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Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19 - PubMed

  • ️Fri Jan 01 2021

doi: 10.1186/s13073-021-00933-8.

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Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19

John F Fullard et al. Genome Med. 2021.

Abstract

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases.

Methods: Three brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain.

Results: Quantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis.

Conclusions: Despite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.

Keywords: Choroid plexus; Gene expression; Microglia; Neuroinflammation; Prefrontal cortex; SARS-CoV-2.

© 2021. The Author(s).

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1

Droplet-based single-nucleus RNA sequencing in the dorsolateral prefrontal cortex (PFC), medulla oblongata (medulla), and choroid plexus (ChP) of 5 COVID-19 patients and 4 controls. A Experimental design. Frozen specimens of the human brain were dissected and subjected to a number of molecular assays, including single-nucleus RNA-sequencing (snRNA-seq), viral genome RNA-seq, and SARS-CoV-2 viral spike protein detection. B Uniform manifold approximation and projection visualization of annotated single-nucleus data (n = 68,557 barcodes). Colors show annotated cell types. C Distribution of canonical gene markers on annotated cell populations. The range of violins is adjusted by the maximum and minimum in each row. D Cell composition of mesenchymal cells and monocytes/macrophage in the choroid plexus stratified by case-control status. Only comparisons across tissues and cell types that survived false discovery rate correction are shown. Ast1 and Ast2 are the 2 groups of astrocytes. End, endothelial cells; Epi, epithelial cells; Ep, ependymal cells; Ex, excitatory neurons; In, inhibitory neurons; LM, lymphocytes; Mes, mesenchymal cells; Mic: microglia; Mo/MP, monocytes/macrophage; Oli, oligodendrocytes; Opc, oligodendrocyte progenitor cell. Peri1 and Peri2 are the two groups of pericytes

Fig. 2
Fig. 2

Differential gene expression and gene set enrichment analyses in COVID-19 patients compared to controls. A Number of differentially expressed genes (DEGs) identified in cell types across three brain regions. Up- and downregulated genes are shown in different colors. Cell types are ranked by the total number of DEGs across three brain regions. Cell types with no more than 10 DEGs in any brain region are omitted. B Gene set activity scores of PFC microglia. The four most significant pathways among 186 KEGG gene sets are shown. The shade of a violin indicates the median activity score of each individual. C Ten differentially expressed genes (FDR < 0.05) in four upregulated pathways are shown. The size of a circle shows the relative weight of a gene that contributed to the activity of a pathway. The relative weight is estimated as the ratio of the expression level of a gene to the sum of expression of all genes in the pathway. Colors show the log fold change

Fig. 3
Fig. 3

Pseudo-temporal trajectory score (PTS) analysis in the microglia-identified gene expression signatures with differential progression patterns in COVID-19 cases. A Pseudo-temporal trajectory in the microglia across three brain regions. Red and blue colors label cells from COVID-19 cases and controls, respectively. B PTS across 5 COVID-19 patients and 4 controls in PFC microglia. The shade of violin plots indicates the median activity score of each individual. C We identified 4 types of gene expression progression patterns over the pseudo-temporal trajectory: increasing (blue), early transient (orange), late transient (green), and decreasing (red). A dashed line shows the profile of a representative gene in each group

Fig. 4
Fig. 4

Gene regulatory network (GRN) analysis revealed transcription factors (TFs) driving transcriptomic dysregulation in COVID-19 patients. A TF module scores in PFC microglia. The shade of a violin indicates the median activity score of each individual. Four upregulated TF modules (IRF8, ATF5, SPI1, TAL1) are shown. B Upregulated TF modules in PFC microglia. Colored nodes show the transcription factors (blue, green, brown, and purple), DEGs (red), and a genetically associated gene based on genome-wide association studies (GWAS) (yellow). Nodes without circles are genes regulated by the transcription factors but are not DEGs. The regulatory network is trimmed to show only 14 DEGs, ranked by P-values, and 10 non-DEG genes regulated by each transcription factor. C Enrichment of the GWAS-associated genes in 4 microglia regulons: IRF8, ATF5, SPI1, and TAL1. Circles show odds ratios for the overlap of nominally significant GWAS gene (n = 285 and 560 for blood and brain, respectively, P ≤ 0.05), imputed from a GWAS comparing hospitalized COVID with respect to the general population, and genes of 4 microglia regulons. Error bars show 95% confidence intervals of estimated odds ratios. “Up” are those that are predicted to be upregulated (n = 140 and 297 for blood and brain, respectively, P ≤ 0.05) and “Down” are those that are predicted to be downregulated (n = 145 and 263 for blood and brain respectively, P ≤ 0.05). Analysis is limited to protein-coding genes only. Significant enrichments (P ≤ 0.05, Fisher’s exact test) are denoted by “*”

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