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Review 1: "Blood Transcriptomics Analysis Offers Insights into Variant-specific Immune Response to SARS-CoV-2"

Overall, both reviewers suggest focusing on the  comparison of deconvolution algorithms and exhibit concerns about the practicality of RNA-seq as a routine diagnostic tool due to cost and complexity factors.

Published onJan 25, 2024
Review 1: "Blood Transcriptomics Analysis Offers Insights into Variant-specific Immune Response to SARS-CoV-2"
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key-enterThis Pub is a Review of
Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2
Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Abstract Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient’s immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient’s disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.Key Points Computational deconvolution of transcriptomes can estimate immune cell abundances in SARS-CoV-2 patients, supplementing missing CBC data.10 million RNA sequencing reads per sample suffice for analyzing immune responses and disease severity, including BCR/TCR identification.

RR:C19 Evidence Scale rating by reviewer:

  • Strong. The main study claims are very well-justified by the data and analytic methods used. There is little room for doubt that the study produced has very similar results and conclusions as compared with the hypothetical ideal study. The study’s main claims should be considered conclusive and actionable without reservation.



Authors have performed the correlation of immune cells composition predicted by the various approaches based on RNA-seq data and CBCs. This is very fascinating to see that immune cell deconvolution results are showing the positive correlation with CBC. Authors have also added the information of BCR/TCR repertoire analysis and suggested that combining with immune cells deconvolution would enhance the sensitivity and help the clinicians for better decision. Additionally authors have carried out analysis for downsampling of the sequencing reads to see what should be the minimum sequencing depth to capture the immune cells information, which may be useful if RNA-seq is going to be adopted in the hospital as a diagnostic approach.

Authors have used the RNA-seq data and computational approaches to propose a very novel idea in the paper entitled "Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2" to use the RNA-seq data as additional diagnostic approach in the hospitals. They have also carried out the analysis to explore the possibilities about what should be the minimum reads depth used to get the biological significant results. There are some minor suggestions, which might be helpful to improve the quality of the draft. These are as follows:

  1. Authors should provide the correlation and other statistics in the result sections. 

  2. Authors can also try to improve the quality of the figures and figure legends for better readability.

  3. Authors should have added one paragraph, where they should compare all the deconvolution approaches and comment which one is outperformed.

  4. One of the major concerns about the application of this work is, who is going to perform all the bioinformatic analysis in hospitals, if this approach is being applied.

  5. How the clinicians should be trained to understand the data and make the decision.

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