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Proteomic and transcriptomic profiling of brainstem, cerebellum and olfactory tissues in early- and late-phase COVID-19

Nature Neuroscience volume 27, pages 409–420 (2024)Cite this article

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Abstract

Neurological symptoms, including cognitive impairment and fatigue, can occur in both the acute infection phase of coronavirus disease 2019 (COVID-19) and at later stages, yet the mechanisms that contribute to this remain unclear. Here we profiled single-nucleus transcriptomes and proteomes of brainstem tissue from deceased individuals at various stages of COVID-19. We detected an inflammatory type I interferon response in acute COVID-19 cases, which resolves in the late disease phase. Integrating single-nucleus RNA sequencing and spatial transcriptomics, we could localize two patterns of reaction to severe systemic inflammation, one neuronal with a direct focus on cranial nerve nuclei and a separate diffuse pattern affecting the whole brainstem. The latter reflects a bystander effect of the respiratory infection that spreads throughout the vascular unit and alters the transcriptional state of mainly oligodendrocytes, microglia and astrocytes, while alterations of the brainstem nuclei could reflect the connection of the immune system and the central nervous system via, for example, the vagus nerve. Our results indicate that even without persistence of severe acute respiratory syndrome coronavirus 2 in the central nervous system, local immune reactions are prevailing, potentially causing functional disturbances that contribute to neurological complications of COVID-19.

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Fig. 1: Proteomic landscape and evaluation of COVID-19-enriched pathways.
Fig. 2: Differential regulation of IFN response in the brainstem.
Fig. 3: Deregulation of proteins and genes involved in synaptic processes during COVID-19.
Fig. 4: Perivascular unit in the brainstem shows inflammatory changes during COVID-19 disease course.
Fig. 5: Study design and multiomics approach.

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Data availability

The snRNA-seq data generated in this study have been deposited in the European Genome–Phenome Archive under the accession number EGAS00001006442. The data are available under controlled access due to the sensitive nature of sequencing data, and access can be obtained by contacting the appropriate data access committee listed for each dataset in the study. Access will be granted to commercial and non-commercial parties according to patient consent forms and data transfer agreements. We have an institutional process in place to deal with requests for data transfer. A response to requests for data access can be expected within 14 days. After access has been granted, the data is available for 2 years. The processed proteomic and clinical data are available in Supplementary Tables 1 and 8. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE132 partner repository with the dataset identifier PXD038693. Raw image files of histological stainings, immunohistochemistry, multi-epitope ligand cartography and spatial transcriptomics generated in this study have been deposited publicly at Zenodo28. The remaining data are available within the article, extended data files or supplementary tables.

Code availability

No custom software codes were used.

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Acknowledgements

We gratefully thank F. Egelhofer, R. Koll, P. Matylewski, V. Wolf and S. Meier for excellent technical assistance and advice. The authors are most grateful to the individuals with COVID-19 and their relatives for consenting to autopsy and subsequent research, which were facilitated by the Biobank of the Department of Neuropathology, Charité–Universitätsmedizin Berlin and the German National Autopsy Network (NATON–01KX2121). Cartoon images were partially created with Biorender.com. This work was also supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy EXC-2049-390688087, as well as SFB TRR 167 and HE 3130/6-1 to F.L.H., CRC130 P17 to H.R. and A.E.H., HA 5354/10-1 to A.E.H. and RA 2491/1-1 to H.R.; by the German Center for Neurodegenerative Diseases Berlin, by the European Union (PHAGO, 115976; Innovative Medicines Initiative-2; FP7-PEOPLE-2012-ITN: NeuroKine) and by the Ministry for Science and Education of Lower Saxony through the program ‘Niedersächsisches Vorab’ to J.F. E.W. and M.L. who are supported by the project ‘Virological and immunological determinants of COVID-19 pathogenesis—lessons to get prepared for future pandemics (KA1-Co-02 ‘COVIPA’)’. We would like to thank the Sequencing Facility at the Max Planck Institute for Molecular Genetics for their services.

Author information

Author notes

  1. These authors contributed equally: Josefine Radke, Jenny Meinhardt, Tom Aschman, Robert Lorenz Chua, Vadim Farztdinov.

  2. These authors jointly supervised this work: Frank L. Heppner, Michael Mülleder, Christian Conrad, Helena Radbruch.

Authors and Affiliations

  1. Institute of Pathology, Universitätsmedizin Greifswald, Greifswald, Germany

    Josefine Radke & Kristin Peters

  2. Department of Neuropathology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Josefine Radke, Jenny Meinhardt, Tom Aschman, Ekaterina Friebel, Valerie Helena Huhle, Ronja Mothes, Shirin Schneeberger, Elisa Schumann, Leona Kawelke, Julia Jünger, Viktor Horst, Simon Streit, Regina von Manitius, Werner Stenzel, Frank L. Heppner & Helena Radbruch

  3. Center of Digital Health, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany

    Robert Lorenz Chua, Sören Lukassen, Foo Wei Ten, Naveed Ishaque, Katharina Jechow, Roland Eils & Christian Conrad

  4. Core Facility High Throughput Mass Spectrometry, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Vadim Farztdinov, Markus Ralser & Michael Mülleder

  5. Department of Neuropathology, University Medical Center Göttingen, Göttingen, Germany

    Jonas Franz, Carolina Thomas & Christine Stadelmann

  6. Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Péter Körtvélyessy

  7. Department of Neurology, Otto von Guerike University Magdeburg, Magdeburg, Germany

    Stefan Vielhaber

  8. Institute of Molecular and Clinical Immunology, Otto von Guerike University Magdeburg, Magdeburg, Germany

    Dirk Reinhold

  9. Department of Rheumatology and Clinical Immunology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Anja E. Hauser

  10. Immune Dynamics, Deutsches Rheuma-Forschungszentrum, a Leibniz Institute, Berlin, Germany

    Anja E. Hauser

  11. Department of Pathology, University Medical Center Ulm, Ulm, Germany

    Anja Osterloh

  12. Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Philipp Enghard

  13. Department of Pathology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Jana Ihlow, Sefer Elezkurtaj & David Horst

  14. Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Florian Kurth & Victor Corman

  15. Institute of Virology, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Marcel A. Müller & Julia Melchert

  16. Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany

    Nils C. Gassen

  17. Max Planck Institute for Molecular Genetics, Berlin, Germany

    Bernd Timmermann

  18. Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité – Universitätsmedizin Berlin, Berlin, Germany

    Camila Fernandez-Zapata & Chotima Böttcher

  19. Institute of Medical Biochemistry and Molecular Biology, Universitätsmedizin Greifswald, Greifswald, Germany

    Elke Krüger

  20. Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Markus Landthaler & Emanuel Wyler

  21. Institut für Biologie, Humboldt Universität, Berlin, Germany

    Markus Landthaler

  22. German Centre for Infection Research (DZIF), associated partner, Berlin, Germany

    Victor Corman

  23. German Center for Neurodegenerative Diseases (DZNE) Berlin, Berlin, Germany

    Frank L. Heppner

  24. Cluster of Excellence NeuroCure, Berlin, Germany

    Frank L. Heppner

  25. German Cancer Consortium (DKTK), Partner Site Charité – Universitätsmedizin Berlin, Berlin, Germany

    Frank L. Heppner

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  1. Josefine Radke
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Contributions

J.R., J. Meinhardt, T.A., R.L.C., V.F, F.L.H., M.M., C.C. and H.R. conceived the project. J.R., J. Meinhardt, T.A., R.L.C., V.F., C.B., J.F., C.T., E.W., V.C., M.M., C.C., H.R., E.F., E.K., R.M., P.K. and C.F.-Z. performed experiments and analyzed data. S.L., F.W.T., N.I., R.L.C., V.F. and M.M. carried out bioinformatics analyses and processed data. S.Streit, R.v.M., J.F., C.T., C.S., A.O., J.I., S.E., W.S., L.K., V.H. and J.J. performed sample acquisition and/or histological analyses. V.H.H., S. Schneeberger, E.S., S.V., D.R., A.E.H., P.E., M.A.M., N.C.G., F.K., J. Melchert., K.J., B.T., M.L. and D.H. performed experiments. M.R., R.E. and C.C. oversaw sequencing of the samples. J.R., J. Meinhardt, T.A., R.L.C., V.F., H.R., E.F., F.L.H. and K.P. prepared figures and wrote the manuscript. J.R., F.L.H., M.M., C.C. and H.R. critically revised the manuscript. All authors critically read, reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Josefine Radke or Helena Radbruch.

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The authors declare no competing interests.

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Nature Neuroscience thanks Robyn Klein, Christopher Mason and Avindra Nath for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Post hoc PCA analysis based on proteomics data of the brainstem.

Post hoc PCA score plots based on (dys-)regulated proteins detected in contrast 1 (acute COVID-19 vs. control, a, b) and detected in contrast 2 (late COVID-19 vs. control, c, d). Score plots on the left show sample clustering in the first two principal components. Ellipses are drawn on the confidence level of 0.8 to underline sample grouping. Criteria used for finding (dys-)regulated proteins are described in the Supplementary Material and Methods section. Post hoc PCA loading plots on the right based on (dys-)regulated proteins detected in the respective contrast show correlation of proteins and different clinical factors with the first two PCs. The features (green arrows) contributing the most to the first two principal components (for example, S100A11, TNS3) are shown and the clinical factors (blue dashed arrows).

Extended Data Fig. 2 Interferon related alterations (PCR, snRNA Seq).

Interferon score determined by qPCR of six ISGs (IFIT1, IFI27, IFI44L, ISG15, RSAD2, SIGLEC1) as previously described38,105 in biologically independent brainstem samples from n = 9 acute, n = 9 late, and n = 6 control cases. There were no statistically significant differences between group means as determined by one-way ANOVA (p = 0.34) (a). Gene expression of homeostatic microglia marker. Expression levels are color coded; the percentage of cells expressing the respective gene is size coded (b).Violin plot comparing the expression of IFN score in acute and late COVID-19 and in controls across all cell populations within the brainstem (c) and olfactory mucosa (d). Significance was calculated for IFN scores with a Mann-Whitney U test (two-sided) with the p-values adjustment with the Benjamini-Hochberg method. This is followed by a Wilcoxon effect size (two-sided) calculation for significant differences only. Significance was calculated using the Mann-Whitney U test, the p-values were corrected with FDR. Asterisks indicate level of statistical significance: ***p < 0.001, **p < 0.01, and *p < 0.05 (for the Wilcoxon effect size and the exact p-values, please refer to Supplementary Table 9) (e). Interferons and their receptor expression in all analyzed snRNA sequencing samples from the brainstem (n = 4 acute, n = 4 late, and n = 4 control cases) (c).

Extended Data Fig. 3 Synaptic alterations and cell frequencies.

Synaptic Ontology Analysis using SynGO39 based on differentially expressed proteins (DEPs) detected in contrast 1 (acute COVID-19 vs. control) and contrast 2 (late COVID-19 vs. control) showing alteration of synaptic processes in the brainstem. Reference sunburst diagrams for synaptic location (a) and synaptic function (b) gene ontologies (GOs) are shown on the left. GO terms representing major synaptic subcompartments (a) or synaptic functions (b) are positioned in the next level from the center. The sunburst plots illustrate that the synapse-associated DEPs were of pre- and postsynaptic origin (a) and mainly involved in biological processes associated with synapse organization and processes in the presynapse (b). The number of (dys-)regulated proteins in each term is indicated by the color scheme in the legend (FDR-adjusted p-value < 0.05; a, b). Bar graph with data points per sample showing the percentage of all cell types in the brainstem in acute (n = 4 individuals) and late (n = 4 individuals) COVID-19 and controls (n = 4 individuals) as detected by snRNA sequencing. Differences in cellular frequencies were calculated with scCODA, a Bayesian model that determines compositional changes in single-cell RNA-seq datasets (significant if FDR adjusted p-value < 0.1). (scCODA; FDR adjusted p-value < 0.1; c). The error bars depict the standard error mean (SEM), and each dot reflects the percentage of cells per sample.

Extended Data Fig. 4 SnRNA Seq UMAPs and SARS-CoV-2 transcripts.

UMAPs of snRNA sequencing of the brainstem (medulla oblongata; a-b) and olfactory mucosa (c-d). Sample overview (a, c) and detection of SARS-CoV-2 transcripts (b, d) are shown. Dot plot showing the expression of selected myelin- and neuronal/synapse-related proteins in cerebrospinal fluid (CSF) of living patients with COVID-19 (n = 38) with either high (C19high) or low (C19low) serum levels of procalcitonin (PCT) compared to non-inflammatory controls (CTRL, n = 28) as well as patients with herpes simplex virus meningitis (HSVM, n = 10; e).

Extended Data Fig. 5 Automated clustering of spatial sequencing.

Heat map of the top 5 most expressed genes per cluster of integrated spatial sequencing data from all sequenced samples. The color represents normalized and scaled (‘SCTtransform’, Seurat) gene expression (a). These unsupervised clusters are distributed similar to the supervised anatomical annotation shown in Fig. 3 with clusters of brainstem nuclei and white matter fiber tracts (b; left panel: acute (patient D10), right panel: late (patient D19)).

Extended Data Fig. 6 Subclustering and pseudotime analysis of oligodendrocytes (snRNA Seq).

The UMAPs show a refined subclustering of oligodendrocytes (left all, right disease groups) (a) and pseudotime analysis (b). The plotting symbol for each cell is colored according to its true pseudotime; trajectories in black. The transitional cell clusters Oligo.OPALIN + 2 and Oligo 1 are significantly reduced during acute COVID-19 (c, e). Marker expression of different oligodendrocyte cell clusters in acute (n = 4 individuals), late (n = 4 individuals), and control (n = 4 individuals) cases (d). Color and size codes are displayed. Differences in cellular frequencies were calculated with scCODA, a Bayesian model that determines compositional changes in single-cell RNA-seq datasets (significant if FDR adjusted p-value < 0.1). The central line depicts the median, boxes represent the interquartile range (IQR), and whiskers show the distribution excluding outliers. Outliers are points outside 1.5 times the IQR (e).

Extended Data Fig. 7 T cell comparison in lung, olfactory mucosa and brain (snRNA Seq).

Integrated UMAPs of natural killer cells (NK), CD4+ and CD8 + T cells within the brainstem, olfactory mucosa and lung in acute and late COVID-19 and controls in the single-cell RNA-seq datasets (a). The Venn diagram represents all unique and shared T cell related transcripts in the brainstem, olfactory mucosa and lung (b). Dot plot of snRNA sequencing activation (c) and cytotoxic markers (d) in CD8 + T cells in the brainstem. Expression levels are color coded; the percentage of cells expressing the respective gene is size coded (c, d). Integrated heatmap of cytotoxic and activation markers in T cells of lung, brainstem and mucosa (e).

Extended Data Fig. 8 Marker gene expression for cell type annotations (snRNA Seq).

Marker gene expression profiles are shown for brainstem (a) and olfactory mucosa (b). Color and size codes are displayed. Dotplots showing expression of endothelial cell type markers (c), macrophages and microglia (d) and neurons (e) in the brainstem samples.

Supplementary information

Supplementary Information

Supplementary material and methods.

Reporting Summary

Supplementary Table 1

Metadata of the autopsy and CSF cohort.

Supplementary Table 2

Interferome dataset (proteomics).

Supplementary Table 3

IFN score (qPCR).

Supplementary Table 4

Description of synaptic proteins.

Supplementary Table 5

Differential gene expression and cell counts (snRNA-seq).

Supplementary Table 6

Spatial sequencing and automated clustering.

Supplementary Table 7

Cell population_scCODA.

Supplementary Table 8

Differentially expressed proteins for all organs.

Supplementary Table 9

IFN score (snRNA-seq).

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Radke, J., Meinhardt, J., Aschman, T. et al. Proteomic and transcriptomic profiling of brainstem, cerebellum and olfactory tissues in early- and late-phase COVID-19. Nat Neurosci 27, 409–420 (2024). https://doi.org/10.1038/s41593-024-01573-y

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