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Broad transcriptional dysregulation of brain and choroid plexus cell types with COVID-19

Though SARS-CoV-2 primarily targets the respiratory system, it is increasingly appreciated that patients may suffer neurological symptoms of varied severity1–3. However, an unbiased understanding of the molecular processes across brain cell types that could contribute to these symptoms in COVID-19 patients is still missing. Here, we profile 47,678 droplet-based single-nucleus transcriptomes from the frontal cortex and choroid plexus across 10 non-viral, 4 COVID-19, and 1 influenza patient. We complement transcriptomic data with immunohistochemical staining for the presence of SARS-CoV-2. We find that all major cortex parenchymal and choroid plexus cell types are affected transcriptionally with COVID-19. This arises, in part, from SARS-CoV-2 infection of the cortical brain vasculature, meninges, and choroid plexus, stimulating increased inflammatory signaling into the brain. In parallel, peripheral immune cells infiltrate the brain, microglia activate programs mediating the phagocytosis of live neurons, and astrocytes dysregulate genes involved in neurotransmitter homeostasis. Among neurons, layer 2/3 excitatory neurons—evolutionarily expanded in humans4—show a specific downregulation of genes encoding major SNARE and synaptic vesicle components, predicting compromised synaptic transmission. These perturbations are not observed in terminal influenza. Many COVID-19 gene expression changes are shared with those in chronic brain disorders and reside in genetic variants associated with cognitive function, schizophrenia, and depression. Our findings and public dataset provide a molecular framework and new opportunities to understand COVID-19 related neurological disease.

inflame brain-barrier cells, including the choroid plexus; and that this barrier-together with peripheral-inflammation is then relayed into the brain parenchyma. 162 We sought to evaluate the predicted activation of brain microglia and migration of 163 peripheral immune cells via enhanced inflammatory signaling from the periphery and brain- Extended Data Fig. 7a-b), consistent with enhanced brain-barrier inflammatory signaling. Our 175 observations suggest that the COVID-19 subpopulation represents a distinct microglial state 176 that shares features with, but is ultimately different from, microglial cell states previously 177 reported in human neurodegenerative disease. 178 We next assessed the predicted increase in peripheral immune cell infiltration. We suggesting a COVID-19 specific-or at least not a pan-viral-CNS response. 188 Because we collected sufficient microglia for DEG analysis, we sought a clearer 189 understanding of the functional ramifications of their COVID-19 activation. As expected, 190 established markers of microglial activation previously linked to pathologic functions such as 191 CD74 and MERTK were strongly upregulated [67][68][69][70][71][72][73][74] . We observed a concomitant loss of the immune checkpoint molecules MEF2C and IRAK3 that restrain runaway inflammation [75][76][77] .

Potential synaptic dysfunction specifically in layer 2/3 excitatory neurons 203
The global induction of microglial phagoptosis programs prompted us to look into other 204 potential signatures of neuronal compromise. Astrocytes play a dual role in both forming the glia 205 limitans barrier 82,83 and supporting homeostatic neuronal function [84][85][86] (Fig. 5a). Given SARS-

206
CoV-2 infection of barrier cells, we thus wondered whether astrocytes would exhibit similar 207 hallmarks of inflammation with concomitant consequences for neuronal health. We indeed 208 observed hallmarks of astrogliosis 87 and viral defense 50 in upregulated GFAP and IFITM3 209 expression, as well as neurotoxicity in the secreted factor CHI3L1 88-90 (Fig. 3b, Fig. 5a Fig. 5a, e) as well as the major potassium uptake channel Kir4.1 (KCNJ10), which 218 reduces neuronal excitability by lowering extracellular potassium, glutamate, and BDNF 102 .

219
We next sought to identify the neuronal subtypes affected in COVID-19. Enrichment 220 analysis implicated potential impairments in synaptic transmission specifically in excitatory 221 neurons (Extended Data Fig. 9). Among COVID-19 patients, those with objective neurological 222 symptoms or detectable virus did not exhibit further deficits in the expression of genes 223 mediating synaptic transmission (Extended Data Fig. 9c, Extended Data Table 1). Glutamate 224 release at synapses is critical for fast and faithful communication between neurons for 225 information processing in sub-second timeframes [103][104][105] . Glutamate is stored in readily releasable pools of vesicles within presynaptic terminals. During synaptic transmission, SNARE proteins play an indispensable role in vesicle docking, priming, fusion, and synchronization of 228 glutamate release into the synaptic cleft 103,104 . Downregulation of the mRNA encoding SNARE 229 components has been found in various neurological diseases [106][107][108][109] , and is sufficient in 230 experimental models to impair both vesicle processing and overall neuronal viability [110][111][112][113][114] . We 231 observed in COVID-19 excitatory neurons a consistent downregulation of major SNARE 232 components (Fig. 5f)  The human neocortex is composed of six layers of interconnected excitatory and 237 inhibitory neurons, with each layer both highly specialized locally and integrated globally. Of 238 these six layers, layers 2 and 3 are disproportionately thickened during the evolution of the 239 human brain, playing a key role in higher brain functions such as cognition, learning, memory,   Table 7). The degree of overlap was particularly high in astrocytes and excitatory neurons.  Table 8) [125][126][127] . We found a strong enrichment of DEGs residing within GWAS hits of various 269 disorders and traits, especially in cognition, schizophrenia, and depression (Fig. 6b). These    with snRNA-seq studies in mind for safety and regulatory reasons, there is a lack of the high-291 quality tissue necessary for such studies 21,23,24 . This has precluded larger study cohorts that 292 could be more representative of COVID-19 patients as a whole. For example, while we and a recent IHC-based study of 43 patient brains 5 (the largest to date) do not find a clear correlation between detectable brain SARS-CoV-2 and glial inflammation or neurological symptoms, this 295 may not be representative of all patients, especially those without pre-existing co-morbidities or from "brain fog" to memory loss to difficulty concentrating 11,145,146 . Several COVID-19 320 perturbations revealed here may be addressable with existing medications 147 . However, as is 321 the case for recent human transcriptomic studies, additional research will be required to link the 322 observed cell type-specific gene expression changes with neurological symptoms 32,33,35,122,123 .  a, Study design. Colored triangles denote brain regions studied for each patient.
b, Uniform Manifold Approximation and Projection (UMAP) of 23,626 nuclei captured from the human medial frontal cortex, colored by cell type and labeled with percent of total nuclei. Note, that as in prior reports 32,34,35 , the 'Endothelial' cluster also exhibits vascular mural cell markers.

Isolation of nuclei from frozen post-mortem medial frontal gyrus
Frozen medial frontal cortex tissue from post-mortem control and COVID-19 patients were obtained from the Stanford/ VA/ NIA Aging Clinical Research Center (ACRC) and the Saarland University Hospital Institute for Neuropathology, with approval from local ethics committees. Group characteristics are presented in Supplementary Table 1. The protocol for the isolation of nuclei was adapted from previous studies 32, 34,35,148,149 , and performed in a BSL2+ Biosafety Cabinet wearing PPE. All procedures were carried out on ice or at 4°C. Briefly, 50 mg of postmortem brain tissue was dounce homogenized in 2 ml of Nuclei EZ Prep Lysis Buffer (Sigma, NUC101) spiked with 0.2 U μl −1 RNase inhibitor (Takara, 2313A) and EDTA-free Protease Inhibitor Cocktail (Roche, 11873580001) before incubating on ice for 5 min in a final volume of 5 ml. Homogenized tissue was filtered through a 100-μm cell strainer (Falcon, 352360), mixed with an equal volume of 50% iodixanol density gradient medium in PBS (OptiPrep, Sigma-Aldrich, D1556) to make a final concentration of 25% iodixanol. 30% iodixanol was layered underneath the 25% mixture. Similarly, 40% iodixanol was layered underneath the 30% iodixanol. In a swinging-bucket centrifuge, nuclei were centrifuged for 20 min at 3,000 r.c.f. After centrifugation, the nuclei were present at the interface of the 30% and 40% iodixanol solutions. Isolated nuclei were resuspended in 1% BSA with 0.2 U μl −1 RNase inhibitor, filtered twice through a 40 μm strainer (Flowmi), and counted on an TC20 automated cell counter (Bio-Rad) after the addition of Trypan Blue. We did not use statistical methods to pre-determine sample sizes, but our sample sizes are similar to those reported in previous publications [30][31][32] .

Isolation of nuclei from frozen post-mortem choroid plexus
Frozen choroid plexus tissue was extracted from the lateral ventricles of post-mortem tissue obtained from the Stanford University Pathology department and the Saarland University Hospital Institute for Neuropathology, with approval from local ethics committees. Group characteristics are presented in Supplementary Table 1. All procedures were carried out on ice or at 4°C, and in a BSL2+ Biosafety Cabinet wearing PPE. Dounce homogenization or enzymatic dissociation successfully used for mice choroid plexi 29 resulted in loss of nuclei integrity and low nuclei complexity (<50 median genes/ nuclei). We hypothesized that like shaking an apple tree, gentle pipetting of choroid plexi tissue in lysis buffer could liberate nuclei without needing to physically disintegrate the fibrous choroid matrix-and thus avoid collateral physical damage to nuclei. Specifically, 40 mg of choroid plexus tissue was thawed in 250 μl of 1% BSA with 0.2 U μl −1 RNase inhibitor until the tissue settled. 5 ml of lysis buffer (10 mM Tris, 10 mM NaCl, 3 mM MgCl2, 0.1% Nonidet P40 substitute (Roche/ Sigma, 11754599001), 0.2 U μl −1 RNase inhibitor, and protease inhibitor) was added and tissue incubated on ice for 10 minutes with gentle swirling every 2 minutes. 5 ml of 1% BSA was added and the tissue triturated 10 times with a 5 ml serological pipette. After centrifugation (500g, 5 min), pelleted nuclei were resuspended in 1% BSA with 0.2 U μl −1 RNase inhibitor; gently triturated 10 times with a 1 ml regular-bore pipette tip; and filtered twice through a 70 μm and then a 40 μm strainer (Flowmi). Debris was inspected on a brightfield microscope and nuclei were counted on an TC20 automated cell counter (Bio-Rad) after the addition of Trypan Blue.

Droplet-based snRNA-sequencing
For droplet-based snRNA-seq, libraries were prepared using the Chromium Next GEM Single Cell 3ʹ v3.1 according to the manufacturer's protocol (10x Genomics), targeting 10,000 nuclei per sample after counting with a TC20 Automated Cell Counter (Bio-Rad). 13 cycles were applied to brain parenchyma samples to generate cDNA, and 15 for choroid plexus samples. All samples underwent 15-16 cycles for final library generation. Generated snRNA-seq libraries were sequenced across two S4 lanes on a NovaSeq 6000 (150 cycles, Novogene).
snRNA-seq quality control Gene counts were obtained by aligning reads to the hg38 genome (refdata-gex-GRCh38-2020-A) using CellRanger software (v.4.0.0) (10x Genomics). To account for unspliced nuclear transcripts, reads mapping to pre-mRNA were counted. As previously published, a cut-off value of 200 unique molecular identifiers (UMIs) was used to select single nuclei for further analysis 34 . As initial reference, the entire dataset was projected onto two-dimensional space using Uniform Manifold Approximation and Projection (UMAP) on the top 20 principal components 150 . Three approaches were combined for quality control: (1) ambient cell free mRNA contamination was removed using SoupX 151 for each individual sample; (2) outliers with a high ratio of mitochondrial (>5%, <200 features) relative to endogenous RNAs and homotypic doublets (> 5000 features) were removed in Seurat 152 ; and (3) after scTransform normalization and integration, doublets and multiplets were filtered out using DoubletFinder with subsequent manual inspection and filtering based on cell type-specific marker genes 153 . Genes detected in fewer than 4 cells were excluded. After applying these filtering steps, the dataset contained 47,678 high-quality nuclei.

Cell annotations
Seurat's Integration function was used to align data with default settings. Genes were projected into principal component (PC) space using the principal component analysis (RunPCA). The first 80 (for global object), 30 (choroid plexus), or 25 (specific cell types) dimensions were used as inputs into Seurat's FindNeighbors, FindClusters (at 0.2 resolution) and RunUMAP functions. Briefly, a shared-nearest-neighbor graph was constructed based on the Euclidean distance metric in PC space, and cells were clustered using the Louvain method. RunUMAP functions with default settings was used to calculate 2-dimensional UMAP coordinates and search for distinct cell populations. Positive differential expression of each cluster against all other clusters (MAST) was used to identify marker genes for each cluster 154 . We annotated cell-types using previously published marker genes 31,33-35 .

Differential gene expression and sub-cluster analysis
Differential gene expression of genes comparing controls, COVID-19, and influenza samplesor comparing cell type sub-cluster markers-was done using the MAST 154 algorithm, which implements a two-part hurdle model. Seurat default Log (fold change) > 0.25 (absolute value), adjusted P value (Bonferroni correction) < 0.05, and expression in greater than 10% of cells were required to consider a gene differentially expressed. Biological pathway and gene ontology enrichment analysis was performed using Enrichr 155 , Metascape 156 , or GeneTrail 3 157 with input species set to Homo sapiens 156 and using standard parameters. Docking, processing, and viral defense genes relevant to SARS-CoV-2 were chosen based on Iadecola, et al 16 . To identify microglia sub-cluster markers, differential expression analysis of cells grouped in each subcluster was performed against the remaining cells within the given cell-type. Markers were defined based on the MAST algorithm using only positive values with Log (fold change) > 0.25 (absolute value), adjusted P value (Bonferroni correction) < 0.01. Enrichment/ overrepresentation of the overlap between markers defining the COVID-19 microglia 2 cluster and the Mathys 34 Alzheimer's disease Mic1 cluster followed the hypergeometric probability, using the set of 17,926 protein-coding genes as background.
Monocle trajectory analysis Monocle3 (v.0.2.1.) was used to generate the pseudotime trajectory analysis in microglia 64 . Cells were re-clustered as above and used as input into Monocle to infer cluster and lineage relationships within a given cell type. Specifically, UMAP embeddings and cell sub-clusters generated from Seurat were converted to a cell_data_set object using SeuratWrappers (v.0.2.0) and then used as input to perform trajectory graph learning and pseudo-time measurement through reversed graph embedding with Monocle.

Viral transcripts
To search for SARS-CoV-2 reads, raw fastq files were subjected to read alignment via Viral-Track 158 or centrifuge 159 using the human (GRCh38) genome reference. For Viral-Track both a collection of 12,163 consensus virus sequences from Virusite 160 (release 2020.3) and 17,133 curated SARS-CoV-2 genomes from NCBI (downloaded on 29-09-2020) were used. For centrifuge, a preprocessed virus index compiled by genexa containing among other viruses 138 SARS-CoV-2 genomes was used. We also adopted a complementary approach 161 focusing on SARS-CoV-2 reads, whereby barcoded but unmapped BAM reads were aligned using STAR to the SARS-CoV-2 reference genome, with a less stringent mapping parameter (outFilterMatchNmin 25-30) than the original Viral-Track pipeline.

Cell-cell communication
Cell-cell interactions based on the expression of known ligand-receptor pairs in different cell types were inferred using CellChatDB 61 (v.0.02). To identify potential cell-cell communication networks perturbed or induced in COVID-19 patient brains, we focused on differentially expressed ligands in choroid plexus epithelium, microglia, and astrocytes; and differentially expressed receptor pairs in excitatory and inhibitory neurons. Briefly, we followed the official workflow and loaded the normalized counts into CellChat and applied the preprocessing functions identifyOverExpressedGenes, identifyOverExpressedInteractions, and projectData with standard parameters set. As database we selected the Secreted Signaling pathways and used the pre-compiled human Protein-Protein-Interactions as a priori network information. For the main analyses the core functions computeCommunProb, computeCommunProbPathway, and aggregateNet were applied using standard parameters and fixed randomization seeds. Finally, to determine the senders and receivers in the network the function netAnalysis_signalingRole was applied on the netP data slot.

GWAS
From the GWAS catalog 126 , we obtained GWAS risk genes for neurological disorders [Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), brain aging, multiple system atrophy (MSA), multiple sclerosis (MS), Parkinson's disease (PD), and narcolepsy], psychiatric disorders [Attention deficit hyperactivity disorder (ADHD), autism, bipolar disorder, depression, psychosis, post-traumatic stress disorder (PTSD), and schizophrenia], and neurobehavior traits [Anxiety, suicidality, insomnia, neuroticism, risk behavior, intelligence, and cognitive function]. We removed gene duplicates and GWAS loci either not reported or in intergenic regions and used a P < 9 × 10 −6 to identify significant associations 32 . Then, since GWAS signals can point to multiple candidate genes within the same locus, we focused on the 'Reported Gene(s)' (genes reported as associated by the authors of each GWAS study). Disorders and traits exhibiting a significant number of genes also perturbed in COVID-19 patients are highlighted. Following gene symbol extraction, we curated the gene set by (1) removing unknown or outdated gene names using the HGNChelper package (v.0.8.6), (2) converting remaining Ensembl gene IDs to actual gene names using the packages ensembldb (v.2.10.0) and EnsDb.Hsapiens.v86 (v.2.99.0), and (3) removing any remaining duplicates. We then calculated the overlap between each set of GWAS genes with the cell type-specific DEGs. Finally, a statistical enrichment of each overlap against background was calculated using a hypergeometric test with the total background size set equal to the number of unique RNAs mapped in our data set (29,431).

Comparison of DEGs in chronic CNS disease
We compiled cell type-specific differentially expressed genes (DEGs) reported in published datasets for Alzheimer's Disease (AD) 34 , Autism spectrum disorder (ASD) 33 , Huntington's disease (HD) 123 , and Multiple sclerosis (MS) 122 . Lists of gene symbols were curated using the aforementioned approach. COVID-19 DEGs that overlap with those found across the selected CNS diseases were called shared, whereas those not previously reported were called unique to COVID-19. Statistical significance calculations of over-representation in DEG overlaps are based on cumulative hypergeometric P values analogous to the procedure described above.
PVCA and PCA analysis Briefly, to conduct the Principal Variance Component Analysis (PVCA), we aggregated the SoupX corrected raw counts for each gene and each biological sample using the aggregateData function of the muscat package (v.1.2.1). The resulting matrix was normalized by dividing each feature of a sample by the total counts from that sample, multiplied by a scaling factor of 100,000 and scaling the result using the function log(x+1). As variables we considered the sample annotation fields "Sample-ID", "Patient-ID", "Sex", "Brain-region", "Disease", "ageBin", and "nNucleiBin". Since the number of nuclei and age are numeric variables but PVCA is designed to support factors, we assigned the values into ordered bins, more specifically, into five half-open (left-closed) intervals of size 1,000 starting at 2,000 for the number of nuclei and five similarly defined intervals of size 10 starting at 51 for the age. We set the algorithm cut-off for the minimal variance out of the total variance being explained to be 95%. For each single annotation variable, or first higher-order combinations of such, at cut-off of 0.005 was applied to consider them explanatory. All variables (or combinations of such) not passing the threshold were summarized as Other in the analysis. The residual is then defined as the remaining proportion of variance not being associated with any of the variables that are explanatory nor informative to a minor proportion. To conduct Principal Components Analysis (PCA) we aggregated the log-normalized cell counts from Seurat for each gene and sample using the aggregateData function from muscat and centered the gene expression vectors before computing eigenvectors.

Immunohistochemistry
Paraffin-embedded human brain tissue (medial frontal cortex, meninges, and choroid plexus) adjacent to tissue processed for snRNA-seq was subjected to immunohistochemistry (IHC). After deparaffinization and rehydration of 1-3 µm sections, peroxidases were blocked by incubation in 1% H2O2 for 15 minutes at room temperature. Heat antigen retrieval was performed by steaming at 98°C in target retrieval solution pH 6.1 (Dako, Carpinteira, CA, USA #S1699) for 30 minutes. Sections were allowed to cool down at room temperature. Following antigen retrieval, sections were incubated for 45 minutes at room temperature with the anti-SARS spike glycoprotein antibody 3A2 (rabbit, Abcam ab272420 1:100 diluted in Dako REAL antibody diluent #S2022), which has been validated in previous publications 20,41 . After three washes with wash buffer (Dako #S3006), the Dako REAL EnVision HRP kit (#K5007) was used for the visualization of the antibody reaction according to the manufacturer's instructions. Sections were counterstained with Mayer's hemalum (Sigma-Aldrich #1.09249). After dehydration, coverslips were mounted with Entellan (Merck #1.07961). Images were acquired with an Olympus BX 40 microscope, equipped with an Olympus SC30 digital microscope camera using the Olympus cellSens software.