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Non-cell autonomous disruption of nuclear architecture as a potential cause of COVID-19 induced anosmia

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Abstract

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.
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Article
Non-cell-autonomous disruption of nuclear
architecture as a potential cause of COVID-19-
induced anosmia
Graphical abstract
Highlights
dDownregulation of odor detection pathways may explain
COVID-19-induced anosmia
dSARS-CoV-2-mediated disruption of nuclear architecture
may impair odor detection
dSARS-CoV-2-mediated nuclear reorganization is non-cell
autonomous
Authors
Marianna Zazhytska, Albana Kodra,
Daisy A. Hoagland, ...,
Benjamin R. tenOever,
Jonathan B. Overdevest,
Stavros Lomvardas
Correspondence
benjamin.tenoever@nyulangone.edu
(B.R.t.),
jo2566@cumc.columbia.edu (J.B.O.),
sl682@cumc.columbia.edu (S.L.)
In brief
SARS-CoV-2 induces non-cell-
autonomous effects in olfactory
epithelium that disrupts nuclear
architecture and downregulates olfactory
receptor expression in olfactory sensory
neurons.
Zazhytska et al., 2022, Cell 185, 1–13
March 17, 2022 ª2022 Elsevier Inc.
https://doi.org/10.1016/j.cell.2022.01.024 ll
Article
Non-cell-autonomous disruption of
nuclear architecture as a potential cause
of COVID-19-induced anosmia
Marianna Zazhytska,
1,15
Albana Kodra,
1,2,15
Daisy A. Hoagland,
3
Justin Frere,
3,14
John F. Fullard,
4,5,6
Hani Shayya,
1,2
Natalie G. McArthur,
8
Rasmus Moeller,
3
Skyler Uhl,
3
Arina D. Omer,
7
Max E. Gottesman,
13
Stuart Firestein,
8
Qizhi Gong,
9
Peter D. Canoll,
10
James E. Goldman,
10
Panos Roussos,
4,5,6,11
Benjamin R. tenOever,
3,14,16,
*
Jonathan B. Overdevest,
12,16,
*and Stavros Lomvardas
1,13,16,17,
*
1
Mortimer B. Zuckerman Mind, and Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
2
Department of Genetics and Development, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons,
Columbia University, New York, NY 10032, USA
3
Department of Microbiology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
4
Center for Disease Neurogenomics, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
5
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
6
Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
7
Baylor Genetics, 2450 Holcombe Blvd, Houston, TX 77021, USA
8
Department of Biological Sciences, Columbia University New York, NY 10027, USA
9
Department of Cell Biology and Human Anatomy, School of Medicine, University of California at Davis, Davis, CA 95616, USA
10
Department of Pathology and Cell Biology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons,
Columbia University, New York, NY 10032, USA
11
Department of Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
12
Department of Otolaryngology, Head and Neck Surgery, Columbia University Irving Medical Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY 10032, USA
13
Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, Vagelos College of Physicians and
Surgeons, Columbia University, New York, NY 10032, USA
14
Present address: Department of Microbiology, New York University, Langone Health, New York, NY 10016, USA
15
These authors contributed equally
16
These authors contributed equally
17
Lead contact
*Correspondence: benjamin.tenoever@nyulangone.edu (B.R.t.), jo2566@cumc.columbia.edu (J.B.O.), sl682@cumc.columbia.edu (S.L.)
https://doi.org/10.1016/j.cell.2022.01.024
SUMMARY
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 under-
standing 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 sen-
sory deficit. Here, we show that both in humans and hamsters, SARS-CoV-2 infection causes widespread
downregulation of olfactory receptors (ORs) and of their signaling components. This non-cell-autonomous
effect is preceded by a dramatic reorganization of the neuronal nuclear architecture, which results in dissi-
pation 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.
INTRODUCTION
Neurological symptoms in COVID-19 patients have immense
importance due to their role in exacerbating initial disease pre-
sentation 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 heterogeneous neurological symp-
toms (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;Gra-
ham 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., 2022,2021b;Butowt and von Bartheld,
2020;Gerkin et al., 2020;Hornuss et al., 2020;Luers et al.,
ll
Cell 185, 1–13, March 17, 2022 ª2022 Elsevier Inc. 1
Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
2020;Tong et al., 2020). Although olfactory deficits are common
in upper respiratory infections, these symptoms are accompa-
nied by rhinorrhea and nasal congestion that insulate olfactory
sensory neurons (OSNs) from odorants. In contrast, anosmia in
COVID-19 is independent from conductive interference. Thus,
the association between COVID-19 and anosmia raises mecha-
nistic 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 hu-
man 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 transcrip-
tion and reduction of interchromosomal OR contacts. Effects of
SARS-CoV-2 infection in nuclear architecture are non-cell auton-
omous and can be induced by neutralized serum from SARS-
CoV-2 infected hamsters. Our data provide a potential explana-
tion for the neurological symptoms caused by a virus with no
tropism for neurons.
RESULTS
To explore mechanisms of COVID-19-induced anosmia, we in-
fected golden hamsters (M. auratus) with SARS-CoV2 and moni-
tored changes over a period of 10 days post-infection (dpi) by
scRNA-seq. This rodent species is a good animal model for
SARS-CoV-2 infection owing to sequence homology between
hamster and human ACE2 and similarity in pathogenesis and
immunological responses (Cleary et al., 2020;Hoagland et al.,
2021;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 (Figure S1A) using previously described markers (Durante
et al., 2020;Fletcher et al., 2017). We detect a decrease of SUS
cells at 1 dpi that is exaggerated at 3 dpi (Figures 1A and 1B),
when SUS representation decreases from 20.6% in mock-in-
fected hamsters to 6% at 3 dpi. SUS diminution coincides with
increase of microglia and other immune cells (Figures 1A and
1B). Both SUS and microglia return to pre-infection representa-
tion in the hamster OE by 10 dpi (Figures 1A and 1B). In contrast,
OSN representation is stable throughout the infection (Figures
1A and 1B).
At 1 and 3 dpi, we detect the viral RNA in 5% of the cells,
followed by complete elimination of the virus at 10 dpi. At 1 dpi,
47% of the total infected cells are SUS, and 40% of which
are infected (Figures 1C–1E and S1B). In contrast, only 6% of
the infected cells are OSNs (Figures 1C, 1E, and S1B). At
3 dpi, microglia and other immune cells also frequently contain
SARS-CoV-2 transcripts (Figures 1C–1E and S1B). Consistent
with this, we detect colocalization of spike protein with
Krt18 and with Aif1/lba-1, SUS and microglia markers, respec-
tively (Figures S1C and S1E). Spike colocalization with OSN
markers is rare and occurs at OE regions of viral shedding
and structural damage (Figures S1C and S1E). Finally, SARS-
CoV-2 presence in OSN axons innervating the olfactory bulb
(OB) is rare, consistent with the infrequent OSN infection in
the OE (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 3 dpi, with the viral RNAs represent-
ing 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-2
populations, we detect upregulation of cyto-
kines 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
3 dpi (Figures 2C and S2). By 10 dpi, 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 3 dpi
(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 (Figures
2E and 2F).
COVID-19 causes downregulation of OR and OR
signaling genes in hamster OEs
Our scRNA-seq analysis could not provide insight to the ef-
fects of the virus into OR expression because we used a
50-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 tran-
scriptional 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 4 dpi 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 last until 10 dpi (Figure 3C), consistent with obser-
vations 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 2 dpi, including
the downregulation of Lhx2, Ebf1, and Ebf2, transcription factors
with key roles in expression of OR and OR signaling genes
ll
2Cell 185, 1–13, March 17, 2022
Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
(Hirota and Mombaerts, 2004;Monahan et al., 2019;Monahan
et al., 2017;Wang et al., 2004;Wang et al., 1997). Downregula-
tion of SUS markers starts at 2 dpi and peaks at 4 dpi before be-
ing restored to pre-infection levels by day 10 (Figures 3D and 3E).
In contrast, OSN responses are delayed and persistent, with key
molecules for olfaction, such as Adcy3 (Wong et al., 2000), re-
maining downregulated through day 10 (Figures 3D and 3E).
Finally, markers of OSN progenitor cells increase, with GBC
markers peaking at 10 dpi (Figures 3D and 3E). This may reflect
progenitor cell activation toward the replenishment of infected
cells of the OE (Fletcher et al., 2017;Gadye et al., 2017). A sum-
mary 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 (Figure S3), and all the significant transcriptional changes
are listed in Table S1.
The most striking transcriptional change observed in infected
OEs is the widespread downregulation of OR genes. Significant
OR downregulation is first observed at 2 dpi, peaks at 4 dpi, and
continues through 10 dpi (Figures 3F and 3H), 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 10 dpi, or the changes observed
in INPs and SUS cells (Figures 3C and 3D). 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 4 dpi that is partially preserved
till 10 dpi (Figure 3I). Some antiviral responses are also sustained
till day 10 (Figure 3J).
SARS-CoV-2 infection induces reorganization of OSN
nuclear architecture
To decipher mechanisms responsible for widespread and sus-
tainable OR downregulation, we directed our studies to a known
regulator of OR expression, the OSN nuclear architecture
GBCs
T-cells
MV2
INPs
SUS cells
HBCs
Fibroblasts
Microglia
OSNs
B-cells
MV1
Olfactory glia
Macrophages
Mock
OSNs
INPs
SUS cells
T-cells
Microglia
HBCs
Macrophages
Fibroblasts
MV1
B-cells
GBCs
MV2
Olfactory glia
1dpi
GBCs
T-cells
MV2
INPs
SUS cells
HBCs
Fibroblasts
Microglia
OSNs
B-cells
MV1
Olfactory glia
Macrophages
3dpi
OSNs
INPs
SUS cells
T-cells
Microglia
HBCs
Macrophages
Fibroblasts
MV1
B-cells
GBCs
MV2
Olfactory glia
10dpi
UMAP1
UMAP2
0.00
0.25
0.50
0.75
1.00
Mock 10dpi
3dpi
1dpi
0.00
0.25
0.50
0.75
1.00
Mock 10dpi
3dpi
1dpi
OSNs
INPs
SUS cells
T-cells
Microglia
HBCs
Macrophages
Fibroblasts
MV1
B-cells
GBCs
MV2
Olfactory glia
UMAP1
UMAP2
0.0
0.1
0.2
0.3
0.4
N
N
NN
N
N
S
S
SSS
S
0.0
0.2
0.4
0.6
N
N
N
N
N
N
S
S
S
SS
S
Percentage of cells with SARS-CoV2
Percentage of cells with SARS-CoV2
1dpi
3dpi
Distribution of OE cell
types during infection
Distribution of SARS-CoV-2-N
across cell types of the OE
Other
Other
Immune cells
Immune cells
Microglia
Microglia
OSN progenitors
OSN progenitors
OSNs
OSNs
SUS
SUS
CoV-2-N
AB
C
E
D
Figure 1. SARS-CoV-2 infects and transiently depletes hamster sustentacular cells
(A) UMAP plots of hamster OEs for mock- and SARS-CoV-2-infected hamsters at 1, 3, and 10 dpi. See also Figure S1A for the distribution of cell-specific markers.
(B) Representation of cell types in mock- and SARS-CoV-2-infected hamster OEs at 1, 3, and 10 dpi. HBCs, GBCs, and INPs combined as OSN progenitor cells,
macrophages, T cells, B cells combined as immune cells, MV1, MV2, olfactory glia, and fibroblasts combined as other.
(C) Feature plot showing expression of N SARS-CoV-2 transcript in hamster OEs. See also Figure S1B for expression of S SARS-CoV-2 transcript.
(D) Bar charts depicts proportion of SARS-CoV-2 N transcript across the cell types of the OE. Color code of cell types same as B. See also Figures S1C and S1E
for histological confirmation of SARS-CoV-2 tropism.
(E) Percentage of cells with N and S transcripts in the annotated clusters (HBCs, GBCs, and INPs combined as OSN progenitor cells; macrophages, T cells, B cells
combined as immune cells; MV1, MV2, olfactory glia, and fibroblasts combined as other) at 1 and 3 dpi. Each panel (A–E) represents two combined biological
replicates, per condition.
ll
Cell 185, 1–13, March 17, 2022 3
Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
(Bashkirova and Lomvardas, 2019;Clowney et al., 2012;Mar-
kenscoff-Papadimitriou et al., 2014;Monahan et al., 2019). OR
gene clusters from multiple chromosomes converge to OSN-
specific genomic compartments, which facilitate stable and sin-
gular OR transcription (Clowney et al., 2012). We therefore asked
if disruption of OR compartments is the cause of OR downregu-
lation 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 10 dpi.
OR gene clusters form robust long-range cis and trans genomic
contacts in hamster OSNs, as shown for OR genes from chromo-
somes 16 and 17 (Figure 4A). SARS-CoV-2 infection reduces
these contacts, starting at 1 dpi and peaking at 3 dpi (Figure 4A).
A contact matrix for all the hamster OR clusters arranged by
chromosome shows strong long-range cis interactions and
widespread trans contacts between them in control samples
(Figure 4B). However, these interactions become reduced as
early as 1 dpi and remain low by 10 dpi (Figures 4B and 4C).
MesAur-Ifitm2
MesAur-Eif2ak2
MesAur-Gstm2
MesAur-Ccl4
MesAur-Ccl3
MesAur-Saa3
MesAur-Cyp2f2
MesAur-Cyp2g1
MesAur-ND4L
MesAur-Isg15
MesAur-Lcn11
Cov2---S
Cov2---M
Cov2---ORF8
Cov2---N
MesAur-Ifitm2
MesAur-Eif2ak2
MesAur-Gstm2
MesAur-Ccl4
MesAur-Ccl3
MesAur-Saa3
MesAur-Cyp2f2
MesAur-Cyp2g1
MesAur-ND4L
MesAur-Isg15
MesAur-Lcn11
Cov2---S
Cov2---M
Cov2---ORF8
Cov2---N
MesAur-Ifitm2
MesAur-Gstm2
MesAur-Cyp2f2
MesAur-Cyp2g1
MesAur-ND4L
1dpi 3dpi 10dpi
024602460246
0
2
4
6
Mock
Expression level
Bpifb4
Cyp2g1
Cyp2f2
Gstm1
Slc22a20
Ptgds
Cyp1a2
Fetub
Aqp3
Lypd2
Aldh2
Atox1
Igfbp5
Rgs5
Sec14l3
Vmo1
Pon1
Gstm2
Ces1d
Agr3
Pgrmc1
Por
Crym
Agr2
Prss33
Sult1c1
Dpys
Cyp2j6
Ccl4
Ddx60
Gbp2
Il1rn
Isg15
Trf
Cd74
Fcer1g
Rsad2
Ccl8
Tnf
Nfkbia
Cxcl10
Ccl5
Saa3
0
20
40
60
-2 -1 0 1 2
avg_logFC
-log10(p_val)
0
2
4
6
10dpi
1dpi
3dpi
Mock
Expression Level
Isg15
0
1
2
3
10dpi
1dpi
3dpi
Mock
Expression Level
Irf7
0
1
2
3
4
10dpi
1dpi
3dpi
Mock
Expression Level
Irf9
0
1
2
3
10dpi
1dpi
3dpi
Mock
Expression Level
Eif2ak2
0
1
2
3
10dpi
1dpi
3dpi
Mock
Expression Level
Mx1
0
1
2
3
10dpi
1dpi
3dpi
Mock
Expression Level
Dtx3l
SUS SUSSUS
control
Adcy3 RNA FISH
Adcy3 IF
SARS-CoV-2
control
4dpi
4dpi
control
4dpi
4dpi
A
F
C
B
Wilcoxon, p = 1.3e−12
100
1000
10000
4d
p
i
mock
IntDen Adcy3 RNA−FISH (log10)
Wilcoxon, p < 2.2e−16
10
20
30
40
Mean Intensity Adcy3 (IF)
4dpi
MOCK
4dpi
mock
ns
p<0.0001
p<0.0001
p<0.0001
p<0.0001
ns
p<0.0001
p<0.0001
ns
p<0.0001
p<0.0001
ns
p<0.0001
p<0.0001
p<0.0001
p<0.0001
p<0.0001
ns
0
1
2
3
4
10dpi
1dpi
3dpi
Mock
Expression Level
Adcy3
0
2
4
6
Expression Level
Gfy
0
1
2
3
4
Expression Level
Cnga2
0
2
4
6
Expression Level
Gng13
0
1
2
3
4
Expression Level
Rtp1
0
1
2
3
Expression Level
Rtp2
10dpi
1dpi
3dpi
Mock
10dpi
1dpi
3dpi
Mock
10dpi
1dpi
3dpi
Mock
10dpi
1dpi
3dpi
Mock
10dpi
1dpi
3dpi
Mock
p<0.0001
p<0.0001
p<0.0001
p=0.05
p<0.0001
p=0.001
p<0.01
p<0.0001
p<0.05
p<0.0001
p<0.0001
p<0.0001
p<0.0001
p<0.001
p<0.01
p<0.0001
p<0.0001
p<0.0001
E
D
Figure 2. SARS-CoV-2 infection induces non-cell-autonomous changes in hamster OSNs
(A) Scatter plots showing average expression of mock- and SARS-CoV-2-infected SUS cells at 1, 3, and 10 dpi. Top differentially expressed genes are shown in
red boxes, hamster genes highlighted with ‘‘MesAur-’’ prefix, SARS-CoV-2 transcripts with ‘‘Cov2-.’
(B) Volcano plot showing upregulated and downregulated genes in SARS-CoV-2
+
vs SARS-CoV-2
SUS cells from infected OEs at 1 and 3 dpi. Significantly
upregulated genes are shown in red, downregulated in blue; top 1% of differentially expressed genes highligh ted on the plot.
(C) Violin plots representing the log-normalized expression of antiviral genes in OSNs from mock- and SARS-CoV-2-infected OEs at 1, 3, and 10 dpi. See also
Figure S2 for feature plots.
(D) Violin plots representing the log-normalized expression of key OSN genes from mock- and SARS-CoV-2-infected OEs at 1, 3, and 10 dpi.
(E) Confocal micrographs of IF-FISH in hamster OE, mock and 4 dpi, shows decreased ADCY3 protein and mRNA levels in tissues infected with SARS-CoV-2.
Left, ADCY3 Ab (green) and SARS-CoV-2 gRNA (magenta). The SARS-CoV-2 probe targets the antisense strand of the S gene, detecting replicating virus. Right,
Adcy3 mRNA FISH (red) is reduced in 4 dpi samples compared with mock.
(F) ACDY3 protein and mRNA quantifications show significantly lower levels in hamster OE 4 dpi compared with mock controls. Top, distribution of mean intensity
ACDY3 antibody staining for individual cells in in control and infected hamster OE sections. Bottom, distribution of integrated density (mean intensity 3DAPI
area) of ADCY3 mRNA FISH signal in control and infected hamster OE sections.
ll
4Cell 185, 1–13, March 17, 2022
Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
Hidden Markov model (HMM) calculation of genomic compart-
ment scores shows widespread reduction of most compart-
ments by 3 dpi, revealing a delayed disruption of genome-wide
nuclear architecture compared with the disruption of OR com-
partments (Figure 4D). However, genomic compartmentalization
remains disrupted 10 dpi, 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-autono-
mous mechanism of nuclear reorganization. Since previous re-
ports implicate cytokines and antiviral responses in olfactory
deficits and OR downregulation (Lane et al., 2005,2010;Rodri-
guez 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 3 dpi and inactivated the
circulating virus by UV irradiation. We applied these sera to the
OEs of naive 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 h (Figures
5B and 5C). HMM confirmed genome-wide changes in compart-
ment 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 naive hamsters (Figure 5E). As expected from the
infection time course, there are no significant changes in
OR transcription at this time point (Figure S4A). However,
there is a trend of OR downregulation across most OR genes,
which is more pronounced than the effects observed at 1 dpi
upon SARS-CoV-2 infection (Figure S4A). Thus, by providing
sera from the peak of the inflammatory response, we likely
accelerated the molecular changes observed during viral
0e+00
5e+06
1e+07
SARS-CoV-2 Counts/ total counts
4dpi 10dpi
2dpi
1dpi
Mock
SARS-CoV-2 viral load
Atf3
Krt17
Socs3
Sfn
Gsto1
Hspb1
Krt14
Apoe
Krt19
Klf4
Krt15
Krt5
Anxa2
Igfbp2
Aqp3
Wnt4
Ccnd2
Crlf1
F3
Perp
Epas1
Csrp1
Dapl1
Sult1e1
Covid19
Crabp2
Neurod1
Ifitm2
Hmgb2
Hes6
Insm1
Sox11
Cks2
Psip1
Hmgn2
Marcksl1
Ranbp1
Cdk4
Impdh2
Tubb5
Nap1l1
Covid19
Gfy
Umodl1
Adcy3
Tspan7
Acbd7
Nrn1l
Sult1d1
Omp
Stom
Ric8b
Chga
Faim2
Nsg1
Olfm1
Fam131c
Them6
Cartpt
Plekhb1
Rtp1
Dcdc2a
Pcolce2
Impdh1
Ctxn3
Ak1
Serpine2
Covid19
Sustentacular
cells
Sec14l3
Ermn
Cyp2g1
Cyp2f2
Ces1d
Gsta4
Lypd2
Calml4
Gchfr
Sec14l2
Gstm2
Tyro3
Tst
Agr2
Dcxr
Galm
Mgst1
Aqp3
AU021092
Clu
Mgll
Covid19
4dpi
4dpi
10dpi
2dpi
1dpi
Mock
4dpi
10dpi
2dpi
1dpi
Mock
4dpi
10dpi
2dpi
1dpi
Mock
4dpi
10dpi
2dpi
1dpi
Mock
-2
-1
0
1
2
-
2
-1
Olfactory Sensory
Neurons
Horizontal Basal
Cells Globose Basal
Cells
Insm1
Stmn3
Crabp1
Elavl3
Hdac2
Arhgdig
Stmn1
Tubb3
Ell3
Lhx2
Qdpr
Marcksl1
Ebf1
Ebf2
Ctxn1
Stmn2
Camk2n1
Gap43
Cotl1
Dpysl3
Vim
Immediate Neuronal
Precursors
Covid19
4dpi
10dpi
2dpi
1dpi
Mock
−4
−2
0
2
4
log2FC
10dpi1dpi 2dpi 4dpi
GBC
HBC INP OSN SUSINP OSN SUS INP OSN SUS INP OSN SUS
GBC
HBC
GBC
HBC
GBC
HBC
4dpi
10dpi
2dpi
1dpi
Mock
OR genes
-2
-1
0
1
-1
log2FoldC
OSN marker genes
A
D
E
B
FHI
Obp2b
Rsad2
Saa3
Arg1
Ambn
Ifit2
Acod1
Cxcl10
Lcn3
Isg15
Amelx
Obp2a
Il1b
Ccl5
Mx1
Irf7
Lcn4
Il1rn
Mx2
Enam
Uba7
Gbp2
Fcgr4
Gbp5
AA467197
Lcn11
Slfn1
Gbp7
Ccl4
Csf3r
Herc6
Zbp1
Apobec1
Ddx60
Ccl2
Nlrc5
Slamf9
Dspp
Oas2
Slc11a1
Tnf
Apol6
Siglec1
Ccl3
Lgals9
F5
Itgal
Cd274
Ifi47
Sec14l3
Il1a
−2
0
2
4
C
TopVar 50 genes
4dpi
10dpi
2dpi
1dpi
Mock
SARS-CoV-2
−2
−1
0
1
Adcy3
Gfy
Gng13
Olfr
Rtp1
4dpi 10dpi
2dpi1dpi
Cnga2
log2FC
Ifng
Il2
Il6
Irf7
Tnf
Immune response
0
2
4
6
8
4dpi 10dpi
2dpi1dpi
Olfactory transduction
log2FC
SARS-CoV-2
SARS-CoV-2
G
J
4dpi
10dpi
2dpi
1dpi
Mock
SARS−CoV−2 entry factors
Ace2
Tmprss4
Cltrn
Ace
Anpep
−1
−0.5
0
0.5
1
Tmprss2
2.9e−12
p <
2
.
22
e−
16
p < 2.22e−16
p < 2.22e−16
p=9.7e−11
0
5
1dpi
2dpi
4dpi
10dpi
log2FoldChange
OR marker genes
p=0.3
p=1.3e−10
p=1.3e−07
p=0.061
p=0.66
−4
0
4
8
1dpi 2dpi 4dpi 10dpi
Figure 3. SARS-CoV-2 infection causes downregulation of hamster OR and OR signaling genes
(A) SARS-CoV-2 genomic counts in hamster OE following intranasal inoculation of SARS-CoV-2 and harvest at 1, 2, 4, and 10 dpi. SARS-CoV-2 raw counts were
normalized to the MesAur1.0 genome reads and plotted as DESeq 2’s median ratio normalization (MRN). No mapped counts were found in the mock-infected OE.
(B) Z-scored expression of SARS-CoV-2 entry genes across infection time course.
(C) Z-scored expression of the 50 genes with highest variance.
(D) Z-scored expression of HBC, GBC, INP, OSN, and SUS markers (left to right) across SARS-CoV-2 infection time course.
(E) Distribution of log2FC for cell-type-specific markers in the OE during SARS-CoV-2 infection time course.
(F) Distribution of log2FC for OR genes during SARS-CoV-2 infection time course.
(G) Distribution of log2FC for OSN markers during SARS-CoV-2 infection time course.
(H) Z-scored expression of OR genes during SARS-CoV-2 infection time course.
(I) Aggregate expression of ORs and OR signaling transduction genes during SARS-CoV-2 infection time course. See also Figure S3.
(J) Antiviral gene expression during SARS-CoV-2 infection time course. Data for each panel (A–J) represent averages from three biological replicates per con-
dition, except for 1 dpi, which is the average of two biological replicates.
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Cell 185, 1–13, March 17, 2022 5
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Article
infection, consistent with the upregulation of genes induced
2 days post-SARS-CoV-2 infection (Figure S4B). SUS markers,
however, are nonresponsive to the signals from the infected
sera (Figure S4C), supporting the notion that SUS cells only
exhibit mostly cell-autonomous transcriptional changes.
SARS-CoV-2 infection induces downregulation of OR
and OR signaling genes in humans
To explore whether our observations from hamsters apply to hu-
mans, 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 (Figure S5A). This is further
supported by scRNA-seq analysis on an autopsy from a control
(noninfected) sample (Zazhytska et al., 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 (Fig-
ure S5B), consistent with recent observations (Khan et al.,
2021). We also detect SARS-CoV-2 RNA in microglia cells re-
cruited to the infected human OEs (Figure S5C), replicating ob-
servations from hamster OEs. Finally, as in hamsters, we did
not observe OSN depletion in infected OEs (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 (Table S2). These variations did not in-
fluence cellular constitution, as quantification of OE, respiratory
epithelium (RE), and immune cells show consistency between
samples (Figures S5E and S5F). Post-mortem time (PMT) had
no obvious effect on the quality of these libraries (Figure S5G).
Surrogate variable analysis (Leek, 2014) identified one control
sample as extreme outlier, resulting in its removal from further an-
alyses (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 vari-
able amounts between samples (Figure 6A). However, represen-
tation of OE and RE markers did not change with viral load
Figure 4. SARS-CoV-2 infection disrupts interchromosomal OR compartments
(A) In situ HiC maps of contacts between OR clusters in cis (top) or trans (bottom) for mock, 1, 3, and 10 dpi hamster from pooled in situ HiC data. 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) 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
10 dpi. 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 10 dpi (shades of red). For each panel (A–D), data represent averages from two biological replicates per condition.
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induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
(Figures S5E and S5F). Unexpectedly, in one of the remaining
five control samples, we detected the RNA genome of a non-
SARS coronavirus, hCoV-OC43 (Figure S5H), which was previ-
ously 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 olfac-
tory deficits (highlighted with striped bar) due to limited solicita-
tion of these symptoms at the early phase of the pandemic. How-
ever, based on current reports, >60% of these subjects may
have experienced olfactory deficits (Butowt and von Bartheld,
2020;Wang et al., 2020).
In most infected samples, we detected elevated levels of cyto-
kines and antiviral genes such as IFN-g(Figures 6B and S5J). GO
analysis of significantly upregulated genes between infected au-
topsies is overrepresented with terms related to immune
response (Figure S5K). We did not detect an overall downregula-
tion of SUS markers (Figure 6C), although some SUS-enriched
genes are downregulated in the infected samples (Figure 6D).
Similarly, we did not detect depletion of OSN markers, but selec-
tive reduction of OSN-enriched genes with established critical
roles in olfaction, such as Adcy3 (Figures 6C and 6D). Control
OEs have higher OR mRNA levels than the infected OEs (Fig-
ure 6E), except for the hCoV-OC43-infected control. PCA anal-
ysis using only OR genes, segregates control from infected sam-
ples, while PCA with the whole transcriptome does not (Figures
6F and 6G), suggesting that reduced OR transcription consti-
tutes 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 res-
piratory markers are not changing upon infection (Figure 6H).
Volcano and boxplots showing the transcriptional effects of
SARS-CoV-2 infection in aggregate further support significant
downregulation of OR and OR signaling genes (Figures 6I and
6J). Finally, GO analysis of downregulated genes in infected
Figure 5. Serum from SARS-CoV2-infected hamsters disrupts genomic OR compartments
(A) The experimental pipeline used to expose naive hamsters OEs serum from SARS-CoV-2 or mock-infected hamsters prior to in situ HiC analysis. Serum was
collected 3 dpi from mock or SARS-CoV-2-infected hamsters, centrifuged, and UV-irradiated before intranasal inoculation to naive hamster OEs for 12.5 h. See
also 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 12.5 h 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 h to serum from mock
(blue) and SARS-CoV-2-infected hamsters at 3 dpi (red).
(E) SARS-CoV-2 genomic counts in inactivated serum of 3 dpi hamster applied to naive hamster compared with the viral load at 1 dpi 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 three biological replicates per condition.
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induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
A
C
BF
E
G
D
K
J
I
H
Figure 6. SARS-CoV-2 infection of human OEs coincides with downregulation of OR/OR signaling genes
(A) SARS-CoV-2 genomic counts from the OE of 18 COVID-19 patients (red) and 4 controls (blue). SARS-CoV-2 raw counts were normalized to the hg38 genom e
reads using DESeq2’s median ratio normalization (MRN). The striped bar highlights the only sample with known anosmia. See also Figure S5 for histological
confirmation of SARS-CoV-2 detection.
(B) Zscore for inflammatory makers for each sample shows variability of inflammatory response among patients. See also Figure S4J for aggreg ate analysis of
log2FC of antiviral/inflammatory markers.
(C) Distribution of log2FC for cell-type-specific markers in the OE. A subset of OSN markers is downregulated.
(D) Z-scored expression of inflammatory makers calculated across samples shows variability of inflammatory response among COVID -19patients. Samples are
ordered according to the number of days after symptoms onset (top). See also Figure S4J.
(E) Distribution of aggregated normalized OR genes counts across all human OEs. Transformed aggregate expression of OE and OSN markers are plotted for
each sample as dots. Infected samples depicted in red and control samples in blue. The self-reported anosmic patient marked with stripes (153) and hCoV-
OC43
+
is marked in light blue (2186).
(F) COVID-19 samples (red) and controls (blue) do no cluster in PCA analysis with all genes (left) but do cluster when only OR genes (right) are considered. Sample
with coronavirus hCoV-OC43 is highlighted in light blue (2186).
(G) Z-scored expression of OR genes (top). Unsupervised clustering using only OR genes distinguish COVID-19 and control samples. Sample 2186 (light blue,
hCov-OC43
+
), clusters with COVID-19 samples. (Bottom) Zscore for Ebf1, Ebf2, and Lhx2, transcription factors with known role in the expression of OR/OR
signaling genes.
(H) MA-plot with OE genes (blue), OR genes (red), and RE genes (black).
(I) Volcano plot of COVID-19 versus control RNA-seq data. Log2FC genes with abs(log
2
FC) R1 highlighted in red. Gene with padj < 0.05 are identified with
blue fonts.
(J) Boxplot representation of the normalized counts (MRN) grouped in COVID-19 positive and control specimens for Adcy3, Cnga2, Gfy, Gng13, aggregate OR,
and Rtp1. Significance was calculated using Wilcoxon test. Significance value for OR downregulation does not change if we omit the most lowly expressed ORs
(shown in the chevron-shaped distribution of the MA plot).
(K) GO analysis for downregulated genes reveals enrichment for genes involved in sensory perception of smell. See also Figure S5K for GO analysis of upre-
gulated genes.
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Article
autopsies shows that ‘‘sensory perception of smell’’ constitutes
the most significantly enriched GO term (Figure 6K).
SARS-CoV-2 infection disrupts interchromosomal OR
compartments in humans
To ask whether OR downregulation in SARS-CoV-2-infected
OEs is caused by changes in nuclear architecture, we estab-
lished a protocol for the isolation of OSN nuclei from OE au-
topsies by FACS (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 (Figures 7A and 7B). Contact
matrixes depicting human OR genes from every chromosome
confirms that OR genes form interchromosomal compartments
(Figures 7C and 7D) that are disrupted in SARS-CoV-2-infected
OEs (Figures 7A–7D), independently of genome-wide changes in
nuclear architecture (Figure 7E). Finally, we identified interchro-
mosomal compartments containing Adcy3 and other genes
with key functions in olfaction that also dissipate in infected sam-
ples (Figure S6B).
DISCUSSION
We provide a molecular explanation for SARS-CoV-2-induced
anosmia and a mechanism by which this virus can alter the iden-
tity 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 his-
tological analyses (Khan et al., 2021), our data suggest that OSN
Figure 7. SARS-CoV-2 infection of human OEs disrupts genomic OR compartments
(A) 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 versus 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 two control and four 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 < 0.05 was computed using Wilcoxon rank test.
(E) HMM score for a given number of compartments indicating differences in genomic compartmentalization between two control (blue) and four infected
samples (red).
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Article
infection by SARS-CoV-2 is too infrequent to account for the re-
ported smell loss. Moreover, the cell-autonomous effects of SUS
infection may be too transient to account for long-lasting olfac-
tory deficits reported by COVID-19 patients, which have a
mean duration of 20 days (Chapurin et al., 2022). Thus, the
most likely explanation for COVID-19-induced anosmia is the
non-cell-autonomous, widespread, and persistent downregula-
tion 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 ham-
sters induces significant and rapid changes in OSN nuclear ar-
chitecture suggests that systemic changes caused by SARS-
CoV-2 infection alter the physiology and function of the cells
that this virus cannot infect.
Comparing the effects of SARS-CoV-2 infection in
hamster and human OEs
Hamster scRNA-seq shows that SARS-CoV-2 predominantly in-
fects 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 transcrip-
tional 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 downre-
gulation 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 hu-
man 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 stron-
ger in regions with high viral load. In contrast, transcriptional
changes in OSNs occur independently of direct infection and
do not corelate with the viral load in the OE, hindering their eluci-
dation by transcriptomic comparison within a sample. Further-
more, there is a delay in OSN transcriptional changes compared
to SUS marker downregulation. Thus, autopsies corresponding
to longer infection periods and comparison with noninfected
samples may be required for the detection of OSN transcrip-
tional 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 tran-
scription factors for OSN physiology, explains the downregula-
tion 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 tran-
scription 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 differentia-
tion, and, thus, their disruption in mature OSNs may be irre-
versible. 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 vi-
rus, their pre and post-infection patterns may be different, due to
the inherent stochasticity of trans OR interactions (Bashkirova et
al., 2020;Tan et al., 2019). Thus, OSNs that were already inner-
vating 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 be-
tween OR genes and other neuronal gene families (Jiang et al.,
2017;Tan et al., 2021). Additional mechanisms, such as sus-
tained expression of antiviral programs (Frere et al., 2022), dam-
age in tissue vasculature, and hypoxia (Thakur et al., 2021), could
also contribute to long-lasting 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 interac-
tions between chromosomes has important implications: if OR
expression ceases every time maladaptive physiological re-
sponses disrupt interchromosomal OR contacts, then olfaction
may act as the ‘‘canary in the coal mine’’ for a variety of human
conditions, from viral infections to neurodegeneration (Albers
et al., 2006).
Limitations of the study
We did not identify the circulating molecule(s) that induce reor-
ganization of OSN nuclear architecture and the OSN signaling
pathway responsible for it. Thus, currently, we can only specu-
late that similar mechanisms apply to other neuronal popula-
tions, 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 and Rtp2)
(Saito et al., 2004), olfactory receptor signaling molecules
(Adcy3 and Gng13) (Liu et al., 2018;Wong et al., 2000), and ion
channels generating odor-evoked axon potential (Cnga2)
(Brunet et al., 1996), provides the most likely explanation for
COVID-19-induced anosmia. Finally, we can only deduce that
COVID-19 infection caused OR and OR signaling gene downre-
gulation in humans, as we cannot measure the expression of
these genes before the infection. Although experiments in
ll
10 Cell 185, 1–13, March 17, 2022
Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
hamsters support this hypothesis, we cannot exclude rodent-
specific mechanisms that preclude direct comparisons between
species.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
dKEY RESOURCES TABLE
dRESOURCE AVAILABILITY
BLead contact
BMaterials availability
BData and code availability
dEXPERIMENTAL MODEL AND SUBJECT DETAILS
BHamsters
BVirus stock and propagation
BHuman samples
dMETHOD DETAILS
BSARS-CoV-2 inoculation
BSerum collection, inactivation and intranasal
inoculation
BRNA-seq
BhCoV-OC43 Identification
BSingle cell RNA-seq and analysis
BRNAscope in hOE
BImmunofluorescence
BFluorescence-activated nuclei sorting
BIn situ Hi-C
BHi-C library preparation and sequencing
BHi-C data processing and analysis
dQUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.cell.
2022.01.024.
ACKNOWLEDGMENTS
We thank members of the Lomvardas lab, Konstantin Popadin, and Muham-
mad 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 grate-
ful to the patients with COVID-19 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 Institu-
tional Review Board of CIUMC (IRB AAAT0689 and AAAS7370). LVG hamsters
(Mesocricetus auratus) were treated in compliance with the rules and regula-
tions of IACUC under protocol number PROTO202000113-20-0743. Funding:
NIDCD 3R01DC018744-01S1 (S.L. and J.B.O.), U01DA052783 (S.L.), NIA
3R01AG065582-01S1 (P.R.), 3R01AG067025-02S3 (P.R.), HHMI Faculty
Scholar Award (S.L.), Zegar Family Foundation (S.L.), and Marc Haas Founda-
tion (BRT). Graphical abstract and schemes were created with BioRender.com.
AUTHOR CONTRIBUTIONS
Conceptualization: M.Z., A.K., M.E.G., J.B.O., and S.L. Methodology: M.Z.,
A.K., D.A.H., B.R.T., J.B.O., and S.L. Software: A.D.O. Formal analysis:
M.Z., A.K., and S.H. Investigation: M.Z., A.K., D.A.H., J.F., J.F.F., R.M., S.U.,
and N.G.M. Resources: J.F.F., H.S., S.F., Q.G., P.D.C., J.E.G., P.R., B.R.T.,
and J.B.O. Data curation: M.Z. and A.K. Writing: M.Z., A.K., J.B.O., and S.L.
Supervision: B.R.T., J.B.O., and S.L. Funding acquisition: P.R., B.R.T.,
J.B.O., and S.L.
DECLARATION OF INTERESTS
The authors declare no competing interests.
INCLUSION AND DIVERSITY
We worked to ensure sex and racial balance in the collection of human OE au-
topsies. We worked to ensure gender balance in our reference list while citing
work relevant to this study.
Received: August 4, 2021
Revised: December 6, 2021
Accepted: January 26, 2022
Published: February 2, 2022
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STAR+METHODS
KEY RESOURCES TABLE
REAGENT or RESOURCE SOURCE IDENTIFIER
Antibodies
Anti-OMP Chen et al. (2005) N/A
Anti-Adcy3 Abcam Cat no. ab123803; RRID:AB_10973698Anti-Iba1;
RRID:AB_2636859
Anti-Iba1 Abcam Cat no. ab178846
Anti-Cytokeratin 18 EMD Millipore Cat no. MAB3234; RRID:AB_94763Anti-Atf5;
RRID:AB_205876
Anti-Atf5 Santa Cruz Biotechnology Cat no. sc-46934
anti-mouse IgG conjugated to Alexa-488 Thermo Fisher Scientific Cat no. A-21202; RRID:AB_141607
anti-rabbit IgG conjugated to Alexa-555 Thermo Fisher Scientific Cat no. A-31572, RRID:AB_162543
anti-chicken IgG conjugated to Alexa-488 Jackson ImmunoResearch Cat no. 703-545-155; RRID:AB_2340375
Molecular Probes
RNAscope Probe for Hs-ADCY3 Acdbio Cat no. 441671
RNAscope Probe for Hs-ATF5 Acdbio cat no. 507471
Opal dye 520 Akoya Biosciences FP1487001KT
Opal dye 570 Akoya Biosciences FP1488001KT
Opal dye 690 Akoya Biosciences FP1497001KT
Chemicals, peptides, and recombinant proteins
OptiPrep density Gradient Medium Sigma-Aldrich cat no. D1556-250ML
Mse I NEB cat no. R0525M
Triton X-100 Sigma cat no. 93443
BSA NEB cat no. B9000S
Biotin-11-dUTP Thermo Fisher Scientific cat no. R0081
Quick ligase kit NEB cat no. M2200L
Proteinase K NEB cat no. P8107S
AMPure XP beads Beckman Coulter A63881
Dynabeads MyOne Streptavidin T1 beads Thermo Fisher Scientific cat no. 65602
T4 DNA ligase reaction buffer NEB cat no. B0202S
16% Formaldehyde Solution Thermo Sciientific cat no. 28906
Critical commercial assays
Truseq RNA Library Prep Kit v2 Illumina 20020597
Direct-zol RNA kits from Zymo Research Zymo Research cat no. R2052
RNAscope Multiplex Fluorescenct v2 Assay Acdbio cat no. 323135
Ovation Ultralow System V2 32 Tecan Genomics cat no. 0344-32
Next GEM Single Cell 50GEM Kit v2 10x Genomics cat no.1000265
Papain Dissociation system Worthington Biochemical cat no. LK003178
Deposited data
All sequencing data for hamster and human samples This manuscript, 4DN portal [https://doi.org/10.1101/2021.02.09.430314]
Experimental models: Organisms/strains
Golden Syrian hamsters (Mesocricetus auratus) Charles River Laboratories LVG hamsters, strain code: 049
Software and algorithms
ImageJ Schneider et al. (2012) https://imagej.nih.gov/ij/
R version 4.0.5 base packages The R Foundatiion https://www.rstudio.com/products/rstudio/
download/
(Continued on next page)
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induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
RESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and raw data should be directed to and addressed to Stavros Lomvardas (sl682@
cumc.columbia.edu)
Materials availability
This study did not generate any materials and unique reagents.
Data and code availability
Human and hamster RNAseq, scRNAseq, in situ HiC, supplemental spreadsheets and detailed experimental protocols have been
deposited to 4DN portal at https://doi.org/10.1101/2021.02.09.430314.
This study did not generate custom computer code.
Any additional information is available from the Lead Contact upon request.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
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 Biomed-
icals), 4mM L-glutamine (Gibco), 10mM HEPES (Fisher Scientific), 0.12% NaHCO
3
, 1% heat-inactivated FBS, and 0.7% Oxoid agar
(Thermo Scientific). SARS-CoV-2 virus stocks used for hamster experiments were passage 3.
Continued
REAGENT or RESOURCE SOURCE IDENTIFIER
Subread Liao et al. (2014) http://subread.sourceforge.net
Deseq2 Love et al. (2014) https://bioconductor.org/packages/release/
bioc/html/DESeq2.html
STAR Dobin et al. (2013) https://github.com/alexdobin/STAR
Samtools Li et al. (2009) http://samtools.sourceforge.net/
sva package in R Zhang et al. (2020) https://bioconductor.org/packages/release/
bioc/html/sva.html
Blast Camacho et al. (2009) https://blast.ncbi.nlm.nih.gov/Blast.cgi
Megahit Li et al. (2015) https://github.com/voutcn/megahit
IGV Robinson et al. (2011) https://software.broadinstitute.org/
software/igv/
R version 4.0.5 ggplot2 package The R Foundation https://cran.r-project.org/web/packages/
ggplot2/index.html
R version 4.0.5 Seurat package The R Foundation https://cran.r-project.org/web/packages/
Seurat/index.html
Cellranger 5.0.1 10X Genomics https://support.10xgenomics.com/single-
cell-gene-expression/software/pipelines/
latest/release-notes
CellBender https://doi.org/10.1101/791699 https://cellbender.readthedocs.io/en/latest/
Juicer Durand et al. (2016) https://github.com/aidenlab/juicer/
Juicebox Robinson et al. (2018) https://github.com/aidenlab/Juicebox
Python version 3.7.10 Python Software Foundation https://www.python.org
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Cell 185, 1–13.e1–e5, March 17, 2022 e2
Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
Human samples
25 patients previously diagnosed with COVID-19 at symptoms presentation and postmortem by SARS-CoV-2 RT-PCR analysis un-
derwent 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 met-
astatic 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 Otolaryn-
gology. Tissues were obtained and preserved for histological, molecular, and microscopic evaluation using separate surgical instru-
ments 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 resec-
tion of the anterior skull base including the cribriform plate. Olfactory epithelium was isolated from the olfactory cleft, spanning turbi-
nate and adjacent septal mucosa prior to being preserved in 1% paraformaldehyde (for HiC), 4% paraformaldehyde (for RNA ISH/IF),
or Trizol (for RNA-seq).
METHOD DETAILS
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 4-6-week-old male golden hamsters purchased from Charles River Labo-
ratories. Hamsters were intraperitonially administered anesthesia of ketamine/xylazine (3:1), [100mg/kg] before inoculation. Inocu-
lations 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.
Serum collection, inactivation and intranasal inoculation
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 naı
¨ve 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 Anal-
ysis (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 DESeq2-generated 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 dis-
tribution, 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 VS-transformed 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.
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Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
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
3ʹv.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 (Fleming et al., 2019) was used to model and
remove systematic biases and background noise, and to remove empty droplets. Post Cellbender h5 matrixes 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() func-
tion 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 compo-
nents 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.
RNAscope in hOE
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, RNAscopeProbe - 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 RNAscopeProbe 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 12mm in human specimens. In hamster, OE and OB were similarly embedded in OCT and sagittal cryo-
sections were collected at a thickness of 8-12mm. 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 mg/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 anti-
bodies were labeled with the following secondary antibodies: for OMP, anti-chicken IgG conjugated to Alexa-488 (2 mg/ml, Jackson
ImmunoResearch cat no. A-11055, RRID:AB_2534102), for Adcy3, anti-rabbit IgG conjugated to Alexa-555 (2 mg/ml, Thermo Fisher
Scientific cat no. 703-545-155, RRID:AB_2340375), for cyto-Krt18, anti-mouse IgG conjugated to Alexa-488 (2 mg/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-mm 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 per-
meabilization. After being quenched with 1.1% Triton-X for 10 min at 37 C, nuclei were digested with 25 U/ml MseI in 13CutSmart
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
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Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
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 23Ampure beads following the standard protocol and eluted in 300 ml water. Biotinylated frag-
ments 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 13Ampure 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 (2 375 bp), or NovaSeq 6000 (2 3150 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 ham-
ster 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.
QUANTIFICATION AND STATISTICAL ANALYSIS
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 num-
ber of replicates can be found in the figure legends and methods details, p-values are indicated on figures.
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Please cite this article in press as: Zazhytska et al., Non-cell-autonomous disruption of nuclear architecture as a potential cause of COVID-19-
induced anosmia, Cell (2022), https://doi.org/10.1016/j.cell.2022.01.024
Article
Supplemental figures
Krt18
SARS-CoV-2
DAPI
1dpi
Distance
(
Pm)
Pixel Intensity
OMP
SARS-CoV-2
0 100 200 300
0
20
40
60
OMP
SARS-CoV-2
20 um
SARS-CoV-2
20 um
’’
OMP
SARS-CoV-2
DAPI
OMP
25
Pm
*
Distance (Pm)
Pixel Intensity
0
10
20
30
40
05812
SARS-CoV-2
AIF1/Iba-1
SARS-CoV-2
DAPI
AIF1/Iba-1
Mature OSNs
INPs
SUS cells
T-cells
Microglia
HBCs
Macrophages
Fibroblasts
MV1
B-cells
GBCs
MV2
Olfactory glia
C1qa
Ccl3
C3
Ltb
Cd37
Tmem119
Cd74
Aif1
Ccl5
Ccl4
Fbln1
Dcn
Lum
Slc12a2
Avil
Hepacam2
Aqp3
F3
Jun
Krt17
Krt5
Krt14
Pafah1b3
Ezh2
Hes6
Neurod1
Neurog1
Cyp2g1
Cyp1a2
Gng8
Gap43
Olig2
Lhx2
Ebf2
Emx2
Atf5
Gde1
Sox11
Rtp1
Chga
Tom1
Gfy
Gng13
Omp
Adcy3
Identity
Percent Expressed
0
25
50
75
-1
0
1
2
Average Expression
SUS cells
T-cells
GBCs
INPs
Microglia
Fibroblasts
HBCs
MV1
Macrophages
OSNs
B-cells
MV2
Olfactory glia
-10
0
10
-10 0 10
UMAP_1
UMAP_2
1dpi
GBCs
T-cells
HBCs
INPs
SUS cells
Microglia
Fibroblasts
OSNs
B-cells
MV1
Macrophages
Olfactory glia
MV2
-10
0
10
-10 0 10
UMAP_1
UMAP_2
3dpi
CoV2--S
SUS cells
T-cells
GBCs
Fibroblasts
Microglia
OSNs
Macrophages
HBCs
INPs
MV1
B-cells
MV2
Olfactory glia
-10
0
10
-10 0 10
UMAP_1
UMAP_2
10dpi
GBCs
T-cells
MV2
INPs
SUS cells
HBCs
Fibroblasts
Microglia
OSNs
B-cells
MV1
Olfactory glia
Macrophages
-10
0
10
-10 0 10
UMAP_1
UMAP_2
Mock
A
DC
B
E
Figure S1. SARS-CoV-2 infects hamster OSNs very infrequently, related 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, and dot color represents expression level. The plot
shows clusters from 68,951 combined cells extracted from eight OEs with two biological replicates per condition.
(legend continued on next page)
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(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 4 dpi.
Rarely OMP-positive cells colocalize 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.
(D) Top, IF-FISH confocal micrograph of OE tissue section showing colocalization of RNA FISH signal of SARS-CoV-2 (magenta) and antibody staining for OMP
(green), suggesting that a small percentage of OSNs is infected. Bottom, IF-FISH in hamster OB section at 4 dpi shows rare RNA-FISH SARS-CoV-2 (magenta)
detected in proximity to glomeruli labeled by OMP protein (green).
(E) RNA-FISH SARS-CoV-2 signal (magenta) in the OE and lamina propria strongly correlates with the microglia marker AIF1/Iba1 (yellow), as highlighted by the
enlarged picture in the white box. The line intensity scan of one single cell shows that SARS-CoV-2 signal is correlated with AIF1/Iba1 suggesting engulfment of
viral particles by microglia.
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Figure S2. Evidence for induction of antiviral programs in OSNs upon SARS-CoV-2 infection of hamster OEs, related to Figure 2
Feature plot of Irf7, Irf9, Isg15, and Eif2ak2 (PKR) expression across clusters. Expression of these genes starts at microglia and immune cells at 1 dpi but expands
on OSNs and other OE-resident cells by 3 dpi (two biological replicates per condition combined).
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A
B
Figure S3. GSE analysis of enriched genes 1 and 10 days post SARS-CoV-2 infection in hamsters, related to Figure 3
(A) GSE analysis for enriched genes at 1 dpi in hamster reveals consequences for neurogenesis and OSNS activity at 1 dpi for three GO domains (biological
process, cellular component, and molecular function).
(B) GSE analysis for enriched genes at 10 dpi in hamster for three GO domains.
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Figure S4. Subtle transcriptional changes in the hamster OE upon exposure to UV-neutralized serum from infected hamsters, related to
Figure 5
(A) Z-scored expression of OR genes from OEs exposed to mock- versus SARS-CoV-2-infected serum for 12.5 h (left) or from OEs that were mock infected versus
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, 1 dpi, 2 dpi, 4 dpi, and 10 dpi) (right).
(C) Violin plots showing the effect of exposure to serum from SARS-CoV2 versus mock-infected hamsters for 12.5 h at the transcription of OR genes, OE, OSN,
and SUS markers. For comparison, we plot the same groups at SARS-CoV-2-infected (1 dpi) versus mock-infected hamsters. For each panel (A–C), data
represent averages of three biological replicates per condition.
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*
Atf5
SARS-CoV-2
DAPI
COVID-19
20um
20um
Atf5
SARS-CoV-2
AIF1/Iba1
SARS-CoV-2
20um
146
OMP
ATF5
Wilcoxon, p = 0.39
0
40
80
Control
189
Covid−19
146
DAPI in 189 and 146
Percentage ATF5 positive cells/
p = 0.0074
p = 0.72
8
0
2
4
6
% of cells positive for AIF1/Iba1
Neuronal layer
(OE)
Lamina propria
0.0
0.2
0.4
0.6
Pearsons Correlation (R)
Atf5 Krt-18 AIF1/Iba1
SARS-CoV-2
Control COVID-19
1
3
10
30
Number of SARS-COV-2 puncta
Apical
Layer
padj = 0.019
padj =
0.00044 padj = 0.78
Neuronal
layer (OE)
Lamina
propria
E
D
C
B
A
Respiratory epithelium
Olfactory epithelium
hOE_102
hOE_116
hOE_99
hOE_167
hOE_128
hOE_136
hOE_134
hOE_132
hOE_147
hOE_146
hOE_163
hOE_118
hOE_153
hOE_114
hOE_110
hOE_107
hOE_170
hOE_150
hOE_145
hOE_169
hOE_189
hOE_2186
hOE_2193
1
10
100
1000
10000
1
10
100
1000
10000
Normalized Counts
Control
Covid 19
Respiratory epithelium
Olfactory epithelium
102
116
99
167
128
136
134
132
147
146
163
118
153
114
110
107
170
150
145
169
189
2186
2193
1
10
100
1000
10000
1
10
100
1000
10000
Normalized Counts
0.0e+00
2.5e 05
5.0e 05
7.5e 05
Control
Covid
102
116
99
167
128
136
134
132
147
146
163
118
153
114
110
107
170
150
145
169
189
2186
2193
Percentage counts
IM_percentage
RE_percentage
OE_percentage
% total counts for OE, RE and immune cell types
1e+02
1e+04
1e+06
102
116
99
167
128
136
134
132
147
146
163
118
153
114
110
107
170
150
145
169
189
2186
2193
Normalized Counts (Sum)
Imm_unique
Olfactory epithelium
Respiratory epithelium
SARS−CoV−2
Sum SARS−CoV−2 counts, OE, RE and Immune cell types markers
F
ORF1b
ns2
HE
S
ns12.9
N
E
M
ORF1a
48
0
hCoV-OC43 in 2186
R=0.11 p=0.32
0
25
50
75
0
10000
20000
30000
Normalized counts
Post mortem time (hours)
Corr House Keeping genes (ACTB, GAPDH, PPIA, PGK1)
and Post mortem time as a proxy of RNA integrity
H
G
-1.00
-0.75
-0.50
-0.25
0.00
Control Covid-19
Covid19
Surrogate Variable 1
Outliers labeled
Correlation between SV1 and Covid19
205
-1.00
-0.75
-0.50
-0.25
0.00
12345
batch
Surrogate Variable 1
Outliers labeled
Correlation between SV1 and batch
205
-1.00
-0.75
-0.50
-0.25
0.00
FM
sex
Surrogate Variable 1
Outliers labeled
Correlation between SV1 and sex
-1.00
-0.75
-0.50
-0.25
0.00
0255075
postmortemtime
Surrogate Variable 1
Outliers labeled
Correlation between SV1 and postmortemtime
-1.00
-0.75
-0.50
-0.25
0.00
0204060
daysinhospital
Surrogate Variable 1
Outliers labeled
Correlation between SV1 and daysinhospital
205
-1.00
-0.75
-0.50
-0.25
0.00
40 60 80
age
Surrogate Variable 1
Outliers labeled
Correlation between SV1 and age
205
205 205
I
20um
Wilcoxon, p = 0.054
Wilcoxon, p = 0.00033
Wilcoxon, p = 0.054
Wilcoxon, p = 0.019
Wilcoxon, p = 0.053
Wilcoxon, p = 0.84
Wilcoxon, p = 0.042
Wilcoxon, p = 0.0018
Wilcoxon,
p = 0.0049
Wilcoxon, p = 0.064
IL1R2 IFNG
TNFSF9 IL20RB IL6
CXCL1
CCL20 IL1RN
TNFRSF17
3
10
30
100
300
1000
3000
3
10
30
30
100
300
1000
1
10
100
1000
10
100
1000
3
10
30
100
30
100
300
1000
10
30
100
300
3
10
30
100
300
Wilcoxon, p = 0.042 Wilcoxon, p = 0.01
IL1B CCL19
30
100
300
1
10
100
1000
Normalized Counts (log10)
CXL11
J
K
regulation of endopeptidase activity
immune response-activating cell surface receptor signaling pathway
receptor-mediated endocytosis
response to lipopolysaccharide
neutrophil mediated immunity
coagulation
blood coagulation
neutrophil activation
calcium-mediated signaling
positive regulation of cytosolic calcium ion concentration
regulation of hemopoiesis
antigen processing and presentation
DNA packaging
interferon-gamma-mediated signaling pathway
leukocyte chemotaxis
regulation of gene silencing
nucleosome assembly
dendritic cell differentiation
antimicrobial humoral immune response mediated by antimicrobial peptide
chromatin organization involved in negative regulation of transcription
T-helper 1 type immune response
neutrophil chemotaxis
negative regulation of gene expression, epigenetic
cellular response to chemokine
DNA replication-dependent nucleosome assembly
interleukin-7-mediated signaling pathway
0
20
40
Fold Enrichment (O/E)
3
4
5
6
7
-log10(FDR)
Upgulated Genes, GO=Biological Process
60
Figure S5. Quality control analyses of human OE autopsies, related 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/Ib a1) 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
2,140 cells in infected and 1,819 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 arrow s.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.
(D) Bar plot of the percentage of Atf5 RNA
+
cells over total number of DAPI-positive cells on sections of COVID-19 (146) and control (189) human OEs. No
significant difference between samples is detected (n = 508 for 146, n = 458 for 189). Cells with more than two RNA puncta were counte d as positive. On the right,
a representative image of sample 146 for Atf5 RNA-FISH (red) and OMP protein (green).
(E) Percentage of total counts of OE, RE, and immune-cell-type genes. Pairwise Wilcoxon test shows no significant difference between samples. Distribution of
normalized counts for OE and RE genes for each sample (bottom panels).
(F) SARS-CoV-2 counts plotted in decreasing order (red) together with normalized counts for OE (blue), RE (black), and immune cells (gray).
(G) Correlation of house-keeping genes (ACTB, GAPDH, PPIA, and PGK1) and post-mortem times were used as a proxy of RNA integrity. Pearson’s test shows no
significant correlation.
(H) Coverage map for hCoV-OC43 in sample 2,186.
(I) Surrogate variable analysis reveals sample 205 as an extreme outlier. SV1 does not correlat e with known variables (COVID-19, processing batch, age, sex,
post-mortem time, or days after symptoms onset), but rather in all cases distinguishes sample 205 from other samples.
(J) Box plot depicting normalized counts between pooled control and infected samples for antiviral genes.
(K) GO analysis for upregulated genes depicting significant enrichment for genes involved in immune/antiviral responses.
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Article
Figure S6. FACS and HiC of OSN nuclei from human OE autopsies, related to Figure 7
(A) FACS data for control and COVID-19 human OE. Fixed DAPI positive, Lhx2/Atf5 double positive nuclei were collected for in situ HiC.
(B) Reduction in HiC contacts in compartments formed by Adcy3, Gng3, Gfy, OMP, Grm7, Gap43, and Rtp1 genes.
ll
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