Single-cell analysis of signalling and transcriptional responses to type I interferons

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   Review Commons

   Jul 22, 2025

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   Reply to the reviewers

   Manuscript number: RC-2023-02191

   Corresponding author: Jan Rehwinkel

   1. General Statements

   The authors wish to thank all three reviewers and the Review Commons team for carefully evaluating our study. We have addressed all points raised as detailed below.

   We have thoroughly revised our bulk RNAseq analysis, which is now performed at the transcript level using the latest GENCODE release. We have updated Figure 3 and associated supplementary figures and tables. This change from gene to transcript level was important for accurate motif analysis as requested by reviewer 2: matching promoters to individual IFN-regulated transcripts – rather than …

   Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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   Reply to the reviewers

   Manuscript number: RC-2023-02191

   Corresponding author: Jan Rehwinkel

   1. General Statements

   The authors wish to thank all three reviewers and the Review Commons team for carefully evaluating our study. We have addressed all points raised as detailed below.

   We have thoroughly revised our bulk RNAseq analysis, which is now performed at the transcript level using the latest GENCODE release. We have updated Figure 3 and associated supplementary figures and tables. This change from gene to transcript level was important for accurate motif analysis as requested by reviewer 2: matching promoters to individual IFN-regulated transcripts – rather than aggregating all promoters per gene – avoids significant signal dilution. This strategy yields higher-resolution expression data and is biologically preferable. Indeed, several well characterised IFN-regulated RNAs (e.g., the ADAR1-202 transcript encoding the p150 isoform) originate from promoters located far from the constitutive promoters of their host genes. In our revised manuscript, we now provide in the new supplementary figure 13 the requested promoter motif analysis. Using two computational approaches – de novo motif search and analysis of a curated motif database – we find strong enrichment of interferon-stimulated response elements (ISREs) in promoters of type I IFN regulated transcripts. No other motifs reached similarly high levels of enrichment, and our analysis did not reveal differences between different type I IFNs. These new data show that all type I IFNs engage a common regulatory pathway, supporting our overall conclusion that different type I IFNs do not induce qualitatively different responses in PBMCs.

   Regrettably, in the process of analysing the bulk RNAseq data at transcript level, we noticed that our original lncRNA analysis contained numerous false positives. Closer inspection showed that many “differentially expressed” LNCipedia models were likely not full-length transcripts and commonly shared a single IFN-induced set of exons that artificially inflated expression estimates for every overlapping model. To correct this issue, we replaced LNCipedia with the latest high-quality non-coding RNA catalogue from GENCODE, most entries of which were defined by full-length RNA sequencing [1]. We also tightened our filtering criteria and now report only transcripts that are robustly expressed in our dataset and are either classified as high-confidence by GENCODE or robustly supported at every splice junction by our RNAseq.

   We hope our manuscript is sufficiently improved and suitable for publication in PLoS Biology. New or revised text is highlighted in green in our revised manuscript.

   2. Point-by-point description of the revisions

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   Reviewer #1

   Evidence, reproducibility and clarity:

   The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN differ in the way they engage IFN signals to produce transcriptome changes. The data lead the authors to conclude that any differences are of quantitative rather than qualitative nature.

   The authors' conclusions are based on a mass cytometry approach to phenotype STAT activation in different cell types, bulk RNA sequencing to study ISG expression in PBMC, and single cell sequencing to study ISG responses in individual cell types. The data are solid, clear and reproducible in biological replicates (eg different blood donors).

   Significance:* While some of the data can be considered confirmatory, the comprehensive analysis of cell-type specificity and IFN-I subtype specificity advances the field and provides a reference for future analyses. The study is complete and there is no obvious lack of a critical experiment. The number of scientists interested in the multitude of open questions around type I IFN is large, thus the study is likely to attract a broad readership.*

   We thank the reviewer for her/his positive assessment of our study.

   The biggest limitation is to my opinion the low sequencing depth of scRNAseq which is clearly the downside of this technology. Using 11 hematopoietic cell types and bulk RNA sequencing the total number of ISG was determined to be 975 by Mostafavi et al. and the core ISG numbered 166. This is in stark contrast to this studies' 10 core ISG. The authors limitations paragraph should discuss the fact that scRNAseq reduces the overall ISG number that can be analyzed.

   Thank you for this valid comment. We amended the limitations paragraph as requested. We agree that the Mostafavi et al. 2016 Cell paper [2] is important but note that there are many differences to our study: Mostafavi et al. use mice, a seemingly very high IFN dose (10,000 Units) and microarrays (not RNAseq).

   A minor point concerns the 25 supplementary figures of the study. There must be a better way to support the conclusions with the necessary data.

   We agree that our supplementary materials are extensive. However, this is not unusual for studies reporting multiple large datasets. We would be delighted to organise our supplementary information differently in due course according to journal guidelines.

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   Reviewer #2

   Evidence, reproducibility and clarity:

   The manuscript entitled “Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

   We thank the reviewer for evaluating our study and the suggestions made.

   *Major Comments: *

   • Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859)* We thank the reviewer for the suggestion to study the promoters of genes regulated by type I IFNs. The analyses outlined below were performed by A. Fedorov, who is now a new co-author of our study. To investigate promoter features that might underlie the observed transcriptional responses across type I IFNs, we first performed a de novomotif search using STREME [3] on our bulk RNAseq dataset (Figure 3). Specifically, we compared the promoters of transcripts that were up- or down-regulated by each IFN subtype (e.g., IFN-β-induced) either with one another or with promoters of robustly expressed RNAs that remained unresponsive to any treatment. No significant motifs emerged from these comparisons, except when we compared promoters of IFN-induced transcripts to the background set of unresponsive RNAs. This comparison consistently yielded strong enrichment of interferon-stimulated response element (ISRE)-like motifs in the promoters of up-regulated RNAs (new Figure S13a).

   Next, we conducted a complementary analysis using known transcription factor (TF) motifs from the JASPAR database [4]. We screened all promoters of annotated RNAs using clustered JASPAR motifs and Z-standardised motif scores relative to all high-confidence GENCODE RNAs, including those not expressed in PBMCs. We reasoned that TFs actively mediating IFN responses would likely bind promoters with high motif scores (Z ≥ 2), while promoters with low scores (Z ≤ -1) would represent an unregulated background. This approach produced two sets of RNAs per TF cluster: putatively regulated and unregulated. We then restricted each set to RNAs expressed in our dataset and associated each transcript with its estimated fold change in response to each type I IFN, regardless of statistical significance. Next, we compared median fold changes between the likely regulated and unregulated sets across all TF clusters and IFN subtypes (Figure S13b). Among all tested TF motifs, only the ISRE-like cluster showed strong and consistent associations with transcriptional changes across all IFN subtypes. We also observed statistically significant but much weaker associations for other TFs, including a known negative regulator of innate antiviral signaling, NRF1 [5]. However, effect sizes for these motifs were dwarfed by those of ISRE-like motifs, suggesting that no JASPAR TFs other than those within the ISRE-like cluster play a major role in PBMCs under our conditions. Overall, these findings support the idea that all type I IFNs engage a common regulatory pathway, differing primarily in the magnitude rather than the nature of their transcriptional effects.

   How do they relate to the activation of kinases by IFN subtypes?

   We did not analyse the activation of the canonical kinases (i.e., TYK2 and JAK1) downstream of IFNAR. This would be interesting and may be possible using phospho-specific antibodies to these kinases in our CyTOF setup. However, this would require a very large investment of time and resources to identify specific antibodies, optimise a new CyTOF staining panel and to acquire and analyse new datasets. We therefore believe this should be pursued as a separate future study.

   *Are there distinct features that dictate differential responses in monocytes and lymphocytes? *

   Following the computational approach described above, we applied STREME to identify DNA motifs that could distinguish promoters associated with monocyte- and lymphocyte-specific ISGs. Regrettably, this analysis did not yield any significant motifs, likely due in part to the limited number of genes in each category.

   • Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.*

   Thank you for this suggestion. We tried using the same scale for all heatmaps. However, given that the values for pSTAT1 are higher than those for other pSTATs, the resulting heatmaps did not show differences for the other pSTATs well. We therefore decided to leave these panels unchanged. Please also note that Figures 2b and S3b provide comparison between pSTATs (and other markers) using the same scale.

   Minor Comments:

   • The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.* Thank you for this suggestion. We have amended sub-headers accordingly.

   • Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.*

   The scales have been enlarged for better visibility as requested.

   • Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb. *

   Thank you for the suggestion. We combined these panels.

   • Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types. *

   Figure 5 and several accompanying supplementary figures already depict ISGs unique to IFN subtypes or cell types. Whilst we appreciate the suggestion, we prefer not to add additional figures to avoid redundancies.

   • The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes. *

   Thank you for this comment. We changed the presentation of the GO analysis in Fig S11 by sorting on p-value (instead of % of hits in category). We hope this shows more clearly that GO category enrichment amongst genes encoding IFN-induced transcripts had high statistical significance (log10 p-values of about -5 or lower for many categories).

   Significance:* ** The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset. *

   *Audience: *** This is a valuable resource for immunologists, virologists, and bioinformaticians.

   Thank you for these encouraging comments.

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   Reviewer #3

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   Evidence, reproducibility and clarity:

   *Summary *

   Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

   Thank you for your positive assessment of our work and for recommending publication.

   *Major comments: *

   *Two of the key conclusions are not very convincing. *

   First, the authors claim that the magnitude of the responses varied between the 5 types of IFNs, however, as they point out in the 'limitation' paragraph, doses of the different IFNs were normalized using bioactivity. Knowing that this bioactivity is based on assays performed on A549 lung cells, this normalization likely induces a bias. How do the authors explain similar antiviral bioactivity but differing magnitudes of modulation of ISG expression? Would the authors expect the same differences of expression between the several IFNs tested in A549 cells? We thus recommend being very cautious when comparing magnitude of the response between the 5 types of IFNs.

   We thank the reviewer for this important point and included the following reasoning in our discussion:

   “An important technical consideration for our study was the normalisation of type I IFN doses used to treat cells (see also ‘Limitations of the study’ below). We relied on bioactivity (U/ml) that is measured by the manufacturer of recombinant type I IFNs using a cytopathic effect (CPE) inhibition assay. In brief, the lung cancer cell line A549 is treated with type I IFN and is infected with the cytopathic encephalomyocarditis virus (EMCV). Control cells not treated with IFN are killed by EMCV, whereas cells treated with sufficient IFN survive. How, then, is it possible that different type I IFNs induce differing magnitudes of STAT phosphorylation and ISG expression despite being used at the same bioactivity? Cell survival in the CPE inhibition assay may be due to one or a few ISGs. Indeed, single ISGs can mediate powerful antiviral defence. For example, MX1 is crucial for host defence against influenza A virus [6]. Thus, similar bioactivity of different IFNs in A549 cells against EMCV-triggered cell death may not reflect the breadth of effects on many ISGs. Moreover, IFN-induced survival of A549 cells following EMCV infection is a binary readout. Induction of the relevant ISG(s) mediating protection beyond a threshold required for cell survival is unlikely to register in this assay. Thus, similar antiviral bioactivity (in the CPE inhibition assay) and differing magnitudes of modulation of ISG expression (at transcriptome level) are compatible.”

   We believe inclusion of this paragraph demonstrates an appropriate level of caution in our data interpretation. Further, we would expect to make similar observations if we were to apply transcriptomic analysis to A549 cells treated with different type I IFNs. However, given our focus in this study on primary, normal cells, we decided not to pursue work with the transformed and lab adapted A549 cell line.

   Second, the qualitatively different responses to type I IFN subtypes claimed by the authors were not apparent. This seems true at the level of the bulk population (Fig. S10) but not at cell-type level (Fig. S15/S16).

   We believe there may be a misunderstanding here. In relation to Figure S10, we do not claim “qualitatively different responses to type I IFN subtypes”. Instead, we conclude that “differences in expression between the different type I IFNs were quantitative” (page 8; lines 229-230, now: 238-239). Moreover, Figures S15/S16 (now: S16/S17) do not refer to analyses of responses to different type I IFN subtypes.

   The authors state (line 311-312) that 'Consistent with our bulk RNAseq data, differences were again quantitative rather than qualitative' at the cell-type level. The response between cell types seems very different to us since a core set of only 10 ISGs are shared by all cell types and all 5 type I IFNs. Knowing that the expression of hundreds, sometimes thousands of genes, are induced by IFN, this seems like a rather small overlap (and thus qualitatively different responses). Fig S15 and S16 nicely illustrate that the responses are qualitatively different between cell-type. Please modify this conclusion accordingly.

   Thank you for highlighting this. The statement in lines 311-312 does not refer to differences between cell types but to differences between type I IFN subtypes. We are sorry this was not clear and changed this sentence (now lines 357-358). Furthermore, we have made it clearer in the revised text that qualitative differences were observed between cell types (e.g. lines 329 and 350-352).

   *No additional experiments are needed to support the claims. However, we believe that two additional analyses could provide useful information. *

   The levels of IFNAR1 and IFNAR2 expressed at the plasma membrane probably vary between cell types and may thus influence the magnitude of the IFN response. While it would be difficult to measure these levels by flow cytometric analysis on the different cell types, could the authors extract information from their scRNAseq analysis on the expression level of IFNAR1/2 in all cell types? This would give a hint about potential differences in expression (and thus in magnitude).

   We analysed IFNAR1/2 transcript levels in our scRNAseq dataset (Figure R1 below). Unfortunately, for many cells, IFNAR1 and IFNAR2 transcripts were not detected (see width of violin plots at zero), probably due to low sequencing depth inherent to scRNAseq analysis. We therefore prefer not to draw conclusions from these data.

   Could the authors investigate further the expression of lncRNAs at the single-cell levels? It would be useful to also define a core set of lncRNAs that are shared between cell types and IFN subtypes. If such a core set does not exist (since lncRNAs are less conserved than coding genes), it would be nice to mention it.

   Thank you for this suggestion. The expression of lncRNAs is generally lower than protein-coding genes, resulting in high drop-out rates in 10X datasets. Indeed, Zhao et al. comment that “current development of single-cell technologies may not yet be optimized for lncRNA detection and quantification” [7]. We only detected a small number of lncRNAs in our scRNAseq analysis, and only four lncRNAs were significantly differentially expressed between cell types. We thus could not perform a meaningful analysis of lncRNAs in our scRNAseq dataset. This is now mentioned in the limitations paragraph at the end of the manuscript.

   Minor comments:

   There is a typo in line 355 Fig.4C =>6C.

   Thank you for spotting this.

   ***Referees cross-commenting** *

   We agree with Reviewer 1 that the low sequencing depth of scRNAseq restricts the analysis and must be discussed in the 'limitation' paragraph. This would explain why the authors identified only 10 ISGs that are common to all cell types and all 5 IFN subtypes. Of note, as a comparison, Shaw et al (10.1371/journal.pbio.2004086) identified a core set of 90 ISGs that are upregulated upon IFN treatment in cells isolated mainly from kidney and skin of nine mammalian species ("core mammalian ISGs"). It is thus expected that stimulated blood cells isolated from a single mammalian species share more than 10 ISGs.

   We amended the limitations section as requested. Shaw et al. [8] used a single type I IFN (universal or IFNα, depending on species) at a very high dose (1000 U/ml). Taken together with the use of bulk RNAseq in this study, it is unsurprising that our work identified fewer core ISGs. We believe our small list of core ISGs is nonetheless both a high confidence and a high utility set of ISGs: these genes are induced by multiple type I IFNs, in all major cell types in blood and their regulation can be measured even when sequencing depth is low.

   Significance (Required)

   *Multiple single-cell RNAseq analysis of PBMCs, stimulated or not, have been previously performed in multiple contexts (for instance with PBMCs isolated from the blood of patients infected with influenza virus or SARS-CoV-2). The technical advance is thus limited. *

   *However, the work represents a conceptual advance for the field since it provides the first single-cell atlas of PBMCs stimulated with five type-I IFN subtypes. The generated datasets represent a great resource for anyone interested in innate immunity (virologists, immunologists and cancerologists). *

   Of note, we are studying innate immunity in the context of RNA virus infection but we have no expertise on scRNA sequencing. We may thus have missed a flaw in the analyses.

   We thank the reviewer for their positive assessment of the advances of our study and the value of our IFN resource.

   A

   B

   C

   D

   Figure R1. IFNAR1/2 expression in scRNAseq data.

   Violin plots showing expression of IFNAR1 (A,C) or IFNAR2 (B,D) in different cell types. In (A,B), data were pooled across conditions. In (C,D), data are shown separately for unstimulated control cells and cells stimulated with different type I IFNs.

   References

   Kaur G, Perteghella T, Carbonell-Sala S, Gonzalez-Martinez J, Hunt T, Madry T, et al. GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing. bioRxiv. 2024. Epub 20241031. doi: 10.1101/2024.10.29.620654. PubMed PMID: 39554180; PubMed Central PMCID: PMCPMC11565817. Mostafavi S, Yoshida H, Moodley D, LeBoite H, Rothamel K, Raj T, et al. Parsing the Interferon Transcriptional Network and Its Disease Associations. Cell. 2016;164(3):564-78. Epub 2016/01/30. doi: 10.1016/j.cell.2015.12.032. PubMed PMID: 26824662; PubMed Central PMCID: PMCPMC4743492. Bailey TL. STREME: accurate and versatile sequence motif discovery. Bioinformatics. 2021;37(18):2834-40. doi: 10.1093/bioinformatics/btab203. PubMed PMID: 33760053; PubMed Central PMCID: PMCPMC8479671. Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R, Castro-Mondragon JA, Ferenc K, Kumar V, et al. JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic acids research. 2024;52(D1):D174-D82. doi: 10.1093/nar/gkad1059. PubMed PMID: 37962376; PubMed Central PMCID: PMCPMC10767809. Zhao T, Zhang J, Lei H, Meng Y, Cheng H, Zhao Y, et al. NRF1-mediated mitochondrial biogenesis antagonizes innate antiviral immunity. The EMBO journal. 2023;42(16):e113258. Epub 20230706. doi: 10.15252/embj.2022113258. PubMed PMID: 37409632; PubMed Central PMCID: PMCPMC10425878. Grimm D, Staeheli P, Hufbauer M, Koerner I, Martinez-Sobrido L, Solorzano A, et al. Replication fitness determines high virulence of influenza A virus in mice carrying functional Mx1 resistance gene. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(16):6806-11. Epub 20070410. doi: 10.1073/pnas.0701849104. PubMed PMID: 17426143; PubMed Central PMCID: PMCPMC1871866. Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J. 2022;20:4381-9. Epub 20220804. doi: 10.1016/j.csbj.2022.08.003. PubMed PMID: 36051880; PubMed Central PMCID: PMCPMC9403499. Shaw AE, Hughes J, Gu Q, Behdenna A, Singer JB, Dennis T, et al. Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses. PLoS Biol. 2017;15(12):e2004086. Epub 2017/12/19. doi: 10.1371/journal.pbio.2004086. PubMed PMID: 29253856.

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2. 

   Review Commons

   Jul 22, 2025

   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 #3

   Evidence, reproducibility and clarity

   Summary

   Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

   Major comments:

   Two of the key conclusions are not very convincing.

   First, the authors claim that the magnitude of the responses varied between the 5 …

   Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

   Learn more at Review Commons

   ---

   Referee #3

   Evidence, reproducibility and clarity

   Summary

   Rigby and collaborators analyzed the signaling responses and changes in gene expression of human PBMCs stimulated with different IFN type I subtypes, using mass cytometry, bulk and single-cell RNA sequencing. Their study represents the first single-cell atlas of human PBMCs stimulated with five type I IFN subtypes. The generated datasets are useful resources for anyone interested in innate immunity. The data and the methods are well presented. We thus recommend publication.

   Major comments:

   Two of the key conclusions are not very convincing.

   First, the authors claim that the magnitude of the responses varied between the 5 types of IFNs, however, as they point out in the 'limitation' paragraph, doses of the different IFNs were normalized using bioactivity. Knowing that this bioactivity is based on assays performed on A549 lung cells, this normalization likely induces a bias. How do the authors explain similar antiviral bioactivity but differing magnitudes of modulation of ISG expression? Would the authors expect the same differences of expression between the several IFNs tested in A549 cells? We thus recommend being very cautious when comparing magnitude of the response between the 5 types of IFNs.

   Second, the qualitatively different responses to type I IFN subtypes claimed by the authors were not apparent. This seems true at the level of the bulk population (Fig. S10) but not at cell-type level (Fig. S15/S16). The authors state (line 311-312) that 'Consistent with our bulk RNAseq data, differences were again quantitative rather than qualitative' at the cell-type level. The response between cell types seems very different to us since a core set of only 10 ISGs are shared by all cell types and all 5 type I IFNs. Knowing that the expression of hundreds, sometimes thousands of genes, are induced by IFN, this seems like a rather small overlap (and thus qualitatively different responses). Fig S15 and S16 nicely illustrate that the responses are qualitatively different between cell-type. Please modify this conclusion accordingly.

   No additional experiments are needed to support the claims. However, we believe that two additional analyses could provide useful information.

   The levels of IFNAR1 and IFNAR2 expressed at the plasma membrane probably vary between cell types and may thus influence the magnitude of the IFN response. While it would be difficult to measure these levels by flow cytometric analysis on the different cell types, could the authors extract information from their scRNAseq analysis on the expression level of IFNAR1/2 in all cell types? This would give a hint about potential differences in expression (and thus in magnitude).

   Could the authors investigate further the expression of lncRNAs at the single-cell levels? It would be useful to also define a core set of lncRNAs that are shared between cell types and IFN subtypes. If such a core set does not exist (since lncRNAs are less conserved than coding genes), it would be nice to mention it.

   Minor comments:

   There is a typo in line 355 Fig.4C =>6C.

   Referees cross-commenting

   We agree with Reviewer 1 that the low sequencing depth of scRNAseq restricts the analysis and must be discussed in the 'limitation' paragraph. This would explain why the authors identified only 10 ISGs that are common to all cell types and all 5 IFN subtypes. Of note, as a comparison, Shaw et al (10.1371/journal.pbio.2004086) identified a core set of 90 ISGs that are upregulated upon IFN treatment in cells isolated mainly from kidney and skin of nine mammalian species ("core mammalian ISGs"). It is thus expected that stimulated blood cells isolated from a single mammalian species share more than 10 ISGs.

   Significance

   Multiple single-cell RNAseq analysis of PBMCs, stimulated or not, have been previously performed in multiple contexts (for instance with PBMCs isolated from the blood of patients infected with influenza virus or SARS-CoV-2). The technical advance is thus limited.

   However, the work represents a conceptual advance for the field since it provides the first single-cell atlas of PBMCs stimulated with five type-I IFN subtypes. The generated datasets represent a great resource for anyone interested in innate immunity (virologists, immunologists and cancerologists).

   Of note, we are studying innate immunity in the context of RNA virus infection but we have no expertise on scRNA sequencing. We may thus have missed a flaw in the analyses.

   Read the original source
3. 

   Review Commons

   Jul 22, 2025

   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

   The manuscript entitled "Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

   Major Comments:

   1. Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in …

   Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

   Learn more at Review Commons

   ---

   Referee #2

   Evidence, reproducibility and clarity

   The manuscript entitled "Single-cell analysis of signalling and transcriptional responses to type I interferon" by Rigby et al. examines the response to type I IFN subtypes in PBMCs using an integrative proteomics and transcriptomics approach. Some of the analysis could be deepened to provide better insights into what governs the magnitude of change in gene expression as well as the cell type-specific response to expression and generate more excitement for the study.

   Major Comments:

   1. Although the authors appropriately conclude that type I IFNs induce qualitatively similar, the response is not quantitatively similar. What elements in the promoters of ISGs make them more responsive to IFN subtypes? (PMID: 32847859) How do they relate to the activation of kinases by IFN subtypes? Are there distinct features that dictate differential responses in monocytes and lymphocytes?
   2. Figure 2a, d-h - Consider using the same scale for all heatmaps. This will allow for comparison of pSTATs median expression. Consider increasing the range in the color scale as some of the subtle changes in STAT phosphorylation across subtypes are not well appreciated. This also applies to Supplementary figures related to Figure 2.

   Minor Comments:

   1. The title of subsections are a bit generic (e.g "Analysis of the signalling response to type I IFNs using mass cytometry". Consider updating them to reflect some of the findings from each analysis.
   2. Figure 3 and S3 - Increase the heatmap scale to better appreciate changes in gene expression.
   3. Consider combining panel a and b in figure S7 for better contrasts of the response to IFNa1 or IFNb.
   4. Figure 4 - The authors could visualize ISGs that are unique across IFN types or cell types.
   5. The gene ontology analysis should be performed with higher statistical stringency to capture the most significant IFN responsive processes.

   Significance

   Significance:

   The authors provide an extensive compendium of cell type specific changes in response to type I IFN stimulation. They have created a public repository which extends the value of this dataset.

   Audience:

   This is a valuable resource for immunologists, virologists, and bioinformaticians.

   Read the original source
4. 

   Review Commons

   Jul 22, 2025

   Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

   Learn more at Review Commons

   ---

   Referee #1

   Evidence, reproducibility and clarity

   The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN …

   Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

   Learn more at Review Commons

   ---

   Referee #1

   Evidence, reproducibility and clarity

   The study can be directly connected to a landmark paper in the field (Mostafavi et al. , Cell 2016). By comparison with this study, the authors use improved technologies to address the question if and how responses to type I IFN differ between human peripheral blood-derived cells types. In line with Mostafavi et al. the authors conclude that only a comparably low number of interferon-stimulated genes (ISG) is induced in all cell types and that considerable differences exist between cell types in the IFN-induced transcriptome. The authors address a second relevant aspect, whether and how the many different subtypes of type I IFN differ in the way they engage IFN signals to produce transcriptome changes. The data lead the authors to conclude that any differences are of quantitative rather than qualitative nature. The authors' conclusions are based on a mass cytometry approach to phenotype STAT activation in different cell types, bulk RNA sequencing to study ISG expression in PBMC, and single cell sequencing to study ISG responses in individual cell types. The data are solid, clear and reproducible in biological replicates (eg different blood donors).

   Significance

   While some of the data can be considered confirmatory, the comprehensive analysis of cell-type specificity and IFN-I subtype specificity advances the field and provides a reference for future analyses. The study is complete and there is no obvious lack of a critical experiment. The number of scientists interested in the multitude of open questions around type I IFN is large, thus the study is likely to attract a broad readership.

   The biggest limitation is to my opinion the low sequencing depth of scRNAseq which is clearly the downside of this technology. Using 11 hematopoietic cell types and bulk RNA sequencing the total number of ISG was determined to be 975 by Mostafavi et al. and the core ISG numbered 166. This is in stark contrast to this studies' 10 core ISG. The authors limitations paragraph should discuss the fact that scRNAseq reduces the overall ISG number that can be analyzed.

   A minor point concerns the 25 supplementary figures of the study. There must be a better way to support the conclusions with the necessary data.

   Read the original source
5. 

   Version published to 10.1101/2023.07.03.547491 on bioRxiv

   Jul 3, 2023