Absolute quantitation of individual SARS-CoV-2 RNA molecules provides a new paradigm for infection dynamics and variant differences
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Abstract
Despite an unprecedented global research effort on SARS-CoV-2, early replication events remain poorly understood. Given the clinical importance of emergent viral variants with increased transmission, there is an urgent need to understand the early stages of viral replication and transcription. We used single-molecule fluorescence in situ hybridisation (smFISH) to quantify positive sense RNA genomes with 95% detection efficiency, while simultaneously visualising negative sense genomes, subgenomic RNAs, and viral proteins. Our absolute quantification of viral RNAs and replication factories revealed that SARS-CoV-2 genomic RNA is long-lived after entry, suggesting that it avoids degradation by cellular nucleases. Moreover, we observed that SARS-CoV-2 replication is highly variable between cells, with only a small cell population displaying high burden of viral RNA. Unexpectedly, the B.1.1.7 variant, first identified in the UK, exhibits significantly slower replication kinetics than the Victoria strain, suggesting a novel mechanism contributing to its higher transmissibility with important clinical implications.
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Reply to the reviewers
1. General Statements [optional]
This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.
We thank the reviewers for their constructive and helpful comments on our manuscript and are delighted to find their consensus that the manuscript represents an important contribution to the field. We provide a detailed response to specific points below. In addition, we propose to include new data showing that our method can be applied to experimentally infected lung tissue. Namely, we show highly sensitive detection of SARS-CoV-2 RNA in infected hamster lung section.
2. Description of the planned …
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Reply to the reviewers
1. General Statements [optional]
This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.
We thank the reviewers for their constructive and helpful comments on our manuscript and are delighted to find their consensus that the manuscript represents an important contribution to the field. We provide a detailed response to specific points below. In addition, we propose to include new data showing that our method can be applied to experimentally infected lung tissue. Namely, we show highly sensitive detection of SARS-CoV-2 RNA in infected hamster lung section.
2. Description of the planned revisions
Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.
Reviewer #1 **Major comments:**
The authors used approaches provided in FISH-quant (Mueller et al, Nat Methods 2013) and big-fish. However, these tools to analyze RNA aggregates were not designed and validated for such massive aggregations as observed by SARS-Cov-2. They were developed for cases such as transcription sites with much smaller aggregations, with a few tens to a hundred molecules. With a regular spot detection approach, usually a few thousand spots can be detected in a cell (e.g. King et al, J Virol 2018), but this depends also on the used microscope and the available cellular volume. Higher RNA concentrations cannot be resolved with a standard approach, because RNA spots start to overlap. Decomposing RNA aggregations can help but will not work reliably for the high RNA densities observed for SARS-Cov-2, especially at later infection time-points. The tools will then not provide accurate estimates anymore. To my knowledge, there is currently not accurate quantification method for such massive RNA levels in smFISH. What has been done in the past, is using cellular intensity as an approximation and perform calibrations with cells having lower and thus still resolvable RNA counts (Raj et al., PLO Biology; https://doi.org/10.1371/journal.pbio.0040309.sg003). The authors proposed three expression regimes (partially resistant, permissive, and super permissive). My concerns here apply mainly to the category super-permissive, where an accurate estimation can't be performed. Here a more cautious quantification should be applied. ____To a lesser extent, this will also apply to some of quantifications of gRNAs per factory, with counts exceeding 100s of molecules. As mentioned above, this does not affect any of the conclusions, but would reflect more accurately what kind of reliable information can be drawn from such experiments.
We agree with the reviewer that approaches like FISH-quant and Big-FISH cannot reliably quantify RNA spots with high spatial density such as our examples of “super-permissive” cells. Single molecule quantitation of such cases is likely to underestimate RNA expression as noted by us and King et al 2018 (doi: 10.1128/JVI.02241-17). Therefore, we integrated the combined smFISH signal intensity within entire cellular volumes and compared to the median intensity of single molecules in cells with lower infection density. We will (i) revise the methods and results sections to explain more carefully and explicitly the quantification of RNA in super-permissive cells. (ii) Provide a calibration plot for the quantitation as previously reported (Raj et al 2006, doi: 10.1371/journal.pbio.0040309).
We agree that high local RNA density has the potential to interfere with quantification of gRNAs within viral factories. We have used the “cluster.decomposition()” function of Big-FISH to quantify viral factories, which is conceptually similar to the “Integrated intensity” mode of FISH-quant. Applying this algorithm to non-super permissive cells allows us to use the mean intensity of a reference single-molecule spot to estimate the number of molecules in a cluster. We are confident such estimates are reliable in the majority of viral factories, which contain less than or equal to 200 single gRNA molecules. We will revise the methods section to clarify this method of analysis.
Reviewer #1 ____**Minor comments:**
1.Page 6; the authors state that "smFISH identifies ... cellular distribution .... within ER-like membranous structures". However, the authors didn't directly show such a localization, could they provide an experiment with an ER stain?
This text was based on previous light microscopy and EM studies that reported SARS-CoV-2 RNA in ER-derived membranes (termed Double Membrane Vesicles - DMVs) or co-localisation of anti-dsRNA (J2) with ER-markers (Cortese et al 2020; Hackstadt et al 202; Mendonca et al 2021)*. We propose to clarify the text on page 6 including the citation of these publications and to tone down our claim that the virus is located in ER-like membranous structures.
*Cortese et al 2020, doi: 10.1016/j.chom.2020.11.003
Hackstadt et al 2021, doi: 10.3390/v13091798
Mendonca et al 2021, doi: 10.1038/s41467-021-24887-y
2.It might be worthwhile pointing out that the probe-sets can be used in different host organisms (Vero - African green monkey; human cell lines).
We propose to revise the text to emphasise more clearly the applicability of SARS-CoV-2 probes for the study of many different host organisms.
3.I really liked the experiment, where the authors showed absence of signal when infecting with another virus & elegant control with the J2 AB. Maybe the authors could explain more clearly that the used a different coronavirus & that based on their sequence alignment no/little signal would be expected.
Thank you for this supportive comment. We plan to follow the reviewer’s suggestion and expand our explanation of the rationale of this experiment in the text.
7.The experiment with the isolated virions shows nicely that the smFISH approach has single-virus sensitivity. Did the authors compare the intensity of these isolated virions with the signal in Fig 1B? This might be a question of personal taste, but to me, this section might actually fit better in the first paragraph of page 4/5, where the authors describe single virions in cells.
Thank you for the interesting question. We have not performed a direct comparison of the spot intensities of intracellular genomic RNA molecules and those from the isolated virions, because isolated SARS-CoV-2 requires poly-L-lysine coating for the coverslip attachment while our infection strategy utilises cells growing on uncoated glass. Nonetheless, the isolated virion spot intensities follow a unimodal distribution, and their shape approximates to the point-spread function of the microscope. Since spots at 2 hpi are largely derived from non-replicative viral genomes and they are measured in the intracellular environment with the same background (autofluorescence), they are a better ‘single RNA molecule’ reference.
We also thank the reviewer for suggesting rearranging the text section. To address this point we plan to move the relevant text to the second paragraph of the Results section.
8.Page 6. The authors state "+ORF-N and +ORF-S single labelled spots, corresponding to sgRNAs, were more uniformly distributed throughout the cytoplasm than dual labelled gRNA". This is difficult to appreciate from the image. Is this something the authors could quantify, e.g. with the metrics proposed by Stueland et al, Scientific Reports 2019?
To address this point, we plan to: (i) present an alternative image illustrating a clearer example of differential spatial localisation of gRNA and sgRNA, and (ii) perform quantification of spatial dispersion indices for gRNA and sgRNA using the suggested method for our revision.
9.Page 6. The authors perform a FISH/IF experiment including a co-localization analysis, where a "limited overlap" with sgRNAs was observed. I was wondering if this overlap could actually be simply due to rather high density of the sgRNAs. Maybe a control analysis by slightly changing the RNA positions could provide insight here, and give a threshold for what's to be expected randomly at a given RNA density.
The reviewer’s comment is correct, in that a high density of sgRNAs and nucleocapsid protein could lead to signal overlap due to chance. This is why we excluded “super-permissive” cells from this analysis. Our co-localisation data showed that gRNA spots had a bimodal nucleocapsid immunofluorescence intensity distribution (data not shown), suggesting nucleocapsid-associated and “free” gRNAs, providing a threshold for this analysis. Nevertheless, we agree with the reviewer that the analysis of randomly positioned transcripts of the same density would provide a valuable control. In our revised MS we will include: (i) a random distribution analysis comparing the overlap between sgRNA and nucleocapsid in the “Observed” and a “Randomised” simulation, and (ii) a plot showing a full distribution of co-localised nucleocapsid immunofluorescence intensity for both genomic and sub-genomic viral RNAs.
10.I don't fully follow the argument about stability on page 8. The authors also see an increase in the RNA levels. Couldn't this increase compensate for loss of RNA due to degradation? Would it be possible to perform an experiment at a very high REMDESIVIR concentrations which would blocks transcription?
Remdesivir is a nucleoside analogue that inhibits viral RNA polymerase activity__. __While this drug inhibits viral replication, the inhibition is incomplete and using higher concentrations results in cellular toxicity. At the present time there are no stronger polymerase inhibitors available, so these experiments are the best approximation possible to assess viral RNA stability. We propose to revise the text to discuss the limitations of Remdesivir for modelling RNA stability.
12.How did the authors define/detect replication factories? I couldn't find information about this in the methods.
This is a good point raised by both the reviewers. Please see [Reviewer 2 General comment #1] for our response.
Reviewer #2 **General comments:**
1.The authors' definition of viral factories, in part as foci with at least 4 gRNA molecules, comes across as arbitrary. Perhaps a clearer explanation of this cutoff would be helpful to the readers' understanding of this definition. Additionally, confirmation of the functionality of such factories by immunofluorescence with anti-RdRp, for example, in addition to identifying staining of gRNAs and (-) sense viral RNAs at each focus could provide valuable support to the authors' conclusions.
We thank both reviewers for requesting further information on our explanation of viral factories. We defined viral factories as smFISH signals with spatially extended foci that exceed the size of the point spread function of the microscope and the intensity of a reference single molecule. We then filtered these candidate factories based on the radius of the signal foci with EM-measured radii of double-membrane vesicles and single-membrane vesicles formed by SARS-CoV-2 (150 nm pre-8hpi and 200 nm post-8hpi) (Cortese et al 2020; Mendoca et al 2021). Our terminology encompasses both replication and viral assembly sites. The threshold of 4 genomic RNA molecules was selected as a technical threshold to limit an over-estimation of viral factories at later timepoints. For our spinning-disk confocal imaging system, we found the threshold of 3-7 RNA molecules provided satisfactory results. We propose to revise both the Results and Methods sections to clarify our rationale for defining and quantifying viral factories.
As the reviewer mentioned, we have shown a partial overlap of positive sense genomic RNAs with negative sense genomic RNAs (Figure 2D, S2C), suggesting these viral factories represent double membrane vesicles. The use of antibodies against the viral polymerase (nsp12) is also a possibility to detect replication centres. However, replication centres are not the only ‘viral factories’ as there are also double-membrane structures where viral particles assemble (Mendoca et al 2021) and they, in principle, lack negative sense RNA and replication machinery, so neither smFISH probes against the negative strand nor a nsp12 antibody will comprehensively detect viral factories. We appreciate the valuable suggestion, but the classification of viral factories into replication and assembly sites would be challenging due to reagent availability and is beyond the scope of this manuscript.
2.The random distribution of super-permissive cells in each cell line was demonstrated early in the infection, primarily at 8 hpi. The authors do not show how this pattern changes over time (8, 10, 12, 16, 24 hpi, for example). Do clusters of super-permissive cells appear at later time points, or does the pattern of 'highly' infected cells remain random for each virus? Any strain-specific differences identified from such patterns may be important for understanding infection progression. Finally, the authors do acknowledge this point, but it cannot be overstated that these data were taken from cell culture systems that have limited similarities to the human respiratory epithelium. A better model for such studies might be primary cultured human bronchial epithelial cells, but of course, these cells are not as readily accessible as the cell lines used in this manuscript.
We share the same view that the presence and the spatial distribution of “super-permissive” cells can provide unique insights into SARS-CoV-2 infection dynamics. Our findings suggest that even at 24 hours post infection (hpi), not all cells become “super-permissive” and the culture maintains a heterogenous population of “partially resistant”, “permissive” and “super-permissive” cells (Figure 3C, S3C-D). We agree with the reviewer that the spatial distribution of “super-permissive” cells at later timepoints is of interest. To address this point, we plan to: (i) analyse the spatial distribution of “super-permissive” cells at 24 hpi, and (ii) compare the distribution of “super-permissive” cells at 24 hpi between VIC and B.1.1.7 strains.
We appreciate the comment on the limitations of the cell culture systems to the human respiratory tract. However, Calu-3 and A549-ACE2 lung epithelial cells have been used in many studies over the last year and we feel it is important to publish single cell quantitation with these models to enable comparison with the published literature. We believe our results provide valuable information on the intrinsic nature of host cell susceptibility to support viral replication. During the review of this manuscript, we applied our smFISH probes to detect SARS-CoV-2 RNA in infected Golden Syrian hamster lung sections, which show an uneven distribution of infected cells. While the identification and spatial characterisation of susceptible cell types in the lung are beyond the scope of this manuscript, we are excited to include this data in our revised paper to demonstrate the utility of this sensitive approach to track spatiotemporal viral infection dynamics.
3.The difference in early replication kinetics between the VIC and B.1.1.7 strains is an exciting finding that may have implications for clinical outcomes and transmissibility of these viruses. However, the authors did not clearly demonstrate how these differences in RNA production correlate to infectious viral load released from these cells (in bulk) at each time point. An explanation of this omission would be helpful.
We will provide data on the level of infectious virus secreted from VIC and B.1.1.7 infected cells at all time points in the revised paper.
In my opinion, findings related to specific cell lines are of much less importance (and are much less biologically relevant) that identification of replicative differences among strains. Such differences could be used, in part, to aid prediction of the transmissibility of VOC, for example. I think this point gets a bit 'lost in the weeds' of the rest of the paper.
To address this comment, we will revise text on the differential replication kinetics of the SARS-CoV-2 strains to make this more prominent in our paper.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.
Reviewer #1 ____**Minor comments:**
4.I might have missed this, but they authors could also mention the positive control data about but Calu3 infected with SARS-COv2. One thing I was wondering: why did the authors use two different cell lines for this experiment?
To address this point, we have added a sentence about a positive control visualising SARS-CoV-2 in Calu-3 cells using our probe set (page 5 – line 17).
The experiments with HCoV-229E were done in Huh-7.5 cells because SARS-CoV-2 and HCoV-229E have distinct cell preferences. Using the J2 antibody we show that the levels of the dsRNA derived from viral replication are similar in the two cell lines and with the two viruses. Therefore, the lack of smFISH signal in HCoV-229E infected cells supports the high specificity of the probe set.
5.Fig 1E. Would be nice to have the intensity scale for all time-points to permit a comparison of image intensities along the different time-points.
6.Fig 3B. Would be important to have intensity scale bars to judge the signal intensities across the different time-points.
The fluorescence intensity scale in Figure 1E is applicable to all timepoints, except for the lower panel at 24 hpi, which was intended to show wider dynamic contrast range. To address this point, we have provided intensity scales for all time-points studied in this figure and also Figure 3B.
11.Fig 3C. maybe indicate the two groups with dashed lines.
We have added a dashed line at the 102 mark in Figure 3C to visually differentiate “partially resistant” and “permissive” cells.
4. Description of analyses that authors prefer not to carry out
Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.
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Referee #2
Evidence, reproducibility and clarity
In "Absolute quantitation of individual SARS-CoV-2 RNA molecules: a new paradigm for infection dynamics and variant differences", Lee and colleagues adapt fluorescence in situ hybridization (FISH) to track viral RNAs at the single-molecule level, illustrating heterogeneity during the infection process with potential for significant clinical implications. The authors have meticulously demonstrated use of this approach to investigate the kinetics of early infections, as well as infection heterogeneity between the original and variant strains. Most notably, the authors have identified differences in early infection kinetics between an …
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #2
Evidence, reproducibility and clarity
In "Absolute quantitation of individual SARS-CoV-2 RNA molecules: a new paradigm for infection dynamics and variant differences", Lee and colleagues adapt fluorescence in situ hybridization (FISH) to track viral RNAs at the single-molecule level, illustrating heterogeneity during the infection process with potential for significant clinical implications. The authors have meticulously demonstrated use of this approach to investigate the kinetics of early infections, as well as infection heterogeneity between the original and variant strains. Most notably, the authors have identified differences in early infection kinetics between an early strain and more transmissible variant.
General Comments:
1.The authors' definition of viral factories, in part as foci with at least 4 gRNA molecules, comes across as arbitrary. Perhaps a clearer explanation of this cutoff would be helpful to the readers' understanding of this definition. Additionally, confirmation of the functionality of such factories by immunofluorescence with anti-RdRp, for example, in addition to identifying staining of gRNAs and (-) sense viral RNAs at each focus could provide valuable support to the authors' conclusions.
2.The random distribution of super-permissive cells in each cell line was demonstrated early in the infection, primarily at 8 hpi. The authors do not show how this pattern changes over time (8, 10, 12, 16, 24 hpi, for example). Do clusters of super-permissive cells appear at later time points, or does the pattern of 'highly' infected cells remain random for each virus? Any strain-specific differences identified from such patterns may be important for understanding infection progression. Finally, the authors do acknowledge this point, but it cannot be overstated that these data were taken from cell culture systems that have limited similarities to the human respiratory epithelium. A better model for such studies might be primary cultured human bronchial epithelial cells, but of course, these cells are not as readily accessible as the cell lines used in this manuscript.
3.The difference in early replication kinetics between the VIC and B.1.1.7 strains is an exciting finding that may have implications for clinical outcomes and transmissibility of these viruses. However, the authors did not clearly demonstrate how these differences in RNA production correlate to infectious viral load released from these cells (in bulk) at each time point. An explanation of this omission would be helpful.
Significance
Adaptation of RNA-based imaging to understand viral infection cycles is critical to the development of antivirals and other mitigation strategies, highlighting the significance of this work. This manuscript represents an almost herculean effort to identify viral replication dynamics using a series of thoughtful and well-controlled experiments. This paper is likely to be valuable to the field, and will serve as a launch pad for future studies in the role of viral RNA production in SARS-CoV-2 infection, clinical outcomes, and transmissibility.
Expertise keywords: influenza virus, virus transmission, oligonucleotide-based imaging and therapeutics
I do not have significant experience with quantitation of fluorescence imaging and signal co-localization in cell images.
Referees cross-commenting
Reviewer 1's comments regarding the application of smFISH and RNA quantitation are very helpful and address some key limitations of the research presented in this manuscript. I agree that the experiments are well thought out and include appropriate controls. I think the reviewer's comments and concerns are fair and that it would be appropriate to ask the authors to address their points.
However, my primary concern remains with the biology and focus of the manuscript. In my opinion, findings related to specific cell lines are of much less importance (and are much less biologically relevant) that identification of replicative differences among strains. Such differences could be used, in part, to aid prediction of the transmissibility of VOC, for example. I think this point gets a bit 'lost in the weeds' of the rest of the paper.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The authors use single-molecule FISH (smFISH) to study the early-time points of SARS-Cov-2 infection/replication. By targeting genome and sub-genomic RNAs, they can decipher different stages during the infection cycle, and identify different cell populations with distinct behavior. By applying both smFISH and IF with the J2 antibody recognizing dsRNA, the authors nicely demonstrate how smFISH is more sensitive, especially during early infection when viral RNA levels are still relatively low. The investigation of the two SARS-Cov-2 strains is well thought through and provides evidence that these strains have similar viral …
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Referee #1
Evidence, reproducibility and clarity
Summary:
The authors use single-molecule FISH (smFISH) to study the early-time points of SARS-Cov-2 infection/replication. By targeting genome and sub-genomic RNAs, they can decipher different stages during the infection cycle, and identify different cell populations with distinct behavior. By applying both smFISH and IF with the J2 antibody recognizing dsRNA, the authors nicely demonstrate how smFISH is more sensitive, especially during early infection when viral RNA levels are still relatively low. The investigation of the two SARS-Cov-2 strains is well thought through and provides evidence that these strains have similar viral uptake and infection rates, but differ in the replication kinetics, opening the door for future investigations. The paper is a pleasure to read and the authors provide a wealth of controls that not only convincingly illustrate the specificity of their approach but also how it provides unique information, complementing both IF and sequencing-based approaches. The provided methods are explained in detail and will allow users to quickly get started. Paper provides not only very interesting biological insights, but also nicely illustrates how smFISH can be used to study infection by providing unique information.
Major comments:
The key conclusions were convincingly presented and, as far as I can judge as a biophysicst with limited experience in SARS-Cov-2 biology, backed-up with the adequate controls and analysis. In general, the authors provide exemplary validations to illustrate the specific of their approach. RNA detection and single-molecule sensitivity is validated in several experiments, by the "standard" probe-splitting approach, where a dual-color labeling of the same RNA is performed, but also by RNAse and Remdesivir treatment. Further, the authors show the specificity of their smFISH probes by applying them to another coronavirus (HCov-229E), where no signal was detected. Further, the authors provide very detailed methods, which should make it easy for other researches to apply these methods in their own research, and also reproduce the results. The imaging data is nicely complimented with quantitative analysis where needed and the provided plots are both adequately chosen and visually pleasing.
However, I have one major concern about the RNA abundance analysis. While this comment concerns some of the analysis, it does not question the obtained conclusions. The authors used approaches provided in FISH-quant (Mueller et al, Nat Methods 2013) and big-fish. However, these tools to analyze RNA aggregates were not designed and validated for such massive aggregations as observed by SARS-Cov-2. They were developed for cases such as transcription sites with much smaller aggregations, with a few tens to a hundred molecules. With a regular spot detection approach, usually a few thousand spots can be detected in a cell (e.g. King et al, J Virol 2018), but this depends also on the used microscope and the available cellular volume. Higher RNA concentrations cannot be resolved with a standard approach, because RNA spots start to overlap. Decomposing RNA aggregations can help but will not work reliably for the high RNA densities observed for SARS-Cov-2, especially at later infection time-points. The tools will then not provide accurate estimates anymore. To my knowledge, there is currently not accurate quantification method for such massive RNA levels in smFISH. What has been done in the past, is using cellular intensity as an approximation and perform calibrations with cells having lower and thus still resolvable RNA counts (Raj et al., PLO Biology; https://doi.org/10.1371/journal.pbio.0040309.sg003). The authors proposed three expression regimes (partially resistant, permissive, and super permissive). My concerns here apply mainly to the category super-permissive, where an accurate estimation can't be performed. Here a more cautious quantification should be applied. To a lesser extent, this will also apply to some of quantifications of gRNAs per factory, with counts exceeding 100s of molecules. As mentioned above, this does not affect any of the conclusions, but would reflect more accurately what kind of reliable information can be drawn from such experiments.
Minor comments:
I have a few minor comments/questions.
1.Page 6; the authors state that "smFISH identifies ... cellular distribution .... within ER-like membranous structures". However, the authors didn't directly show such a localization, could they provide an experiment with an ER stain?
2.It might be worthwhile pointing out that the probe-sets can be used in different host organisms (Vero - African green monkey; human cell lines).
3.I really liked the experiment, where the authors showed absence of signal when infecting with another virus & elegant control with the J2 AB. Maybe the authors could explain more clearly that the used a different coronavirus & that based on their sequence alignment no/little signal would be expected.
4.I might have missed this, but they authors could also mention the positive control data about but Calu3 infected with SARS-COv2. One thing I was wondering: why did the authors use two different cell lines for this experiment?
5.Fig 1E. Would be nice to have the intensity scale for all time-points to permit a comparison of image intensities along the different time-points.
6.Fig 3B. Would be important to have intensity scale bars to judge the signal intensities across the different time-points.
7.The experiment with the isolated virions shows nicely that the smFISH approach has single-virus sensitivity. Did the authors compare the intensity of these isolated virions with the signal in Fig 1B? This might be a question of personal taste, but to me, this section might actually fit better in the first paragraph of page 4/5, where the authors describe single virions in cells.
8.Page 6. The authors state "+ORF-N and +ORF-S single labelled spots, corresponding to sgRNAs, were more uniformly distributed throughout the cytoplasm than dual labelled gRNA". This is difficult to appreciate from the image. Is this something the authors could quantify, e.g. with the metrics proposed by Stueland et al, Scientific Reports 2019?
9.Page 6. The authors perform a FISH/IF experiment including a co-localization analysis, where a "limited overlap" with sgRNAs was observed. I was wondering if this overlap could actually be simply due to rather high density of the sgRNAs. Maybe a control analysis by slightly changing the RNA positions could provide insight here, and give a threshold for what's to be expected randomly at a given RNA density.
10.I don't fully follow the argument about stability on page 8. The authors also see an increase in the RNA levels. Couldn't this increase compensate for loss of RNA due to degradation? Would it be possible to perform an experiment at a very high REMDESIVIR concentrations which would blocks transcription?
11.Fig 3C. maybe indicate the two groups with dashed lines.
12.How did the authors define/detect replication factories? I couldn't find information about this in the methods.
Significance
The authors their established smFISH approach for the detection of SARS-Cov-2 RNA. As mentioned above, they provide extensive validations and detailed protocols (including the necessary probe sequences). This should allow also relative newcomers to the field to quickly perform these experiments. While the technical advance might not be major, the convincing presentation will certainly be appealing for an audience which has not be using imaging-based approaches to study (early) viral infection events and was relying more on other approaches, such as sequencing or bulk-PCR.
There are a few papers using smFISH to study SARS-Cov-2, but to my knowledge this study provides the most detailed analysis of the early time-points of infection, where smFISH with its sensitivity really shines. This paper not only provide new insights about SARS-Cov-2 biology, but is very nicely illustrating what kind of unique information smFISH can provide and how this complements orthogonal approaches such as single-cell RNA-seq. Hence, this will certainly be interesting for virologists/biologists working on this pathogen by providing new insight about the replication kinetics, but can also help them to potentially integrate smFISH into their own research.
I'm a biophysicist working on transcriptional regulation. I contributed to development of both experimental methods and analysis tools to study single-molecule FISH data. I have only limited expertise in virology, and thus not evaluate in detail the biological findings concerning SARS-Cov-2.
Referees cross-commenting
I completely agree with the assessment of reviewer #2 and have nothing to add.
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SciScore for 10.1101/2021.06.29.450133: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Then, cells were incubated with J2 primary antibody (Scicons 10010200) at 0.5 µg/ml or human anti-N primary antibody (Ey2B clone 1:2000) (Huang et al., 2020) for 2 h at room temperature. J2suggested: (SCICONS Cat# 10010200, RRID:AB_2651015)anti-Nsuggested: (Wesley CS; J Cell Biol. 2000 Cat# N, RRID:AB_2570096)Ey2Bsuggested: (Krasnov AN; Nucleic Acids Res. 2005 Cat# e(y2b, RRID:AB_2568395)Experimental Models: Cell Lines Sentences Resources Cell culture: Vero E6, A549-ACE2 (kind gift from Bartenschlager lab) (Klein et al., 2020) and Huh-7.5 cells were maintained in standard DMEM, Calu-3 cells in Advanced … SciScore for 10.1101/2021.06.29.450133: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Antibodies Sentences Resources Then, cells were incubated with J2 primary antibody (Scicons 10010200) at 0.5 µg/ml or human anti-N primary antibody (Ey2B clone 1:2000) (Huang et al., 2020) for 2 h at room temperature. J2suggested: (SCICONS Cat# 10010200, RRID:AB_2651015)anti-Nsuggested: (Wesley CS; J Cell Biol. 2000 Cat# N, RRID:AB_2570096)Ey2Bsuggested: (Krasnov AN; Nucleic Acids Res. 2005 Cat# e(y2b, RRID:AB_2568395)Experimental Models: Cell Lines Sentences Resources Cell culture: Vero E6, A549-ACE2 (kind gift from Bartenschlager lab) (Klein et al., 2020) and Huh-7.5 cells were maintained in standard DMEM, Calu-3 cells in Advanced DMEM both supplemented with 10% fetal bovine serum, 2mM L-glutamine, 100 U/mL penicillin and 10μg/mL streptomycin and non-essential amino acids. Vero E6suggested: RRID:CVCL_XD71)Calu-3suggested: BCRJ Cat# 0264, RRID:CVCL_0609)Viral strains were propagated in Vero-E6 cells as described (Wing et al., 2021). Vero-E6suggested: NoneSimilarly, HCoV-229E (Andrew Davidson lab (Bristol) and Peter Simmmonds lab (Oxford)) virus was propagated in Vero E6 cells and TCID50 was performed in Huh-7.5 cells. Huh-7.5suggested: RRID:CVCL_7927)Software and Algorithms Sentences Resources Candidate sequences were BLAST screened against custom human transcriptome and intron database to score number of off-target base-pair matches, then 35 – 48 sequences with the least match scores were chosen per probe set. BLASTsuggested: (BLASTX, RRID:SCR_001653)For in silico probe sequence specificity analysis, selected oligonucleotide sequences were aligned against SARS-CoV-1 (NC_004718), SARS-CoV-2 (NC_045512), MERS-CoV (NC_019843), HCoV-229E (NC_002645), HCoV-NL63 (NC_005831), HCoV-OC43 (NC_006213), HCoV-HKU1 (NC_006577), Human (GCF_000001405.39), and African green monkey (GCF_015252025.1) RefSeq genome or transcriptome assembly using ‘bowtie2’ (2.4.4) (Langmead and Salzberg, 2012). RefSeqsuggested: (RefSeq, RRID:SCR_003496)For 20x stitched images, CellMask channel was deconvolved with constrained iterative module using cellSens (5 iterations, default spinning disk PSF, Olympus), then following Cellpose parameters were used: model_type=cyto, diameter=55, flow_threshold=0, cellprob_threshold=-6. cellSenssuggested: NoneInfected cells were counted using ImageJ “3D object counter” or manually. ImageJsuggested: (ImageJ, RRID:SCR_003070)Nearest neighbour distances were calculated using the KDtree algorithm (Maneewongvatana and Mount, 1999) implemented in python (scipy.spatial.KDTree). pythonsuggested: (IPython, RRID:SCR_001658)Sequencing libraries were prepared using the Illumina Total RNA Prep with Ribo-Zero Plus library kit (Cat# 20040525) according to manufacturer’s guidelines. Ribo-Zero Plussuggested: NoneRNA-sequencing analysis: Genomes: We downloaded the human genome primary assembly and annotation from ENSEMBL (GRCh38.99) and the SARS-CoV-2 RefSeq reference genome from NCBI (NC_045512.2). ENSEMBLsuggested: (Ensembl, RRID:SCR_002344)Alignment and gene counts: We performed a splice-site aware mapping of the sequencing reads to the combined human and SARS-CoV-2 genome and annotation using STAR aligner (2.7.3a) (Dobin et al. 2013). STARsuggested: (STAR, RRID:SCR_004463)First, we performed library size correction and variance stabilisation with regularized–logarithm transformation implemented in DESeq2 (1.28.1) (Love et al. 2014). DESeq2suggested: (DESeq, RRID:SCR_000154)SARS-CoV-2 sub-genomic RNA expression: To assess relative levels of viral sub-genomic and genomic RNA expression, we tallied the alignments (using GenomicRanges and GenomicAlignments R packages (Lawrence et al., 2013)) mapping to the region unique to the genomic RNA and the shared region and normalised for their respective lengths. GenomicRangessuggested: (GenomicRanges, RRID:SCR_000025)Statistics, data wrangling and visualisation: Statistical analyses were performed in R (3.6.3) and RStudio (1.4) environment using an R package “rstatix” (0.7.0). RStudiosuggested: (RStudio, RRID:SCR_000432)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although single cell RNA-seq analyses can overcome some of these issues (Fiege et al., 2021; Ravindra et al., 2021), their low coverage and lack of information regarding the spatial location of cells remains significant limitations. In this study we show smFISH is a sensitive approach that allows the absolute quantification of SARS-CoV-2 RNAs at single molecule resolution. Our experiments show the detection of individual gRNA molecules within the first 2h of infection that most likely reflect incoming viral particles. However, we also observed small numbers of foci comprising several gRNAs that were sensitive to RDV treatment, demonstrating early replication events. We believe these foci represent ‘replication factories’ as they co-stain with FISH probes specific for negative sense viral RNA and sgRNA. These data provide the first evidence that SARS-CoV-2 replication occurs within the first 2h of infection and increases over time. This contrasts to our observations with the J2 anti-dsRNA antibody where viral dependent signals were apparent at 6 hpi (Cortese et al., 2020; Eymieux et al., 2021). We noted that co-staining SARS-CoV-2 infected cells with J2 and ORF1a FISH probe set showed a minimal overlap, suggesting that infection may induce changes in cellular dsRNA. The observation that infection can perturb mitochondrial function provides a possible explanation for these observations (Appelberg et al., 2020; Mullen et al., 2021). Importantly, mitochondrial dsRNAs can engage M...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 42 and 43. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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