9 would play a role in immune responses. Moreover, pseudotime trajectory analysis discovered that hemocytes differentiate from a single subpopulation to four hemocyte populations, indicating active hematopoiesis in the crustacean. The authors explored cell growth- and immune-related genes in each cluster and suggested putative functions of each …
Reviewer #1 (Public Review):
Summary and Strength:
Single-cell RNA sequencing is the most appropriate technique to profile unknown cell types and Koiwai et al. made good use of the suitable tool to understand the heterogeneity of shrimp hemocyte populations. The authors profiled single-cell transcriptomes of shrimp hemocytes and revealed nine subtypes of hemocytes. Each cluster recognizes several markers, and the authors found that Hem1 and Hem2 are likely immature hemocytes while Hem5 to Hem9 would play a role in immune responses. Moreover, pseudotime trajectory analysis discovered that hemocytes differentiate from a single subpopulation to four hemocyte populations, indicating active hematopoiesis in the crustacean. The authors explored cell growth- and immune-related genes in each cluster and suggested putative functions of each hemocyte subtype. Lastly, scRNA-seq results were further validated by in vivo analysis and identified biological differences between agranulocytes and granulocytes. Overall, conclusions are well-supported by data and hemocyte classifications were carefully performed. Given the importance of aquaculture in both biology and industry, this study will be an extremely useful reference for crustacean hematopoiesis and immunity. Moreover, it will be a good example and prototype for cell-type analysis in non-model organisms.
Weaknesses:
The conclusions of this paper are mostly well supported by data, but some aspects of data analysis QC and in vivo lineage validation need to be clarified.
It is not a trivial task to perform genome-wide analyses of gene expression on species without sufficient reference genome/transcriptome maps. With this respect, the authors should have de novo assembled a transcriptome map with a careful curation of the resulting transfrags. One of the weaknesses of this study is the lack of proper evaluation for the assembly results. To reassure the results, the authors would need to first assess their de novo transcripts in detail and additional data QC analysis would help substantiate the validity.
The authors applied SCTransform to adjust batch effects and to integrate independent sequencing libraries. SCTransform performs well in general; however, the authors would need to present results on how batch effects were corrected along with before and after analysis. In addition, the authors would need to check if any cluster was primarily originated from a single library, which could be indicative of library-specific bias (or batch effects).
Hem6 cells lack specific markers and some cells in this cluster are scattered throughout the other clusters (Fig. 1 & 2). Based on the pattern, it is possible that these cells are continuous subsets of other clusters. It would be good if the authors could group these cells with Hem7 or other clusters based on transcriptomic similarities or by changing clustering resolution. Additionally, they may also be a result of doublets, and it is unclear whether doublets were removed. Hem6 cells require additional measures to fully categorize as a unique subset.
The authors took advantage of FACS sorting, qRT-PCR, and microscopic observation to verify in silico analyses and defined R1 and R2 populations. While the experiments are appropriate to delineate differences between the two populations, it is not sufficient to determine agranulocytes as a premature population (Hem1-4) and granulocytes as differentiated subsets (Hem5-9). To better understand the two groups (ideally nine subtypes), additional in vivo experiments would be essential. For example, proliferation markers (BrdU or EdU) could be examined after FACS sorting R1 and R2 cells to show R1 cells (immature hemocytes) are indeed proliferating as indicated in the analyses.
FACS-sorted R1 or R2 population does not look homogeneous based on the morphology and having two subgroups under nine hemocyte subtypes may not be the most appropriate way to validate the data. The better way to prove each subtype is to use in situ hybridization to validate marker gene expressions and match with morphology.
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Evaluation Summary:
This study provides identification of different subpopulations of blood cells and gives new insights in putative hemocyte lineage relationships by single cell RNA sequencing. The main conclusions are fairly well supported by the data and this manuscript will be of high interest to crustacean immunologists and readers in the field of aquaculture.
(This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)
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