Wenxiang Gao1, Jiarui Liao1,
Haoyan Guan1, Yueqi Sun2
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[1]. The prevalence of AR has been reported to be
approximately 2% to 25% in children [2] and 1%
to greater than 40% in adults [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[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 [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 [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-), 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+,GATA3+,
CXCR4+, CCR3+),
Treg(FOXP3+, IKZF2+), Naïve
T(TCF7+, SELL+,
LEF1+, CCR7+) and Cytotoxic
CD8(GZMB+, GZMK+,
NKG7+, CST7+,
PPF1+, TRGC2+) 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>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(not show). 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(CD14+ FCGR3A-). Mono2
(CD14+ FCGR3A+), Mono3
(CD14+ FCGR3A++)cells were
identified. However, Mono4 related genes (CD14-FCGR3A- KLRC4+KLRK1+TCRBV3S1+)[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+CCL8+CXCL10+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+OLIG1+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.
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