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Brain-wide alterations revealed by spatial transcriptomics and proteomics in COVID-19 infection

Nature Aging volume 4, pages 1598–1618 (2024)Cite this article

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Abstract

Understanding the pathophysiology of neurological symptoms observed after severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection is essential to optimizing outcomes and therapeutics. To date, small sample sizes and narrow molecular profiling have limited the generalizability of findings. In this study, we profiled multiple cortical and subcortical regions in postmortem brains of patients with coronavirus disease 2019 (COVID-19) and controls with matched pulmonary pathology (total n = 42) using spatial transcriptomics, bulk gene expression and proteomics. We observed a multi-regional antiviral response without direct active SARS-CoV2 infection. We identified dysregulation of mitochondrial and synaptic pathways in deep-layer excitatory neurons and upregulation of neuroinflammation in glia, consistent across both mRNA and protein. Remarkably, these alterations overlapped substantially with changes in age-related neurodegenerative diseases, including Parkinson’s disease and Alzheimer’s disease. Our work, combining multiple experimental and analytical methods, demonstrates the brain-wide impact of severe acute/subacute COVID-19, involving both cortical and subcortical regions, shedding light on potential therapeutic targets within pathways typically associated with pathological aging and neurodegeneration.

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Fig. 1: Upregulation of antiviral defense genes and dysregulation of cell type markers in the frontal lobe and pons of patients with severe acute/subacute COVID-19.
Fig. 2: Consensus network analysis revealed shared mechanisms overlapping with neurodegenerative disorders.
Fig. 3: hdWGCNA indicated shared pathways between severe acute/subacute COVID-19 and neurodegenerative disorders.
Fig. 4: Pervasive downregulation of neurodegeneration-associated mitochondrial and synaptic vesicle pathways in the brain of patients with severe acute/subacute COVID-19.
Fig. 5: Bulk tissue RNA and proteomic profiling of the frontal lobe and pons confirm the biological processes implicated by spatial transcriptomics.
Fig. 6: Proteomic profiling of eight brain regions of patients with severe acute/subacute COVID-19 identifies shared changes.
Fig. 7: Neuronal and mitochondrial dysfunction in the substantia nigra in the midbrain of patients with severe acute/subacute COVID-19.

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Data availability

All processed data are available in the main text or the supplementary materials and source data. The proteomics data have been deposited to MassIVE with accession number MSV000094297 (ftp://massive.ucsd.edu/v07/MSV000094297/). Raw data of GeoMx spatial transcriptomics (.DCC, .pkc files and Q3 normalized counts) and nCounter direct gene expression assays (.RCC files) have been deposited in the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) and are accessible through GEO Series with accession number GSE274267.

Code availability

The code used in this study can be found at https://github.com/dhglab/Brain-wide-alterations-revealed-by-spatial-transcriptomics-and-proteomics-in-COVID-19-infection.

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Acknowledgements

We thank the autopsy service chief and staff at the Department of Pathology and Laboratory Medicine, G. Fishbein, K. Ellis, C. E. Ramos and G. Ceballos for facilitating the tissue procurement. We thank C. K. Williams at the section of neuropathology for helpful discussion and advice on tissue processing. We thank members of the Geschwind laboratory (R. Kawaguchi, L. Bricks, Y. Tan, X. Huan and L. O. Chen) for helpful discussion and advice on data analyses. This work was funded by the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research (BSCRC); the UCLA COVID-19 Research Award (OCRC no. 20-70) to D.H.G., H.V.V. and T.Z.; the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (20231126) to D.H.G.; the Translational Research Fund of the UCLA Department of Pathology and Laboratory Medicine to T.Z and H.V.V.; and the UCLA Intercampus Medical Genetics Training Program (T32GM008243) to T.Z. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. Liuliu Pan

    Present address: Duality Biologics, Shanghai, China

Authors and Affiliations

  1. Section of Neuropathology, Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Ting Zhang, Christopher Kazu Williams, Shino Magaki & Harry V. Vinters

  2. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Ting Zhang, Christopher Kazu Williams, Harry V. Vinters & Daniel H. Geschwind

  3. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Ting Zhang & Daniel H. Geschwind

  4. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Ting Zhang & Daniel H. Geschwind

  5. Translational Pathology Core Laboratory, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Yunfeng Li

  6. Technology Access Program, Bruker Spatial Technology, Seattle, WA, USA

    Liuliu Pan

  7. Proteome Research Center, University of California, Los Angeles, Los Angeles, CA, USA

    Jihui Sha & James Wohlschlegel

  8. Proof of Principle Team, Translational Science, Bruker Spatial Technology, Seattle, WA, USA

    Michael Bailey

  9. Center for Systems Biomedicine, University of California, Los Angeles, Los Angeles, CA, USA

    Emmanuelle Faure-Kumar

  10. Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA

    James Wohlschlegel

  11. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Shino Magaki, Xinmin Li & Harry V. Vinters

  12. Technology Center for Genomics & Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA

    Chao Niu, Yoojin Lee, Yu-chyuan Su & Xinmin Li

  13. Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, USA

    Daniel H. Geschwind

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Contributions

Experiments were designed by D.H.G, H.V.V. and T.Z. Spatial transcriptomics data were generated by T.Z., Y. Li, L.P., C.N., Y. Lee, Y.-C.S. and X.L. Bulk RNA direct gene expression (nCounter) data were generated by T.Z., Y. Li, M.B. and E.F.-K. Proteomics data were generated by T.Z., Y. Li, J.S. and J.W. Data analysis was performed by T.Z., Y.-C.S., J.S. and D.H.G. The manuscript was written by T.Z., S.M., H.V.V. and D.H.G. The work was supervised by D.H.G. and H.V.V.

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Correspondence to Harry V. Vinters or Daniel H. Geschwind.

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Extended data

Extended Data Fig. 1 Gene expression profile from the frontal grey matter correlates with nCounter bulk tissue direct gene expression.

a) Bar plots showing the number of differentially expressed molecules in different assays (BH-adjusted p < 0.1, MSstatsTMT31 for proteomics; DESeq232 for nCounter direct gene expression; lme433 for spatial transcriptomics). b) Venn diagrams and Pearson’s correlation of overlapped differentially expressed genes from nCounter direct gene expression of the frontal lobe and spatial transcriptomics of frontal grey matter (p = 1.60E-11, GeneOverlap34, Pearson’s correlation, two-sided, r = 0.77, p = 1.62E-33) and frontal white matter (p = 0.60, GeneOverlap34, Pearson’s correlation, two-sided, r = -0.42, p = 0.72). c) 3 SARS-CoV2 transcripts in nCounter direct gene expression (basal ganglia) indicated there was no increased expression of viral mRNA (N = 6 for controls, N = 6 for COVID-19 patients, two-tailed unpaired t test). d) Bar plots showing the results of cell type enrichment analysis (EWCE38) of NRGN neuron from Yang, et al, 202125 and its celltype markers’ enrichment when comparing to the major cell types (level 1) and subclass cell types (level 2) in Wamsley, et al, 202439 and Bakken, et al, 202140. The red dashed line represents a threshold of 2.5 for the sd_from_mean. Cell types with a sd_from_mean value > 2.5 and a BH-adjusted p < 0.05 are considered statistically significant.

Source data

Extended Data Fig. 2 Increased ACE2 and FURIN protein expression in the frontal cortex of severe acute/subacute COVID-19 patients.

Anti-ACE2 (a) and Anti-FURIN (b) immunohistochemistry highlight the increased protein expression at the vascular/perivascular structures (magenta arrowhead) in the white matter of the COVID-19 patients (N = 9) comparing to the non-COVID controls (N = 8), one-tailed unpaired t-test of area percentage for positive immunoreactivity, p = 3.95E-2 (ACE2) and p = 3.00E-3 (FURIN).

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Extended Data Fig. 3 Increased NRP1 protein expression in the frontal cortex of severe acute/subacute COVID-19 patients.

a) Anti-NRP1 immunohistochemistry in the frontal cortex of non-COVID controls (N = 8) and COVID-19 patients (N = 9) are displayed. Within the non-COVID controls, NRP1 immunoreactivity has a predominant astrocytic expression pattern (cyan arrowhead) in five out of eight cases, while a predominant neuronal expression pattern (purple arrowhead) is observed in the remaining three. In the COVID-19 patients, NRP1 immunoreactivity is seen more frequently in both neurons and astrocytes. Moreover, increased neuronal immunoreactivity is found in the nucleus of five out of nine patients, and in the cytosol of two out of nine patients. The overall immunopositive area percentage is increased in the COVID-19 patients (p = 3.20E-3, one-tailed unpaired t-test). b) Anti-Fibrin immunohistochemistry in the frontal white matter of non-COVID controls (N = 5) and COVID-19 patients (N = 4). Increased intravascular and perivascular fibrin deposition (yellow arrowhead), indicative of thrombi formation and blood brain barrier/neurovascular unit defect, is seen in both non-COVID controls and COVID-19 patients (one-tailed unpaired t-test of area percentage for positive immunoreactivity, p = 2.68E-1).

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Extended Data Fig. 4 Network dendrogram, EWCE results of WGCNA and GO enrichment analysis of hdWGCNA.

a) Consensus WGCNA network dendrogram from co-expression topological overlap of genes in frontal grey matter and pontine nuclei spatial transcriptomics. b-d) Dot plots illustrating cell type enrichment analysis (EWCE38) results of consensus modules, using major cell types from Wamsley, et al, 202439(b), subclass cell types from Bakken, et al, 202140(c), and Yang, et al, 202125(d). The color intensity corresponds to the sd_from_mean of each cell type. The greyscale of the dot edge corresponds to the FDR-adjusted p value for each cell type. The dot size in each row represents the relative abundance across all the modules. e) Top GO enrichment terms of all the excitatory neuron modules (EnrichR56).

Source data

Extended Data Fig. 5 Additional GO enrichment, PPI and MCODE network analyses of differentially expressed proteins in eight regions.

a) The graph reflects the same networks depicted in Fig. 6b (downregulated GO cluster, left panel) and Fig. 6c (upregulated GO cluster, right panel), but with the regional information displayed as a pie chart for each gene. Each GO term is proportioned within the pie chart and color-coded based on the number of proteins under the term from the corresponding region (key below)42. Most of the terms displayed are shared by more than three regions. b) Merged protein-protein interaction and MCODE network analyses42 of differentially expressed proteins in eight regions. Top GO terms for each MCODE network are selected for labeling.

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Extended Data Fig. 6 Brain-wide differential gene expression in severe acute/subacute COVID-19 patients shows minimal overlap with hypoxia-related changes.

a) Volcano plots depict the NES values from Gene Set Enrichment Analysis (GSEA60,61, GeneSet Hallmark) and FDR-q values across 10 areas of the frontal lobe, pons, and midbrain from spatial transcriptomics results. Notably, hypoxia-related genes are not significantly enriched in any of the profiled areas. b) Venn diagrams illustrate the results of comparison between the top 500 differentially up-and down-regulated genes observed in primary cortical neurons under hypoxia conditions68 and those identified in the grey matter of COVID-19 patients (BH-adjusted p < 0.05) in this study, no significant overlap are identified (for upregulated genes, the comparisons between Zhang, et al68 and frontal grey matter, pontine nuclei, and substantia nigra, p = 9.04E-1, 8.13E-1, and 1.78E-1, respectively; for downregulated genes, the corresponding p = 9.35E-1, 1.00, 3.71E-1, GeneOverlap34). We also compared the 500 upregulated genes of mouse brains under hypoxic conditions for 2 days (gene pattern 1) and 7 days (gene pattern 2)69 to the upregulated genes in frontal grey matter, finding no significant overlap (p = 4.70E-1 and 1.88E-1, GeneOverlap34).

Source data

Extended Data Fig. 7 Increased amyloid deposition including senile plaques and cerebral amyloid angiopathy, not Tau deposition, in severe acute/subacute COVID-19 patients.

a) Anti-Aβ42 immunohistochemistry highlights the amyloid deposition including senile plaques (orange arrowhead) and cerebral amyloid angiopathy (CAA, purple arrowhead) in the COVID-19 patients (N = 9) which are not seen in the non-COVID controls (N = 8) (one-tailed unpaired t-test of area percentage for positive immunoreactivity, p = 4.61E-2). b) Anti-phospho-Tau immunohistochemistry does not detect significant positive immunoreactivity and difference in positive immunoreactivity area between non-COVID control (N = 8) and COVID-19 patient (N = 9) (one-tailed unpaired t-test, p = 4.84E-1) groups. Rare neurons containing neurofibrillary tangles and neuropil threads (cyan arrowhead) are observed. As positive controls, Aβ42 immunopositivity highlights the characteristic senile plaques and cerebral amyloid angiopathy of advanced stage Alzheimer’s disease (Braak stage V-VI). Anti-phospho-Tau antibody highlights abundant neurofibrillary tangles and neuropil threads (cyan arrowhead), and neuritic plaques (yellow arrowhead) in AD patient. Anti-Aβ42 and phospho-Tau IHCs were repeated once.

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Zhang, T., Li, Y., Pan, L. et al. Brain-wide alterations revealed by spatial transcriptomics and proteomics in COVID-19 infection. Nat Aging 4, 1598–1618 (2024). https://doi.org/10.1038/s43587-024-00730-z

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