Abstract
Background
Heart failure (HF) is a prevalent and critical cardiac condition that
leads to profound structural and functional changes in the heart.
Although traditional treatments have shown partial efficacy, the
long-term outcomes remain suboptimal. Emerging research has highlighted
the pivotal role of oxidative stress and ferroptosis in HF progression.
This study investigates a new therapeutic approach utilizing
antioxidant polyphenol nanoparticles loaded with a STAT3 agonist
(PN@Col) to target these pathways and improve age-related HF.
Results
Key cells and genes contributing to HF progression were identified via
analysis of the GEO database, with single-cell RNA sequencing
(scRNA-seq) and AUCell analysis used to evaluate differential gene
expression. The STAT3 gene was highlighted as essential, and its
functionality was further validated in vitro through cell experiments,
confirming its impact on cardiomyocytes (CMs) in HF. Following the
development of PN@Col, in vitro experiments showed that PN@Col
effectively reduced oxidative stress and ferroptosis in CMs. In vivo
studies in elderly HF mice demonstrated significant improvements in
cardiac function following PN@Col treatment.
Conclusions
PN@Col offers a promising therapeutic approach to age-related HF by
mitigating oxidative stress and ferroptosis in cardiomyocytes. These
findings provide a solid scientific foundation for PN@Col as a
potential novel treatment strategy for HF, supporting further
exploration toward clinical application.
Graphical Abstract
[30]graphic file with name 12951_2025_3317_Figa_HTML.jpg
Supplementary Information
The online version contains supplementary material available at
10.1186/s12951-025-03317-x.
Keywords: Heart failure, Oxidative stress, Ferroptosis, Signal
transducer and activator of transcription 3, Polyphenol nanoparticles
Introduction
Heart failure (HF) is a serious and common cardiovascular disease
characterized by the heart's inability to effectively pump enough blood
to meet the body's needs [[31]1, [32]2]. This condition significantly
reduces patients' quality of life and markedly increases mortality
rates [[33]3, [34]4]. Globally, the incidence and prevalence of HF have
been steadily increasing, imposing a heavy burden on public health
[[35]5]. Current clinical management of HF primarily relies on
pharmacological treatments such as beta-blockers,
angiotensin-converting enzyme (ACE) inhibitors, and angiotensin II
receptor blockers (ARBs), which alleviate symptoms by easing cardiac
workload and improving circulation [[36]6, [37]7]. Additionally,
mechanical support devices like ventricular assist devices (VADs) and
cardiac resynchronization therapy (CRT), along with eventual heart
transplant surgery, are available [[38]8, [39]9]. While these
interventions can offer short-term symptom relief and improve quality
of life, the long-term prognosis remains suboptimal, leading many
patients to eventually necessitate heart transplants [[40]10, [41]11].
Therefore, there is an urgent need to develop new therapeutic
strategies aimed at enhancing the long-term prognosis and quality of
life for individuals with HF.
In recent years, an increasing body of research indicates that
oxidative stress plays a crucial role in the pathophysiological
processes of HF [[42]12, [43]13]. Oxidative stress refers to the
imbalance between the generation and clearance of free radicals and
reactive oxygen species (ROS) in the body, resulting in cellular and
tissue damage [[44]14, [45]15]. Elevated levels of ROS not only
directly harm cardiomyocytes (CMs) but also exacerbate the progression
of HF by triggering inflammatory responses and various cell death
pathways such as apoptosis and ferroptosis [[46]16, [47]17].
Ferroptosis is a novel programmed cell death mechanism characterized by
intracellular iron overload and lipid peroxidation reactions [[48]18,
[49]19]. In HF, the occurrence of ferroptosis significantly damages
CMs, making targeted therapies against oxidative stress and
ferroptosis, such as antioxidants and iron chelators, a prominent focus
in HF research [[50]19, [51]20]. Nevertheless, the effective delivery
of these therapeutic molecules and maximizing their efficacy remain
significant challenges in current research efforts.
Polyphenols are a category of natural compounds with potent antioxidant
activity, widely present in fruits, vegetables, and tea [[52]21]. These
compounds not only directly scavenge free radicals and reduce oxidative
stress but also exert protective effects by modulating various cellular
signaling pathways [[53]21]. In recent years, with the advancement of
nanotechnology, polyphenol nanoparticles (PNs) have emerged as a novel
drug delivery system [[54]22, [55]23]. Unlike fully synthetic
nanoparticles, these polyphenol nanoparticles have higher
bioavailability and targeting ability, allowing them to stably exist in
vivo, effectively deliver drugs to specific lesion sites, and be
enzymatically degraded, dissolved, or broken down into non-toxic small
molecules or metabolites through physicochemical actions [[56]24,
[57]25]. Furthermore, loading therapeutic molecules onto PNs can
further enhance their therapeutic effects. For example, utilizing PNs
to deliver antioxidants and ferroptosis regulators can not only improve
drug stability and bioavailability but also effectively synergize to
suppress oxidative stress and ferroptosis, thereby alleviating the
progression of HF [[58]22, [59]26]. Consequently, employing PNs for
therapeutic molecule delivery holds promise as an effective strategy
for treating HF.
Signal transducer and activator of transcription 3 (STAT3) is a crucial
transcription factor involved in regulating various cellular processes,
including cell proliferation, differentiation, apoptosis, and immune
responses [[60]27, [61]28]. Recent studies have revealed the
significant role of STAT3 in the occurrence and progression of HF
[[62]29, [63]30]. Specifically, STAT3 plays a critical role in CMs'
biological functions by regulating ferroptosis and inflammatory
responses [[64]31, [65]32]. For instance, activation of STAT3 can
decrease ferroptosis, protect CMs, and thereby improve the prognosis of
HF [[66]33]. Consequently, targeted modulation of STAT3 activity holds
promise as a novel therapeutic strategy for treating HF. This study
innovatively proposes the use of a novel antioxidant PN loaded with a
STAT3 agonist (PN@Col) as a new therapeutic approach for age-related
HF. We systematically evaluated the effectiveness of this strategy
through in vitro cell experiments and in vivo mouse models,
investigating its potential mechanisms of action.
This study aimed to explore the therapeutic effects of PN@Col in
improving age-related HF by modulating CM ferroptosis. Specifically, we
initially identified key cell types and genes crucial in the
progression of HF through single-cell RNA sequencing (scRNA-seq)
analysis. Subsequently, we validated the essential role of STAT3 in
regulating CM ferroptosis through in vitro cell experiments and
prepared PN@Col. Through both in vitro and in vivo experiments, we
assessed the impact of PN@Col on the biological functions of CMs and
ferroptosis, investigating its therapeutic effects in an age-related HF
mouse model. The results of the study demonstrated that PN@Col
significantly suppressed CM ferroptosis, improved cellular biological
functions, and notably enhanced heart function in aged HF mice. This
discovery provides new insights and strategies for the treatment of
age-related HF, holding significant scientific and clinical
implications. Through this research, we have provided scientific
evidence and theoretical support for enhancing the prognosis of
age-related HF patients clinically, promoting the application and
advancement of antioxidant PNs in cardiac disease treatment.
Results and discussion
Cellular heterogeneity and cell type analysis in human heart tissue of HF
To investigate the impact of cellular heterogeneity in heart tissue of
HF patients, we analyzed scRNA-seq data from the GEO database. After
data quality control cells with fewer than 2,000 expressed genes (UMI)
were filtered out (Fig. S1A), and cells with mitochondrial
expression < 5% or ribosomal gene expression > 20% were retained.
Statistical analysis was performed using perCellQCMetrics (Fig. S1B-C).
Linear scaling (ScaleData) and PCA (RunPCA) identified the first two
PCs (Fig. S1D), followed by a heatmap of gene expression profiles for
PC_1 to PC_7 (Fig. S1E) and cell distribution visualization (Fig. S1F).
RunHarmony was applied to reduce batch effects, improving clustering
accuracy (Fig. S1G–I).
Subsequently, we employed the UMAP algorithm to perform non-linear
dimensionality reduction on the first 7 PCs, clustering all cells into
30 cell clusters (Fig. S2A). The distribution of cells within each cell
cluster is depicted in Fig. S2B-C, while the proportion of cell numbers
in each cell cluster is illustrated in Fig. S2D-E.
To identify the identities of the cell clusters obtained from the
aforementioned clustering, we initially screened for marker genes
within each cell cluster. Subsequently, we visualized the expression
distribution of these marker genes through heatmaps and UMAP, as
illustrated in Fig. S3. By annotating cells based on the marker genes
of each cell and the marker genes of each cell cluster, we identified
12 cell types, namely adipocytes, CM, endocardial_cells,
endothelial_cells, epicardial_cells, fibroblasts, lymphatics,
Macrophages, mast_cells; neuronal_cells; pericytes; and T_cells
(Fig. [67]1A–B). The expression distribution of marker genes for each
cell type is shown in Fig. [68]1C and Fig. S4. Additionally, we
displayed the expression patterns of marker genes used to identify
cells among the cell subtypes and clusters in Fig. S5, indicating the
accurate cell identification outcomes of this study. Different cell
types can be clearly distinguished based on classic marker gene
expression.
Fig. 1.
[69]Fig. 1
[70]Open in a new tab
Cell Type Distribution and Expression Features of scRNA-seq Data. A
Visualization of cell annotation results grouped based on UMAP
clustering, where each color represents a specific cell type; B
Visualization of cell annotation results based on UMAP clustering, with
each color representing a distinct cell type, left panel for the
Control group (n = 28), right panel for the HF group (n = 17); C
Expression patterns of known lineage-specific marker genes across
different cell types in Control and HF groups, with darker blue
indicating higher average expression levels and larger circles
representing more cells expressing the gene; D Proportional
distribution of cell types in each sample, with each color representing
a specific cell type; E Bar graph comparison of cell numbers for each
cell type, where red represents the Control group (n = 28) and blue
represents the HF group (n = 17); F Box plot showing changes in cell
type proportions, with red indicating the Control group (n = 28) and
blue indicating the HF group (n = 17), * denotes statistical
significance compared to the Control group, **p < 0.01, ***p < 0.001
Furthermore, we elaborated on the cellular composition distributions
within different groups of the 12 cell subtypes (Fig. [71]1D). Using a
Wilcox. For the test, we conducted an analysis to identify changes in
cell proportions in the HF group compared to the control group. The
analysis revealed a significant decrease in the quantities of CMs and
pericytes in the HF group, while the numbers of fibroblasts,
lymphocytes, macrophages, and T cells showed a significant increase
(Fig. [72]1E–F), aligning with the alterations in cell composition
observed in HF.
The scRNA-seq analysis results above indicate that the HF group and
control group can be classified into 30 clusters, successfully
identifying 12 cell subtypes. Notably, in the HF group, a significant
decrease in the quantities of CMs and pericytes was observed, while
fibroblasts, lymphocytes, macrophages, and T cells exhibited a
significant increase.
Analysis of key cells and their characteristics influencing the progression
of HF
To understand the functional differences behind the quantities and
variations of CMs, macrophages, and T cells and to identify the cells
that play a crucial role along with their expression characteristics,
we analyzed scRNA-seq data of three cell types. We re-clustered these
three cell types using UMAP analysis, where CMs were re-clustered into
two subtypes, namely CM_1 and CM_2. Compared to the Control group, the
number of cells in the CM_1 subtype decreased while it increased in the
CM_2 subtype (Fig. [73]2A, Fig. S6). Macrophages were also re-clustered
into two subtypes, Macrophages_1 and Macrophages_2. In comparison to
the Control group, the number of cells in the Macrophages_1 subtype
increased while it decreased in the Macrophages_2 subtype (Fig. [74]2B,
Fig. S7). The T cells were re-clustered into four cell subtypes: NKT
(Natural Killer T cells), CD8^+ Effector T cells, T memory cells, and
Proliferate NKT cells. The changes in the quantity of each T cell
subset were not significant (Fig. [75]2C, Fig. S8).
Fig. 2.
[76]Fig. 2
[77]Open in a new tab
Analysis of Key Cell Expression Characteristics. A UMAP clustering
results showing the distribution of CM_1 and CM_2 cells, with different
colors indicating distinct CM subtypes; B UMAP clustering results
displaying the distribution of Macrophages_1 and Macrophages_2 cells,
with different colors representing different macrophage subtypes; C
UMAP clustering results illustrating the distribution of various T cell
subsets, with different colors denoting different T cell subsets; D
Total number and interaction strength of cell communications in the
Control group (n = 28), where the thickness of lines in the network
graph represents the number of pathways and the size of circles in the
boxplot indicates the strength of interactions; E Total number and
interaction strength of cell communications in the HF group (n = 17),
with similar visualization as in D; F Differences in AUC scores for CM
subtypes, macrophage subtypes, and T cell subsets between HF group
(n = 17) and Control group (n = 28) based on an aging gene set; G
Differences in AUC scores for CM subtypes, macrophage subtypes, and T
cell subsets between HF group (n = 17) and Control group (n = 28) based
on a ferroptosis gene set; H Differences in AUC scores for CM subtypes,
macrophage subtypes, and T cell subsets between HF group (n = 17) and
Control group (n = 28) based on an inflammation gene set. * denotes
statistical significance compared to the Control group, *p < 0.05,
**p < 0.01, ***p < 0.001
To further explore the critical roles of various subtypes of the three
cell types in the progression of HF, we investigated the intercellular
communication mediated by ligand-receptor interactions. Utilizing the R
programming language with the ‘‘CellChat’’ package, we analyzed the
communication and interactions between cell phenotypes. The analysis
revealed that compared to the Control group, in the HF group, there was
a decrease in the total number of pathway interactions between CM_1
cells and an increase in the total number of pathway interactions
between Macrophages_1 cells. Additionally, the signaling strength
received by CM_2 decreased, while it increased for Macrophages_1 and
Macrophages_2, with no significant changes in the communication
quantity and strength of other cell types (Fig. [78]2D–E). These
findings further underscore the significance of the interaction between
CMs and macrophages in the progression of HF.
In order to screen for cell populations regulated by aging,
inflammation, and ferroptosis in HF, this study conducted AUCell
analysis based on gene signatures related to aging, inflammation, and
ferroptosis. Furthermore, Wilcox. The test was utilized to explore the
differences in aging, inflammation, and ferroptosis scores between the
disease group and the control group. The analysis results indicate that
there are significant differences in the aging-related gene AUCell
scores among the subtypes of CM_1, CM_2, Macrophages_1, Macrophages_2,
NKT, Proliferate_NKT, and CD8^+ Effector T cell in the normal and HF
groups (Fig. [79]2F, Fig. S9–S11). Furthermore, significant intergroup
differences were observed in the ferroptosis-related gene AUCell scores
among the subtypes of CM_1, CM_2, Macrophages_1, and NKT cells
(Fig. [80]2G, Fig. S12–S14), while only Macrophages_1, NKT, and CD8^+
Effector T cell subtypes showed significant differences in the
inflammation-related gene AUCell scores between groups (Fig. [81]2H,
Fig. S15–S17). Therefore, it is speculated that the aging and
ferroptosis related to CMs may influence the occurrence and development
of HF.
The research findings above indicate that CMs are crucial cells
mediating intercellular communication in the progression of HF.
Moreover, aging and ferroptosis related to CMs may play an essential
role in the pathological process of HF.
Further screening key genes related to aging and ferroptosis in CMs
To investigate key genes associated with aging and ferroptosis in CMs,
we extracted genes from the scRNA-seq dataset of CM_1 and CM_2 cells
and performed differential expression analysis. In CM_1 cells, we
identified 616 DEGs, among which 277 genes showed high expression in HF
samples, and 339 genes showed low expression (Fig. [82]3A–B). Likewise,
in CM_2 cells, we found 947 DEGs, with 368 genes upregulated in HF
samples and 579 genes downregulated (Fig. [83]3C–D).
Fig. 3.
[84]Fig. 3
[85]Open in a new tab
Identification of Key Genes Associated with Aging and Ferroptosis in
CMs. A Volcano plot illustrating differential gene expression in CM_1
cells from Control and HF groups, where blue dots to the right of the
dotted line represent genes with lower expression in the HF group and
red dots to the left indicate genes with higher expression in the HF
group; B Heatmap showing the expression of the top 20 DEGs in CM_1
cells, with blue indicating downregulation, red indicating
upregulation, and darker colors reflecting greater significance of
differential expression; C Volcano plot displaying differential gene
expression in CM_2 cells from Control and HF groups, using a similar
color scheme as in (A); (D) Heatmap presenting the expression of the
top 20 DEGs in CM_2 cells, following the color scheme as in (B); (E)
Venn diagram depicting the intersection of DEGs in CM_1 and CM_2 cells,
aging-related genes, and ferroptosis-related genes; F Bubble plot of GO
enrichment analysis for the 17 intersecting genes, with circle color
indicating significance of enrichment (from blue to red) and circle
size representing the number of enriched genes; G Bubble plot of KEGG
enrichment analysis for the 17 intersecting genes, using the same
visualization as in (F); H Protein–protein interaction analysis of the
17 intersecting genes (Combined score = 0.4); I PPI network diagram of
the 17 intersecting genes, with color gradient from green to blue and
circle size indicating the degree value of genes; J Statistical
analysis of PPI network interaction sites for the 17 intersecting
genes, visualized in a graph; K Friends analysis of semantic similarity
of GO terms among the 17 intersecting genes; L Pyscenic analysis of
transcription factor regulatory differences in all core cells,
highlighting abnormally activated transcription factors in HF samples
The DEGs were subjected to GO functional enrichment analysis and KEGG
pathway enrichment analysis. The results revealed that in CM_1 cells,
the DEGs were associated with BP, including oxidative phosphorylation,
aerobic respiration, and cellular respiration (Fig. S18A). In terms of
CC, these genes were mainly enriched in the cytoplasm, inner
mitochondrial membrane protein complex, and mitochondrial
protein-containing complex (Fig. S18B). Regarding MF, the enrichment
was primarily observed in oxidoreduction-driven active transmembrane
transporter activity, cytoskeletal protein binding, and primary active
transmembrane transporter activity (Fig. S18C). Furthermore, the KEGG
pathway enrichment analysis indicated that these DEGs were involved in
signaling pathways such as Diabetic cardiomyopathy, Oxidative
phosphorylation, Thermogenesis, and Prion disease (Fig. S18D).
In CM_2 cells, the DEGs were found to be associated with BP, such as
aerobic respiration, cellular respiration, and energy derivation by
oxidation of organic compounds (Fig. S19A). The enrichment analysis of
CC showed that these genes were mainly enriched in the cytoplasm, inner
mitochondrial membrane protein complex, and intracellular anatomical
structure (Fig. S19B). Regarding MF, the enrichment was primarily
observed in oxidoreduction-driven active transmembrane transporter
activity, cytoskeletal protein binding, and primary active
transmembrane transporter activity (Fig. S19C). Additionally, the KEGG
pathway enrichment analysis revealed that these DEGs were involved in
signaling pathways, including Diabetic cardiomyopathy, Cardiac muscle
contraction, Oxidative phosphorylation, and Thermogenesis (Fig. S19D).
The intersection of DEGs in CM_1 and CM_2 cells related to aging and
ferroptosis resulted in 17 common genes, namely: BACH1, AR, WWTR1,
YAP1, FTL, MAP1LC3A, PEBP1, FTH1, LINC00472, HSPA5, PPP1R13L, GOT1,
STAT3, PRDX6, HSF1, SAT1, and IDH2 (Fig. [86]3E). These 17 intersecting
genes exhibited consistent expression trends in both CM_1 and CM_2
cells (Table S1-S2). Subsequently, these 17 intersecting genes were
subjected to GO functional enrichment analysis and KEGG pathway
enrichment analysis. The analysis revealed that these 17 intersecting
genes were associated with BP, such as multicellular organism growth,
regulation of DNA-templated transcription in response to stress, and
regulation of transcription from RNA polymerase II promoter in response
to stress. In terms of CC, these genes were primarily enriched in the
transcription regulator complex, autophagosome, and secondary lysosome.
Regarding MF, the enrichment was predominantly observed in DNA-binding
transcription factor binding, transcription corepressor activity, and
ferrous iron binding (Fig. [87]3F). Furthermore, the KEGG pathway
enrichment analysis of these intersecting genes showed their
involvement in signaling pathways, including Ferroptosis, Necroptosis,
Carbon metabolism, and Biosynthesis of amino acids (Fig. [88]3G).
Using STRING for protein–protein interaction analysis of the 17
intersecting genes (Fig. [89]3H), we obtained the PPI network and
interaction rankings of these genes, providing detailed information in
F[90]ig. [91]3I–J. STAT3, PRDX6, and HSPA5 genes were identified as
having pivotal roles in this protein interaction network. Additionally,
we utilized the GOSemSim package to calculate the semantic similarity
of GO terms among the 17 intersecting genes. This analysis assessed the
functional closeness of these genes by evaluating the shared GO terms
they possessed. By comparing the functional similarity among genes, we
can identify genes that are significantly associated or functionally
aberrant. The analysis revealed that the STAT3 gene exhibited the
highest functional similarity (Fig. [92]3K), suggesting its potential
central role in the corresponding BP. Furthermore, we analyzed the
differential regulation of all core cell transcription factors, and by
grouping, we further selected transcription factors that were
abnormally activated in HF samples. The analysis showed significant
inhibition of the activation of the STAT3 factor in HF samples
(Fig. [93]3L).
These research findings indicate that the expression regulation of the
STAT3 gene in CMs of HF samples may play a crucial role in the
occurrence and development of HF, with STAT3 potentially being
significantly associated with the aging and ferroptosis of CMs.
Activation of STAT3 inhibits CMs ferroptosis and promotes functional repair
To further investigate the impact of the STAT3 gene on the biological
functions of CMs and cell ferroptosis, we designed three shRNA
sequences targeting STAT3. We constructed STAT3 silenced and
overexpressed CMs through lentiviral transfection of these sequences
individually (Fig. [94]4A). Transfection efficiency was validated using
RT-qPCR and Western blot, and the sequence sh-STAT3-3, showing the most
optimal transfection efficiency, was selected for subsequent
experiments (Fig. S20).
Fig. 4.
[95]Fig. 4
[96]Open in a new tab
Impact of STAT3 Gene on the Biological Functions of CMs. A Schematic
diagram of experimental procedure involving lentivirus transfection for
silencing or overexpression of STAT3; B Viability changes of CMs in
different intervention groups at 12, 24, 36, and 48 h detected by CCK-8
assay; C Assessment of proliferative capacity of CMs in different
intervention groups using EDU experiment, with EDU-positive cells shown
in red indicating cells in the proliferative phase, and blue
representing DAPI-stained cell nuclei (scale bar = 50 μm); D
Statistical analysis of EDU staining results; E Assessment of apoptosis
in CMs in different intervention groups using Annexin V/PI double
staining flow cytometry, with the bar graph showing the percentage of
cells in Q2 and Q3 quadrants indicating apoptotic cells; F Evaluation
of migration of CMs in different intervention groups using wound
healing assay (scale bar = 100 μm); G Evaluation of migration capacity
of CMs in different intervention groups using Transwell assay (scale
bar = 50 μm). Quantitative data in the figures are presented as
Mean ± SD. Each cell experiment was repeated 3 times. * denotes
comparison between two groups, **p < 0.01, ***p < 0.001, ****p < 0.0001
Next, we conducted experiments to assess the changes in the biological
functions of CM cells in different intervention groups. Results from
CCK8 and EDU assays revealed that compared to the sh-NC group, the
sh-STAT3 group exhibited significantly reduced cell viability and
proliferation capabilities. In contrast, when compared to the oe-NC
group, the oe-STAT3 group demonstrated significantly enhanced cell
viability and proliferation abilities (Fig. [97]4B–D). Apoptosis levels
were assessed by flow cytometry, showing that silencing STAT3 induced
increased apoptosis in CMs cells, while overexpressing SIRT1 inhibited
CMs cell apoptosis (Fig. [98]4E). The results from Transwell and wound
healing assays indicated that, compared to the sh-NC group, the
migration ability of CMs cells in the sh-STAT3 group was significantly
weakened, while in comparison to the oe-NC group, the migration
capability of CMs cells in the oe-STAT3 group was notably increased
(Fig. [99]4F–G). These experimental findings suggest that silencing
STAT3 in CM cell lines inhibits cell proliferation and migration while
promoting CM cell apoptosis. On the other hand, overexpressing STAT3
enhances cell proliferation and migration while suppressing cell
apoptosis.
Initially, we investigated the regulatory role of STAT3 in iron balance
by analyzing the expression of iron metabolism-related genes using the
FerroOrange probe. As shown in (Fig. [100]5A–B), within 24 h of
knocking down STAT3, an increase in intracellular Fe^2+ content was
observed, while overexpressing STAT3 led to a significant reduction in
intracellular Fe^2+ levels.
Fig. 5.
[101]Fig. 5
[102]Open in a new tab
Influence of STAT3 on CMs Cell Ferroptosis. A Representative
fluorescence images of CMs transfected with different lentiviruses for
intracellular iron levels identified using FerroOrange (Scale
bar = 100 μm); B Statistical analysis of iron content in CMs from
different intervention groups using assay kits; C GSH/GSSG ratio in CMs
from different intervention groups; D–E Visualization of ROS production
in CMs using DCFH-DA under confocal laser scanning microscopy and
statistical analysis using flow cytometry (Scale bar = 25 μm); F
Measurement of MDA content in different intervention groups; G
Representative confocal images of CMs stained with C11-BODIPY 581/591.
Red indicates unoxidized lipids, while green represents oxidized lipids
(Scale bar = 25 μm); H–I Transcription levels of ferroptosis-related
genes SLC7A11 and GPX4 detected by RT-qPCR; J Observation of
mitochondrial morphology in cells using TEM, with red arrows pointing
to mitochondrial structures (scale bar = 1 μm); K Detection of
mitochondrial membrane potential (MMP: mitochondrial membrane
potential) in cells from different intervention groups using JC-1
assay. Quantitative data in the figures are presented as Mean ± SD.
Each cell experiment was repeated 3 times. * denotes comparison between
two groups, **p < 0.01, ***p < 0.001, ****p < 0.0001
To further validate the impact of STAT3 on redox balance, we compared
oxidative stress indicators and peroxidation products. In the sh-STAT3
group, a significant decrease in the GSH/GSSG ratio was observed, while
in the oe-STAT3 group, the GSH/GSSG ratio significantly increased,
indicating a mitigation of oxidative stress levels (Fig. [103]5C).
Confocal laser scanning microscopy examination and flow cytometry
fluorescence quantification results both demonstrated that CM cells
treated with STAT3 knockdown exhibited an increase in ROS production,
whereas cells overexpressing STAT3 showed a significant reduction in
ROS generation (Fig. [104]5D–E).
The results indicated that, compared to the sh-NC group, the sh-STAT3
group exhibited a significant increase in MDA levels, while the
oe-STAT3 group showed a marked decrease in MDA levels when compared to
the oe-NC group (Fig. [105]5F). Evaluation of lipid peroxidation levels
using C11-BODIPY581/591 also revealed a significant upregulation after
STAT3 knockdown and downregulation after STAT3 overexpression
(Fig. [106]5G). Compared to the sh-NC group, the sh-STAT3 group showed
a significant decrease in mRNA transcription levels of GPX4 and
SLC7A11, while the oe-STAT3 group exhibited a significant increase in
mRNA transcription levels of GPX4 and SLC7A11 when compared to the
oe-NC group (F[107]ig. [108]5H–I).
Ferroptosis is accompanied by characteristic mitochondrial
morphological changes [[109]34, [110]35]. As depicted in Fig. [111]5J,
the sh-STAT3 group of CMs displayed mitochondrial shrinkage, reduced
cristae, and increased membrane density, while the oe-STAT3 group of
CMs exhibited mitochondrial damage repair. Assessment of mitochondrial
membrane potential using JC-1 revealed that knocking down STAT3
significantly increased mitochondrial membrane potential
hyperpolarization in CMs, whereas overexpressing STAT3 markedly
decreased mitochondrial membrane potential hyperpolarization in CMs
(Fig. [112]5K), suggesting that STAT3 may influence ferroptosis in CMs
by impacting mitochondrial function. In conclusion, these results
confirm the involvement of STAT3 in regulating and inhibiting
ferroptosis in CMs.
Overall, the study results indicate that STAT3 is involved in
regulating and inhibiting ferroptosis in CMs, thus promoting the
growth, proliferation, and migration of CMs while inhibiting cell
apoptosis.
Successful preparation of PN@Col
We synthesized a novel antioxidant PN by combining EGCG with glycine
(Gly) and loaded the STAT3 agonist Colivelin into this nanoparticle,
named PN@Col [[113]36, [114]37] (Fig. [115]6A). Subsequently, we
employed TEM to observe the shape and structure of the nanoparticles.
As depicted in Fig. [116]6B, the prepared nanoparticles are likely to
be spherical in three-dimensional space, with PN@Col particles
relatively larger than PN particles. Further confirmation from SEM
images revealed that the prepared nanoparticles are spherical and that
lipid particles increased in size after Colivelin loading
(Fig. [117]6C). Particle size analysis provided additional
verification, as the nanosizer detected that the average diameter of PN
was 112.62 ± 8.67 nm, with a Zeta potential of -31.28 mV, whereas the
average diameter of PN@Col was 226.71 ± 16.92 nm, with a Zeta potential
of -18.66 mV. Compared to PN, PN@Col exhibited a slightly higher Zeta
potential and a slight increase in diameter (Fig. [118]6D–E),
indicating that PN@Col exhibits good integrity. Subsequently, we used
high-performance liquid chromatography (HPLC) to confirm the successful
drug loading by measuring the drug loading capacity and encapsulation
efficiency. The results indicated that PN@Col successfully loaded
Colivelin, with a Colivelin content of 12.6 ± 1.5% (n = 6) and an
encapsulation efficiency of 69.7 ± 13.2%. We then evaluated its in
vitro release profile in PBS (pH 7.4) containing 0.1% Tween 80. The
results showed that 11–12% and 76–83% of the loaded Colivelin were
released after 3 h and 24 h, respectively, with no further release
observed in the subsequent 48 h (Fig. [119]6F).
Fig. 6.
[120]Fig. 6
[121]Open in a new tab
Synthesis and Characterization of Novel Antioxidant PNs. A Schematic
diagram of the synthesis steps of PN@Col nanoparticles; B Morphological
features of PN (top) and PN@Col (bottom) observed using TEM (scale bar:
100 nm/200 nm); C Morphology of PN (top) and PN@Col (bottom) observed
using SEM (scale bar: 5 μm/1 μm); D Particle size distribution of PN
(left) and PN@Col (right) analyzed by nanoparticle tracking; E Zeta
potential analysis of PN (left) and PN@Col (right) by nanoparticle
tracking; F In vitro release of Colivelin from PN@Col nanoparticles. No
significant differences were observed between samples at each time
point. Data are presented as the mean with a standard deviation from
three independent samples prepared under the same conditions; G
Fluorescence microscopy images of CMs cells cultured with PN (left) and
PN@Col (right) for 24 h, with green fluorescence staining indicating
viable cells (scale bar = 25 μm); (H) DPPH and ABTS radical scavenging
activities of PN and PN@Col; I Antioxidant capacity assessment of PN
and PN@Col. Quantitative data in the figures are presented as
Mean ± SD. Each experiment was repeated 3 times. ‘‘ns’’ indicates no
statistically significant difference between the two groups
We evaluated the viability of cells treated with different
nanoparticles using fluorescence microscopy. The results demonstrated a
significant increase in the survival rate of CM cells in the PN@Col
treatment group compared to the PN treatment group (Fig. [122]6G).
Furthermore, we assessed the radical scavenging ability and antioxidant
activity of PN@Col by measuring DPPH and ABTS free radicals. The
results indicated that both PN and PN@Col exhibited strong radical
scavenging abilities (Fig. [123]6H). Using Trolox as the reference
antioxidant for the ABTS assay to quantify the antioxidant capacity and
express it as equivalent antioxidant capacity, the calculations
revealed that both PN and PN@Col possessed robust antioxidant
capabilities (F[124]ig. [125]6I). Importantly, the loading of Colivelin
did not affect the radical scavenging and antioxidant abilities of PNs.
These findings collectively demonstrate the successful development of a
polyphenol nanoparticle loaded with a STAT3 agonist (PN@Col).
PN@Col inhibits CMs ferroptosis and promotes functional recovery
To investigate the impact of PN@Col on the biological functions of CMs,
we induced ferroptosis in CMs by treating them with Erastin, followed
by treatment with nanoparticles and assessment of their biological
functions. Initially, Western blot analysis revealed that compared to
the control group, the expression of p-STAT3 protein in CM cells was
significantly downregulated in the Erastin-treated group. However,
following treatment with Colivelin and PN@Col, there was an
upregulation of p-STAT3 protein expression in the cells, with PN@Col
demonstrating a stronger upregulation capacity (Fig. [126]7A). Results
from CCK-8 and EDU staining assays indicated that compared to the
control group, the viability and proliferative capacity of CM cells in
the Erastin-treated group significantly decreased. However, following
treatment with Colivelin and PN@Col, there was a partial recovery in
both cell viability and proliferative capacity, with the PN@Col
treatment group showing the most pronounced recovery effects
(Fig. [127]7B–C). The study findings from cell scratch and Transwell
assays demonstrate that Erastin suppresses the migration ability of
CMs. Furthermore, compared to the control group treated with unloaded
drug nanoparticles (PNs), the addition of Colivelin and PN@Col shows a
partial restoration of cell migration capability. Particularly, the
cell migration ability is significantly enhanced in the PN@Col
treatment group (Fig. [128]7D–E).
Fig. 7.
[129]Fig. 7
[130]Open in a new tab
Impact of PN@Col on the Biological Functions of CMs. A Expression
changes of p-STAT3 protein in CMs cells from different treatment groups
detected by Western blot; B Viability changes of CMs in different
treatment groups at 12, 24, 36, and 48 h assessed by CCK-8 assay; C
Evaluation of proliferative capacity of CMs in different treatment
groups using EDU experiment, with red fluorescence indicating
EDU-positive cells in the proliferative phase and blue fluorescence
representing DAPI-stained cell nuclei (scale bar = 50 μm); D Assessment
of cell migration in different intervention groups of CMs using wound
healing assay (scale bar = 100 μm); E Evaluation of migration capacity
of CMs in different treatment groups using Transwell assay (scale
bar = 50 μm); F Detection of apoptosis in CMs from different treatment
groups using Annexin V/PI double staining flow cytometry, with bar
graph representing the percentage of cells in Q2 and Q3 quadrants
indicating apoptotic cells; G Representative fluorescence images of
intracellular iron levels in CMs from different treatment groups
identified using FerroOrange (Scale bar = 25 μm); H Visualization of
ROS production in CMs using DCFH-DA under confocal laser scanning
microscopy and statistical analysis using flow cytometry (Scale
bar = 25 μm); I Representative confocal images of CMs stained with
C11-BODIPY 581/591. Red indicates unoxidized lipids, while green
represents oxidized lipids (Scale bar = 25 μm); J GSH/GSSG ratio in CMs
from different treatment groups; K Measurement of MDA content in CMs
from different treatment groups; L–M Transcription levels of
ferroptosis-related genes SLC7A11 and GPX4 detected by RT-qPCR; N
Observation of mitochondrial morphology in cells using TEM, with red
arrows indicating mitochondrial structures (scale bar = 1 μm); O
Detection of mitochondrial membrane potential (MMP) in cells from
different treatment groups using JC-1 assay. Quantitative data in the
figures are presented as Mean ± SD. Each cell experiment was repeated 3
times. * denotes comparison between two groups, *p < 0.05, **p < 0.01,
***p < 0.001, ****p < 0.0001
Subsequently, we assessed the apoptosis of CM cells treated with
different reagents using flow cytometry. The results revealed that
Erastin could induce apoptosis in CM cells. However, treatment with
Colivelin and PN@Col inhibited apoptosis in the cells, with PN@Col
demonstrating a significantly stronger capacity to suppress apoptosis
compared to Colivelin (Fig. [131]7F).
Further detection of ferroptosis-specific indicators revealed
significant differences: compared to the control group, the model and
PNs groups exhibited elevated levels of intracellular Fe^2+, ROS, lipid
peroxidation, and MDA in CMs, along with a significant decrease in
GSH/GSSG ratio. In contrast, the colivelin and PN@Col groups showed
significant reductions in intracellular Fe^2+, ROS, MDA levels, and
lipid peroxidation compared to the model group. Additionally, the
GSH/GSSG ratio was significantly increased in these groups.
Furthermore, the PN@Col group demonstrated a significantly greater
ability to restore ferroptosis-specific indicators compared to the
colivelin group (Fig. [132]7G-K).
To further validate our findings, we examined the expression of GPX4
and SLC7A11 mRNA. The RT-qPCR results revealed that compared to the
control group, the model and PNs groups exhibited a significant
decrease in mRNA expression levels of GPX4 and SLC7A11. In contrast,
the colivelin and PN@Col groups showed a notable restoration in the
expression levels of GPX4 and SLC7A11 compared to the model and PNs
groups. Importantly, PN@Col demonstrated a significantly greater
ability to restore their expression compared to colivelin
(Fig. [133]7L-M).
Additionally, as illustrated in Fig. [134]7N, compared to the control
group, the model and PNs groups displayed mitochondrial shrinkage,
reduced cristae, and increased membrane density. Conversely, the
colivelin and PN@Col groups exhibited mitochondrial repair, increased
cristae, and reduced membrane density relative to the model group.
Importantly, PN@Col showed a significantly stronger capacity to repair
mitochondrial damage compared to colivelin. The results of the JC-1
assay assessing mitochondrial membrane potential changes revealed that
the mitochondrial membrane potential hyperpolarization in CMs was
notably elevated in the model group compared to the control group. In
contrast, both the colivelin and PN@Col groups showed a significant
reduction in mitochondrial membrane potential hyperpolarization
compared to the model and PNs groups, with PN@Col demonstrating a
significantly superior ability to restore the mitochondrial membrane
potential compared to colivelin (Fig. [135]7O).
These research findings indicate that PN@Col antioxidant PNs can
inhibit ferroptosis in CMs, regulate biological functions, promote
functional recovery, and suppress cell apoptosis.
PN@Col significantly improves cardiac function in age-related HF mice
To investigate the impact of PN@Col on cardiac function in age-related
HF mice further, we validated the HF elderly mouse model constructed
through transverse aortic constriction surgery and treated them through
tail vein injection of nanoparticles. After 28 days of treatment, the
mice in each group underwent an assessment of cardiac functional
parameters.
The results of the ultrasound diagnostic equipment revealed that
compared to the sham group, mice in the model and PNs groups exhibited
significantly increased IVSD, LVEDD, and LVESD, as well as decreased
LVPWT, LVEF, and LVFS. In comparison to the model and PNs groups, mice
in the colivelin and PN@Col groups displayed significant reductions in
IVSD, LVEDD, and LVESD, while LVPWD, LVEF, and LVFS showed significant
increases. Additionally, the therapeutic effect of PN@Col was
significantly more pronounced than that of Colivelin (Fig. [136]8A–B;
Fig. S21A–F).
Fig. 8.
[137]Fig. 8
[138]Open in a new tab
Impact of PN@Col on Age-related HF Mouse Cardiac Function. A
Representative cardiac ultrasound images of mice in different treatment
groups; B Representative left ventricular pressure waveforms, with a
scale of 40 mmHg vertically and 100 ms horizontally; C Anatomical
diagrams of mouse hearts from different treatment groups; D H&E stained
images of mouse heart tissues from different treatment groups, with
quantification of cross-sectional area of the heart tissues(scale bar:
500 μm/50 μm); E Assessment of CM size with WGA staining results (scale
bar: 50 μm); F Evaluation of myocardial interstitial fibrosis
deposition in mouse ventricles with Sirius Red staining (left) and
Masson staining (right), where CVF (Collage Volume Fraction) quantifies
the ratio of Masson-stained blue area to total heart chamber area
(scale bar: 500 μm/50 μm), fibrotic area in mice indicated by picro
sirius red-positive area (scale bar: 50 μm); G Western blot analysis of
BNP, β-MHC, and collagen I protein expression changes in heart tissues
from different treatment groups; (H) TUNEL staining detecting apoptosis
in mouse myocardial tissue, with red fluorescence representing
apoptotic cells and blue fluorescence from DAPI nuclear staining (scale
bar: 50 μm); I Immunohistochemical staining to assess the expression of
p-STAT3 protein in mouse heart tissues (scale bar: 50 μm); J Prussian
blue staining for detecting iron ion content in mouse heart tissue
(scale bar: 50 μm); K Detection of ROS levels in mouse heart tissues
from different treatment groups; L Assessment of GSH/GSSG ratio in
mouse heart tissues from different treatment groups; M Measurement of
MDA content in mouse heart tissues from different treatment groups; N
RT-qPCR analysis of transcription levels of ferroptosis-related genes
Slc7a11 and Gpx4 in mouse heart tissues from different treatment
groups; O Representative TEM images of mouse heart tissues from
different treatment groups(scale bar: 2 μm). Quantitative data in the
figures are presented as Mean ± SD, with 6 mice per group in each
experiment, where * represents a comparison between two groups, **
indicates p < 0.01, *** indicates p < 0.001, and **** indicates
p < 0.0001
Hemodynamic assessment results demonstrated that compared to the sham
group, mice in the model group exhibited a significant increase in
LVEDP and a significant decrease in LVSP, maximal rate of pressure rise
in the left ventricle (+ dP/dt), and maximal rate of pressure decrease
in the left ventricle (-dP/dt). In comparison to the model and PNs
groups, mice in the colivelin and PN@Col groups showed a significant
reduction in LVEDP, while LVSP, maximal rate of pressure rise, and
maximal rate of pressure decrease in the left ventricle significantly
increased (Fig. S21G–J). The examination of ventricular mass index
revealed that relative to the sham group, mice in the model group had
significantly increased left and right ventricular mass indices (LVMI
and RVMI). In contrast, compared to the model and PNs groups, mice in
the colivelin and PN@Col groups displayed a significant decrease in
LVMI and RVMI, with PN@Col demonstrating a significantly greater
capacity to restore cardiac function in mice than Colivelin (Fig.
S21K–L).
Following the euthanasia of mice, the hearts were harvested and
subjected to H&E staining. Results revealed that compared to the sham
group, the hearts of mice in the model and PNs groups exhibited
significant hypertrophy with a noticeable increase in cross-sectional
area. Treatment with Colivelin and PN@Col alleviated cardiac
hypertrophy in mice post-treatment, with PN@Col demonstrating superior
ability in restoring heart size compared to Colivelin (Fig. [139]8C–D).
Utilizing WGA staining, we measured the cross-sectional area of CMs.
Our study findings indicated a significant increase in CM
cross-sectional area in the model and PNs groups mice, which was
mitigated by treatment with Colivelin and PN@Col, with PN@Col showing a
more favorable therapeutic effect (Fig. [140]8E).
Additionally, we conducted a comprehensive in vivo toxicity assessment.
HE staining analysis of the kidney and liver revealed no significant
damage after PN@Col injection (Fig. S22A). Cytokine levels measured by
ELISA indicated that PN@Col injection did not trigger harmful
inflammatory responses (Fig. S22B).
Results from Sirius Red staining and Masson's trichrome staining
demonstrated a significant increase in interstitial fibrosis deposition
area in the myocardium of mice in the model and PNs groups compared to
the sham group. However, following treatment with Colivelin and PN@Col,
the degree of fibrosis notably decreased, with PN@Col exhibiting
stronger inhibitory capacity against myocardial fibrosis
(Fig. [141]8F). The process of myocardial hypertrophy is accompanied by
complex changes in gene expression levels, which are causally linked to
the occurrence and progression of the hypertrophic phenotype. BNP,
β-MHC, and collagen I gene re-expression during myocardial hypertrophy
is well recognized [[142]38, [143]39]. Therefore, simultaneous
detection of BNP, β-MHC, and collagen I gene expression will be of
great significance for determining myocardial hypertrophy and its
prognosis. Western blotting showed that, compared to the sham group,
BNP, β-MHC, and collagen I protein levels were significantly increased
in the hearts of the model and PNs groups. After treatment with
Colivelin and PN@Col, these protein levels were alleviated, with the
reduction in BNP, β-MHC, and collagen I protein levels in the PN@Col
group being significantly stronger than that in the Colivelin group
(Fig. [144]8G). TUNEL staining confirmed that both Colivelin and PN@Col
could suppress apoptosis of CMs in HF mice, with PN@Col showing a more
pronounced inhibitory effect (Fig. [145]8H).
Furthermore, we evaluated the expression of p-STAT3 protein in the
cardiac tissues of mice from different treatment groups. Analysis
revealed a decrease in p-STAT3 protein expression in the myocardial
tissues of mice in the model and PNs groups compared to the sham group.
In contrast, treatment with Colivelin and PN@Col led to a significant
upregulation of p-STAT3 protein expression compared to the model group,
confirming the ability of PN@Col to activate STAT3 (F[146]ig. [147]8I).
In addition, we assessed specific markers of ferroptosis, revealing
that compared to the sham group, mice in the model group exhibited a
significant increase in Fe^2+, ROS, and MDA levels in cardiac tissues,
accompanied by a notable decrease in the GSH/GSSG ratio. Conversely,
compared to the model and PNs groups, the cellular levels of Fe^2+,
ROS, and MDA in the Colivelin and PN@Col groups markedly decreased,
while the GSH/GSSG ratio significantly increased. Furthermore, the
PN@Col group demonstrated a significantly stronger ability to restore
specific markers of ferroptosis compared to the Colivelin group
(Fig. [148]8J–M).
To further validate our findings, we examined the expression of Gpx4
and Slc7a11 mRNA. Results from RT-qPCR demonstrated that compared to
the sham group, the mRNA levels of Gpx4 and Slc7a11 in the model and
PNs groups showed a significant decrease. In contrast, the expression
levels of Gpx4 and Slc7a11 in the Colivelin and PN@Col groups exhibited
a noticeable restoration compared to the model group, with PN@Col
showing a significantly stronger ability to restore their expression
levels than Colivelin (Fig. [149]8N). TEM revealed typical
mitochondrial abnormalities associated with ferroptosis in the model
group, including mitochondrial ridge destruction and membrane integrity
impairment. Following treatment with Colivelin and PN@Col, the
aforementioned ultrastructural damages were alleviated, with PN@Col
demonstrating a superior therapeutic effect over Colivelin
(Fig. [150]8O).
The results of the study indicate that PN@Col treatment can
significantly improve cardiac function in HF mice by inhibiting CM
ferroptosis.
HF is a fatal cardiac disease that poses a significant threat to global
health due to its high incidence and mortality rates [[151]40,
[152]41]. Oxidative stress and ferroptosis have been widely recognized
as important pathological mechanisms in the progression of HF [[153]42,
[154]43]. Against this background, this study aims to explore a novel
therapeutic strategy utilizing PN@Col to improve age-related HF. The
findings indicate that PN@Col not only effectively suppresses oxidative
stress and ferroptosis but also significantly enhances cardiac function
in mice with HF. The comprehensive application of scRNA-seq analysis,
in vitro cell experiments, and in vivo mouse models validates the
potential of PN@Col in the treatment of HF.
Previous studies have indicated that oxidative stress plays a critical
role in the occurrence and development of HF, and conventional
antioxidants and iron chelators can, to some extent, slow the
progression of the condition [[155]44, [156]45]. However, these
treatment methods often have issues such as low bioavailability and
poor targeting. In contrast, the PN@Col used in this study offers
higher stability and specificity. Unlike traditional antioxidants,
PN@Col can stably exist within cells and effectively deliver a STAT3
agonist to CMs, significantly enhancing the therapeutic effects.
Furthermore, PN@Col demonstrates multiple synergistic actions by not
only directly scavenging free radicals but also regulating the
ferroptosis pathway, further protecting CMs.
STAT3 is a crucial transcription factor extensively involved in
processes such as cell proliferation, differentiation, apoptosis, and
immune response [[157]46, [158]47]. Previous studies have confirmed the
essential role of STAT3 in the pathological progression of HF, with its
activation significantly reducing ferroptosis and inflammation in CMs
[[159]46, [160]48]. Through scRNA-seq analysis, this study further
validates the key role of STAT3 in HF. In vitro experimental results
demonstrate that the STAT3 agonist markedly enhances CM viability,
suppresses ferroptosis, and improves cellular biological functions. In
comparison to other studies, the utilization of a nanoparticle delivery
system in this study enhances the bioavailability and therapeutic
effects of the STAT3 agonist [[161]49–[162]51].
PN@Col significantly improves the condition of HF by inhibiting ROS
generation and regulating ferroptosis. In vitro experiments demonstrate
that PN@Col effectively reduces ROS levels in CMs, decreasing the
damage caused by oxidative stress. Additionally, PN@Col significantly
reduces the rate of ferroptosis in CMs by modulating the relevant
signaling pathways. In vivo, experiments further validate the
therapeutic effects of PN@Col, as mice models receiving PN@Col
injections show a substantial improvement in cardiac function, as well
as a noticeable reduction in the hypertrophy and fibrosis of myocardial
tissue. These results indicate that PN@Col not only exhibits potent
antioxidant and anti-ferroptosis effects at the molecular and cellular
levels but also significantly enhances cardiac function in the overall
animal model.
PN@Col has shown vast potential in clinical applications. Firstly, its
high bioavailability and specificity make it an ideal candidate for
treating HF. Secondly, PN@Col's multiple synergistic actions not only
provide antioxidant effects but also regulate ferroptosis, offering a
new strategy for the comprehensive treatment of HF. Furthermore, the
nanoparticle structure of PN@Col enhances drug delivery systems,
improving stability and efficacy in vivo. However, PN@Col also faces
challenges in clinical applications, such as large-scale production and
quality control, necessitating further research and optimization.
Compared to previous studies, this research demonstrates significant
advantages in innovation and effectiveness. While previous strategies
for antioxidant and ferroptosis regulation have partially alleviated
the symptoms of HF, issues such as low bioavailability and poor
targeting have been identified. In this study, the use of the PN
delivery system not only greatly improved the drug's bioavailability
but also enhanced the therapeutic effects through synergistic actions.
Furthermore, by combining scRNA-seq analysis with in vivo and in vitro
experiments, this research systematically evaluated the effectiveness
and mechanism of PN@Col, providing new directions and insights for
future research in HF treatment.
This study, through scRNA-seq analysis, identified CMs as key cells
mediating intercellular communication in the progression of HF. It was
also discovered that CM-related aging and ferroptosis may play
significant roles in the pathological process of HF. Further analysis
led to the identification of the key gene STAT3 associated with CM
aging and ferroptosis. Building on these findings, the research
successfully developed PNs using EGCG and glycine and loaded the STAT3
agonist Colivelin into them, resulting in a novel antioxidant PN named
PN@Col. Through in vivo and in vitro experiments, the study
comprehensively investigated the potential inhibitory effect of PN@Col
in age-related HF mice. The results demonstrate that this innovative
treatment strategy can inhibit ROS generation, prevent CMs from
undergoing ferroptosis, and significantly improve cardiac function in
age-related HF mice.
Conclusion
This study has made significant progress in improving age-related HF by
developing and validating PN@Col, demonstrating its potential
scientific and clinical value. From a scientific standpoint, the
research not only unveiled the critical role of STAT3 in regulating CM
ferroptosis and inflammatory responses but also validated the
effectiveness and mechanism of PN@Col through scRNA-seq and
multi-layered experiments, providing new perspectives and data support
for the pathophysiological research of HF. In terms of clinical
applications, PN@Col, as a novel treatment strategy, has shown
tremendous potential in enhancing the long-term prognosis and quality
of life for HF patients, particularly through its synergistic effects
in antioxidant and ferroptosis inhibition. This offers a promising
avenue for more effective and precise clinical treatment options.
Although this study has made significant progress at multiple levels,
it still has some limitations. Firstly, the sample size of the study is
relatively small, particularly in vivo experiments, which are limited
to mouse models, potentially impacting the generalizability and
applicability of the results. Secondly, while the in vitro and in vivo
experiments demonstrate that PN@Col exhibits promising therapeutic
effects, its long-term efficacy and safety have not been thoroughly
validated, and further large-scale clinical trials are needed.
Additionally, this study primarily focuses on the specific pathological
mechanisms of HF, lacking a comprehensive exploration of other
potential pathogenic factors and mechanisms. Finally, optimization of
the production process of PN@Col and the drug delivery system requires
further research to ensure its stability and feasibility in clinical
applications.
Future research should focus on validating PN@Col in large-scale
clinical trials to assess its long-term efficacy and safety, as well as
exploring its applicability in different types of HF patients.
Furthermore, it is essential to optimize the production process and
drug delivery system of PN@Col further to enhance its stability and
bioavailability, ensuring its feasibility and cost-effectiveness in
clinical applications. Simultaneously, potential applications of PN@Col
in other heart diseases or related pathological conditions should be
investigated to expand its therapeutic scope. By combining various
omics technologies and the principles of precision medicine, further
elucidation of the molecular mechanisms of PN@Col in regulating CM
function can provide a theoretical basis and technical support for
developing more efficient treatment strategies. Through ongoing
research and exploration, PN@Col holds the promise of bringing new
breakthroughs and hope for the treatment of HF and other cardiovascular
diseases.
Materials and methods
Ethical statement
This study strictly adhered to relevant ethical principles and
regulations concerning animal experimentation. All experimental
procedures were approved by the Institutional Animal Care and Use
Committee of Shengjing Hospital of China Medical University (Ethical
Approval Number 2024PS359K). All animals were housed and cared for in
environments meeting humane standards, and experiments were conducted
with efforts to minimize pain. At the conclusion of the experiments,
all mice were euthanized humanely under ether anesthesia.
Downloading and preprocessing scRNA-seq data of heart tissue samples from HF
patients
Data download
The [163]GSE183852 dataset was obtained from the Gene Expression
Omnibus (GEO) database ([164]http://www.ncbi.nlm.nih.gov/geo/). This
dataset includes samples from 28 non-diseased donors (Control group
samples obtained post-brain death donation) and 17 individuals with
Dilated (non-ischemic) cardiomyopathy (DCM) obtained from the left
ventricular (LV) heart tissue specimens (HF group samples). The DCM
tissues were collected from implanted left VADs in individuals or from
transplanted hearts. After filtering, the expression matrix of each
sample was normalized using the NormalizeData function in the "Seurat"
package.
Quality control
Initially, employ the R package "DropletUtils" to assess the expression
status of each cell and filter out barcodes with no cell expression.
Subsequently, further filter based on the number of Unique Molecular
Identifiers (UMI) in each cell, removing cells with UMI counts less
than 200. Next, utilize the ‘‘scater’’ package to analyze gene
expression in cells and filter out cells with mitochondrial gene
expression exceeding 5% and ribosomal gene expression lower than 20%.
Finally, perform gene-level statistics.
scRNA-seq data analysis
Principal component analysis (PCA) and batch effect removal
Initially, the top 2000 genes displaying the most significant
differences between cells were selected using the ‘‘Seurat’’ package’s
FindVariableFeatures function. Focusing on these genes in downstream
analysis helps highlight biological signals within the single-cell
dataset. Subsequently, the expression data was scaled linearly using
the ‘‘Seurat’’ package’s ScaleData function, followed by linear
dimensionality reduction analysis using the ‘‘Seurat’’ package’s RunPCA
function. Finally, the ‘‘harmony’’ package's RunHarmony function was
employed to mitigate sample-to-sample variations.
Cell dimensionality reduction and clustering
Following batch effect correction, the principal components (PCs) with
the highest standard deviation were selected first. Subsequently, cell
clustering analysis was performed using the ‘‘Seurat’’ package’s
FindNeighbors and FindClusters functions. Uniform manifold
approximation and projection (UMAP) was then conducted using the
‘‘Seurat’’ package's RunUMAP function.
Identification of marker genes
Initially, differential expression genes between each cluster and all
other cells were calculated using the ‘‘Seurat’’ package’s FindMarkers
function (|log2FC|≥ 0.1, minimum cell group expression ratio of 0.25,
p-value ≤ 0.05), thus identifying marker genes (top 500 logFC genes).
Cell annotation
The cells were annotated based on existing marker genes, and cluster
visualization was conducted. Subsequently, the proportions of different
cell types in each group were calculated to determine the variance in
cell type proportions among groups, aiding in the identification of
core cell clusters. Using the identified core cell clusters, further
dimensionality reduction clustering was performed to determine marker
genes for subtyping and to calculate cell type proportions in each
sample, assessing inter-group proportion differences. Subsequently,
subtyping of core cell clusters (including CMs, macrophages, and T
cells) was carried out using UMAP, enabling dimensionality reduction
clustering analysis of single-cell data. Common markers for CM and
macrophage subtypes were not identified; thus, annotation was performed
using a heuristic clustering approach.
Differential analysis
The differential expression analysis of the core cell groups (HF vs
Control) was conducted using the ‘‘Seurat’’ package’s FindMarkers
function. The selection criteria were set as fold change (FC) > 1.2 and
p-value < 0.05.
Functional enrichment analysis
The intersecting genes were subjected to Gene Ontology (GO) enrichment
analysis utilizing the "ClusterProfiler" package in R. This analysis
included exploration of biological processes (BP), molecular functions
(MF), and cellular components (CC), followed by the visualization of
the GO enrichment results through bubble charts and gene network
diagrams. A cutoff of |log2FC|> 1 and P-value < 0.05 was applied for
filtering. Subsequently, based on the |log2FC| values, candidate target
genes underwent Kyoto Encyclopedia of Genes and Genomes (KEGG)
enrichment analysis using the ‘‘ClusterProfiler’’ package in R, with
resulting bubble charts and gene network diagrams.
Venn analysis
A set of 728 genes related to ferroptosis was obtained from the FerrDb
database ([165]http://www.zhounan.org/ferrdb/current/). Additionally,
5740 genes associated with senescence were extracted from the GeneCards
database ([166]https://www.genecards.org/) using the search term
‘‘senescence.’’ Intersection genes were identified by conducting a Venn
analysis in R using the ‘‘VennDiagram’’ package between the
aforementioned two gene sets and the differentially expressed genes
(DEGs) in CM_1 and CM_2.
AUCell scoring
In order to identify cell clusters regulated by aging, inflammation,
and ferroptosis in HF, this study conducted AUCell analysis on
individual cells based on gene labels associated with aging,
inflammation, and ferroptosis. The differences in aging, inflammation,
and ferroptosis scores between the disease group and the control group
were explored using the Wilcoxon test.
Pyscenic Analysis
To investigate transcriptional factor regulatory variances among all
core cells, abnormally activated transcription factors were further
selected from HF samples according to grouping. SCENIC was employed to
infer the regulatory activity of each transcription factor based on the
expression levels within the core cell subtypes.
Cell communication analysis
Cell-to-cell communication analysis was conducted based on the specific
expression genes of various cell types and ligand-receptor
relationships collected from the literature. Cell interaction analysis
was performed separately according to the grouping to select abnormal
aging, inflammation, and related ligand-receptor axes. The ‘‘cellchat’’
package was utilized to infer cell-to-cell communication based on the
expression values of receptor-ligand genes corresponding to various
cell types, thereby constructing a network of receptor-ligand pairs
between cells.
Cell culture and treatment
Human CMs (CP-H076, Wuhan Punose Life Sciences, Wuhan, China) were
cultured using a human CM complete medium (CM-H076, Wuhan Punose Life
Sciences, Wuhan, China). Human embryonic kidney cells HEK-293 T
(Bio-72947, Beijing Bio-Bos Biological Technology Co., Ltd) were
cultured in DMEM high glucose medium (11,965,084, Thermo Fisher
Scientific, USA) containing 10% FBS (10100147C, Thermo Fisher, USA) and
1% penicillin–streptomycin (100 U/mL penicillin and 100 μg/mL
streptomycin, 15,140,163, Thermo Fisher, USA). The cells were
maintained in a humidified incubator at 37 °C with 5% CO[2] (Heracell™
Vios 160i CR CO[2] Incubator, 51,033,770, Thermo Scientific™, Germany).
Passaging was carried out when the cells reached 80% ~ 90% confluency.
Treat CM cells with 5 μM ferroptosis inducer Erastin (HY-15763,
MedChemExpress, USA) to induce cell ferroptosis. After treatment for
24 h, further treat the cells with different reagents for another 24 h
and conduct subsequent biochemical tests [[167]52, [168]53].
Lentivirus transduction
CMs were silenced using lentivirus infection, with lentivirus packaging
services provided by Shengwu Bioengineering (Shanghai, China). The
pHAGE-puro plasmid series and auxiliary plasmids pSPAX2 and pMD2.G
(catalog numbers #118,692, #12,260, and #12,259, respectively,
purchased from Addgene, USA) were utilized for gene overexpression. For
gene silencing, the pSuper-retro-puro plasmid series and auxiliary
plasmids gag/pol and VSVG (catalog numbers #113,535, #14,887, and
#8454, respectively, obtained from Addgene, USA) were employed. The
aforementioned plasmids were co-transfected with lentivirus packaging,
facilitated by Shengwu Bioengineering. The constructed plasmids were
co-transfected into HEK293T cells using Lipofectamine 2000 reagent
(11,668,030, Thermo Fisher, USA), followed by harvesting the
supernatant after 48 h of cell culture. The supernatant,
post-centrifugation using a 0.45 μm filter, was collected as the virus,
which was concentrated after 72 h by centrifugation of the supernatant.
The two virus preparations were mixed, and viral titer was determined.
During the logarithmic growth phase, cells were digested with trypsin
and seeded at a density of 1 × 10^5 cells per well in a 6-well plate.
Following 24 h of routine culture, when cell confluency reached
approximately 75%, the cells were infected with lentivirus packaging at
a multiplicity of infection (MOI) of 10 and a working titer of about
5 × 10^6 TU/mL, supplemented with 5 μg/mL polybrene (TR-1003, Merck,
USA) in the medium. After 4 h of infection, an equal volume of medium
was added to dilute the polybrene, and 24 h post-infection, the fresh
medium was replaced. For the establishment of stable cell lines, cells
were further cultured in a medium containing 2 μg/mL puromycin
(E607054, Shengwu Bioengineering, Shanghai, China). During passaging,
the puromycin concentration was gradually increased in a stepwise
manner at 2, 4, 6, 8, and 10 μg/mL to perform resistance screening and
obtain stable transduced cell lines. Cells were collected once they no
longer exhibited cell death in the presence of a puromycin-containing
medium. The efficiency of knockout was confirmed through Western blot
and RT-qPCR analyses. Lentivirus sequences for silencing are detailed
in Table S3, with subsequent selection of the most effective silencing
sequence for experimental validation [[169]54].
Cell groups were categorized as follows: sh-NC CMs (control cells with
lentivirus silencing STAT3); sh-STAT3 CMs (cells with silenced STAT3);
oe-NC CMs (control cells with lentivirus overexpressing STAT3); and
oe-STAT3 CMs (cells overexpressing STAT3).
Detection of gene expression by RT-qPCR
After one week of HF model construction or at the end of treatment,
mice were euthanized, and total RNA from tissues and cells was
extracted using the Trizol reagent kit (A33254, Thermo Fisher, USA).
The extracted RNA was reverse transcribed using the reverse
transcription kit (RR047A, Takara, Japan) to obtain the corresponding
cDNA. The reaction system was prepared using the SYBR® Premix Ex TaqTM
II kit (DRR081, Takara, Japan), and the samples were subjected to
RT-qPCR using a real-time fluorescence quantitative PCR instrument
(ABI7500, Thermo Fisher, USA). The PCR program was designed as follows:
an initial denaturation at 95 ℃ for 30 s, followed by cycling with
denaturation at 95 ℃ for 5 s, annealing at 60 ℃ for 30 s for 40 cycles,
final extension at 95 ℃ for 15 s, extension at 60 ℃ for 60 s, and a
further extension at 90 ℃ for 15 s to generate amplification curves.
GAPDH was used as an internal reference, with three replicates for each
RT-qPCR setting, and the experiment was repeated three times. The
expression ratio of the target gene in the experimental group compared
to the control group was calculated using the 2^−ΔΔCT method, where
ΔΔCT = ΔCt [experimental group]—ΔCt [control group], and ΔCt = Ct
[target gene]—Ct [internal reference gene]. Ct represents the cycle
threshold when the real-time fluorescence intensity reaches the set
threshold, indicating logarithmic growth of amplification. The primer
details are provided in Table S4.
Western blot analysis
Total proteins from tissues and cells were extracted using RIPA (Radio
Immunoprecipitation Assay) lysis buffer (P0013B, Beyotime, Shanghai,
China) containing 1% PMSF (Phenylmethanesulfonyl fluoride) following
the manufacturer's instructions. The protein concentration of each
sample was determined using a BCA protein assay kit (P0011, Beyotime,
Shanghai, China) on the supernatant, and protein concentrations were
adjusted to 1 μg/μL. Each sample was then boiled at 100 ℃ for 10 min to
denature the proteins and stored at – 80 ℃ for later use. SDS-PAGE gels
(8–12%) were prepared based on the expected size of the target protein
bands, and 50 μg of protein samples were loaded into each lane using a
micropipette. The gel was electrophoresed at a constant voltage of 80 V
to 120 V for 2 h. Transblotting was performed at a constant current of
250 mA for 90 min to transfer the proteins from the gel to a PVDF
membrane (1,620,177, Bio-Rad, USA).
The membrane was blocked with 1 × TBST containing 5% non-fat milk at
room temperature for 1 h. The blocking solution was then discarded, and
the membrane was washed with 1 × TBST for 10 min. Primary antibodies
(antibody details in Table S5) were incubated overnight at 4 ℃,
followed by three washes with 1 × TBST for 10 min each at room
temperature. Subsequently, the membrane was washed three times with
1 × TBST for 5 min each at room temperature. The membrane was then
incubated with HRP-conjugated goat anti-rabbit IgG (ab6721, dilution
1:5000, Abcam, Cambridge, UK) or goat anti-mouse IgG (ab205719,
dilution 1:5000, Abcam, Cambridge, UK) secondary antibodies at room
temperature for 1 h. After three washes with 1 × TBST buffer at room
temperature for 5 min each, the membrane was immersed in ECL reaction
solution (1,705,062, Bio-Rad, USA) and incubated at room temperature
for 1 min. The liquid was removed, the membrane was covered with
plastic wrap, and band exposure imaging was performed on the Image
Quant LAS 4000C gel imaging system (GE Healthcare, USA). The relative
expression levels of proteins were determined by comparing the
grayscale values of the target bands to the reference bands (GAPDH as
internal control), reflecting the relative protein expression levels.
Protein expression levels were assessed with three replicates for each
experiment.
CCK-8 proliferation assay
CMs were digested and re-suspended, and the cell concentration was
adjusted to 1 × 10^5 cells/mL before seeding 100 μL per well into a
96-well plate for overnight incubation. Following the manufacturer's
instructions for the CCK-8 assay kit (C0041, Beyotime, Shanghai,
China), cell viability was assessed using the CCK-8 method at 12, 24,
36, and 48 h post-culturing. At each time point, 10 μL of the CCK-8
detection solution was added, and the plate was then placed in a 37 °C,
5% CO[2] incubator for 2 h. The absorbance at 450 nm was measured using
a microplate reader (BioTek, USA) to calculate cell viability according
to the following formula [[170]55].
[MATH:
Cellvia
bility
(%)=ODsample-ODblank<
mi mathvariant="italic">ODcontrol-ODblank<
mo>×100 :MATH]
OD[sample] represents the optical density value of the drug-treated
samples. OD[control] refers to the optical density value of the control
group (untreated cells under normal growth conditions). OD[blank]
denotes the optical density value of the blank control (comprising
culture medium and CCK-8 reagent, but without cells).
EDU staining
CMs were seeded in a 24-well plate with a density of 1 × 10^5 cells per
well. Each group of cells was triplicated. 5-Ethynyl-2'-deoxyuridine
(EdU) solution (ST067, Beyotime, Shanghai, China) was added to the
culture medium to achieve a concentration of 10 µmol/L, followed by 2 h
of incubation in a CO[2] incubator. After removal of the culture
medium, cells were fixed at room temperature for 15 min in PBS solution
containing 4% paraformaldehyde, washed twice with PBS containing 3%
BSA, permeabilized with 0.5% Triton-100 in PBS at room temperature for
20 min, washed again with PBS containing 3% BSA, and incubated with 100
µL of staining solution per well at room temperature in the dark for
30 min. Cell nuclei were stained with DAPI (C1002, Beyotime, Shanghai)
for 5 min. The coverslip was then examined under a fluorescence
microscope (Model: FM-600, Shanghai Pudan Optical Instrument Co., Ltd)
to observe 6–10 random fields per well and record the number of
positive cells in each field. The EdU labeling rate was calculated as
the percentage of positive cells out of the total cells (positive
cells + negative cells) × 100% [[171]55]. Each experiment was repeated
three times.
Transwell migration assay
To assess the migration capability of CMs, Transwell inserts with a
pore size of 8 μm on polycarbonate membranes (3428, Corning, USA) were
used. The lower chambers were pre-filled with 600 µL of culture medium
containing 20% FBS and incubated at 37 °C for 1 h. After treating the
cells, they were resuspended in an FBS-free medium, seeded at a
concentration of 1 × 10^6 cells/mL in the upper chambers, and incubated
at 37 °C with 5% CO[2] for 24 h. The Transwell inserts were then
removed, washed twice with PBS, fixed with 5% glutaraldehyde, stained
with 0.1% crystal violet at 4 °C for 5 min, washed again with PBS, and
surface cells were removed using a cotton ball. The cells were observed
under a light microscope, and images were taken of 5 random fields. The
average number of cells that migrated across the insert was calculated
for each group. Each experiment was repeated three times [[172]55].
Wound healing assay
On the bottom of a 6-well plate, lines were marked at 0.5–1 cm
intervals using a ruler and marker, with each well crossed by at least
5 lines. CMs were seeded into the wells at a density of 5 × 10^5 cells
per well. When cells reached 100% confluence, a 200 μL pipette tip was
used to create a scratch perpendicular to the marked lines on the back.
Suspended cells were washed off with sterile PBS and replaced with a
complete medium containing 2% serum. The scratches were observed at 0 h
and 24 h under an optical microscope (model: DM500, Leica) to measure
the gap distance between the wound edges. Images were captured under an
inverted microscope to assess cell migration in each group. The
distance between the scratches was analyzed using Image-Pro Plus 6.0
software, and the wound healing rate was calculated using the following
formula [[173]55].
[MATH:
Woundhe
alingra<
/mi>te=distance0h-dista<
mi>nce24h<
/msub>distance0h :MATH]
Here, distance[0 h] and distance[24 h] represent the gap distance
between the cells at 0 h and 24 h after scratching, respectively.
Flow cytometry analysis
The apoptosis level of CMs was assessed using the Annexin V-FITC/PI
assay kit (C1062L, Beyotime, Shanghai, China). Cells were seeded in
6-well plates, with 1 × 10^6 cells per well. Following cell collection,
195 µL of Annexin V-FITC binding buffer was added to resuspend the
cells. Subsequently, 5 µL of Annexin V/FITC solution and 10 µL of PI
solution were added, followed by a 15 min incubation at room
temperature in the dark. Flow cytometry analysis was conducted within
20 min to determine the percentage of apoptotic cells. The apoptosis
rate was calculated as the sum of apoptotic cells in the Q1-UR (upper
right) and Q1-LR (lower right) quadrants [[174]55].
Detection of Fe2 + Ions
The FerroOrange probe (F374, Dojindo, Japan) was used to assess the
levels of Fe^2+ ions inside the cells. Pre-treated CMs were seeded on
confocal culture dishes, washed with Hank's balanced salt solution
(HBSS; 13,150,016, Gibco), and then incubated with 1 μM FerroOrange for
30 min. The cells were observed under a confocal laser scanning
microscope (LSM780, Zeiss). Simultaneously, the iron content in cells
and tissues was determined using an iron content detection kit (MAK025,
Sigma-Aldrich). For tissue samples, homogenization was conducted by
adding 1 mL of distilled water, followed by centrifugation to obtain
the supernatant. Cell samples were collected and centrifuged directly
to obtain the supernatant. Subsequently, 100 µL of the supernatant from
each well was transferred to a flat-bottom 96-well UV detection plate.
A mixture of 35 µL of reagent, 5 µL of reagent B, and 150 µL of reagent
C was thoroughly combined with the samples, followed by a 5 min
incubation at room temperature. The absorbance was read at 359 nm using
an Epoch microplate spectrophotometer. The Fe^2+ content was normalized
to the protein concentration. Each experiment was repeated three times.
TEM
CMs and heart tissue were prepared for TEM. The samples were fixed
overnight at 4 °C in a 2.5% glutaraldehyde solution (P1126, Beijing
Solabao Technology Co., Ltd., Beijing, China), followed by fixation in
a 1% osmium tetroxide solution (115,355, ECHO Chemicals, Chengdu,
China) for 1–2 h at room temperature. Dehydration was carried out in
graded ethanol (50%, 70%, 80%, 90%, and 95%), followed by treatment
with pure acetone and overnight incubation in pure embedding resin. The
samples were then embedded after infiltration and heated overnight at
70 °C to complete embedding. Thin sections of 70–90 nm thickness were
obtained using a Reichert ultramicrotome. These sections were stained
with lead citrate and 50% ethanol-saturated uranyl acetate for 15 min
each, enabling observation under TEM.
Measurement of mitochondrial membrane potential
Cells were seeded into a 6-well culture plate using the JC-1
mitochondrial membrane potential assay kit (40706ES60, Yisheng
Biotechnology, Shanghai, China), with 1 mL of JC-1 staining working
solution added to each well and thoroughly mixed. The cells were then
incubated at 37 °C for 20 min in a cell culture incubator. CCCP
provided in the kit (50 mM) was added to the cell culture medium at a
1:1000 ratio, diluted to 50 μM, and the cells were treated for 20 min
as a positive control. After the incubation at 37 °C, the supernatant
was removed, and the cells were washed twice with JC-1 staining buffer
(1 ×). Subsequently, 2 mL of cell culture medium containing serum and
phenol red was added. The observation was conducted under a fluorescent
microscope or a confocal laser scanning microscope, with the excitation
light set at 490 nm and emission light at 530 nm to detect JC-1
monomers.
Biochemical analysis
The levels of reduced glutathione (GSH) and oxidized glutathione (GSSG)
within cells and tissues were determined using the GSH and GSSG assay
kit (S0053, Beyotime) following the manufacturer's instructions. The
absorbance of samples was measured at 450 nm using a microplate reader
(E8051, Promega), and quantification was performed using a standard
curve.
To detect the malondialdehyde (MDA) content, the MDA assay kit (BC0025,
Solarbio) manufacturer's instructions were followed. For MDA detection,
the supernatant of cell homogenates and the prepared working reagent
were transferred to a 96-well plate. The absorbance was measured at
600 nm, 532 nm, and 450 nm using an automated microplate reader
(Infinite200, Tecan, Beijing) for further calculations.
Cells grown on confocal culture dishes were stained with 5 μM
C11-BODIPY581/591 (D3861, Thermo Fisher, USA) in the dark for 15 min
and observed under a fluorescent microscope. The fluorescence
properties change from red to green when oxidized by free radicals.
Detection of ROS
ROS levels were measured using a ROS detection kit (S0033S, Beyotime,
Shanghai). DCFH-DA was diluted 1:1000 in serum-free medium to a final
concentration of 10 μM/L, and cells were incubated at 37 °C for 20 min.
After washing three times with serum-free medium, fluorescence
intensity was observed using a fluorescence microscope (FV-1000/ES,
Olympus, Japan) and analyzed with Image J. Each experiment was repeated
three times.
For tissue ROS measurement, a tissue ROS detection kit (HR8821,
Biovision) was used. Tissue samples (50 mg) were homogenized,
centrifuged at 100 × g for 3 min at 4 °C, and the supernatant
collected. A mixture of 200 μL supernatant and 2 μL DHE probe was
incubated in a 96-well plate at 37 °C in the dark for 15–30 min.
Fluorescence intensity was measured at an excitation wavelength of
488–535 nm and an emission wavelength of 610 nm using a microplate
reader (E8051, Promega). Tissue ROS levels were expressed as
fluorescence intensity (RFU) per mg of protein. Each experiment was
performed in triplicate.
Preparation of novel antioxidant PNs
Sixty-one milligrams of EGCG (HY-13653, MedChemExpress, USA) were
dissolved in a 5% ethanol solution containing 10 μL of formaldehyde and
37% triethylamine (TEA, T0886, Sigma-Aldrich, USA) in 40 mL and
vigorously stirred for dissolution at 30 ℃. Subsequently, 10 mg of
glycine (Gly, HY-Y0966, MedChemExpress, USA) was added to the solution,
and the reaction was continuously stirred for 48 h. The nanoparticles
were obtained by centrifugation at 10,000 r/min-1 for 10 min to yield
PNs, which were then purified by washing three times with 10 mL of
water and resuspended in 5 mL of water for storage at 4 ℃.
To dissolve the PNs, 2 mg of the nanoparticles were mixed with 100 μL
of ethanol. Subsequently, 2 mg of the STAT3 agonist Colivelin (referred
to as Col, HY-P1061, MedChemExpress, USA) was added to the mixture and
vigorously stirred at 30 ℃. The nanoparticles were then reassembled in
a deionized water and ethanol solution. After centrifugation at
10,000 r/min^−1 for 10 min, the nanoparticles were washed three times
with 10 mL of water, followed by freeze-drying to obtain PN@Col.
Morphology, size, and distribution of nanoparticles
The size and shape of nanoparticles were observed using TEM. Prior to
scanning, 20 μL of freshly prepared nanoparticle samples in suspension
were loaded onto carbon-coated copper electron microscopy grids.
Negative staining was performed using 1% uranyl acetate (CD106833,
Guangzhou Wei Pharmaceuticals Technology Co., Ltd., Guangzhou, China)
for 5 min. This method provided high-quality contrast and sharpness for
clear and distinct visualization of the samples. Subsequently, the
grids were washed thrice with PBS, the excess phosphotungstic acid
solution was removed using filter paper, and the grids were left
semi-dried. The images were observed at 100 kV using a Hitachi H7650
TEM (Hitachi, Japan).
The scanning electron microscopy (SEM) procedure for detecting lipid
nanoparticles involves the following steps: Lipid nanoparticles were
examined using a scanning electron microscope (S-4800, Hitachi, Japan)
under an accelerating voltage of 3 kV. The sample was loaded onto a
conductive adhesive tape mounted on the SEM sample stub and coated with
a thin layer of gold using a sputter coater (Cressington Scientific
Instruments, Watford, UK) for 60 s before observation with SEM.
Nanoparticle Tracking Analysis (NTA): 20 μg of nanoparticles were
dissolved in 1 mL of PBS and vortexed for 1 min to ensure uniform
dispersion of LNP@Ket. The nanoparticle size distribution was directly
observed and measured using a ZetaView Nanoparticle Tracking Analyzer
(Particle Metrix, Germany) through dynamic light scattering (DLS). The
average size of the exosomes and the Polymer Dispersity Index (PDI)
were determined, and the Zeta potential value of the exosomes was
measured by conducting three measurements for each sample.
PN@Col drug loading and encapsulation efficiency test
Freeze-dried PN@Col was accurately weighed, dissolved in a 50:50
mixture of acetonitrile (AcN, Thermo Fisher) and water, and analyzed by
HPLC (Ascentis C18 column, 25 cm × 4.6 mm, 5 µm, Agilent). The mobile
phase was AcN:water (50:50) at a flow rate of 1 mL/min. Colivelin was
detected at 227 nm using a UV detector. Drug loading was calculated as
the weight percentage of Colivelin in PN@Col, and encapsulation
efficiency was determined based on its weight [[175]56].
PN@Col in vivo and in vitro drug release testing
For the release kinetics study, in vitro, the PN@Col containing
Colivelin was suspended in 1 mL of PBS (Procell, [176]PB180327, China)
containing 0.1% Tween 80 (Sigma, P1754-1L, USA) and incubated in a
rotating shaker at 37 °C. At regular time points, the PN@Col suspension
was centrifuged at 10,000 rpm for 10 min, and 0.9 mL of the supernatant
was collected and replaced with fresh buffer. After the final
collection at 72 h, the remaining PN@Col was freeze-dried and dissolved
in a 50:50 mixture of AcN and water. The released samples and remaining
PN@Col were analyzed by HPLC [[177]56].
Assessment of antioxidant capacity
Antioxidant activity was evaluated using DPPH and ABTS free radical
scavenging assays.
For the DPPH assay, varying concentrations of nanoparticles were added
to a freshly prepared 0.4 mM DPPH methanol solution and incubated at
25 °C for 30 min under subdued light. Absorbance was measured at 517 nm
using a UV–visible spectrophotometer (TU-1901, Persee, China).
For the ABTS assay, ABTS stock solution was prepared by mixing 7 mM
ABTS (5 mL) with 140 mM potassium persulfate (88 μL) and incubating in
the dark for 10–16 h. The stock was diluted 20-fold with 80% methanol
to adjust the absorbance to 0.75 ± 0.05 at 734 nm. In a 96-well plate,
100 μL of the methanol nanoparticle solution at varying concentrations
and 100 μL ABTS working solution were mixed and incubated at 25 °C in
the dark for 30 min. Absorbance was measured at 734 nm using a
microplate reader. The scavenging activity was calculated using:
[MATH: Y=1-Asample-AcontrolA<
mn>0×100% :MATH]
A[sample] is the absorbance of DPPH or ABTS solutions after adding
nanoparticles, A[control] represents the absorbance of DPPH or ABTS
solutions without samples, and A[0] indicates the absorbance of DPPH or
ABTS solutions lacking antioxidants.
The antioxidant capacity was compared using Trolox (HY-101445,
MedChemExpress, USA) as a reference. The equivalent antioxidant
capacity (TEAC) was calculated as:
[MATH: EC50(Trolox)250.34×1EC50(sample)×1000=Antioxidantcapacity(mmolTEg-1 :MATH]
).
Where 250.34 represents the molecular weight of Trolox. EC[50] is the
concentration of the sample found in the linear relationship that
inhibits 50% of free radicals.
Detection of live cell density
CMs were seeded in a 12-well plate at a density of 1 × 10^5 cells per
well, with each group of cells having three replicate wells. After
incubating for 12 h to allow cell adhesion, the cells were treated with
either PN or PN@Col for another 12 h. Subsequently, live cells were
labeled with fluorescein diacetate (FDA, F1303, Thermo Fisher, USA) and
observed under a fluorescence microscope for image analysis. The
percentage of viable cells was calculated based on the proportion of
fluorescent cells.
Establishment of an aged mouse model of HF
Aged SPF C57BL/6 mice over 24 months of age (219, Beijing Vital River
Laboratory Animal Technology Co., Ltd., Beijing, China), weighing
18–25 g, were housed in SPF animal facilities with controlled lighting
of 12 h light/12 h dark, humidity maintained at 60–65%, and temperature
between 22 and 25 °C. The mice were provided ad libitum access to food
and water and acclimated for 1 week before the experiment, during which
their health status was monitored. The experimental procedures and
animal protocols were approved by the Institutional Animal Care and Use
Committee.
Mice were anesthetized with 2% isoflurane in 100% O₂ (1 L/min) and
intubated using a 20G catheter. Ventilation was maintained via a
respirator (MiniVent Model 845, Harvard Apparatus) delivering 2%
isoflurane in oxygen at 1 L/min, with a tidal volume of 150 µL and a
respiratory rate of 150 bpm. After hair removal and disinfection
(iodine and 70% alcohol, three times), a midline incision was made from
the manubrium to the xiphoid process. A partial sternotomy was
performed to expose the aortic arch using a rib spreader. Adipose
tissue was cleared, and a titanium micro-clip was placed, reducing the
aortic arch cross-section by ~ 50%.
The rib spreader was removed, and the ventilator’s expiratory circuit
was briefly occluded (2–3 s) to induce heart overinflation, increasing
the tidal volume to 200 µL. The thoracic wall and skin were sutured,
tidal volume was reset to 150 µL, and isoflurane was gradually reduced
to 0.5% until spontaneous respiration resumed. The endotracheal tube
was removed, and mice recovered in a heated cage for 30 min to 1 h.
Sham-operated mice underwent the same procedure without micro-clip
placement. All mice were maintained for 8 weeks before CO[2] euthanasia
and further interventions.
Mice were randomly divided into five groups (n = 6 per group). Three
days post-HF model construction, treatments were administered via daily
tail vein injections for 28 days: Sham group: Thoracotomy without
aortic manipulation, received 100 μL PBS; PNs group: 100 μL PNs
(equivalent to 2 mg/kg colivelin) in PBS; Model group: HF mice received
100 μL PBS; Colivelin group: 100 μL PBS with 2 mg/kg colivelin; PN@Col
group: 100 μL PBS with 2 mg/kg PN@Col.
Cardiac function was assessed every 7 days. After 28 days, mice were
euthanized for biochemical analysis of cardiac tissues [[178]37,
[179]53, [180]57].
Echocardiographic examination Protocol
Echocardiographic examinations were performed using the Visual Sonics
Vevo 2100 system (Visualsonics Inc., Toronto, Canada) equipped with a
21 MHz linear array transducer. The procedure was conducted as follows.
Mice were positioned supine on a heated platform under continuous
anesthesia with 2% isoflurane gas. The chest fur was shaved using an
electric shaver and then depilated with hair removal cream. Parasternal
long-axis (PLAX) and short-axis views were obtained. In the PLAX view
at the level of the aortic sinus, measurements were taken of the left
ventricular posterior wall thickness (LVPWT), interventricular septum
thickness (IVST), left ventricular end-diastolic dimension (LVEDD),
left ventricular end-systolic dimension (LVESD), left ventricular
ejection fraction (LVEF), and left ventricular fractional shortening
(LVFS) [[181]58, [182]59].
ELISA for inflammatory cytokines
Inflammatory cytokines TNF-α (Abcam, #ab252354), IL-1β (Cloud-clone,
#SEA563Si96T), and IFN-γ (Chenglin, #AD0081Mk) were measured using
ELISA kits following the manufacturer’s instructions. Required
microplates were prepared, with unused plates stored at 4 °C. Reagents
were brought to room temperature for 20 min before use.
Standards and samples were diluted as instructed, and 10 µL of each was
added to wells, sealed, and incubated at 37 °C for 90 min. Wells were
washed four times, then incubated with biotinylated antibodies at 37 °C
for 60 min, followed by enzyme-conjugated solution for 30 min. After
washing, 100 µL of chromogenic substrate was added and incubated in the
dark at 37 °C for 10–20 min. The reaction was stopped with 100 µL of
stop solution, and absorbance was measured at 450 nm [[183]60].
Hemodynamic parameter assessment in mice
Mice were positioned supine and secured on the surgical table under
continuous anesthesia with 2% isoflurane gas. A multi-channel
physiological signal acquisition system (MP150, BIOPAC, USA) was
utilized to measure left ventricular systolic pressure (LVSP), left
ventricular end-diastolic pressure (LVEDP), maximum rate of rise of
left ventricular pressure (+ dp/dt), and maximum rate of fall of left
ventricular pressure (− dp/dt) [[184]58, [185]59].
Hematoxylin and eosin (H&E) staining
H&E staining was performed on cardiac tissue to observe pathological
changes using a H&E staining kit (C0105S, Beyotime, Shanghai, China).
Following the completion of treatment, mice were euthanized, and
sections of the the heart, kidney, and liver tissues were fixed in 4%
paraformaldehyde (P0099, Beyotime, Shanghai, China), dehydrated,
cleared, and embedded in paraffin. Thin sections of 5 μm were prepared
using a microtome, followed by baking, deparaffinization, hydration to
water, staining with hematoxylin, rinsing with distilled water,
immersion in 95% ethanol, eosin staining, differentiation with 70%
hydrochloric acid ethanol, dehydration, clearing, and mounting with
neutral resin. The slide preparation was then examined under an optical
microscope to assess the morphological changes in the mouse cardiac
tissue [[186]53].
Masson staining
A Masson trichrome staining kit (DC0032, Leagene Biotechnology,
Beijing, China) was utilized to assess the level of atrial fibrosis in
the tissue sections. After dewaxing 4 μm thick sections in water, the
slides were stained with hematoxylin for 5–10 min, differentiated in
acidic alcohol for 5–15 s, rinsed in water, and then immersed in
Masson's blue solution for 3–5 min. Subsequently, after another water
rinse, slides were stained with Ponceau S for 5–10 min, washed in
phosphomolybdic acid solution for 1–2 min, counterstained with aniline
blue solution for 1–2 min, dehydrated in ethanol, cleared in xylene,
and finally mounted for observation. Images were captured at
200 × magnification using an Olympus BX51 microscope (Tokyo, Japan) and
analyzed with ImagePro Plus 6.0 imaging software. Three random slices
were taken from each mouse, from apex to base of the heart, with 5
fields of view observed on each slide under high magnification, in
accordance with a single-blind protocol. The collagen volume fraction
(CVF) was quantified by measuring the ratio of the blue-stained area to
the total atrial area.
Sirius red staining
Following the completion of the treatment, mice were euthanized, and
6 μm thick sections of mouse heart tissue were dewaxed in water. The
sections were then stained with hematoxylin for 10–20 min,
differentiated in acid differentiation solution for 10 s, rinsed in
running water for 10 min, and subsequently stained with Sirius Red dye
(365,548-5G, Sigma-Aldrich, USA) for 1 h at room temperature. After a
water rinse, the slides were dehydrated, cleared, and finally mounted
for observation. Three random slices were taken from each mouse, with 5
fields of view observed on each slide under high magnification. The
ImagePro Plus 6.0 software was utilized to perform a semi-quantitative
analysis of the Picro Sirius Red-positive area.
Wheat germ agglutinin (WGA) staining
After fixing mouse heart tissues in 4% paraformaldehyde for 24 h, the
tissues underwent dehydration using a series of ethanol concentrations,
followed by clearing in xylene and embedding in paraffin. Subsequently,
tissue sections were stained with WGA staining solution ([187]W11261,
Thermo Fisher, USA), which binds specifically to sugars on the cell
membrane. The slides were then mounted with a neutral mounting medium
to complete the slide preparation. Following staining, the samples were
observed under a fluorescence microscope to analyze the cellular
structure and changes in the heart tissue.
Immunohistochemical staining
Paraffin-embedded tissue sections were air-dried overnight, baked at
60 °C for 20 min, deparaffinized in xylene (2 × 10 min), and rehydrated
through graded ethanol to distilled water. Antigen retrieval was
performed in citrate buffer (pH 6.0) using a microwave (8 min),
followed by cooling to room temperature. Sections were washed with PBS
(3 × 3 min), treated with 3% H₂O₂ for 10 min, and blocked with goat
serum (20 min, room temperature).
Sections were incubated overnight at 4 °C with rabbit anti-p-STAT3
antibody (ab76315, 1:100, Abcam), washed, then incubated with goat
anti-rabbit IgG (ab6721, 1:5000, Abcam) for 30 min, followed by
Streptavidin–Biotin Complex (SABC, P0603, Beyotime) at 37 °C for
30 min. Staining was developed using a DAB kit (P0203, Beyotime) for
6 min, and counterstained with hematoxylin (30 s).
Sections were dehydrated through graded ethanol, cleared in xylene
(2 × 5 min), sealed with neutral resin, and examined under a
brightfield microscope (BX63, Olympus). Five high-power fields per
slide were analyzed using Image-Pro Plus 6.0, with experiments repeated
three times.
Detection of apoptosis in cells using TUNEL assay
Mouse heart tissues were fixed in 4% paraformaldehyde for 15 min,
washed with PBS three times, and permeabilized in 0.1% Triton-X 100 in
PBS for 3 min. Subsequently, the cardiac tissues were stained using the
TUNEL assay kit (C1090, Beyotime, Shanghai, China) to detect apoptosis.
This process involved incubating the samples with 50 μL of
biotin-labeled solution at 37 °C in the dark for 60 min, followed by
three washes with PBS. Then, 0.3 mL of the labeling reaction
termination solution was added, followed by three more washes with PBS.
Next, 50 μL of Streptavidin-HRP working solution was applied to the
samples and left to incubate at room temperature for 30 min, followed
by another three washes with PBS. The samples were incubated with
0.5 mL of DAB chromogen at room temperature for 5 min, followed by
three washes with PBS. Subsequently, the samples were counterstained
with DAPI (10 μg/mL) for 10 min. The cell apoptosis ratio for each
group was then calculated using the Image Pro Plus 6.0 software after
observing the images of different groups under a confocal microscope.
Prussian blue staining for iron accumulation assessment in tissues
The Solarbio iron staining kit (Prussian blue staining, G1424, Solarbio
Science & Technology, Beijing, China) was utilized to determine iron
accumulation in tissues. Initially, mouse heart tissues were excised
and fixed in 4% paraformaldehyde. Following fixation, the tissues
underwent dehydration, clearing, embedding in paraffin, and sectioning
into 5 μm thick slices. The slices were deparaffinized to water after
baking at 60 °C, followed by staining with the Prussian blue staining
reagent to detect iron ions in the tissues. Post-staining, the slices
were rinsed with distilled water, underwent rapid clearing, and were
finally seal-mounted using a neutral mounting medium. The stained
tissue sections were observed under an optical microscope, and analysis
was performed using Olyvia software to assess the accumulation of iron
ions in the tissues.
Statistical analysis
The data were derived from at least three independent experiments,
presented as mean ± standard deviation (Mean ± SD). For comparisons
between the two groups, a two-sample independent t-test was employed.
Regarding comparisons among three or more groups, a one-way analysis of
variance (ANOVA) was utilized. In cases where the ANOVA results
indicated significant differences, Tukey's Honestly Significant
Difference (HSD) post-hoc test was conducted to compare differences
between each group. For non-normally distributed or inhomogeneous
variance data, the Mann–Whitney U test or Kruskal–Wallis H test is
applied. All statistical analyses were performed using GraphPad Prism 9
(GraphPad Software, Inc.) and the R programming language. A
significance level of 0.05 was set for all tests, with a two-tailed
p-value less than 0.05 considered statistically significant.
Supplementary Information
[188]12951_2025_3317_MOESM1_ESM.zip^ (32.4MB, zip)
Supplementary material 1. Fig. S1. Quality Control, Filtering, and PCA
of scRNA-seq Data. Note: (A) Cell expression plot showing the
relationship between the total gene expression counts per single cell
(x-axis) and the number of genes detected (y-axis) in each cell; (B)
Histograms on the upper left display the distribution of gene
expression levels in single cells, while the ones on the upper right
show the average intensity of gene expression in each sample. The two
plots at the bottom illustrate the impact of different gene detection
thresholds on intra-sample cells, determining the gene expression
threshold used for subsequent analysis; (C) Proportional relationship
between different gene expression levels and total cell counts to
assess dominant genes in the dataset and their expression uniformity
within samples; (D) PCA results of the top 15 highly and lowly
expressed genes in the top 2 PCs; (E) Heat map showing the top 20
predominantly associated gene expressions with PC_1 to PC_7 in PCA,
where yellow indicates upregulation and purple indicates downregulation
of gene expression; (F) Sample distribution revealed by PCA, indicating
potential groupings or patterns between samples with different colors
and markers; (G) Distribution of standard deviations of PCs, with
important PCs having larger standard deviations, and the red line
represents the screening threshold; (H) Distribution of cells after
Harmony batch correction, each point representing a cell, where red
points denote Control group samples (n=28) and blue points represent HF
group samples (n=17); (I) Distribution of cell features after Harmony
batch correction, with features values gradually increasing from blue
to orange. Fig. S2. Cell Clustering Analysis of scRNA-seq Data. Note:
(A) UMAP visualization of cell clustering results, where each branch on
the dendrogram represents a unique cluster of cells, aiding in the
identification and interpretation of different cell types or states;
(B) Group visualization of UMAP clustering results, illustrating the
aggregation and distribution of cells from different source samples,
with each point representing a cell and different colors indicating
distinct clusters; (C) UMAP clustering results displaying the
aggregation and distribution of cells from different source samples,
with each point representing a cell, and different colors indicating
distinct clusters, where the left plot represents the Control group
(n=28) and the right plot represents the HF group (n=17); (D) Bar graph
comparing the cell numbers in each cell cluster, with red representing
the Control group (n=28) and blue representing the HF group (n=17); (E)
Box plot showing the changes in cell cluster proportions, with red
representing the Control group (n=28) and blue representing the HF
group (n=17), where * indicates comparison with the Control group, *p
<0.05, **p <0.01, ***p <0.001, ****p <0.0001. Fig. S3. Expression of
Marker Genes in Various Cell Clusters. Note: (A) Expression patterns of
the top 10 marker genes in each of the 30 cell clusters, where each row
represents a gene and each column represents a cell cluster. The color
gradient (from purple to yellow) indicates variations in gene
expression levels, where yellow signifies high expression and purple
signifies low expression. The color bar at the top of each cell cluster
denotes their assignment; (B) Distribution plot showing the expression
levels of the most significant marker genes across the 30 cell
clusters, with darker blue indicating higher average expression levels.
Fig. S4. Expression Distribution of Marker Genes in Various Cell
Subtypes. Note: (A) Distribution of CM cell marker gene expressions;
(B) Distribution of pericyte cell marker gene expressions; (C)
Distribution of endothelial cell marker gene expressions; (D)
Distribution of macrophage cell marker gene expressions; (E)
Distribution of epicardial cell marker gene expressions; (F)
Distribution of mast cells marker gene expressions; (G) Distribution of
endocardial cell marker gene expressions; (H) Distribution of
fibroblast cell marker gene expressions; (I) Distribution of T cells
marker gene expressions; (J) Distribution of adipocytes marker gene
expressions; (K) Distribution of lymphatics cell marker gene
expressions; (L) Distribution of neuronal cells marker gene
expressions. Darker shades of blue in the figures indicate higher
average expression levels. Fig. S5. Distribution of Marker Gene
Expressions. Note: (A) Violin plot showing the expression of marker
genes used for cell identification in different cell types, with each
color representing a distinct cell type; (B) Violin plot showing the
expression of marker genes used for cell identification in different
cell clusters, with each color representing a cluster. Fig. S6.
Analysis of Distribution and Expression Characteristics of CM Subtypes.
Note: (A) UMAP distribution of CM cells in all samples, with different
colors representing different samples; (B) Distribution of CM cells in
Control group (n=28) and HF group (n=17), with red representing Control
group (n=28) and blue representing HF group (n=17); (C) Visualization
of UMAP clustering results, reclassifying CMs into two cell clusters,
with different colors indicating different cell clusters; (D)
Expression status of marker genes in each cell cluster; (E)
Classification of CMs into CM_1 and CM_2 cells based on marker genes;
(F) UMAP clustering results showing the cell distribution of CM_1 and
CM_2, with different colors indicating different subtypes of CMs; (G)
Heatmap of the top 10 genes expressed in CM_1 and CM_2; (H) Expression
patterns of selected cell-specific marker genes in different cell
subtypes, with darker blue indicating higher average expression levels
and larger circles representing more cells expressing the gene; (I)
Expression distribution of marker genes for different CM subtypes; (J)
Proportions of CM_1 and CM_2 cells in each sample; (K) Bar chart
comparing the cell numbers of different CM subtypes, with red
representing Control group (n=28) and blue representing HF group
(n=17); (L) Box plot showing the changes in proportions of CM subtypes,
with red representing Control group (n=28) and blue representing HF
group (n=17), * indicates significance compared to Control group, **p
<0.01.Fig. S7. Analysis of Distribution and Expression Characteristics
of Macrophage Subtypes. Note: (A) UMAP distribution of Macrophage cells
in all samples, with different colors representing different samples;
(B) Distribution of Macrophage cells in Control group (n=28) and HF
group (n=17), with red representing Control group (n=28) and blue
representing HF group (n=17); (C) Visualization of UMAP clustering
results, reclassifying macrophages into two cell clusters, with
different colors indicating different cell clusters; (D) Expression
status of marker genes in each cell cluster; (E) Classification of
macrophages into Macrophage_1 and Macrophage_2 cells based on marker
genes; (F) UMAP clustering results showing the cell distribution of
Macrophage_1 and Macrophage_2, with different colors indicating
different subtypes of macrophages; (G) Heatmap of the top 10 genes
expressed in Macrophage_1 and Macrophage_2; (H) Expression patterns of
selected cell-specific marker genes in different cell subtypes, with
darker blue indicating higher average expression levels and larger
circles representing more cells expressing the gene; (I) Expression
distribution of marker genes for different macrophage subtypes; (J)
Proportions of Macrophage_1 and Macrophage_2 cells in each sample; (K)
Bar chart comparing the cell numbers of different macrophage subtypes,
with red representing Control group (n=28) and blue representing HF
group (n=17); (L) Box plot showing the changes in proportions of
macrophage subtypes, with red representing Control group (n=28) and
blue representing HF group (n=17), * indicates significance compared to
Control group, ***p <0.001.Fig. S8. Analysis of Distribution and
Expression Characteristics of T Cell Subsets. Note: (A) UMAP
distribution of T cells in all samples, with different colors
representing different samples; (B) Distribution of T cells in Control
group (n=28) and HF group (n=17), with red representing Control group
(n=28) and blue representing HF group (n=17); (C) Visualization of UMAP
clustering results, reclassifying T cells into 4 cell clusters, with
different colors indicating different cell clusters; (D) Expression
status of marker genes in each cell cluster; (E) Classification of T
cells into NKT (Natural killer T cells), CD8+_Effector_T cells,
T_memory cells, and Proliferate_NKT cells based on marker genes; (F)
UMAP clustering results showing the distribution of various T cell
subsets, with different colors indicating different T cell subsets; (G)
Heatmap of the top 10 genes expressed in each T cell subset; (H)
Expression patterns of selected cell-specific marker genes in different
cell subtypes, with darker blue indicating higher average expression
levels and larger circles representing more cells expressing the gene;
(I) Expression distribution of marker genes for different T cell
subsets; (J) Proportions of each T cell subset in each sample; (K) Bar
chart comparing the cell numbers of different T cell subsets, with red
representing Control group (n=28) and blue representing HF group
(n=17); (L) Box plot showing the changes in proportions of T cell
subsets, with red representing Control group (n=28) and blue
representing HF group (n=17).Fig. S9. Analysis of Age-Related AUCell in
CMs. Note: (A) Distribution of the number of genes detected in each
single CM; (B) AUCell analysis results of age-related gene sets at the
single-cell level in CMs, where the curve plot represents the activity
distribution of the gene set, and the highlighted area (AUC) shows the
concentration trend of gene activity regions; (C) UMAP visualization
plot based on expression, with each cell colored according to its AUC
score of the age-related gene set; (D) Violin plot showing the score
differences of CM subtypes in aging; (E) Differences in AUC scores of
CM subtypes based on age-related gene sets between HF group (n=17) and
Control group (n=28), * indicates significance compared to Control
group, ***p <0.001.Fig. S10. Analysis of Age-Related AUCell in
Macrophages. Note: (A) Distribution of the number of genes detected in
every single macrophage; (B) AUCell analysis results of age-related
gene sets at the single-cell level in macrophages, where the curve plot
represents the activity distribution of the gene set and the
highlighted area shows the concentration trend of gene activity
regions; (C) UMAP visualization plot based on expression, with each
cell colored according to its AUC score of the age-related gene set;
(D) Violin plot showing the score differences of macrophage subtypes in
aging; (E) Differences in AUC scores of macrophage subtypes based on
age-related gene sets between HF group (n=17) and Control group (n=28),
* indicates significance compared to Control group, ***p <0.001.Fig.
S11. Analysis of Age-Related AUCell in T Cell Subsets. Note: (A)
Distribution of the number of genes detected in each single T cell; (B)
AUCell analysis results of age-related gene sets at the single-cell
level in T cell subsets, where the curve plot represents the activity
distribution of the gene set, and the highlighted area (AUC) shows the
concentration trend of gene activity regions; (C) UMAP visualization
plot based on expression, with each cell colored according to its AUC
score of the age-related gene set; (D) Violin plot showing the score
differences of T cell subsets in aging; (E) Differences in AUC scores
of T cell subsets based on age-related gene sets between HF group
(n=17) and Control group (n=28), * indicates significance compared to
Control group, **p <0.01, ***p <0.001. Fig. S12. Analysis of CMs
Ferroptosis-Related AUCell Scores. Note: (A) Distribution of the number
of genes detected in each single CM; (B) AUCell analysis results of the
ferroptosis-related gene set at the single-cell level in CMs, where the
curve represents the activity distribution of the gene set, and the
highlighted area (AUC) indicates the concentrated trend of gene
activity region; (C) UMAP visualization based on expression, with cells
color-coded according to the AUC scores of the ferroptosis-related gene
set; (D) Violin plot showing the score differences of CM subtypes in
ferroptosis; (E) Differences in the AUCell scores based on the
ferroptosis gene set in CM subtypes between the HF group (n=17) and the
Control group (n=28), * indicates significant difference compared to
the Control group, with ***p <0.001. Fig. S13. Analysis of Macrophages
Ferroptosis-Related AUCell Scores. Note: (A) Distribution of the number
of genes detected in each single macrophage; (B) AUCell analysis
results of the ferroptosis-related gene set at the single-cell level in
macrophages, where the curve represents the activity distribution of
the gene set and the highlighted area (AUC) indicates the concentrated
trend of gene activity region; (C) UMAP visualization based on
expression, with cells color-coded according to the AUC scores of the
ferroptosis-related gene set; (D) Violin plot showing the score
differences of macrophage subtypes in ferroptosis; (E) Differences in
the AUCell scores based on the ferroptosis gene set in macrophage
subtypes between the HF group (n=17) and the Control group (n=28), *
indicates significant difference compared to the Control group, with
***p <0.001. Fig. S14. Analysis of T Cell Ferroptosis-Related AUCell
Scores. Note: (A) Distribution of the number of genes detected in each
single T cell; (B) AUCell analysis results of the ferroptosis-related
gene set at the single-cell level in T cells, where the curve
represents the activity distribution of the gene set, and the
highlighted area (AUC) indicates the concentrated trend of gene
activity region; (C) UMAP visualization based on expression, with cells
color-coded according to the AUC scores of the ferroptosis-related gene
set; (D) Violin plot showing the score differences of T cell subsets in
ferroptosis; (E) Differences in the AUCell scores based on the
ferroptosis gene set in T cell subsets between the HF group (n=17) and
the Control group (n=28), * indicates significant difference compared
to the Control group, with ***p <0.001. Fig. S15. Analysis of CMs
Inflammation-Related AUCell Scores. Note: (A) Distribution of the
number of genes detected in each single CM; (B) AUCell analysis results
of the inflammation-related gene set at the single-cell level in CMs,
where the curve represents the activity distribution of the gene set,
and the highlighted area (AUC) indicates the concentrated trend of gene
activity region; (C) UMAP visualization based on expression, with cells
color-coded according to the AUC scores of the inflammation-related
gene set; (D) Violin plot showing the score differences of CM subtypes
in inflammation; (E) Differences in the AUCell scores based on the
inflammation gene set in CM subtypes between the HF group (n=17) and
the Control group (n=28). Fig. S16. Analysis of Macrophages
Inflammation-Related AUCell Scores. Note: (A) Distribution of the
number of genes detected in each single macrophage; (B) AUCell analysis
results of the inflammation-related gene set at the single-cell level
in macrophages, where the curve represents the activity distribution of
the gene set and the highlighted area (AUC) indicates the concentrated
trend of gene activity region; (C) UMAP visualization based on
expression, with cells color-coded according to the AUC scores of the
inflammation-related gene set; (D) Violin plot showing the score
differences of macrophage subtypes in inflammation; (E) Differences in
the AUCell scores based on the inflammation gene set in macrophage
subtypes between the HF group (n=17) and the Control group (n=28), *
indicates significant difference compared to the Control group, with
***p <0.001. Fig. S17. Analysis of T Cell Inflammation-Related AUCell
Scores. Note: (A) Distribution of the number of genes detected in each
single T cell; (B) AUCell analysis results of the inflammation-related
gene set at the single-cell level in T cells, where the curve
represents the activity distribution of the gene set, and the
highlighted area (AUC) indicates the concentrated trend of gene
activity region; (C) UMAP visualization based on expression, with cells
color-coded according to the AUC scores of the inflammation-related
gene set; (D) Violin plot showing the score differences of T cell
subsets in inflammation; (E) Differences in the AUCell scores based on
the inflammation gene set in T cell subsets between the HF group (n=17)
and the Control group (n=28), * indicates significant difference
compared to the Control group, **p <0.01. Fig. S18. Differential Gene
Enrichment Analysis in CM_1 Cells. Note: (A) Bubble diagram (left) and
gene network diagram (right) of GO-BP enrichment analysis of 616 DEGs
in CM_1 cells; (B) Bubble diagram (left) and gene network diagram
(right) of GO-CC enrichment analysis of 616 DEGs in CM_1 cells; (C)
Bubble diagram (left) and gene network diagram (right) of GO-MF
enrichment analysis of 616 DEGs in CM_1 cells; (D) Bubble diagram
(left) and gene network diagram (right) of KEGG enrichment analysis of
616 DEGs in CM_1 cells. In the bubble diagrams, the color of the
circles represents the significance of enrichment, with colors ranging
from blue to red indicating increasing significance, and the size of
the circles represents the number of enriched genes. Fig. S19.
Differential Gene Enrichment Analysis in CM_2 Cells. (A) Bubble diagram
(left) and gene network diagram (right) of GO-BP enrichment analysis of
947 DEGs in CM_2 cells; (B) Bubble diagram (left) and gene network
diagram (right) of GO-CC enrichment analysis of 947 DEGs in CM_2 cells;
(C) Bubble diagram (left) and gene network diagram (right) of GO-MF
enrichment analysis of 947 DEGs in CM_2 cells; (D) Bubble diagram
(left) and gene network diagram (right) of KEGG enrichment analysis of
947 DEGs in CM_2 cells. In the bubble diagrams, the color of the
circles represents the significance of enrichment, with colors ranging
from blue to red indicating increasing significance, and the size of
the circles represents the number of enriched genes. Fig. S20.
Verification of lentivirus Transfection Efficiency. Note: (A) RT-qPCR
to detect the change in STAT3 mRNA expression in CMs cells after
silencing STAT3 with different sequences transfection; (B) Western blot
to detect the change in STAT3 protein expression in CMs cells after
silencing STAT3 with different sequences transfection; (C) RT-qPCR to
detect the change in STAT3 mRNA expression in CMs cells after
overexpressing STAT3 with different sequences transfection; (D) Western
blot to detect the change in STAT3 protein expression in CMs cells
after overexpressing STAT3 with different sequences transfection.
Quantitative data are presented as Mean ± SD, with each cell experiment
replicated 3 times, * indicates comparison between two groups, ****p
<0.0001. Fig. S21. Results of Ultrasound Diagnostic Instrument and
Hemodynamic Measurements. (A-F) Statistical charts of parameters
including IVSD, LVEDD, LVESD, LVPWD, LVEF, and fractional shortening
detected by ultrasound diagnostic instrument in the heart tissues of
each group of mice; (G-J) Statistical charts of parameters including
LVEDP, LVSP, maximum rate of rise of left ventricular pressure
(+dp/dt), and maximum rate of fall of left ventricular pressure
(-dp/dt) detected by hemodynamic measurements in each group of mice;
(K-L) Detection results of LVMI and RVMI in mice. Quantitative data are
presented as Mean ± SD, with 6 mice in each group, * indicates
comparison between two groups, *p <0.05, **p <0.01, ***p <0.001, ****p
<0.0001. Fig. S22. In vivo toxicity evaluation after PN@Col
administration. (A) HE staining to detect kidney and liver damage in
mice, with 6 mice per experimental group (scale bar: 50 μm); (B)
Cytokine detection using ELISA kits.
Acknowledgements