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INTRODUCTION
Stroke is among the most fatal neurological diseases, and
it is the second leading cause of death in those aged >60
years and the fifth leading cause of death in those aged
<15 years [1–3]. Strokes are clinically classified into
ischemic stroke (IS), hemorrhagic stroke, and transient
ischemic attack, with IS accounting for 80% of all stroke
cases [3, 4]. IS is not only a major cause of death but is
also responsible for a significant number of disability-
adjusted life years, which increased by 138.6% from
1990 to 2019 [5]. Therefore, improving the prognosis of
IS is crucial for alleviating the disease burden.
Senescence is a fundamental biological process
characterized by a general decline in tissue function,
increased susceptibility to neurological diseases, and
decreased resistance to inflammation and infection [6].
Typically, IS is accompanied by accelerated sensory-
motor and neurocognitive decline, which are signs of
senescence [7, 8]. Accordingly, advanced age is a known
risk factor for IS [9]. Furthermore, IS is strongly
www.aging-us.com AGING 2023, Vol. 15, No. 12
Research Paper
Integrative analysis of single-cell and bulk RNA sequencing unveils
the senescence landscape in ischemic stroke
Longhui Fu1,2,*, Beibei Yu 1,2,*, Yongfeng Zhang1,2, Shuai Cao4, Boqiang Lv1,2, Yunze Tian1,2,3,
Huangtao Chen1,2, Shijie Yang1,2, Yutian Hu1,2, Jinghua Hua1,2, Pengyu Ren1,2, Jianzhong Li1,3,
Shouping Gong1,2,5
1Xi’an Jiaotong University, Xi’an, China
2Department of Neurosurgery, Second Affiliated Hospital of Xi’an Jiao Tong University, Xi’an, China
3Department of Thoracic Surgery, Second Affiliated Hospital of Xi’an Jiao Tong University, Xi’an, China
4Department of Orthopedics, Civil Aviation General Hospital, Chaoyang, Beijing, China
5Xi’an Medical University, Xi’an, China
*Co-first author
Correspondence to: Pengyu Ren, Jianzhong Li, Shouping Gong; email: renpengyu.xjut@mail.xjtu.edu.cn; jianzhong-
0520@163.com, https://orcid.org/0000-0002-7846-2921; shpingg@126.com, https://orcid.org/0000-0002-1723-938X
Keywords: ischemic stroke, aging, cellular senescence, single-cell RNA-seq, bioinformatics
Received: April 6, 2023 Accepted: May 27, 2023 Published: June 28, 2023
Copyright: © 2023 Fu et al. This is an open access article distributed under the terms of the Creative Commons Attribution
License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are credited.
ABSTRACT
Ischemic stroke (IS) is a fatal neurological disease that occurs when the blood flow to the brain is disrupted,
leading to brain tissue damage and functional impairment. Cellular senescence, a vital characteristic of aging, is
associated with a poor prognosis for IS. This study explores the potential role of cellular senescence in the
pathological process following IS by analyzing transcriptome data from multiple datasets (GSE163654,
GSE16561, GSE119121, and GSE174574). By using bioinformatics methods, we identified hub-senescence-
related genes such as ANGPTL4, CCL3, CCL7, CXCL16, and TNF and verified them using quantitative reverse
transcription polymerase chain reaction. Further analysis of single-cell RNA sequencing data suggests that MG4
microglial is highly correlated with cellular senescence in MCAO, and might play a crucial role in the
pathological process after IS. Additionally, we identified retinoic acid as a potential drug for improving the
prognosis of IS. This comprehensive investigation of cellular senescence in various brain tissues and peripheral
blood cell types provides valuable insights into the underlying mechanisms of the pathology of IS and identifies
potential therapeutic targets for improving patient outcomes.
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associated with cellular senescence, a major cause of
aging [10]. Cellular senescence refers to the permanent
state of cell cycle arrest, which is a defense mechanism
that prevents unwanted damage to cells [11]. The
inability of cells to re-enter the cell cycle in response to
irreversible growth arrest, resistance to apoptosis,
production of the senescence-associated secretory
phenotype (SASP), mitochondrial dysfunction, and
changes in DNA and chromatin levels are common
pathophysiological processes of cellular senescence [12].
High levels of inflammatory cytokines and SASP have
been detected in the IS-pedunculated region [13]. Various
studies have shown that cellular senescence intervention
improves the prognosis of patients with IS and is a
promising therapeutic approach [14, 15]. There are good
reasons to believe that cellular senescence plays an
important role in the pathophysiological process of IS,
and there are solid grounds for the assertion that cellular
senescence is crucial to the pathophysiology of IS.
Identifying senescent cells in vivo remains challenging,
although cellular senescence can drive a variety of age-
related disease manifestations through aging-related
secretory phenotypes. Recently published gene sets
related to senescent cells can aid in identifying in vivo
cellular senescence [16]. Moreover, senescence can vary
significantly in different cell types. The senescence of
endothelial, smooth muscle and immune cells is believed
to participate in the senescence of blood vessels, and the
senescence of immune cells is believed to promote the
aging of other cell types [17, 18]. Additionally, the
senescence of neurons and glial cells is widespread in
neurodegenerative diseases [19]. However, few studies
have examined cellular senescence after IS, and there is
a lack of research on the relationship between cellular
senescence and a wide range of cell types in the brain. In
this study, we identified hub genes for cellular
senescence in IS using bioinformatics and experimental
validation and explored their biological pathways. Using
single-cell RNA sequencing (scRNA-seq), we evaluated
the hub senescence-related gene (HSRG) expressions in
various cell types and mapped the developmental
trajectories of microglia and cellular communication
networks. Finally, we predicted potential therapeutic
drugs based on the HSRGs. The approach used in this
study is depicted in the flow diagram (Figure 1).
MATERIALS AND METHODS
Microarray datasets
SenMayo is a recently published gene set that
includes 125 and 118 unrepeatable genes in humans
and mice, respectively [16]. The gene set was
downloaded from the supplementary information of
the original article. The Gene Expression Omnibus
(“http://www.ncbi.nlm.nih.gov/geo/”) is an open-
source database that provides gene expression profiles
for our study. Four datasets including GSE163654,
GSE16561, GSE119121, and GSE174574 were used
(Table 1). Bulk RNA-sequencing (bulk RNA-seq) of
brain tissue from three sham-operated rats and three
middle cerebral artery occlusion (MCAO) rats in
GSE163654 was used for differential expression
analysis. GSE16561 contains bulk RNA-seq data of
peripheral blood from 39 patients with IS and 24
patients with normal groups, which were used for
expression and immune cell infiltration analyses.
Figure 1. The flowchart of data preparation and analysis.
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Table 1. Detailed information of the gene expression matrixes and platform.
GEO dataset
Platform
Species
Tissue
Country
Author
GSE163654
GPL17117
Rat
Penumbras tissue of brains
Canada
Tymianski M et al.
GSE16561
GPL6883
Human
Peripheral blood
USA
Barr TL et al.
GSE119121
GPL6247
Rat
Blood
Belgium
Dagonnier M et al.
GSE174574
GPL21103
C57BL/6
Brain
China
Zheng K, Hao J
GSE119121 contains bulk RNA-seq data from rat
peripheral blood from the MCAO and sham groups
used for the temporal analysis of gene expression.
Finally, scRNA-seq data of the brain tissue from
GSE174574 with three sham group mice and three
MCAO group mice were processed and used for cell
communication analysis.
Differential expression analysis
The R software (v4.2.1, R Foundation, Vienna, Austria)
was used for all analyses and visualizations in this study.
To create the analysis matrix, all original bulk RNA-seq
matrices were normalized and coupled with the
associated RNA probes. Data with non-mRNA
expression loss and no corresponding gene names were
excluded. The differentially expressed genes (DEGs)
were screened using the criteria |log2 (fold change) | >0.5
and a p-value of <0.05. Heatmaps and volcano plots were
generated using the “heatmap” and “ggplot2” packages,
respectively. Finally, a Venn diagram was created using
the website http://www.bioinformatics.com.cn/ for the
Venn analysis.
Pathway enrichment analysis and protein-protein
interaction network
Gene ontology (GO) and Kyoto Encyclopedia of Genes
and Genomes (KEGG) pathway analysis of DEGs were
performed and visualized using the “org.Mm.eg.db” and
“clusterProfiler” packages. The free website, STRING
(https://www.string-db.org/), was used to analyze
functional protein association networks of DEGs. A
minimum interaction score of ≥0.150 was defined as the
cut-off value, and the resulting protein-protein
interaction network was visualized using Cytoscape
software. Finally, the hub genes were ascertained by
visualizing the bulk RNA-seq of DEGs in GSE16561
using the “reshape2” and “ggpubr” packages.
Animal and establishment of the MCAO model
The Medical Experimental Animal Center (Xi’an
Jiaotong University) provided 12 pathogen-free male
Sprague-Dawley rats (weight: 280–300 g). A modified
Zea-Longa model, in which the coil occlusion was
permanently placed in the middle cerebral artery, was
used to create a rat permanent MCAO model [20]. The
rats (n = 8) were randomly allocated to either the sham
group or the MCAO group, with four rats in each group.
The Longa scale was used to assess the neurobehavioral
scores of rats in each group two hours after MCAO.
Animals with no neurological impairment following
surgery were excluded from the study. The rats were
euthanized three days after the operation via
intraperitoneal injection. The brains were removed and
sliced before being put in 2% triphenyl tetrazolium
chloride (TTC) (Solarbio Life Science, Beijing, China)
and incubated at 37° C for 30 minutes.
Quantitative reverse transcription polymerase chain
reaction (RT-qPCR)
Three rats from each group were anesthetized 48 hours
after surgery, and tissue samples were collected from the
ischemic penumbra. The samples were immediately
stored in liquid nitrogen, and total RNA was extracted
from each sample using the TRIzol reagent (Sinopharm
Chemical Reagents Co., Ltd., China). The extracted RNA
was reverse-transcribed into complementary DNA using
SweScript All-in-One RT SuperMix (Wuhan Saiwei
Biotechnology Co., Ltd., China). Table 2 shows the
primer sequences used in the study. The 2-Ct method was
used to calculate the relative mRNA expression, which
was then compared to that of the normal group
(glyceraldehyde 3-phosphate dehydrogenase mRNA
expression). A student’s t-test was used for statistical
comparisons, and differences with a p-value of <0.05
were considered statistically significant.
Construction of a prediction model
The nomogram model, calibration, decision, and
clinical impact curves were based on the expression
data of HSRGs in GSE16561, implemented by the
“rms” and “rmda” packages. The receiver operating
characteristic (ROC) curve was also plotted through the
“ROCR” package, and the calibration, decision curve
analysis (DCA), and clinical impact curves were drawn.
Temporal analysis of expression
GSE119121 contains the bulk RNA-seq of MCAO
rats at different time points, and the DEGs expression
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Table 2. Specific primers used for quantitative real-time PCR.
Gene
Forward
Reverse
Angptl4
CATGGCTGCCTGCGGTAACG
AGTTGCTGGATCTTGCTGTTCTGAG
Ccl3
CACCGCTGCCCTTGCTGTTC
GGAATTTGCCGTCCATAGGAGAAGC
Ccl7
GATCTCTGCCGCGCTTCTGTG
TGGATGAATTGGTCCCATCTGGTTG
Cxcl16
CAGTTTCAGAGCACCCAGCAGTC
GCCTAGCCTCCAGACCATAGCC
Tnf
CACCACGCTCTTCTGTCTACTGAAC
TGGGCTACGGGCTTGTCACTC
was described by a heatmap and a violin plot using
the “heatmap,” “reshape2,” and “ggpubr” packages.
Simultaneously, the mean value of hub gene expression
at different time points was calculated, and a line graph
was drawn.
Immune cell infiltration analysis
To analyze immune cell infiltrations in GSE16561 and
calculate merged expression data, we used the
CIBERSORT method, which is a technique for
analyzing different immune cell types in tissues [21].
The samples were filtered using a p-value of <0.05, and
a bar plot was generated to show the percentage of each
immune cell type in each sample. The “pheatmap”
package was used to generate a heatmap of the 22
immune cells and a heatmap describing the hub gene
expression in immune cells as well. The package
“vioplot” was used to compare and visualize the levels
of 22 immune cells in IS and normal samples. Using
the “corrplot” package, a correlation heatmap was
generated that revealed the correlation of 22 different
types of infiltrating immune cells.
ScRNA-seq data processing and cell communication
analysis
GSE174574 contains the scRNA-seq of three sham group
mice and three MCAO group mice. ScRNA-seq data
were processed using the “Seurat” package for
unsupervised graph-based clustering before analysis [22].
The following were the screening criteria for the cells
examined: Cells with 500–6,000 unique molecular
identifiers and 35% of mitochondrial genes judged to be
of high quality were eliminated from further research.
The normalized data function was used to normalize the
quality-controlled data, and then the find variable
features tool was used to select 2000 highly variable
genes. The mutual principal component analysis tool
“Seurat” was used to integrate the data. The proportion of
cells was determined by selecting the top 20 main
components for the visualization of dimensionality
reduction using uniform manifold approximation and
projection (UMAP). The “SingleR” package was used for
cell type identification, in which “MouseRNAseqData”
was used as a reference. Additionally, the “cellcall”
package was used to infer intercellular communication
[23]. To determine differentiation trajectories for major
clusters with large cell numbers, the “monocle3” package
was used for cell trajectory analysis [24].
Drug screening and molecular docking
The DSigDB database contains the Food and Drug
Administration-approved drugs and experimental
compounds (http://tanlab.ucdenver.edu/DSigDB) and
is a free website with the DSigDB interface
(https://maayanlab.cloud/Enrichr/). Drugs and compounds
were predicted using Enrichr, based on hub genes. The
screening criterion was adj. p <0.05, and the ranking
was based on the comprehensive score. The protein was
converted to the PDBQT file format so that AutoDock 4
software could recognize and read the modified protein.
To prepare the ligands for docking, charges were added
and optimized. Three PDBQT files were identified:
rigid DEG proteins, flexible proteins, and drug ligands.
Finally, we used AutoDock 4 software to perform
molecular docking.
RESULTS
Identification of the senescence-related genes (SRGs)
The DEGs between the six-hour rat MCAO groups and
sham groups in GSE163654 were discovered and are
shown in a volcano plot (Figure 2A). Among them, 326
genes were upregulated and 199 genes were
downregulated. Subsequently, according to the Venn
plot, 14 upregulated DEGs (CCL3, Jun, CCL4, Il1a,
VGF, VEGFA, IL1B, ANGPTL4, TNF, CCL2, CXCL16,
GEM, ICAM1, CCL7) and two downregulated SRGs
(CXCL12, SELPLG) were involved in the senescence-
related SenMayo dataset (Figure 2B). Specifically, 92
edges were linked between 16 corresponding proteins in
the protein-protein interaction network (Figure 2C).
A heatmap shows the expression of these 16 genes
(Figure 2D). Based on these 16 genes, GO/KEGG
functional enrichment analysis was performed. The
subsequent GO/KEGG functional enrichment analysis
showed that these genes were highly enriched in
leukocyte migration, positive regulation of the ERK1
and ERK2 cascades, cytokine activity, and cytokine
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receptor binding (Figure 2E). Finally, to identify the
conservation of these genes between species, we
compared the expression of these 16 genes in human
peripheral blood between the IS and normal groups
(Figure 2F). Thirteen of these 16 genes were expressed
in both mice and humans, and five (ANGPTL4, CCL3,
CCL7, CXCL16, and TNF) showed statistically
significant differences.
SRGs expression in the peripheral blood of the rat
By analyzing the GSE119121 expression matrix, we
visualized the expression of these 16 SRGs in the
peripheral blood of the MCAO and sham groups
(Figure 3A). We further compared the expression trends
of these SRGs at five different time points (1, 2, 3, 6,
and 24 hours) (Figure 3B). Notably, CXCL16 expression
and GEM decreased in the MCAO group but tended to
recover after 24 hours. The expression levels of VGF,
CCL3, and CCL4 decreased at later time points.
Meanwhile, the expression levels of the other 11 genes
increased at different time points within 24 hours in the
MCAO group, and most of them recovered 24 hours
after the operation. Additionally, the expression of five
SRGs was significantly different at some points after the
MACO operation compared with that before the
operation. Finally, a line graph was drawn to describe
the variation trends of the five hub genes (Figure 3C).
Thus, ANGPTL4, CCL3, CCL7, CXCL16, and TNF have
better species conservation in the peripheral blood of rats
and humans and were identified as HSRGs for further
research.
Figure 2. Discovery of SRGs in rat MCAO model and human peripheral blood. (A) The volcano plot for DEGs of brain tissue in
GSE163654. The genes related to cellular senescence were labeled. Red represents high gene expression and blue represents low expression.
(B) The Venn plot for the distribution of DEGs. (C) The protein-protein interaction network for SRGs. (D) The heatmap for SRGs in GSE163654.
(E) GO/KEGG pathway analysis and protein interaction network of SRGs. The color of the proteins corresponds to the pathway and the
number shows the count of genes. (F) The violin plot for SRGs of human peripheral blood in GSE16561. *p < 0.05, **p < 0.01, ***p < 0.001.
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Validation of HSRGs by RT-qPCR in the MCAO
model
We performed RT-qPCR in the MCAO model to
demonstrate the critical role of HSRGs in IS. To verify
the success of our MCAO model, TTC staining was
performed on the brain tissue of MCAO rats (Figure
4A). TTC staining stains the normal brain tissue red,
while the infarct lesions appear white, allowing a good
evaluation of the infarct in the brain. According to the
RT-qPCR results, the expression levels of all five genes
differed significantly (p <0.05) (Figure 4B). Gene
expression levels were higher in the brain tissues of rats
in the MCAO model than in the sham group, and the
expression trend was consistent with that shown in
Figure 2D between the MCAO and sham groups in
GSE163654.
Construction of a clinical prediction model
Based on the expression levels of HSRGs in GSE16561,
we constructed a nomogram prediction model (Figure
4C). To verify the effectiveness of the model, ROC,
calibration, DCA, and clinical impact curves were plotted
(Figure 4D–4G). The area under the curve (AUC) of the
prediction model was approximately 0.956, and the
calibration curve showed good calibration. The DCA
curve showed that this predictive model could yield
significantly greater net benefits for making clinical
decisions. In terms of the clinical impact curve, the
prediction model determined that the population at risk
for IS was strongly matched to the actual population
when the threshold probability was >65% of the
predicted score probability value, confirming the good
clinical efficiency of the prediction model.
Immune cell infiltration analysis
The CIBERSORT algorithm was used to predict
immune cell infiltration in the IS and normal groups.
The bar plot and heatmap displayed the percentage of
each of the 22 types of immune cells in each human
blood sample from GSE16561 (Figure 5A, 5B).
Correlation analysis of immune cells revealed that
resting mast cells and activated mast cells had the most
significant negative correlation, while naïve B cells and
CD8 T cells, follicular helper cells, resting mast cells
and activated CD4+ memory T cells, M2 macrophages
and monocytes, resting dendritic cells and M1
macrophages, neutrophils and activated mast cells had a
significant positive correlation (Figure 5C). The violin
plot of the immune cell infiltration difference showed
that, in comparison to the normal group, patients with
IS had lower levels of CD8+ T cells and activated
NK cells (Figure 5D). Finally, we analyzed HSRG
expression in 22 types of immune cells (Figure 5E).
Figure 3. Expression of SRGs in the rat peripheral blood and identification of HSRGs. (A) The heatmap for SRGs in GSE119121 at
different time points. (B) The violin plot for SRGs in GSE119121. (C) The line graph describes the variation trend of HSRGs expression at
different time points.
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ScRNA-seq reveals the cellular senescence pattern
after IS
Cell clusters were identified by UMAP analysis in
MCAO and sham-operated mice (Figure 6A). We
further annotated the cell clusters through the “SingleR”
package and mapped them to the UMAP (Figure 6B).
Nine cell clusters were identified: astrocytes,
endothelial cells, epithelial cells, fibroblasts,
granulocytes, microglia, monocytes, natural killer cells,
and oligodendrocytes. Subsequently, HSRG expression
in each cell cluster was mapped onto UMAP diagrams
and quantified (Figure 6C, 6D). Finally, we used the
“AUCell” package to evaluate the total hub gene
expressions in all types of cells and cells with an AUC
value greater than 0.078 were adopted (Figure 6E).
Significantly, HSRGs were highly expressed in
microglia and monocytes (Figure 6F). Based on the
UMAP and violin plots, microglia and monocytes had
the highest senescence scores, as shown in Figure 6G.
Intercellular communication and internal signaling
based on scRNA-seq
The intercellular communication in the MCAO and
sham groups is shown, respectively (Figure 7A).
Monocytes, granulocytes, and microglia were more
involved in cellular communication as receptors in the
sham group. Subsequently, we identified microglia and
monocytes as receivers and assessed their cellular
interactions with astrocytes and monocytes (Figure 7B,
7C). Further analysis of transcription factors (TFs)
involved in cellular communication revealed that Mef2c
and Myc were activated when microglia served as
recipients, whereas Fos, Nfkb1, and Stat1 were activated
when monocytes served as recipients. Finally, we
presented TF activities in receiver cells using a TF
enrichment plot, and all TFs were activated in
monocytes, microglia, and granulocytes (Figure 7D).
Cell trajectory analysis of microglia
The cell trajectories of the microglia are presented as
3D images in Figure 8A. Individual clustering and
UMAP mapping showed that microglia were divided
into four clusters (Figure 8B). To annotate these
microglial cell sub-clusters, we identified the top five
cell marker genes in each cluster (Figure 8D). The
marker genes include “P2RY12”, “SIGLECH”,
“GPR34”, “mt-ATP8”, “SELPLG” of MG1, “CCL12”,
“TNF”, “ADAMTS1”, “SOCS3”, “CCL2” of MG2,
“SPP1”, “LGALS3”, “LPL”, “LILRB4A”, “LILR4B”
of MG3, and “CTLA2A”, “IGFBP7”, “CLDN5”,
“PGLYRP1”, “SLC2A1” of MG4. Analysis of the
differences in the number of cell clusters showed that
MG1 was the main microglia in the sham group,
Figure 4. Validation of HSRGs by rat MCAO model and construction of prediction model. (A) TTC staining verification of rat MCAO
model. (B) Validation of quantitative real-time PCR analysis. (C) Nomogram of HSRGs for predicting IS. A calibration curve (D), Clinical decision
analysis (E, F), and ROC curve (G) of the nomogram.
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whereas MG2, MG3, and MG4 were the main microglia
in the MCAO group (Figure 8E). Additionally,
ANGPTL4 showed specificity for MG4, and its
expression was higher in the MCAO group. In MG2-4,
CCL7, CXCL16, and TNF were highly expressed in the
MCAO group. Although CCL3 was expressed in
different subgroups, its expression level was higher in
the MCAO group (Figure 8F). Finally, the cell
trajectory of microglia was determined to explore their
divergent trajectory.
Figure 5. Immune cell infiltration analysis in human peripheral blood. (A, B) The landscape of immune infiltration between IS and
normal groups in GSE16561. (C) Correlation matrix of all 22 immune cell subtype compositions. Higher, lower, and the same correlation levels
are displayed in red, blue, and white. (D) Comparison of 22 immune cell subtypes between patients in IS and normal groups. (E) The heatmap
for HSRGs in 22 immune cell subtype compositions.
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Drug screening and molecular docking
Small-molecule compounds that may bind to
ANGPTL4, CCL3, CCL7, CXCL16, and TNF were
identified using the DSigDB database; the top 10
compounds are listed in Table 3. Among these, retinoic
acid had the highest combined score (608812). We then
drew a structural diagram of retinoic acid, which can
bind to ANGPTL4, CCL3, CCL7, CXCL16, and TNF
(Figure 9A–9F).
DISCUSSION
Senescence has long been a significant issue for
researchers and has accelerated since the occurrence of
IS [6]. Cellular senescence is one of the significant
causes of senescence and has recently attracted
considerable attention [11]. SenMayo is a set of genes
that accurately describes and assesses cellular
senescence [16]. In this study, we aimed to identify the
HSRGs involved in IS and cellular senescence in brain
tissue by the SenMayo gene set. A nomogram model
was constructed based on HSRGs and was evaluated
preliminarily to predict cellular senescence in patients
with IS. Immune activity plays a vital role in cellular
senescence after IS, which has been discussed in detail
using immune cell infiltration analysis. To further
elucidate the mechanism, scRNA-seq analysis was
performed to determine the cellular localization of
HSRGs, intercellular communication, and cellular
Figure 6. The scRNA-seq reveals the expression of HSRGs in mouse brains. (A) Cluster analysis of scRNA-seq in GSE174574 dataset.
Red represents the cells in the MCAO group and blue represents the cells in the Sham group. (B) Cell cluster identification was obtained in
(A). Different colors represent different cell clusters, with a total of 9 identified. (C) Distribution of HSRGs expression in different cell clusters.
Compared with the Sham group, red represents the high expression of genes in the IS group. (D) Quantified expression of HSRGs in different
cell clusters. (E) The distribution of cell AUC value, an AUC value greater than 0.078 were adopted. (F) Distribution of HSRGs expression in
different cell clusters based on AUC value. (G) Quantified AUC value of HSRGs in different cell clusters.
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trajectory. Finally, small molecules that can bind to hub
gene expression proteins are considered potential drugs
for alleviating cellular senescence after IS. Overall, this
study combined multiple bioinformatic analysis
methods and experimental verification to conduct a
rigorous discussion of cellular senescence after IS at
different transcriptome levels, providing a reference for
further research in this field.
Functional enrichment analysis revealed that SRGs
were primarily related to leukocyte migration and
cytokine-cytokine receptor interactions. This suggests
that the immune response plays a significant role in
cellular senescence after an IS. However, while the
immune response can be protective, the invasion of
innate immune cells into the brain and meninges during
the acute phase can exacerbate ischemic damage [25].
Additionally, peripheral organs can become a second
“battlefield” for the immune response after IS. Danger
signals are released from damaged brain cells into the
circulatory system, which then activates systemic
immunity, causing severe immunosuppression, life-
threatening infections, and a poor prognosis [26]. In the
chronic phase, antigen presentation initiates an adaptive
immune response against the brain, which may underlie
the neuropsychiatric sequelae [25]. Studies have also
shown that microglia, astrocytes, foam cells, and
lymphocytes are activated in IS, forming glial scars that
persist for ten years later and are associated with
cognitive decline [27]. During the acute phase of IS, a
significant number of injured immune cells secrete
various cytokines, while some cells exhibit a SASP
pattern, which is an important indicator of cellular
senescence after IS [28].
Figure 7. Intercellular communication analysis based on scRNA-seq. (A) The intercellular communication in the MCAO and sham
groups. Microglia (B) and monocytes (C) as receivers assessed the cellular interactions with astrocytes and monocytes. (D) The TF enrichment
plot in monocytes, microglia, and granulocytes.
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After validating with rat and human blood samples and
RT-qPCR of rat brain tissue, ANGPTL4, CCL3, CCL7,
CXCL16, and TNF were identified as HSRGs.
ANGPTL4 is a protein associated with endothelial cell
integrity, inflammation, oxidative stress, and lipid
metabolism and may be involved in the pathogenesis
of IS [29]. CCL3 and CCL7, as chemokines, and
CXCL16, as chemokine ligands, are associated with
the recruitment and activation of inflammatory cells,
neuronal survival, and neoangiogenesis, and are
important mediators of IS [30]. Previous studies have
reported that CCL3 may play an important role in
neutrophil recruitment and the development of
atherosclerosis [31]. Moreover, Waśkiel-Burnat et al.
recently reported that CCL7 may be a significant
biomarker of atherosclerosis [32]. Additionally, CXCL16
is implicated in the immune inflammatory response to
atherosclerotic plaques, from antigen recognition to the
migration and infiltration of immune cells into areas of
inflammation [33, 34]. At the same time, TNF is not
only associated with neuroinflammation after IS but also
promotes SASP-stimulated lysosomal extravasation,
Figure 8. Cell trajectory analysis and identification of microglia. (A) 3D images of cell trajectories and the microglia part are amplified.
(B) Individual clustering and UMAP mapping for microglia. (C) Cell trajectories of the microglia. (D) The bubble pattern of the top five cell
marker genes in four microglia clusters. (E) Distribution of four cell clusters in the IS and Sham groups. (F) Quantified expression of HSRGs in
four cell clusters.
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Table 3. The top 10 compounds bind to HSRGs.
Term
Adjusted P-value
Combined score
Genes
Retinoic acid
0.012148
608812
CCL7, CCL3, ANGPTL4, TNF, CXCL16
Roflumilast
0.001246
15085.69
CCL3, TNF
indinavir
0.001246
13656.83
CCL3, TNF
PCI-24781
0.001246
12459.14
CCL7, CCL3
Lopinavir
0.001609
8557.58
CCL3, TNF
Antimycin A
0.002311
5832.819
CCL3, TNF
isoproterenol
5.20E-04
5550.691
CCL7, CCL3, TNF
Honokiol
0.002577
5105.231
CCL3, TNF
15-Acetyldeoxynivalenol
5.20E-04
4787.728
CCL3, ANGPTL4, TNF
palmitic acid
0.003584
3921.078
CCL3, TNF
leading to cellular senescence [35]. Sequencing data
from the brain tissue in the MCAO model demonstrated
good agreement with our PCR validation results, but a
degree of variability was observed in the peripheral
blood. For the lack of data from human brain tissue, we
combined data from human peripheral blood to
determine HSRGs. In human peripheral blood, CCL3
and TNF showed decreased expression after IS, which
may be related to the blood-brain barrier. However, it
still indicates that the expression changes of HSRGs
after IS are more sensitive than other SRGs. Moreover,
we noticed that the expression of Cxcl16 and Tnf in rat
peripheral blood was different from that in humans, and
the Temporal analysis showed that their expression trend
changed again 6 hours after MCAO. We supposed that
the difference is due to different stages of disease
development at different points in time. Overall, the
HSRGs we identified were species-conserved and
showed some efficacy in predicting the onset of IS.
Our study also suggests that HSRGs expression may
change over time and that predictive models may need
to be adjusted over time. After IS, damaged neuronal
cells release large amounts of senescence-associated
cytokines that affect immune cell function [13].
Therefore, we focused on the different immune cell type
expressions in the peripheral blood of patients with IS.
Notably, the IS group had lower numbers of CD8+
Figure 9. Molecular docking of proteins corresponding to HSRGs and retinoic acid. (A) The structural diagram of retinoic acid. (B–F)
Docking simulation of proteins and small molecule compounds.
www.aging-us.com 5509 AGING
T cells and activated NK cells. Additionally, HSRGs
were significantly differentially expressed in neutrophils,
naïve B cells, CD8+ T cells, and T-cell follicular helper
cells, particularly in neutrophils and CD8+ T cells, where
all hub genes were differentially expressed. CD8+ T cells
in the peripheral blood migrate to the brain parenchyma
after IS [36]. Ritzel et al. recently reported that CD8+ T
cells enhance inflammation and leukocyte recruitment
and act as a marker of senescence of the central nervous
system [37]. Moreover, the majority of research
suggested that the dynamics of NK cells in IS are
characterized by an increase in the brain and a decrease
in the peripheral blood, which was consistent with our
results [38, 39]. Meanwhile, brain ischemia weakens NK
cell-mediated immune defenses by interfering with
neurogenic and intracellular pathways [40].
Among the various cell types in IS, microglia and
monocytes are prone to show a cellular senescence
phenotype in the brain tissue (Figure 6). Microglia play
a key role in IS as resident central nervous system
immune cells and are a double-edged sword for neural
healing [41, 42]. Raffaele et al. have shown that
microglia release microcytes that enhance the prognosis
of IS by limiting the senescence of immune cells and
promoting the formation of oligodendrocytes [43].
Furthermore, senescence-associated microglia can
substantially affect brain homeostasis, particularly iron
storage and metabolism, leading to senescence-related
susceptibility and poor functional recovery after IS
[44, 45]. Several studies have also indicated that the
cellular senescence of monocytes is an important
feature of immune-senescence that can delay or
accelerate the establishment of atherosclerotic plaques
[46, 47]. The present study further resolved the issue of
communication between these cells and glial cells. We
found that the intensity of cellular communication
between granulocytes, microglia, and monocytes, which
act as receivers in the MCAO group, was significantly
increased compared to other cells, further illustrating
the important role of the immune response after IS
(Figure 7A). Taken together, we suggest that
intervention in the cellular senescence phenotype of
immune cells, especially microglia and monocytes, may
be the key to reducing senescence and improving the
prognosis of IS.
Microglia are intrinsic brain cells that are important for
senescence. After subpopulation analysis of microglia,
MG4 was found to be closely related to cellular
senescence owing to higher levels of hub gene
expression in the MCAO group. Based on marker
genes, MG4 cells were identified as vessel-associated
microglia, maintaining blood-brain barrier integrity via
Claudin-5 expression, a tight-junction protein. Vessel-
associated microglia maintain BBB integrity at first by
expressing the tight-junction protein Claudin-5 and
making physical contact with endothelial cells, while
microglia phagocytose astrocytic end-feet and disrupt
BBB function during chronic inflammation [48].
Furthermore, retinoic acid was identified as a small
compound that could bind to HSRGs. The positive
effect of retinoic acid in improving the prognosis of IS
possibly relies on improving blood-brain barrier
disruption and reducing apoptosis and neuronal damage,
which has been demonstrated in animal studies but is
still lacking in clinical studies [49–51]. The reversal
effects of retinoic acid on cellular senescence
phenotypes have also been documented [52, 53]. We
believe that retinoic acid could be used as a possible
medication to ameliorate the cellular senescence
phenotype and improve the prognosis of IS.
Even though we rigorously discussed the senescence of
various cell types after IS, this study has some
limitations. First, although a clinical prediction model
constructed based on HSRGs was verified, further
verification using external data is lacking. Additionally,
vessel-associated microglia have been identified to play
an important role in cellular senescence, and further
flow cytometry to verify their function. Moreover,
owing to the lack of data from neuronal cells, the
cellular senescence of neurons after IS was not
discussed in this study. Finally, retinoic acid has been
identified as a potential drug for improving the cellular
senescence phenotype and prognosis of IS; however,
further experimental validation is required.
Abbreviations
IS: ischemia stroke; SASP: senescence-associated
secretory phenotype; scRNA-seq: single-cell RNA
sequencing; HSRG: hub senescence-related gene; bulk
RNA-seq: bulk RNA-sequencing; MCAO: middle
cerebral artery occlusion; DEGs: differentially
expressed genes; GO: gene ontology; KEGG: Kyoto
Encyclopedia of Genes and Genomes; TTC: triphenyl
tetrazolium chloride; RT-qPCR: reverse transcription
polymerase chain reaction; ROC: receiver operating
characteristic; DCA: decision curve analysis; UMAP:
uniform manifold approximation and projection; SRGs:
senescence-related genes; AUC: area under the curve;
TFs: transcription factors.
AUTHOR CONTRIBUTIONS
S.G. and P.R. designed the experiments; L.F. and B.Y.
performed the bulk RNA-seq data analysis; L.F., Y.Z.,
and S.C. designed and performed scRNA-seq data
analysis; Y.T. and B.L. performed the establishment of
the MCAO model; H.C. and S.Y. performed the qPCR
experiments; Y.H. and H.H. bred the mice; L.F., B.Y.,
www.aging-us.com 5510 AGING
P.R., J.L., and S.G. prepared the manuscript; All the
authors edited the manuscript.
ACKNOWLEDGMENTS
We thank Mr. Xiaofei Wang at Biomedical
Experimental Center of Xi’an Jiaotong University for
their assistance with the experiment and data analysis.
CONFLICTS OF INTEREST
The authors declare that the research was conducted in
the absence of any commercial or financial relationships
that could be construed as a potential conflict of
interest.
ETHICAL STATEMENT
The animal study was reviewed and approved by Xi’an
Jiaotong University.
FUNDING
This work was supported by Xi‘an Science and
Technology Plan (21YXYJ0116), the Key Research and
Development Project of Shaanxi Province (Grant
No.2022ZDLSF04-01, and No.2019KW-071), The
National Natural Science Foundation of China (Grant
No. 81971766, and Grant No. 81903268), and China
Postdoctoral Science Foundation (No.2021M692577).
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