Evidence map›Paper›PMID 38307916›Full record

ArticleScientific reports2024

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2.

Markus Hoffmann, Lina-Liv Willruth, Alexander Dietrich, Hye Kyung Lee, Ludwig Knabl, Nico Trummer, Jan Baumbach, Priscilla A Furth, Lothar Hennighausen, Markus List

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. How Genomic and Structural Context Could Shape JAK-STAT Variant Pathogenicity.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Blood gene expression network expression strongly relates to brain amyloid burden.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  7. Article
  8. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Markus Hoffmann *Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany. markus.hoffmann@nih.gov.
Lina-Liv Willruth *Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Alexander Dietrich *Data Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Hye Kyung LeeNational Institute of Diabetes, Digestive, and Kidney Diseases, Bethesda, MD, 20892, USA.
Ludwig KnablTyrolPath Obrist Brunhuber GMBH, Zams, Austria.
Nico TrummerData Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany.
Jan BaumbachChair of Computational Systems Biology, University of Hamburg, Hamburg, Germany.
Priscilla A FurthInstitute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany.
Lothar HennighausenInstitute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2 a, 85748, Garching, Germany.
Markus ListData Science in Systems Biomedicine, TUM School of Life Sciences, Technical University of Munich, Freising, Germany. markus.list@tum.de.

Funding

Immune transcriptomes of SARS-CoV-2 infected populationsZIADK075155 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI HENNIGHAUSEN, LOTHAR · 2020 to 2024
$8.1M
Deutsche Forschungsgemeinschaft 422216132European Union's Horizon 2020 research and innovation program No 777111German Federal Ministry of Education and Research (BMBF) within the framework of the *e:Med* research and funding concept 01ZX1908A / 01ZX2208A*German Federal Ministry of Education and Research (BMBF) within the framework of the *e:Med* research and funding concept *grants 01ZX1910D / 01ZX2210D*VILLUM Young Investigator Grant nr.13154
6 · The paper itself

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 propose that RNA-seq should be considered a 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 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 196 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 BLASTp-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.

Indexed as

COVID-19SARS-CoV-2Gene Expression ProfilingHumansImmunityReceptors, Antigen, T-CellSequence Analysis, RNAReceptors, Antigen, T-Cell

Identifiers

PMID38307916
PMCPMC10837437

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.