key: cord-0815312-bd0fx842
authors: Zazhytska, Marianna; Kodra, Albana; Hoagland, Daisy A.; Frere, Justin; Fullard, John F.; Shayya, Hani; McArthur, Natalie G.; Moeller, Rasmus; Uhl, Skyler; Omer, Arina D.; Gottesman, Max E.; Firestein, Stuart; Gong, Qizhi; Canoll, Peter D.; Goldman, James E.; Roussos, Panos; tenOever, Benjamin R.; Overdevest, Jonathan B.; Lomvardas, Stavros
title: Non-cell autonomous disruption of nuclear architecture as a potential cause of COVID-19 induced anosmia
date: 2022-02-02
journal: Cell
DOI: 10.1016/j.cell.2022.01.024
sha: 2764619714dd7218877b252ab285c3ff9e943120
doc_id: 815312
cord_uid: bd0fx842

SARS-CoV-2 infects less than 1% of cells in the human body, yet it can cause severe damage in a variety of organs. Thus, deciphering the non-cell autonomous effects of SARS-CoV-2 infection is imperative for understanding the cellular and molecular disruption it elicits. Neurological and cognitive defects are among the least understood symptoms of COVID-19 patients, with olfactory dysfunction being their most common sensory deficit. Here, we show that both in humans and hamsters SARS-CoV-2 infection causes widespread downregulation of olfactory receptors (OR) and of their signaling components. This non-cell autonomous effect is preceded by a dramatic reorganization of the neuronal nuclear architecture, which results in dissipation of genomic compartments harboring OR genes. Our data provide a potential mechanism by which SARS-CoV-2 infection alters the cellular morphology and the transcriptome of cells it cannot infect, offering insight to its systemic effects in olfaction and beyond.

Neurological symptoms in COVID-19 patients have immense importance due to their role in exacerbating initial disease presentation and their persistence (Chippa et al., 2021; Ellul et al., 2020; Proal and VanElzakker, 2021) . Anosmia emerged as one of the most common, and yet heterogenous neurological symptoms (Nalbandian et al., 2021) . Early studies correlated higher propensity for acute olfactory loss with a more indolent course, but subsequent work suggested elevated prevalence of smell loss across most COVID-19 cases (Garrigues et al., 2020; Graham et al., 2021) . Usually, smell loss is transient, with patients recovering over 6 weeks. However, for ~10% of patients this resolution is elusive, resulting in persistent olfactory dysfunction (Boscolo-Rizzo et al., 2021a; Boscolo-Rizzo et al., 2021b; Butowt and von Bartheld, 2020; Gerkin et al., 2020; Hornuss et al., 2020; Luers et al., 2020; Tong et al., 2020) . Although olfactory deficits are common in upper respiratory infections, these symptoms are accompanied by rhinorrhea and nasal congestion that insulate olfactory sensory neurons (OSN) from odorants. In contrast, anosmia in COVID-19 is independent from conductive interference. Thus, the association between COVID-19 and anosmia raises mechanistic questions, as OSNs do not express host cell entry proteins (Bilinska et al., 2020; Brann et al., 2020; Chen et al., 2020) , and they are not infected by SARS-CoV-2 (Khan et al., 2021) .

To gain insight into COVID-19 induced anosmia, we explored the consequences of SARS-CoV-2 infection in hamster and human autopsies of the olfactory epithelium (OE). Experiments in hamsters revealed transient recruitment of various immune cells to the OE and rapid upregulation of antiviral genes in OSNs. Further, scRNA-seq revealed preferential SARS-CoV-2 infection and transient depletion of Sustentacular (SUS) cells, followed by their restoration by day 10 post infection (dpi). Although we do not detect OSN depletion, we report significant downregulation of olfactory receptor (OR) genes and of key genes of the OR signaling pathway. OR gene downregulation is preceded by rapid and persistent reorganization of nuclear architecture and disruption of genomic OR compartments. Analysis of human OE autopsies confirmed that SARS-CoV-2 infection correlates with significant decrease of OR and OR signaling gene transcription and reduction of interchromosomal OR contacts. Effects of SARS-CoV-2 infection in nuclear architecture are non-cell autonomous and can be induced by neutralized serum from SARS-CoV-2 infected hamsters. Our data provide a potential explanation for the neurological symptoms caused by a virus with no tropism for neurons.

To explore mechanisms of COVID-19 induced anosmia we infected golden hamsters (M. auratus) with SARS-CoV2 and monitored changes over a period of 10 days post infection (dpi) by scRNA-J o u r n a l P r e -p r o o f seq. This rodent species is a good animal model for SARS-CoV-2 infection due to sequence homology between hamster and human ACE2, and similarity in pathogenesis and immunological responses (Cleary et al., 2020; Imai et al., 2020; Sia et al., 2020) . We performed scRNA-seq at mock and SARS-CoV-2 infected OEs at 1,3, and 10 dpi. We analyzed a total of 68,951 cells and identified 13 cell types (Supplemental Figure S1A) using previously described markers (Durante et al., 2020; Fletcher et al., 2017) . We detect a decrease of SUS cells at 1dpi that is exaggerated at 3dpi ( Figure 1A , B), when SUS representation decreases from 20.6% in mock infected hamsters to 6% at 3dpi. SUS diminution coincides with increase of microglia and other immune cells ( Figure 1A , B). Both SUS and microglia return to pre-infection representation in the hamster OE by 10dpi ( Figure 1A , B). In contrast, OSN representation is stable throughout the infection ( Figure 1A , B).

At 1 and 3dpi we detect the viral RNA in ~5% of the cells, followed by complete elimination of the virus at 10dpi. At 1dpi 47% of the total infected cells are SUS, and ~40% of which are infected ( Figure 1C -E, Supplemental Figure S1B ). In contrast, only 6% of the infected cells are OSNs ( Figure 1C -E, Supplemental Figure S1B ). At 3dpi microglia and other immune cells also frequently contain SARS-CoV-2 transcripts ( Figure 1C -E, Supplemental Figure S1B ). Consistent with this, we detect colocalization of Spike protein with Krt18 and with Aif1/lba-1, SUS and microglia markers, respectively (Supplemental Figure S1C , E). Spike colocalization with OSN markers is rare and occurs at OE regions of viral shedding and structural damage (Supplemental Figure S1C , E). Finally, SARS-CoV-2 presence in OSN axons innervating the olfactory bulb (OB) is rare, consistent with the infrequent OSN infection in the OE (Supplemental Figure S1D ).

To identify transcriptional changes caused by SARS-CoV-2 infection, we first analyzed SUS cells which are directly infected by this virus. There are significant differences between mock and infected SUS cells at 1 and 3dpi, with the viral RNAs representing the most enriched genes in the infected samples ( Figure 2A ). If we split the SUS cells of the infected OEs into SARS-CoV-2 + and SARS-CoV-2populations, we detect upregulation of cytokines and chemokines and downregulation of SUS-specific markers in the SARS-CoV-2 + cells ( Figure 2B ), consistent with cell autonomous transcriptional consequences induced by infection.

Since SARS-CoV-2 infects OSNs infrequently, we asked if the infection elicits non-cell autonomous transcriptional changes in these neurons. Indeed, OSNs activate antiviral responses at 3dpi ( Figure 2C , Supplemental Figure S2 ). By 10dpi transcription of antiviral genes is reduced, concomitantly with the clearance of the virus from the OE. Importantly, genes essential for the sense of smell, such as Adcy3, are significantly downregulated in OSNs at 3dpi ( Figure 2D ). Consistent with this, Adcy3 RNA ISH and IF show significant reduction of Adcy3 mRNA and protein at infected hamster OEs, even in regions with little detectable virus ( Figure 2E , F).

Our scRNA-seq analysis could not provide insight to the effects of the virus into OR expression because we used a 5'-based cDNA synthesis approach. This approach is most sensitive for the detection of the SARS-CoV-2 but inadequate for detection OR mRNAs due to poor annotation in the hamster genome. To overcome this, and to quantify rigorously transcriptional changes, we complemented our analysis with bulk RNA-seq. We collected infected OEs at 1,2,4 and 10 dpi.

This approach confirmed high viral loads in the hamster OE that increase till 4dpi before complete elimination by day 10 ( Figure 3A ). Downregulation of SARS-CoV-2 host entry factors suggests depletion of cells that can be infected by this virus ( Figure 3B ). Further, we detect strong upregulation of antiviral genes that lasts until 10dpi ( Figure 3C ), consistent with observations in other tissues (Blanco-Melo et al., 2020; Hoagland et al., 2021) .

In addition to the aforementioned alterations, we detect an early wave of transient transcriptional changes in SUS cells and immediate neuronal precursors (INPs), followed by delayed, transcriptional changes in OSNs and globose basal cells (GBCs). Reduction in INP markers is mostly restricted to 2dpi, including the downregulation of Lhx2, Ebf1, and Ebf2, transcription factors with key roles in expression of OR and OR signaling genes (Hirota and Mombaerts, 2004; Monahan et al., 2019; Monahan et al., 2017; Wang et al., 2004; Wang et al., 1997) . Downregulation of SUS markers starts at 2dpi and peaks at 4dpi before being restored to pre-infection levels by day 10 ( Figure 3D , E). In contrast, OSNs responses are delayed and persistent, with key molecules for olfaction, like Adcy3 (Wong et al., 2000) remaining downregulated through day 10 ( Figure 3D , E). Finally, markers of OSN progenitor cells increase, with GBC markers peaking at 10dpi ( Figure 3D , E). This may reflect progenitor cell activation towards the replenishment of infected cells of the OE (Fletcher et al., 2017; Gadye et al., 2017) .

A summary of other processes that may be affected at the early and late stages of the infection is shown in GSEA plots for 1 and 10 dpi (Supplemental Figure S3 ) and all the significant transcriptional changes are listed in Supplemental Table 1 .

The most striking transcriptional change observed in infected OEs is the widespread downregulation of OR genes. Significant OR downregulation is first observed at 2dpi, peaks at 4dpi, and continues through 10dpi ( Figure 3F , H), when other OSN markers have recovered ( Figure 3G ). This pattern is distinct from the changes in the most variable genes in the OE, whose expression is fully restored by 10dpi, or the changes observed in INPs and SUS cells ( Figure 3C , D). Genes with critical role in olfaction follow the pattern of OR gene expression, as we also detect strong and significant downregulation of Adcy3, Gng13, Cnga2, Rtp1 and Gfy, at 4dpi that J o u r n a l P r e -p r o o f is partially preserved till 10dpi ( Figure 3I ). Some antiviral responses are also sustained till day 10 ( Figure 3J ).

To decipher mechanisms responsible for widespread and sustainable OR downregulation, we directed our studies to a known regulator of OR expression, the OSN nuclear architecture (Bashkirova and Lomvardas, 2019; Clowney et al., 2012; Markenscoff-Papadimitriou et al., 2014; Monahan et al., 2019) . OR gene clusters from multiple chromosomes converge to OSN-specific genomic compartments, which facilitate stable and singular OR transcription (Clowney et al., 2012) . We therefore asked if disruption of OR compartments is the cause of OR downregulation upon SARS-CoV-2 infection.

We performed in situ HiC in hamster OEs from control (mock infected) and SARS-CoV-2 infected samples at 1,3 and 10dpi. OR gene clusters form robust long range cis and trans genomic contacts in hamster OSNs, as shown for OR genes from chromosomes 16 and 17 ( Figure 4A ). SARS-CoV-2 infection reduces these contacts, starting at 1dpi and peaking at 3dpi ( Figure 4A ).

A contact matrix for all the hamster OR clusters arranged by chromosome shows strong longrange cis interactions and widespread trans contacts between them in control samples ( Figure   4B ). However, these interactions become reduced as early as 1dpi and remain low by 10dpi ( Figure 4B , C). HMM calculation of genomic compartment scores shows widespread reduction of most compartments by 3dpi, revealing a delayed disruption of genome wide nuclear architecture compared to the disruption of OR compartments ( Figure 4D ). However, genomic compartmentalization remains disrupted 10 days post infection, when the virus is already cleared from the OE.

Observations of widespread and persistent disruption of OSN genomic compartments is consistent with a non-cell autonomous mechanism of nuclear reorganization. Since previous reports implicate cytokines and antiviral responses in olfactory deficits and OR downregulation (Lane et al., 2010; Lane et al., 2005; Rodriguez et al., 2020) , we hypothesized that we could disrupt trans OR contacts by imitating the systemic effects of SARS-CoV-2 infection without the virus. We collected serum from mock and SARS-CoV-2 infected hamsters at 3dpi and inactivated the circulating virus by UV irradiation. We applied these sera to the OEs of naïve hamsters by intranasal inoculation ( Figure 5A ). Strikingly, in situ HiC revealed significant reduction of trans OR contacts upon OE exposure to infected sera for 12.5 hours ( Figure 5B , C). HMM confirmed genome wide changes in compartment scores between the two sample groups ( Figure   5D ). RNA-seq did not detect the viral genome on serum exposed OEs, confirming that we did not transfer active SARS-CoV-2 from infected to naïve hamsters ( Figure 5E ). As expected from the J o u r n a l P r e -p r o o f infection time course, there are no significant changes in OR transcription at this time point (Supplemental Figure S4A ). However, there is a trend of OR downregulation across most OR genes, that is more pronounced than the effects observed at 1dpi upon SARS-CoV-2 infection (Supplemental Figure S4A) . Thus, by providing sera from the peak of the inflammatory response we likely accelerated the molecular changes observed during viral infection, consistent with the upregulation of genes induced 2 days post SARS-CoV-2 infection (Supplemental Figure S4B ). SUS markers, however, are non-responsive to the signals from the infected sera (Supplemental Figure S4C ), supporting the notion that SUS cells only exhibit mostly cell-autonomous transcriptional changes.

To explore if our observations from hamsters apply to humans, we analyzed the consequences of SARS-CoV-2 infection of human OE autopsies. We identified a region at the roof of the nasal cavity bridging the superior septum and middle turbinate bones that is highly enriched for OSNs as demonstrated by detection of the mature OSN-specific olfactory marker protein (OMP) and the OSN-enriched LDB1 (Supplemental Figure S5A ). This is further supported by scRNA-seq analysis on an autopsy from a control (non-infected) sample (Zazhytska M, 2021) . RNA ISH in section of OE autopsies from infected patients shows enrichment of the SARS-CoV-2 RNA at the non-neuronal layers of the OE (Supplemental Figure S5B ), consistent with recent observations (Khan et al 2021) . We also detect SARS-CoV-2 RNA in microglia cells recruited to the infected human OEs (Supplemental Figure S5C ), replicating observations from hamster OEs. Finally, as in hamsters, we did not observe OSN depletion in infected OEs (Supplemental Figure S5D ).

Upon establishing histological similarities between hamster and human SARS-CoV-2 infection, we performed bulk RNA-seq in 6 control and 18 infected human OE autopsies.

Autopsies were donated from both male and female patients, representing a variety of ages, duration of infection, hospitalization, treatment, and post-mortem intervals (Supplemental Table   2 ). These variations did not influence cellular constitution, as quantification of OE, respiratory epithelium (RE) and immune cells show consistency between samples (Supplemental Figure   S5E , F). Postmortem time (PMT) had no obvious effect on the quality of these libraries (Supplemental Figure S5G ). Surrogate variable analysis (Leek, 2014) identified one control sample as extreme outlier, resulting in its removal from further analyses (Supplemental Figure   S5I ). The remaining samples were subjected to batch adjustment corrections using ComBat-seq (Love et al., 2014; Zhang et al., 2020) , and then were further analyzed.

SARS-CoV-2 RNA is detected in every infected OE, with variable amounts between samples ( Figure 6A ). However, representation of OE and RE markers did not change with viral J o u r n a l P r e -p r o o f load (Supplemental Figure S5E , F). Unexpectedly, in one of the remaining 5 control samples we detected the RNA genome of a non-SARS coronavirus, hCoV-OC43 (Supplemental Figure S5H) , which was previously shown to infect the RE and OE (Dube et al., 2018) . This sample, highlighted with light blue, was not pooled with control samples. We have information only on one patient about olfactory deficits (highlighted with striped bar) due to limited solicitation of these symptoms at the early phase of the pandemic. However, based on current reports, >60% of these subjects may have experienced olfactory deficits Wang et al., 2020) .

In most infected samples we detected elevated levels of cytokines and antiviral genes such as IFN- ( Figure 6B , Supplemental Figure S5J ). GO analysis of significantly upregulated genes between infected autopsies is over-represented with terms related to immune response (Supplemental Figure S5K ). We did not detect an overall downregulation of SUS markers ( Figure   6C ), although some SUS-enriched genes are downregulated in the infected samples ( Figure 6D ).

Similarly, we do not detect depletion of OSN markers, but selective reduction of OSN-enriched genes with established critical roles in olfaction, such as Adcy3 ( Figure 6C , D). Control OEs have higher OR mRNA levels than the infected OEs ( Figure 6E ), except for the hCoV-OC43-infected control. PCA analysis using only OR genes, segregates control from infected samples, while PCA with the whole transcriptome does not ( Figure 6F , G), suggesting that reduced OR transcription constitutes one of the few distinctive features between infected OEs. OR downregulation generally tracks with downregulation of Lhx2 and Ebf1/2, but two samples with low OR expression have high Lhx2 and Ebf1/2 expression ( Figure 6G ).

Comparisons between individual autopsies are replicated in comparisons of pooled control and infected samples. MA plots depicting the levels of OR genes (red), OE genes (blue), and RE genes (black) in control and infected samples, supports a bona fide transcriptional downregulation of OR genes, as OE and respiratory markers are not changing upon infection ( Figure 6H ). Volcano and box plots showing the transcriptional effects of SARS-CoV-2 infection in aggregate, further support significant downregulation of OR and OR signaling genes ( Figure 6I , J). Finally, GO analysis of downregulated genes in infected autopsies shows that "sensory perception of smell" constitutes the most significantly enriched GO term ( Figure 6K ).

To ask if OR downregulation in SARS-CoV-2 infected OEs is caused by changes in nuclear architecture, we established a protocol for the isolation of OSN nuclei from OE autopsies by FACS (Supplemental Figure S6A ). In situ HiC on FAC-Sorted nuclei confirmed that the OR-specific long range cis and trans genomic contacts are conserved in humans ( Figure 7A , B). Contact matrices depicting human OR genes from every chromosome confirms that OR genes form J o u r n a l P r e -p r o o f interchromosomal compartments ( Figure 7C , D) that are disrupted in SARS-CoV-2 infected OEs ( Figure 7A -D), independently of genome wide changes in nuclear architecture ( Figure 7E ). Finally, we identified interchromosomal compartments containing Adcy3 and other genes with key functions in olfaction that also dissipate in infected samples (Supplemental Figure S6B ).

We provide a molecular explanation for SARS-CoV-2 induced anosmia and a mechanism by which this virus can alter the identity and function of cells that lack entry receptors. Consistent with absence of ACE2 and TMPRSS2 from OSNs (Bilinska et al., 2020; Brann et al., 2020; Chen et al., 2020) , and recent histological analyses (Khan et al., 2021) , our data suggest that OSN infection by SARS-CoV-2 is too infrequent to account for the reported smell loss. Moreover, the cell autonomous effects of SUS infection may be too transient to account for long-lasting olfactory deficits reported by COVID-19 patients, which have a mean duration of ~20 days (Chapurin et al., 2021) . Thus, the most likely explanation for COVID-19 induced anosmia is the non-cell autonomous, widespread, and persistent downregulation of OR and OR signaling genes. The ability of the virus to alter the OSN transcriptome, solves a puzzle that emerged from numerous studies in various organs: the virus is only infecting a small fraction of cells, yet it elicits devastating and often life-threatening physiological disruption (Thakur et al., 2021) . The demonstration that UV-neutralized serum from infected hamsters induces significant and rapid changes in OSN nuclear architecture, suggests that systemic changes caused by SARS-CoV-2 infection alter the physiology and function of the cells that this virus cannot infect.

Hamster scRNA-seq shows that SARS-CoV-2 predominantly infects SUS cells, resulting in cell autonomous transcriptional changes and transient depletion of this cell population. In human OEs, where the viral load is lower, cell autonomous transcriptional changes could not be detected by RNA-seq, probably due to infrequent SUS infection. However, these changes were detected by spatial transcriptomics that compared human OE regions with different viral loads (Khan et al., 2021) . Moreover, in both hamster and human OEs we detect strong, persistent, and widespread downregulation of OR genes as well as downregulation of Adcy3 and other key genes for odor perception, providing a plausible explanation for COVID-19 induced anosmia.

The insight afforded by hamster studies explains why spatial transcriptomics detected downregulation of SUS markers in human OE regions with high SARS-CoV-2 load, but no reduction of OR and OR signaling molecules (Khan et al., 2021) . SUS marker downregulation is cell autonomous, thus expected to be stronger in regions with high viral load. In contrast, J o u r n a l P r e -p r o o f transcriptional changes in OSNs occur independently of direct infection and do not corelate with the viral load in the OE, hindering their elucidation by transcriptomic comparison within a sample.

Furthermore, there is a delay in OSN transcriptional changes compared to SUS marker downregulation. Thus, autopsies corresponding to longer infection periods, and comparison with non-infected samples may be required for the detection of OSN transcriptional changes by this elegant approach.

Disruption of genome architecture as a "nuclear memory" for persistent anosmia COVID-19 induced downregulation of Lhx2 and Ebf, key transcription factors for OSN physiology, explains the downregulation of a plethora of genes involved in odor perception. In hamsters, however, disruption of OR compartments precedes Lhx2/Ebf downregulation and persists after their restoration. Further, in two infected human OEs (146, 147), both OR transcription and OR compartmentalization are disrupted, while Lhx2 and Ebf levels are near control levels. Thus, although COVID-19 induced Lhx2/Ebf downregulation is likely to have major role in the downregulation of OR and OR signaling genes, widespread disruption of OR compartments may be the first insult in OSN physiology and, importantly, a form of "nuclear memory" that delays restoration of OR transcription. This is because OR compartments may form only during differentiation, and, thus, their disruption in mature OSNs may be irreversible. If OSNs cannot reactivate OR transcription, then the sense of smell in COVID-19 patients will recover only after these OSNs are replaced, a process that takes from weeks to months.

If OR contacts could be restored after the elimination of the virus, their pre-and postinfection patterns may be different, due to the inherent stochasticity of trans OR interactions(Elizaveta Bashkirova, 2020; Tan et al., 2019) . Thus, OSNs that were already innervating a glomerulus may activate a different OR from the one originally chosen, resulting in odor misrepresentation in the OB and altered odor perception. This sensory confusion may also be exacerbated by Adcy3 downregulation, as this molecule plays important roles in OSN axon guidance and the stabilization of OR expression (Imai et al., 2006; Lyons et al., 2013; Zou et al., 2007) . Long-term deficits in nuclear architecture could be applicable to other neuronal populations, since adult CNS neurons also assemble long-range cis and trans genomic compartments between OR genes and other neuronal gene families (Jiang et al., 2017; Tan et al., 2021) . Additional mechanisms, such as sustained expression of antiviral programs , damage in tissue vasculature and hypoxia (Thakur et al., 2021) , could also contribute to longlasting neurological deficits, including the loss of smell (Lane et al., 2010) . In either case, the realization that the sense of smell relies on extremely "fragile" genomic interactions between chromosomes has important implications: If OR expression ceases every time maladaptive J o u r n a l P r e -p r o o f physiological responses disrupt interchromosomal OR contacts, then olfaction may act as the "canary in the coalmine" for a variety of human conditions, from viral infections to neurodegeneration (Albers et al., 2006) .

We did not identify the circulating molecule(s) that induce reorganization of OSN nuclear architecture and the OSN signaling pathway responsible for it. Thus, at present we can only speculate that similar mechanisms apply to other neuronal populations, a concept that we have not explored. Furthermore, we did not establish that the reported downregulation in OR and OR signaling genes is responsible for COVID-19 induced anosmia but we infer this from the phenotypes of knockout mice. Reduced expression of genes involved in every step of odor detection, such as receptor proteins (ORs) (Buck and Axel, 1991) , olfactory receptor chaperones (Rtp1, Rtp2) (Saito et al., 2004) , olfactory receptor signaling molecules (Adcy3, Gng13) (Liu et al., 2018; Wong et al., 2000) , and ion channels generating odor-evoked axon potential (Cnga2) (Brunet et al., 1996) , provide the most likely explanation for COVID-19 induced anosmia.

Finally, we can only deduce that COVID-19 infection caused OR and OR signaling gene downregulation in humans, as we cannot measure the expression of these genes before the infection. Although experiments in hamsters support this hypothesis, we cannot exclude rodentspecific mechanisms that preclude direct comparisons between species.

We thank members of the Lomvardas lab, Konstantin Popadin, and Muhammad Saad Shamim for helpful analysis notes, David Weisz for assistance with software and Gary Struhl for the help with imaging. We thank Drs. Axel, Zuker, Maniatis and Rizvi for helpful comments and suggestions. We are grateful to the diseased patients and their families for giving their consent to autopsies, and neuropathologists and staff of the CU biobank for assistance in acquiring OE autopsies. The study was approved by the ethics and Institutional Review Board of CIUMC (IRB AAAT0689, AAAS7370). LVG hamsters (Mesocricetus auratus) were treated in compliance with the rules and regulations of IACUC under protocol number PROTO202000113-20-0743. 

The authors declare no competing interests.

We worked to ensure sex and racial balance in the collection of human OE autopsies. We worked to ensure gender balance in our reference list while citing work relevant to this study. (B) Pairwise heatmap shows reduction of in situ HiC contacts between OR clusters (n=46 clusters) that increases as SARS-CoV-2 infection progresses. (C) Violin plot depicting the mean number of normalized trans in situ HiC contacts between OR clusters genome wide at 100-kb resolution for mock, 1,3, and 10dpi. Every dot indicates aggregated contacts for each OR-to-OR cluster pair in trans, p value was computed using Wilcoxon rank test. (D) HMM score for a given number of compartments indicating differences in genomic compartmentalization for mock (blue) and SARS-CoV-2 infected hamsters at 1,3 and 10dpi (shades of red). For each panel (A-D) data represent averages from 2 biological replicates per condition.

The experimental pipeline used to expose naïve hamsters OEs serum from SARS-CoV-2 or mock infected hamsters prior to in situ HiC analysis. Serum was collected 3dpi from mock or SARS-CoV-2 infected hamsters, centrifuged and UV-irradiated before intranasal inoculation to naïve hamster OEs for 12.5 hours. See also Supplemental Figure S4. (B) Pairwise heatmap of in situ HiC contacts between OR clusters (n=46 clusters) from hamster OEs. The heatmap on the left is from OEs exposed to serum from mock infected hamsters, whereas on the right is from OEs exposed to serum from SARS-CoV-2 infected hamsters. (C) The mean number of normalized trans in situ HiC contacts between OR clusters genome wide at 100-kb resolution for mock and 12.5h SARS-CoV-2 serum treated hamster. Every dot indicates aggregated contacts for each OR-to-OR cluster pair in trans, p value was computed using Wilcoxon rank test. (D) HMM score for a given number of compartments indicating differences in genomic compartmentalization upon OE exposure for 12.5 hours to serum from mock (blue) and SARS-CoV-2 infected hamsters at 3dpi (red). (E) SARS-CoV-2 genomic counts in inactivated serum of 3dpi hamster applied to naïve hamster compared to the viral load at 1dpi hamster. SARS-CoV-2 raw counts were normalized to the MesAur1.0 genome reads and plotted as DESeq2's median ratio normalization (MRN). No mapped counts were found in the mock-infected OE. For each panel (A-D) data represent averages of 3 biological replicates per condition. 

In situ HiC maps from human OSNs depicting contacts between OR clusters in cis. Control is the lower triangle below the diagonal, and COVID-19 the upper triangle. Pixel intensity represents normalized number of contacts between pair of loci. Maximum intensity indicated at the top of each scale bar. Genomic position of OR clusters indicated as green bars; arrows indicate the same OR compartments for both conditions. (B) Contact maps revealing decrease in trans in situ HiC contacts in COVID-19 + OE vs control. Pixel intensity represents normalized number of contacts between pair of loci. Maximum intensity indicated at the top of each scale bar. Genomic position of OR clusters indicated as green bars; arrows indicate the same OR compartments for both conditions. (C) Heatmap depicting contacts between every human OR gene cluster (n=82 OR clusters) arranged by chromosome. In situ HiC was performed on FAC-Sorted OSNs from 2 control and 4 infected human OE autopsies. Reduction in OR contacts is observed both in trans and in cis. (D) Violin plot depicting the mean number of normalized trans HiC contacts between OR clusters genome wide at 100-kb resolution for each sample. Every dot indicates aggregated contacts for each OR-to-OR cluster pair in trans, p value was computed using Wilcoxon rank test. (E) HMM score for a given number of compartments indicating differences in genomic compartmentalization between 2 control (blue) and 4 infected samples (red). Figure S1 (relevant to figure 1) (A) Dot plot showing expression of cell markers across clusters. Cell types are listed on y axis showing expression of 45 selected genes identified by log fold change; genes are listed along x axis. Dot size reflects percentage of cells in a cluster expressing each gene, dot color represents expression level. The plot shows clusters from 68,951 combined cells extracted from 8 OEs with 2 biological replicates per condition. (B) Feature plot depicting expression of S SARS-CooV-2 transcript in hamster olfactory epithelium. Cell types are same as in Figure 1A (n=2 biological replicates for each sample.) (C) Representative confocal micrograph of IF-FISH experiment labeling RNA-FISH SARS-CoV-2 (magenta) and OMP protein (green) in hamster OE at 4dpi. Rarely OMP positive cells co-localize with SARS-CoV-2. No viral particles are detected in the axon bundles (asterisk). The line intensity scan drawn at the center of the OE section shows a discrete distribution of the pixel intensity of the two channels. Supplemental Figure S4 (relevant to figure 5) (A) Z-scored expression of OR genes from OEs exposed to mock vs SARS-CoV-2 infected serum for 12.5 hours (left) or from OEs that were mock infected vs SARS-CoV-2 infected for 1 day (right). (B) Z-scored expression of the top 40 most variable genes upon serum exposure (left). For comparison, z-scored expression of top 40 genes at 1 dpi identified in SARS-CoV-2 infected hamster and harvested at different time points (mock, 1dpi, 2dpi, 4dpi, 10dpi) (right). (C) Violin plots showing the effect of exposure to serum from SARS-CoV2 vs mock infected hamsters for 12.5 hours at the transcription of OR genes, OE, OSN and SUS markers. For comparison we plot the same groups at SARS-CoV-2 infected (1dpi) vs mock infected hamsters. For each panel (A-C) data represent averages of 3 biological replicates per condition. Figure S5 (relevant to figure 6 ) (A) (Top) En bloc resection of the cribriform plate along with underlying mucosa from the olfactory cleft, which contains OE more superiorly and respiratory epithelium below. (Bottom) Section of this human olfactory epithelium stained for OMP (green) and LDB1 (red), OSN specific and OSN enriched markers, respectively. Nuclei are labeled with DAPI (blue). (B) Confocal micrograph of RNA FISH for SARS-CoV-2 gRNA (magenta) and OSN/OSN progenitor marker ATF-5 (green) in COVID-19 + human OE. The SARS-CoV-2 probe targets the antisense strand of the S gene, detecting replicating virus. Nuclei are stained with DAPI. SARS-CoV-2 signal is detected in the apical layers of the epithelium (asterisk), proximal to SUS cells, and in the basal layer where HBCs reside. Correlation of the RNA-FISH SARS-CoV-2 signal and markers for OSNs (ATF5), sustentacular cells (Krt-18) and microglia (AIF1/Iba1) is measured by Pearson's correlation coefficient (R) in COVID-19 (red) and control (blue) human OE autopsies (top right panel). Quantification of RNA FISH signal was measured as local maxima at the apical, neuronal, and basal layers for a total of 2140 cells in infected and 1819 cells in control OEs. Only apical and basal layers have significantly enriched signal in infected OEs. (C) RNA-FISH SARS-CoV-2 signal (magenta), detected in the neuronal layer (OE), marked in between the two white lines, and lamina propria. S probe signal (magenta) strongly correlates with microglia marker AIF1/Iba1 (yellow) immunofluorescence (IF) signal, as indicated by arrows. The number of AIF1/Iba1 positive cells is measured over the number of total cells counted (right panel). In COVID-19 patients a significantly enrichment of microglia is observed in the lamina propria, while in the neuronal layer (OE) more variability between images is observed. This study did not generate custom computer code. Any additional information is available from the Lead Contact upon request.

Hamsters LVG Golden Syrian hamsters (Mesocricetus auratus) were treated and euthanized in compliance with the rules and regulations of IACUC under protocol number PROTO202000113-20-0743. Only adult male hamsters were used for experiments. All experiments were performed on dissected olfactory epithelium tissue or on dissociated cells prepared from whole olfactory epithelium tissue. Dissociated cells were prepared using papain (Worthington Biochemical) and FAC-sorted as previously described.

Virus stock and propagation Infectious work was performed at a CDC/USDA-approved BSL-3 facility at the Icahn School of Medicine at Mount Sinai. SARS-CoV-2 (clinical isolate UAS/WA1/2020) virus was propagated in Vero E6 cells in DMEM supplemented with 0.35% BSA. Infectious titer of virus was determined by plaque assay in Vero E6 cells using an overlay of Modified Eagle Medium (Gibco), 0.2%BSA (MP Biomedicals), 4mM L-glutamine (Gibco), 10mM HEPES (Fisher Scientific), 0.12% NaHCO3, 1% heat-inactivated FBS, and 0.7% Oxoid agar (Thermo Scientific). SARS-CoV-2 virus stocks used for hamster experiments were passage 3.

Human samples 25 patients previously diagnosed with COVID-19 at symptoms presentation and postmortem by SARS-CoV-2 RT-PCR analysis underwent full body autopsy at Columbia University Irving Medical Center (New York, NY, USA). The study was approved by the ethics and Institutional Review Board of Columbia University Medical Center (IRB AAAT0689, AAAS7370). Specimens noted to have metastatic cancer and non-SARS coronavirus were removed from further analysis. Brain tissue and nasal epithelium, including the olfactory region, were retrieved under a collaborative effort by the Department of Neuropathology and the Department of Otolaryngology. Tissues were obtained and preserved for histological, molecular, and microscopic evaluation using separate surgical instruments to prevent cross-contamination. 7 control specimens were collected in similar fashion from deceased individuals who had no clinical history of COVID-19 and had negative SARS-CoV-2 PCR at the time of their presentation and again prior to post-mortem dissection. Nasal tissues, including olfactory and respiratory epithelium were harvested from the skull base using an en-bloc resection of the anterior skull base including the cribriform plate. Olfactory epithelium was isolated from the olfactory cleft, spanning turbinate and adjacent septal mucosa prior to being preserved in 1% paraformaldehyde (for HiC), 4% paraformaldehyde (for RNA ISH/IF), or Trizol (for RNA-seq).

SARS-CoV-2 inoculation All hamster infections were performed in a BSL-3 animal facility at the Center for Comparative Medicine and Surgery at the Icahn School of Medicine at Mount Sinai (New York, NY) using 4-6week-old male golden hamsters purchased from Charles River Laboratories. Hamsters were intraperitonially administered anesthesia of ketamine/xylazine (3:1), [100mg/kg] before inoculation. Inoculations were performed by intranasally administering 100 plaque-forming units (pfu) in a total volume of 100ul per hamster, diluted in PBS. For infected serum experiments whole blood of mock and infected animal at 3dpi was centrifuged to extract serum following UV neutralization of any viral particles remained. Total volume of 100ul per animal was intranasally inoculated in the same fashion as virus administration. Golden hamsters were provided thermal support after infection until recovery from anesthesia. Before sacrifice, the animals were anesthetized and then perfused with 60mL of PBS through the heart.

The blood from a mock and 3dpi infected hamster with 100pfu of SARS-CoV-2 virus was collected from aorta upon euthanasia. Serum was separated via centrifugation following subsequent UV-C inactivation at dose of 100J/m2. Inoculations were performed into naïve hamsters by intranasally administering 100ul of mock or 3dpi inactivated serum per hamster upon anesthesia. 12.5h after serum inoculation hamsters were sacrificed, OE was dissociated and subjected for HiC and bulk RNA-seq.

RNA-seq RNA was extracted using Direct-zol RNA kits from Zymo Research. 50ng-1ug of total RNA was used to prepare DNA libraries with Truseq RNA Library Prep Kit v2 followed by 75 HO paired-end and multiplexed sequencing. Reads were aligned to human genome (hg38), Mesocricetus auratus (MesAur1.0) and SARS-CoV-2 (wuhCor1) using Subread (Liao et al., 2013) and the raw read counts were assembled using featureCounts pipeline (Liao et al., 2014) . Deseq2 was used to detect differences between conditions from the human samples and from the hamster biological replicates. Because of the inherent sources of biological and technical variability, we performed surrogate variable analysis to identify outliers between our samples for human samples. The Surrogate Variable Analysis (SVA) was performed using the "sva" package in R (Leek, 2014) . The number of surrogate variables were estimated with the num.sv() function using the DESeq2generated normalized counts and "model = ~Covid19" (plus or minus) (Love et al., 2014) . One variable was estimated using the default method. The svseq() was thus run with n.sv=1, and all samples exhibited a tight distribution, except sample "205", which was an extreme outlier, and thus was excluded from subsequent analysis. The remaining 23 samples were subject to batch correction using Combat-seq (Zhang et al., 2020) . Subsequently, Deseq2 was used to determine the transcriptional consequences of COVID-19 infection in these autopsies and hamster samples. Two biological replicates were used for 1dpi and three biological replicates were used for control, 2dpi, 4dpi and 10 dpi. Z-score expression was calculated for each gene on DeSeq VStransformed data (Variance Stabilizing Transformation) across samples in humans and across all time points in hamster. Heatmaps were generated using R function pheatmap(). Aggregated OR expression refers to the sum of all counts for annotated OR genes in each species.

hCoV-OC43 Identification Sequencing reads from sample 2186 were mapped to the human genome using STAR, and unmapped reads were output using the "--outReadsUnmapped Fastx" parameter (Dobin et al., 2013) . These reads were then provided as input into "megahit", a short read assembly algorithm using default settings. The resultant contig assemblies were blasted against the NCBI nucleotide collection using "blast+" command line tools. Blast results were filtered for "virus" in the "sskingdom" output format variable. hCoV-OC43 was the only human virus identified in the sample. Unmapped reads from STAR were then aligned to the hCoV-OC43 genome (ACC: NC_006213) using bwa, converted to bam, sorted, and indexed using SAMtools, and visualized using IGV.

Single cell RNA-seq and analysis Cells were dissociated according to the Worthington Papain Dissociation System by incubating fresh olfactory tissue with papain and Calcein VIolet for 40 min at 37 °C. Following dissociation, the live Celcein Violet-positive cells were sorted (MACSQuant Tyto cell sorter -Miltenyi Biotech) and assayed for scRNA-seq. Library preparation was performed accordingly to Chromium Single Cell 3ʹ v.3 Protocol for human samples and Chromium Next GEM Single Cell 5' v.2 Protocol for hamster, respectively, and sequenced on NextSeq. Cell Ranger pipelines were used to generate fastq files which subsequently were aligned against hybrid hg38/ wuhCor1 and MesAur1.0/ wuhCor1 genomes. After alignment resulting in 8 datasets Cellbender (Stephen J Fleming, 2019 ) was used to model and remove systematic biases and background noise, and to remove empty droplets. Post Cellbender h5 matrices of 8 samples were aggregated into Seurat (Stuart et al., 2019) object using Read10X() function. Cells with more than 400 UMIs, expressed 500 and 6000 genes and less than 5% of mitochondrial genes were kept for further analysis. Data were normalized using LogNormalize() function with scale factor of 10,000. FindVariableFeatures() function with 2000 genes and the selection method set to "vst" was used to find variable features. To identify integration anchor genes among the 8 samples the FindIntegrationAnchors() function was used with 30 principal components and 2000 genes, then with IntegrateData() all data was combined into one Seurat object. The data was scaled using the ScaleData() function. Then PCA analysis was performed to reduce dimensionality and the first 30 principal components were used UMAP plots. The number pf PC was chosen based on JackStraw and elbow plots. Clustering was performed using FindClusters() function. Identified 13 clusters were visualized with UMAP (tSNE in case of human samples) and annotated using known marker genes for each cell type. Differential expression analysis was performed using the default two-sided non-parametric Wilcoxon rank sum test with Bonferroni correction using all genes in the dataset.

Dissected tissue was fixed in freshly prepared 4% PFA for 24 hrs at 4C and soaked sequentially at 10%, 20% and 30% sucrose 1X PBS for cryopreservation. The tissue was embedded in OCT and 10 um thick sections were mounted on SUPERFROS Plus Gold slides. To detect the S gene transcripts of SarsCov2, RNAscope® Probe -V-nCoV2019-S-sense, cat no. 845708, was incubated for 2 hr at 40C, in pre-treated sections as indicated by the RNAscope Multiplex Fluorescenct v2 Assay kit. Zeiss Zen2012 SP1 (v8.1.9.484) was used for capturing confocal images. Same conditions were applied for detection of RNAscope® Probe for Hs-ADCY3(cat no. 441671) and Hs-ATF5 (cat no. 507471). Autofluorescence of the human OE sections was removed post-acquisition using ImageJ add-on function Autofluorescence Identifier (AFid) (Baharlou et al., 2019) . If followed by immunofluorescence, tissue was permeabilized with 1XPBS 0.1% Triton X 100 and blocked in a solution of 4% Donkey serum and 1x PBS 0.1% Triton X 100 for 30 minutes at RT, before incubation with primary antibodies for 2 hrs at RT at the concentrations described below.

Immunofluorescence Dissected tissue was fixed in freshly prepared 4% PFA for 24 hrs at 4C. OE was embedded in OCT and coronal cryosections were collected at a thickness of 12μm in human specimens. In hamster, OE and OB were similarly embedded in OCT and sagittal cryosections were collected at a thickness of 8-12μm. Antigen retrieval was performed with 0.01M citric acid buffer (pH 6.0) for 10 minutes at 99C. Sections were rinsed in PBS and after permeabilization with 1x PBS 0.1% Triton X 100, slides were incubated in blocking solution (4% donkey serum +5% nonfat dry milk + 4% BSA + 0.1% Triton X-100) for 30 minutes at RT. Tissue sections were stained with primary antibodies against OMP (Chen et al., 2005) (1:50 dilution) and NP (1:200 dilution, MyBiosource cat no. MBS8574840), Anti-Iba1 (1:250 dilution, Abcam cat no. ab178846). Nuclei were labeled with DAPI (2.5 μg/ml, Thermo Fisher Scientific cat no. D3571), Anti-Cytokeratin 18 (1:250, EMD Millipore cat no. MAB3234), Anti-Adcy3 (1:250, Abcam cat no. ab123803). Primary antibodies were labeled with the following secondary antibodies: for OMP, anti-chicken IgG conjugated to Alexa-488 (2 μg/ml, Jackson ImmunoResearch cat no. A-11055, RRID:AB_2534102), for Adcy3, anti-rabbit IgG conjugated to Alexa-555 (2 μg/ml, Thermo Fisher Scientific cat no. 703-545-155, RRID:AB_2340375), for cyto-Krt18, anti-mouse IgG conjugated to Alexa-488 (2 μg/ml, Thermo Fisher Scientific cat no. A-212-2, RRID:AB_2340375). Confocal images were collected with a Zeiss LSM 700 and image processing was carried out with Fiji (NIH).

Fluorescence-activated nuclei sorting Frozen 1% PFA-fixed tissue was mechanically crushed using Covaris Impactor and then nuclei were extracted with OptiPrep Density Gradient Medium according to the Sigma Millipore protocol. Following extraction and filtering two times through a 35-µm cell strainer, nuclei were stained with Lhx2/Atf5 antibodies for human samples. Next DAPI/Lhx2/Atf5 triple positive nuclei were sorted on a BD Aria II or BD Influx cell sorter for HiC experiments.

In situ Hi-C Depending on the sample, between 30 and 100 thousand nuclei were used for in situ Hi-C. Sorted nuclei were lysed and processed through an in situ Hi-C protocol as previously described with a few modifications. In brief, cells were lysed with 10 mM Tris pH 8 0.2% Igepal, 10 mM NaCl. Pelleted intact nuclei were then resuspended in 0.5% SDS and incubated for 20 min at 62 °C for nuclear permeabilization. After being quenched with 1.1% Triton-X for 10 min at 37 °C, nuclei were digested with 25 U/µl MseI in 1× CutSmart buffer for 1.5 hours at 37 °C. Following digestion, the restriction enzyme was inactivated at 62 °C for 20 min. For the 45-min fill-in at 37 °C, biotinylated dUTP was used instead of dATP to increase ligation efficiency. Ligation was performed at 25 °C for 30 min with rotation after which nuclei were centrifuges. To degrade proteins and revers crosslinks pellets were incubated overnight at 75 °C with proteinase K. Each sample was transferred to Pre-Slit Snap-Cap glass mictoTUBE and sonicated on a Covaris S220 for 90 sec.

Hi-C library preparation and sequencing Sonicated DNA was purified with 2× Ampure beads following the standard protocol and eluted in 300 µl water. Biotinylated fragments were enriched as previously described using Dynabeads MyOne Strepavidin T1 beads. The biotinylated DNA fragments were prepared for next-generation sequencing directly on the beads by using the Nugen Ovation Ultralow kit protocol as described (Monahan et al., 2019) . DNA was amplified by 7 cycles of PCR. Beads were reclaimed and amplified unbiotinylated DNA fragments were purified with 1× Ampure beads. The quality and concentration of libraries were assessed using Agilent Bioanalyzer and Qubit Quantification Kit. Hi-C libraries were sequenced paired-end on NextSeq 500 (2 × 75 bp), or NovaSeq 6000 (2 × 150 bp).

Hi-C data processing and analysis Raw fastq files were processed using the Juicer single CPU BETA version on AWS. Human data were aligned against hg19 and hamster reads were aligned to MesAur1.0_HiC.fasta.gz using BWA 0.7.17 mem algorithm. Hamster genome assembly was obtained from the DNA Zoo Consortium (Dudchenko et al., 2017) and polished with generated HiC data for mock hamster. After reads are aligned, merged, and sorted, chimaeras and duplicates are removed, and finally Hi-C contact matrices are generated by binning at various resolutions and matrix balancing. In this paper we present data with stringent cutoff of MAPQ >30. Hi-C matrices used in this paper were matrix-balanced using Juicer's built-in Knight-Ruiz (KR) algorithm. Matrices were visualized using Juicebox (Robinson et al., 2018) .

Cumulative interchromosomal contacts at the 100kb resolution were constructed by calling Juicer Tools dump function to extract genome wide normalized data from a .hic file and subsequently analyzed as previously described (Monahan et al., 2019) . Briefly, we counted all OR-OR cluster combinations and measured the interchromosomal contacts that map within OR clusters. These counts were then aggregated per genomic bin. The same was done for any genomic contact outside the region of interest to estimate average 'background' contact intensity which was subsequently subtracted from intensity observed within the OR cluster.

Obtained value for each cluster was used for visualization on violin plots. Of note, the intensity of random contacts was higher in COVID-19 samples that resulted in negative values on violin plots.

For human OR cluster annotation, we used genes annotated in HORDE database. For the hamster, firstly, we annotated OR genes via alignment of transcripts present in Ensemble against MesAur1.0_HiC.fasta.gz; next, we defined OR cluster as a stretch of OR genes in vicinity of 50 kb not interrupted by another non OR gene. 82 OR clusters were annotated for human, while for the hamster we were able to annotate only 46 clusters due to poor OR gene annotation in hamster genome in general.

A hidden Markov model (HMM) was used to assess the presence of genomic compartments (Rao et al., 2014) . As we previously described (Monahan et al., 2019) , we extracted subset of normalized interchromosomal contacts to construct 500 kb contact matrix in a manner that 500 kb loci on odd chromosomes emerged as rows while the same size loci on even chromosomes appeared on the columns. We tested 2-21 components to construct HMMs for odd vs even chromosomes; we found that 9 components reveal the existence of trans OR cluster specific compartments. A score was calculated using hmmlearn to deduce the likelihood of the given number of compartments. The same was done for the transposed even vs odd chromosome matrix. The mean value for given compartment was used for graphical visualization.

Statistical tests were performed in R using base packages for statistical analysis and ggplot2 for visualization. For all data, a p-value < 0.05 was considered to be statistically significant. Statistical details for each experiment including statistical tests applied and number of replicates can be found in the figure legends and methods details, p-values are indicated on figures. 

−1 0 0.5 1 9 9 1 6 7 1 3 6 1 5 3 1 0 2 1 0 7 1 1 0 1 3 2 1 5 0 1 3 4 1 7 0 1 2 8 1 4 5 1 4 7 1 6 9 1 8 9 1 6 3 1 4 6 1 1 6 1 1 4 1 1 8 2 1 9 3 2 1 8 6 detection of chemical stimulus involved in sensory perception detection of chemical stimulus involved in sensory perception of smell striated muscle contraction sensory perception of smell muscle adaptation striated muscle cell development actin filament-based movement cellular component assembly involved in morphogenesis actin-mediated cell contraction skeletal muscle contraction myofibril assembly skeletal muscle adaptation actin-myosin filament sliding muscle filament sliding 

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