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\begin{document}

\title{Single-cell transcriptomics reveals the alteration of peripheral blood
mononuclear cells in AR patient challenge by allergen}



\author[1]{Wenxiang Gao}%
\author[1]{Jiarui Liao}%
\author[1]{Wen-xiang Gao}%
\author[2]{Yue-Qi Sun}%
\affil[1]{Sun Yat-sen University First Affiliated Hospital Department of Otorhinolaryngology}%
\affil[2]{The Seventh Affiliated Hospital Sun Yat-sen University}%


\vspace{-1em}



  
  \date{May 15, 2024}


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\textbf{Single-cell transcriptomics reveals the alteration of peripheral
blood mononuclear cells in AR patient challenge by allergen}

\textbf{Wenxiang Gao\textsuperscript{1}, Jiarui Liao\textsuperscript{1},
Haoyan Guan\textsuperscript{1}, Yueqi Sun\textsuperscript{2}}

1 The First Affliated Hospital of Sun-Yat Sen University ENT

2 The Seventh Affliated Hospital of Sun-Yat Sen University

To the Editor:

Allergic rhinitis (AR) is one of the most common diseases
globally\textsuperscript{{[}1{]}}. The prevalence of AR has been
reported to be approximately 2\% to 25\% in children
\textsuperscript{{[}2{]}} and 1\% to greater than 40\% in adults
\textsuperscript{{[}1,3{]}}. The prevalence of confirmed AR in adults in
Europe ranged from 17\% to 28.5\%. Classical symptoms of AR are nasal
itching, sneezing, rhinorrhea, and nasal congestion. Ocular symptoms are
also frequent\textsuperscript{{[}4,5{]}}; The symptoms of AR can reduce
quality of life and school and work performance and is also a frequent
reason for outpatient treatment in ENT department which may costs large
medical expenses. AR is a risk factor for asthma
\textsuperscript{{[}4,5{]}}, and uncontrolled moderate-to-severe AR
affects asthma control.

The pathogenesis of AR is closely related to the changes of immune cells
after allergen challenge. When AR patients are challenged by allergens,
the immune cells in the body will produce a series of changes, producing
Th2 related inflammation. The symptom occurred in 24 hours is called
early-onset reaction, while in 72 hours is called late-onset reaction. T
cells are the most important effector cells, while monocytes are also
involved in the response transformed by the recruitment of inflammatory
factors. B cells and NK cells are also are also important participating
cells \textsuperscript{{[}6,7,8{]}}. We have a certain understanding of
the pathogenesis of allergic rhinitis, but we have not known the dynamic
changes of immune cells in AR patients after exposure to allergens which
sequence from the level of single cell technology. What's more, the
dynamic changes of immune cells and gene expression in peripheral blood
after allergen challenge are of great help to explore the pathogenesis
of allergic rhinitis and specific treatment for specific immune cells or
allergen stimulated immunotherapy, but there is no relevant report. Here
we use 10x single-cell RNA sequencing to present the dynamic changes of
immune cells in PBMC of patients with allergic rhinitis before and 24
and 72 hours after exposure to allergens.

A 26 years old lady, who was diagnosed as only dust-mite specific
allergy moderate to severe allergic rhinitis in October 2023 was
volunteered to join our research after our strict screening. She has no
history of other diseases and no medication within half a year, and has
a healthy live-style. Although her peripheral blood eosinophils were
higher than normal, she did not have asthma and airway
hyperresponsiveness, with lung function (FVC FEV1) in a normal level.
(The general information of the patients is shown in Table E1) After
preparation, we conducted a nasal allergen challenge test on this
patient. PBMCs were extracted from peripheral blood of patients before
challenge, 24 hours after challenge and 72 hours after challenge, and
then sequenced for 10x single cell RNA respectively (FIG1 A). We
evaluated the subjective and objective symptom scores before and after
nasal provocation to ensure that the patient was successful. (Table E2).

The total number of estimated cells for three PBMC sample were 14,202.
The results of single cell sequencing showed that the three PBMC samples
were divided into 13 clusters of cells without supervision (FIG1 B). The
top 10 differentially regulated genes in the 13 clusters cell were
showed in the heatmap report scaled expression (FIGE1). Differentially
expressed genes fall into categories. These 13 clusters of cells were
partial differences in cluster among three samples. Cluster 1 almost
disappeared 72 hours after challenge, while cluster 6 only appeared
before challenge, and clusters 7 almost appeared 72 hours after
challenge (FIG1 C). These differences of these cell subsets will be
further explained in the follow-up supervised analysis. It shows that
there are indeed changes in peripheral blood PBMC of AR patients after
exposure to allergens. Then we first identified 4 cell types by the
specific markers (FIG E2 A), including: B cells (MS4A1, CD79A),
Monocytes (CD14, FCGR3A), natural killer cells (NKG7, GNLY and
CD3\textsuperscript{-}), T cells (CD3D, CD3G). Interesting, we found the
cluster 6 cells which only appeared in before challenge sample expressed
HBA1, PPBP, LYZ, IGKC, G0S2, XPB1, HPRT1 and ID2 gene, but negative for
the CD34 gene (FIG E2 B). Based on gene expression, we define this
cluster as HSC-GSF cells. So we finally identified 5 cell-type groups in
three PBMC sample. They are Monocytes, T cells, HSC-GSF cells, NK cells
and B cells (FIG1 D). The proportion of T cells and NK cells increased
gradually from pre-challenge to 72 hours, from 42.4\% (pre-challenge) to
53.8\% (24h) to 60.9\% (72h) of T cells and 2.8\% (pre-challenge) to
6.6\% (24h) to 8.4\% (72h) of NK cells. While the proportion of
Monocytes were highest at 24h after-challenge (34.9\%), but nearly the
same level at pre-challenge and 72h (24.3\% vs 25.5\%), which is
consistent with the fact that a large number of monocytes will be
recruited by some chemokines during the early stage of allergy but not
the final effector cells. However, B cells were in highest proportion at
pre-challenge (6.3\%), lowest at 24h (4.6\%). Finally, the HSC G-SCF
cells seem to have all transformed after the allergen challenge (FIG1 E,
F). We use pseudotime analysis and found the trajectory of HSC-GSF
clusters may differentiation into T cell (cluster 2 3 4 5), Monocyte
(cluster 1 7 11) and NK cell (cluster 8) from pre-challenge to 24h and
72h after-challenge.

We further used dimensionality reduction techniques to analyze subgroups
and transcriptional gene expression of each group of cells. The T cell
group can be divided into 8 clusters by dimensional reduction analysis.
The clusters of T cells can be preliminarily distinguished by CD4 and
CD8a CD8b (FIG2 A). Then we further divided T cells major to
Th2(CCR4\textsuperscript{+},GATA3\textsuperscript{+},
CXCR4\textsuperscript{+}, CCR3\textsuperscript{+}),
Treg(FOXP3\textsuperscript{+}, IKZF2\textsuperscript{+}), Na\selectlanguage{ngerman}ïve
T(TCF7\textsuperscript{+}, SELL\textsuperscript{+},
LEF1\textsuperscript{+}, CCR7\textsuperscript{+}) and Cytotoxic
CD8(GZMB\textsuperscript{+}, GZMK\textsuperscript{+},
NKG7\textsuperscript{+}, CST7\textsuperscript{+},
PPF1\textsuperscript{+}, TRGC2\textsuperscript{+}) cells according to
their functional express gene (FIG2 B). The proportion of Th2 were
highest at 24h after challenge (42.4\%), while lowest at 72h (23.4\%) of
all the Th2 cell (FIG2 B). And the functional gene of Th2 were also at
highest expression at 24h, the t-SNE plot of GATA3 and CXCR4 show
greater increase at 24h after challenge than pre-challenge, but
decreased at 72h. And the hi-expression (Log2\textgreater{}2) t-SNE plot
of CXCR4 showed the expression intensity higher than two of CXCR4 gene
in Th2 cells at 24 hours after challenge. The expression of CCR4 did not
show much difference in three sample. (FIG E3A). However, Treg cells
were at the top level at 72h, to 47.1\% of all the Treg cells, while 24h
and pre-challenge were almost at the same level (27.6\% vs 25.3\%) (FIG2
B). In accordance with this, Foxp3 and IKZF2, the functional genes of
Treg, was also in the strongest expression at 72 hours after challenge
(FIG E3B). This suggests that the body has already started its own
immune regulation function from 72 hours after allergen challenge by
increasing the proportion of Treg cells and inhibit the progress of Th2
inflammation. Cytotoxic CD8 were at highest level at 24h, approximate to
44\%, and secondly at the 72h (32.6\%), but only 23.5\% at pre-challenge
(FIG2 B). From the expression of functional genes, the main functional
genes (GZMB, GZMK, NKG7, CST7, PPF1, TRGC2) of cytotoxic CD8 cells were
stronger at 24 hours and 72 hours after challenge than before (FIG E3B).
It is suggested that cytotoxic CD8 cells are also involved in allergic
reaction. Naïve T cells were almost at the same level in three sample
(30.3\%, 33.8\%, 34.9\%). Its major genes expression was also relatively
stable in three samples\selectlanguage{english}(not show\selectlanguage{english}). From the pseudotime analysis, we
found the Naive CD4 T (cluster 2) cells may transform to Treg cells
(cluster 7) instead of Th2 cells (cluster 1) and the Naive CD8 T cells
(cluster 4 5) may transform to Cytotoxic CD8 cells (cluster 3 6) after
challenge. (FIG2 C).

Then we focus on the Monocyte. We used dimension reduction analysis and
further divided the Monocyte into 6 unbiased cluster in t-SNE. Then
Mono1\selectlanguage{english}(CD14\textsuperscript{+} FCGR3A\textsuperscript{-}\selectlanguage{english}). Mono2
\selectlanguage{english}(CD14\textsuperscript{+} FCGR3A\textsuperscript{+}\selectlanguage{english}), Mono3
\selectlanguage{english}(CD14\textsuperscript{+} FCGR3A\textsuperscript{++}\selectlanguage{english})cells were
identified. However, Mono4 related genes
\selectlanguage{english}(CD14\textsuperscript{-}FCGR3A\textsuperscript{-}
KLRC4\textsuperscript{+}KLRK1\textsuperscript{+}TCRBV3S1\textsuperscript{+}\selectlanguage{english})\textsuperscript{{[}9{]}}were
not detected, so we identified three Monocytes type (FIG E4A). The
proportion of Mono1 cells were the lowest among the three types of
Monocyte, which were 4.2\% (pre-challenge), 2.1\%(24h), 1.9\% (72h) in
the three samples (FIG 2D). There was no significant difference in the
expression intensity of the top five genes (FECR1A NDRG2 NRARP AXL AFF3)
in Mono1 among the three samples (FIG E4B). The proportion of Mono3
cells were highest in 24h (20.3\%) (FIG 2D), while the top five
expressed gene (MS4A4A CDKN1C HES4 CD79B C1QA) did not show significant
difference among three sample (FIG E4C). Mono2 occupied the largest
share as well as the biggest variation of all the three-type Monocyte in
three sample. The proportion of Mono2 cells were slightly lower at 24
hours (77.6\%), but about the same level at pre-challenge (84.3\%) and
72 hours after challenge (84.5\%) (FIG 2C). However, the Mono2 subsets
in the three samples were almost completely different.
CCL2\textsuperscript{+}CCL8\textsuperscript{+}CXCL10\textsuperscript{+}subsets
were predominant before challenge, which is indicated that Mono2 mainly
exists in the form of chemotactic before allergen challenge in the body.
However, at 24 hours after challenge, the chemotactic Mono2 subgroup
disappeared and replaced by two another Mono2 subgroups with EGR2, EGR3
positive and OSM positive. . When it comes to 72 hours, the cell Mono2
subsets changed to IERSL\textsuperscript{+}OLIG1\textsuperscript{+}Mono2
(FIG 2D). This also confirmed that at the gene level of single cell,
after 72 hours of allergic reaction, Monocytes were not the main cells
involved in inflammation. The top ten genes of Mono2 in three sample
were showed in the FIG E4D.

Finally, B cells and NK cells did not show multiple subgroups on the
FIG1 B \& C t-SNE plot, so we did not conduct dimensional reduction
analysis. Changes in the proportion of B cells and NK cells after
allergen challenge have been described previously. We compared the genes
expressed in B cells and found that the top ten genes in B cells were
down regulated after allergen challenge. Of which, FCRLA, IGHD and IGHG2
are closely related to the immune function of B cells, suggest that the
function of B cells may be weakened after allergen stimulation (FIG
E5A). This is different from what we knew before. On the contrary, the
expression of the top ten genes in NK cells increased significantly
after challenge. The expression of KLRF1, NCR1, CD160 gene are closely
related to the function of NK cells, which indicates that NK function is
up-regulated after allergen challenge. NK cells may play a more
important role in allergic reactions than we have previously know(FIG
E5B).

For the first time, we analyzed the dynamic changes of PBMC in AR
patients after allergen challenge by single cell sequencing technology,
and simulated the clinical pathogenesis of AR. Total of 5 groups of
cells were identified in the pre- and pro-challenge, including T cell,
NK cell, B cell, Monocyte and HSC-G-CSF cell. HSC-G-CSF cell only
existed before challenge, and completely disappeared at 24h and 72h
after challenge. It may transform into T cells, NK cells and Monocyte by
the pseudotime analysis. The proportion of Th2 in functional T cells was
the highest at 24h while Treg was the highest at 72 hours, suggesting
that the peak of inflammation may be in 24 hours and the body has
already started the self-regulation mode at 72 hours. According to the
proportion of CD8 toxic T cells and the expression of functional genes,
it may also be involved in allergic reaction. Except for Mono4, other
three subsets of Monocytes can be identified. The largest proportion
Mono2 subset showed significant difference pre- and pro- challenge. From
the perspective of gene expression, Mono2 seems did not participate in
the inflammatory reaction of late-onset after 72 hours. Surprisingly,
the proportion of B cells and the expression of functional genes
decreased after challenge, suggesting that the role of B cells in
allergic diseases may not be as high as expected. But on the contrary,
the proportion of NK cells and functional genes were enhanced after
challenge. The role of NK cells in allergic rhinitis can be further
studied to find out something new.

We used single cell sequencing technology to study the dynamic changes
of peripheral blood PBMC in the pathogenesis of AR. Although it is only
a descriptive case, there are innovative findings, which are helpful for
understanding the pathogenesis of Allergic rhinitis and follow-up
research and database establishment.

REFERENCES

Bousquet J, Khaltaev N, Cruz AA, Denburg J, Fokkens WJ, Togias A, et al.
Allergic Rhinitis and its Impact on Asthma (ARIA) 2008 update (in
collaboration with the World Health Organization, GA(2)LEN and
AllerGen). Allergy 2008; 63(suppl 86):8-160.

Asher MI, Montefort S, Bjorksten B, Lai CK, Strachan DP, Weiland SK, et
al. Worldwide time trends in the prevalence of symptoms of asthma,
allergic rhinoconjunctivitis, and eczema in childhood: ISAAC Phases One
and Three repeat multicountry cross-sectional surveys. Lancet
2006;368:733-43

3. Katelaris CH, Lee BW, Potter PC, Maspero JF, Cingi C, Lopatin A, et
al. Prevalence and diversity of allergic rhinitis in regions of the
world beyond Europe and North America. Clin Exp Allergy 2012;42:186-207.

4. Leynaert B, Bousquet J, Neukirch C, Liard R, Neukirch F. Perennial
rhinitis: an independent risk factor for asthma in nonatopic subjects:
results from the European Community Respiratory Health Survey. J Allergy
Clin Immunol 1999;104:301-4.

5. Gergen PJ, Turkeltaub PC. The association of individual allergen
reactivity with respiratory disease in a national sample: data from the
second National Health and Nutrition Examination Survey, 1976-80 (NHANES
II). J Allergy Clin Immunol 1992;90:579-88.

6. Guerra S, Sherrill DL, Baldacci S, Carrozzi L, Pistelli F, Di Pede F,
et al. Rhinitis is an independent risk factor for developing cough apart
from colds among adults. Allergy 2005;60:343-

7. Gill MA. The role of dendritic cells in asthma. J Allergy Clin
Immunol, 2012, 129(4): 889-901.

8. Bart N Lambrecht, The immunology of asthma, nature immunology, 2015,
16(1):45-56

9. Villani et al. Single-cell RNA-seq reveals new types of human blood
dendritic cells, monocytes, and progenitors. Science 356, 283 (2017)

\textbf{Method}

\textbf{General information of the patient}

The selected patient was a 25-year-old female who has no history of
smoke/drink/asthma/ diabetes/autoimmune disease/blood system
disease/heart disease/surgery/blood transfusion, and has no experience
of upper respiratory tract infection or application of corticosteroids/
antihistamines in recent half year. The patient has been diagnosed as
allergic to dust mite only, with a symptom of urticaria. Lung function
and blood routine examination were also performed. The lung function was
normal, but the blood routine showed that the proportion and quantity of
eosinophils increased. Detailed information were showed in Table E1.

\textbf{Nasal allergen provocation test}

\textbf{Preparation}

The provocation test is performed at the temperature of 14-20\selectlanguage{english} in a
quiet room which is equipped with first-aid measures such as
epinephrine, dexamethasone, antihistamines, tracheotomy packages, and
oxygen inhalation devices. We first prepared well the solvent solution,
allergen solution (HDM 1.0mg/ml, Wolwobiotech, China) in two different
marker provocation device (the sterile syringe). Then the patient's
scores of total nasal symptom scores (TNSS) , visual analog scale (VAS)
and anterior nasal endoscope and nasal flow test were assessed at
baseline and after nasal provocation.

\textbf{Nasal provocation}

a) Patient will challenge by the allergen solvent first as solvent
control. A special nasal spray device will be used for nasal provocation
tests. Two doses will be sprayed on each side of the nasal cavity, with
an average of 0.05 mL each dose, one in the direction of the common
nasal meatus, and the other in the direction of the middle meatus.
Patient should to take a deep breath before spraying, then hold breath
during the spraying procedure, take deep breath afterwards. Then we will
evaluate the symptom and nasal examination within 10 minutes in
chronological order. We found the solvent provocation did not cause more
than 50\% of the symptoms increased or 30\% nasal flow decreased. (Table
E2). So the allergen solvent challenge is negative, we can do allergen
solution provocation.

b) The nasal allergen provocation was performed at least 10 mins after
solvent control. Same as solvent provocation, two doses spray will be
given on each side of patient's nasal cavity, with an average of 0.05 mL
each dose, one in the direction of the common nasal meatus, and the
other in the direction of the middle meatus. Patient should to take a
deep breath before spraying, then hold breath during the spraying
procedure, take deep breath afterwards. Then we will evaluate the
symptom and nasal examination within 10 minutes in chronological order.
The provocation is defined as positive when subjective measures is
clearly positive or objective measures is clearly positive or both
subjective and objective measures are moderately positive, based on the
EAACI Position paper on the standardization of nasal allergen
challenges. After allergen challenge, the symptom score increased by
more than 50\% and the nasal airflow decreased by more than 30\%
compared with baseline and solvent control. The nasal provocation test
was successful (Table E2).

\textbf{Observation after provocation}

Observe the patient at least every 0.5h. Make sure the patient is
informed of the possible delayed allergic reaction in 12h after
provocation.

\textbf{Isolation of PBMCs}

20ml peripheral blood would be collected from the patient before
provocation and 24h, 72h after allergen nasal challenge. Peripheral
blood was collected in the heparin anticoagulant according to a standard
manner, then transferred to a 50 mL centrifuge tube and diluted with 20
mL PBS. 2.5 mL Ficoll-Hypaque Solution (Meijingbio, China) were added to
the tube and mixed gently. Centrifuge at 400g for 25 minutes, and
transfer PBMC cell layer to another tube. Add enough HBBS and mix
thoroughly. After centrifuging at 300g for 5 minutes, the supernatant
was discarded and the cell pellet was re-suspended in HBSS to repeat the
centrifuging again.

\textbf{Preparation of Single Cell Suspension}

Cells were transported in DMEM (ThermoFisher Scientific, Waltham, MA) on
ice immediately after above procurement. This suspension was then
filtered using a 40\selectlanguage{greek}µ\selectlanguage{english}m strainer (ThermoFisher Scientific) and residual
cell clumps were discarded. The suspension was immediately placed on
ice. After centrifuging at 300g at 4\selectlanguage{ngerman}°C for 6 minutes, the supernatant
was discarded and the cell pellet was re-suspended in DMEM and placed on
ice prior to staining for loading.

\textbf{Sequencing library construction using the 10x Chromium platform}

We Approximately 15000-20000 cells were partitioned into nanoliter
droplets to achieve single cell resolution for a maximum of 5000
individual cells per sample. The resulting cDNA was tagged with a common
16-nt cell barcode and 10- to 12-nt unique molecular identifier during
the reverse transcription (RT) reaction. Full-length cDNA from poly
A--tailed mRNA transcripts was enzymatically fragmented and selected to
optimize the cDNA amplicon size (\textasciitilde{}400 bp) for library
construction (10x Genomics). The single cell library concentration was
quantified by quantitative PCR (qPCR) analysis (Kapa Biosystems) to
produce appropriate cluster counts for the NovaSeq 6000 platform
(Illumina). We generated 28--98 bp (3\selectlanguage{english}' v3 libraries) or 2--150 bp (5\selectlanguage{english}'
libraries and TotalSeq libraries) sequence data targeting
\textasciitilde{}50 K read pairs/cell for the gene expression library,
which provided digital gene expression profiles for each individual
cell.

\textbf{Single-cell RNA-seq data preprocessing:}

The Cell Ranger software pipeline (version 3.1.0) provided by
10xGenomics was used to demultiplex cellular barcodes, map reads to the
genome and transcriptome using the STAR aligner, and down-sample reads
as required to generate normalized aggregate data across samples,
producing a matrix of gene counts versus cells. We processed the unique
molecular identifier (UMI) count matrix using the R package Seurat
(version 3.1.1). To remove low quality cells and likely multiplet
captures, which is a major concern in microdroplet-based experiments, we
apply a criteria to filter out cells with UMI/gene numbers out of the
limit of mean value +- 2 fold of standard deviations assuming a Guassian
distribution of each cells' UMI/gene numbers. Following visual
inspection of the distribution of cells by the fraction of mitochondrial
genes expressed, we further discarded low-quality cells where
\textgreater{}10\% of the counts belonged to mitochondrial genes. After
applying these QC criteria, 14202 single cells in total remained and
were included in downstream analyses. Library size normalization was
performed in Seurat on the filtered matrix to obtain the normalized
count.

Top variable genes across single cells were identified using the method
described in Macosko et al. Briefly, the average expression and
dispersion were calculated for each gene, genes were subsequently placed
into 13 bins based on expression. Principal component analysis (PCA) was
performed to reduce the dimensionality on the log transformed
gene-barcode matrices of top variable genes. Cells were clustered based
on a graph-based clustering approach, and were visualized in 2-dimension
using tSNE. Likelihood ratio test that simultaneously test for changes
in mean expression and in the percentage of expressed cells was used to
identify significantly differentially expressed genes between clusters.
Here, we use the R package SingleR, a novel computational method for
unbiased cell type recognition of scRNA-seq, with the reference
transcriptomic datasets `Human Primary Cell Atlas' (Mabbott et al. 2013)
to infer the cell of origin of each of the single cells independently
and identify cell types.

Differentially expressed genes (DEGs) were identified using the
FindMarkers function of Seurat{[}1{]} package. P value \textless{} 0.05
and \textbar{}log2foldchange\textbar{} \textgreater{} 0.58 was set as
the threshold for significantly differential expression.

\textbf{Funding}

This study was supported by Youth Science Foundation, National Natural
Science Foundation of China {[}Grant Number 82000950{]} and Guangzhou
Municipal Science and Technology Program {[}Grant Number 202201011050{]}\selectlanguage{english}
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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Table-E2/Table-E2}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

