Scientific ReportsBioinformaticsPeer-reviewed journal article
Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2
Markus HoffmanniD, Lina-Liv WillruthiD, Alexander DietrichiD, Hye‐Kyung LeeiD, Ludwig KnabliD, Nico TrummeriD, Jan BaumbachiD, Priscilla A. FurthiD, Lothar G. HennighauseniD, Markus ListiD
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 patients 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 patients 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 PointsO_LIComputational deconvolution of transcriptomes can estimate immune cell abundances in SARS-CoV-2 patients, supplementing missing CBC data. C_LIO_LI10 million RNA sequencing reads per sample suffice for analyzing immune responses and disease severity, including BCR/TCR identification. C_LI
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 13
- FWCI
- 1.77
- Field percentile
- top 16%
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