Abstract

Objective

   To explore the differential gene expression in peripheral blood immune
   cells of individuals with type 2 diabetes mellitus (DM), comparing
   those with and without non-proliferative diabetic retinopathy (NPDR).

Methods

   From a pool of 126 potential participants, 60 were selected for
   detailed analysis. This group included 12 healthy donors (HDs), 22
   individuals with DM, and 26 with NPDR. We analyzed peripheral blood
   mononuclear cells (PBMCs) using RNA sequencing and quantitative PCR
   (qPCR) to pinpoint differentially expressed genes (DEGs). Western blot
   and flow cytometry were also employed to evaluate the protein
   expression of specific genes.

Results

   In patients with NPDR compared to those with DM alone, MerTK—a gene
   implicated in inherited retinal dystrophies due to its mutations—was
   notably downregulated in PBMCs. Through flow cytometry, we assessed the
   protein levels and cellular distribution of MerTK, finding a
   predominant expression in monocytes and myeloid-derived suppressor
   cells (MDSCs), with a marked reduction in CD4+ and CD8+ T cells, as
   well as in natural killer T (NKT) cells. Patients with DM demonstrated
   a significant deviation in the PBMCs composition, particularly in B
   cells, CD4+ T cells, and NK cells, when compared to HDs.

Conclusions

   The study indicates that MerTK expression in T cells within PBMCs could
   act as a viable blood biomarker for NPDR risk in patients with DM.
   Furthermore, the regulation of T cells by MerTK might represent a
   critical pathway through which DM evolves into NPDR.

   Keywords: diabetes mellitus, retinopathy, non-proliferative diabetic
   retinopathy, PBMC (peripheral blood mononuclear cells), MERTK

Introduction

   Diabetic retinopathy (DR), a major ocular complication of diabetes
   mellitus (DM), is a primary cause of vision loss, especially in
   individuals over 50 years old ([45]1). Clinically, DR is classified
   into non-proliferative diabetic retinopathy (NPDR) and advanced
   proliferative diabetic retinopathy (PDR) ([46]2). NPDR involves
   vascular basement membrane thickening and microaneurysm formation,
   progressing to PDR with severe risks due to neovascularization ([47]3).
   Current DR treatments mainly address symptoms and show varied patient
   outcomes. Early retinal function and cellular changes preceding visible
   microangiopathy in DR suggest the necessity for early intervention
   ([48]4).

   Research has shown that individuals with DM and DR experience changes
   in the composition of immune cells in their bloodstream ([49]5, [50]6).
   Bo Li et al. have unveiled a significant association between 35
   distinct immune cell phenotypes and the risk of developing DR ([51]7).
   This alteration in peripheral immune cells is believed to play a
   crucial role in chronic inflammation, setting the stage for the
   development and progression of DR ([52]8, [53]9). This process is not
   only pivotal in the early stages of NPDR but also in the progression to
   more severe forms of the condition, such as PDR. PDR is marked by
   severe complications, including neovascularization, vitreous
   hemorrhage, and tractional retinal detachment ([54]10). Peripheral
   inflammation is thought to exacerbate retinopathy by altering the
   intraocular environment and disrupting the vascular protective
   mechanisms in the retina, as indicated by Yokomizo et al ([55]11).
   Thus, targeting the activity of circulating immune cells presents a
   potential strategy for the early diagnosis and prevention of DR.

   Circulating immune cells have systemic effects on inflammation and can
   penetrate tissues by crossing the parenchyma-blood barriers, occurring
   during both health and disease states ([56]12). The movement of
   specific immune cell subsets into and out of tissues is vital for
   health and disease progression. Particularly in the retina, the
   interactions between these immune cells and retinal cells can either
   worsen or alleviate disease-related changes ([57]13–[58]15),
   highlighting the critical role of peripheral immune regulation in
   retinopathy’s development and progression. Understanding the mechanisms
   by which these cells affect retinal diseases is crucial for creating
   targeted treatments. Such therapies could adjust immune responses to
   prevent or manage retinopathy effectively, opening new pathways for
   intervention.

   This study seeks to elucidate the pivotal molecular alterations in
   circulating immune cells that drive the advancement of DM to NPDR. We
   analyzed peripheral blood mononuclear cells (PBMCs) from patients with
   and without NPDR to achieve this. Through RNA sequencing (RNA-seq), we
   discovered a significant reduction in the expression of the Mer
   tyrosine kinase (MerTK) gene in patients with NPDR, particularly noting
   its marked decrease in circulating T cells. MerTK, a member of the
   Tyro/Axl/Mer receptor family, is predominantly expressed on various
   immune cells such as monocytes/macrophages, dendritic cells, and
   microglia ([59]16). It plays a vital role in phagocytosis and clearing
   apoptotic cells, thereby preventing prolonged inflammation. This
   phagocytic function is critical in maintaining tissue functionality and
   internal environment stability ([60]17). MerTK has been extensively
   researched in the context of retinal pigment epithelium and
   macrophages, where its mutations are linked to severe retinitis
   pigmentosa and the dysregulation of macrophage functions in
   phagocytosis and inflammation ([61]18). Therefore, our findings suggest
   a critical role for MerTK in circulating T cells, potentially
   influencing the development of DR.

Methods

Study subjects

   We enrolled 126 donors from the First People’s Hospital of Guangzhou
   between 2022 and 2023, and 12 HDs, 22 patients with DM (type 2 diabetes
   mellitus), and 26 patients with NPDR (type 2 diabetes mellitus with
   non-proliferative diabetic retinopathy) were selected for analysis
   ([62] Figure 1 ). Diagnostic criteria adhered to the Clinical Diagnosis
   and Treatment Criteria for Diabetic Retinopathy in China. Briefly, all
   participants were subjected to stereoscopic fundus photography for the
   detection of NPDR utilizing a Non-Mydriatic Fundus Camera through
   undilated pupils. For every participant, two fundus images centered on
   the fovea and optic disc for each eye were taken in a darkened room.
   Two experienced ophthalmologists from the First People’s Hospital of
   Guangzhou, working independently and in a blinded fashion, assessed
   each photograph for DR assessment. In cases of discordant evaluations,
   a third ophthalmologist would make the ultimate decision. The severity
   of DR in each eye was established, and the subject’s overall
   classification was determined by the severity observed in the more
   affected eye.

Figure 1.

   [63]Figure 1
   [64]Open in a new tab

   Flow chart of participants enrollment and groups. There were a total of
   126 participants, 66 of whom were excluded by exclusion criteria,
   leaving 60 participants, including 26 in the NPDR group, 22 in the DM
   group and 12 in the HD group. 5 samples of NPDR and 4 samples of DM
   were used for RNA-seq. 7 samples of NPDR and 7 samples of DM were used
   for flow cytometry and qPCR of MerTK. 3 samples of NPDR, 3 samples of
   DM and 3 samples of HD were used for western blot analysis. 18 samples
   of NPDR,15 samples of DM and 9 samples of HD were used for flow
   cytometry.

   Inclusion criteria included confirmed DM diagnosis and accurate
   clinical diagnosis of NPDR grading; diagnosed with type 2 DM; the
   duration of diabetes ranged from 1 to 20 years; and the participants’
   ages ranged from 40 to 90 years. Exclusion criteria encompassed if they
   were younger than 18 years old or had type 1 diabetes mellitus; other
   diabetic microangiopathies like nephropathy or neuropathy; autoimmune,
   neurological, or malignant diseases; major cardiovascular and
   cerebrovascular events; other systemic infections or inflammatory
   diseases; recent usage of hormones, immunosuppressants, or
   anti-inflammatory drugs; and laser photocoagulation, steroid or
   anti-vascular endothelial growth factor treatment in the past week.

   We collected participants’ age, gender, duration of diabetes, and blood
   pressures. Venous blood samples were drawn after an overnight fast. All
   biochemical analyses were performed in our hospital, including
   glycosylated hemoglobin A1c (HbA1c), total cholesterol (TC),
   triglyceride (TG), high-density lipoprotein cholesterol (HDL),
   low-density lipoprotein cholesterol (LDL), and estimated glomerular
   filtration rate (eGFR). This study complied with the Declaration of
   Helsinki for research involving human subjects and was approved by the
   research ethics committee of the institute (K-2022-157-01). Informed
   consent was obtained from all participants per ethical standards. Flow
   chart of participants enrollment is in [65]Figure 1 . Detail of
   participants are in [66]Supplementary Tables 1 –[67] 4 .

PBMCs preparation

   Peripheral blood (5mL) was collected in EDTA-containing tubes (Sanli,
   Liuyang, China) and processed within 2 hours. Lymphocyte Separation
   Medium (LSM, biosharp, Beijing, China) was added to the blood before
   centrifugation ([68]19). The PBMCs layer was transferred to a new tube,
   washed with PBS (labgic, Beijing, China), and treated with red blood
   cell lysis buffer (solarbio, Beijing, China) at room temperature for 5
   minutes. After a final PBS wash, the PBMCs were obtained ([69]20).

Flow cytometry and UMAP analysis

   PBMCs were incubated with a mixture of antibodies for 25 minutes at 4°C
   in the dark. After PBS washing, cells were suspended in 300 uL PBS and
   analyzed using a BD flow cytometer. FlowJo 10.8.1 software was used for
   data analysis. Those antibodies used are in [70]Supplementary Table 5 .

   UMAP analysis was conducted using the “UMAP plugin” in FlowJo 10.8.1.
   High-quality cells were selected with “FlowAI” for gating and doublet
   discrimination, followed by random selection of 10000 cells per sample
   using the “Downsample plugin”. Samples were merged into a new FCS file
   and visualized with UMAP in FlowJo. UMAP outputs were used for “FlowSOM
   plugin” clustering with nine predefined clusters. “ClusterExplorer
   plugin” was then used to present the FlowSOM results. Data from the
   “ClusterExplorer plugin” were normalized in R software for final
   visualization and cellular annotation accordingly.

Gating strategies

   Flow cytometry-acquired cells were gated and classified into various
   immune cells. Myeloid cells were identified as monocytes (CD14+) and
   their subpopulations, and lymphoid lineage cells included B cells
   (CD19+), T cells (CD3+ and its subpopulations), NK cells (CD56+), and
   NKT cells (CD3+CD56+). The details are in [71]Figure 2C .

Figure 2.

   [72]Figure 2
   [73]Open in a new tab

   Verification the transcript and protein level of MerTK in NPDR and DM
   group. (A) qPCR verificated the expression level of MerTK in NPDR and
   DM groups. (B) The protein expression of MerTK in PBMCs was measured by
   western blot analysis, and the ratio of MerTK to β-actin was
   calculated. n = 3 per group. (C) Gating strategies used in flow
   cytometry analyses to identify subsets of immune cells. Blood sample
   were isolated into PBMCs and then examined by flow cytometry.
   Representative image showing forward-scatter area (FSC-A) vs
   side-scatter area (SSC-A) used to exclude debris/dead cells and the
   gated population was then plotted as FSC-A vs FSC-H revealing single
   cells. CD16 vs CD15 showing Low-density eosinophils (CD16-CD15+),
   Low-density neutrophils (CD16+CD15+) and PBMCs; CD3 vs CD19 showing B
   cells (CD3-CD19+), No-T B cells (CD3-CD19-) and T cells (CD3+CD19-);
   CD4 vs CD8 showing CD4+T cells (CD4+CD8-) and CD8+T cells (CD4-CD8+);
   HLA-DR vs CD14 showing Monocytes (CD14+), then CD14 vs CD16 showing
   monocyte population comprising of three subsets: Classical monocytes
   (CD16-CD14+), Intermediate monocytes (CD16+CD14+) and Non-classical
   monocytes (CD16+CD14-); CD14 vs CD19 showing CD14-CD19- cells, then
   CD56 VS CD3 showing NK cells (CD56+CD3-) and NKT cells (CD56+CD3+).
   CD11b VS HLA-DR showing Myeloid-derived suppressor cells (MDSCs,
   CD11b+HLA-DR-), then CD15 vs CD14 showing MDSCs population comprising
   of three subsets: E-MDSCs (CD15-CD14-), PMN-MDSCs (CD15+CD14-) and
   M-MDSCs (CD15-CD14+). (D) Representative histograms of MerTK in PBMCs.
   (E) Flow cytometry verificated the expression level of MerTK in NPDR
   and DM groups. n = 7 per group. The significance of the difference
   between the results was determined by unpaired T-tests. Data is
   presented as mean ± SEM, *P<0.05, **P<0.01.

Quantitative RT-PCR

   RNA was extracted from PBMCs using TRIzol (Invitrogen, Waltham, MA,
   USA) and quantified spectrophotometrically. cDNA was synthesized from
   1μg of RNA using a kit (agbio, Hunan, China). qPCR was performed using
   SYBR-Green PCR Master Mix (agbio, Hunan, China) on a QuantStudioTM 5
   system (Thermo Fisher Scientific, Waltham, MA, USA). MerTK gene
   expression was normalized to GAPDH and analyzed using the 2-ΔΔCt method
   ([74] Table 1 ).

Table 1.

   Sequences of MerTK and GAPDH.
   MerTK Forward primer GTACCAGCCTGCCTTGATGT
   Reverse primer GTGTTTGAAGGCAAGAGGCG
   GAPDH Forward primer AAAGCCTGCCGGTGACTAAC
   Reverse primer TAGGAAAAGCATCACCCGGAG
   [75]Open in a new tab

Western blot analysis

   PBMCs were lysed on ice for 30 minutes using Western & IP lysis buffer
   (Beyotime, Jiangsu, China) containing phenylmethanesulfonyl fluoride
   (Beyotime, Jiangsu, China). Protein extracts (40 μg) were separated on
   a 10% SDS-PAGE gel and transferred onto polyvinylidene fluoride
   membranes (Millipore, Massachusetts, USA). Membranes were blocked with
   5% non-fat milk at room temperature for 2 hours, then incubated
   overnight at 4°C with the following primary antibodies: rabbit
   anti-MerTK (1:1000, Proteintech, Wuhan, China) and mouse anti-β-actin
   (1:4000, Bioworld, Dublin, USA). After washing, membranes were
   incubated for 1 hour at room temperature with HRP-conjugated secondary
   antibodies: goat anti-rabbit IgG (H+L) (1:2000, Proteintech, Wuhan,
   China) and goat anti-mouse IgG (1:2000, Proteintech, Wuhan, China).
   Bands were visualized using the SuperSignal West Pico PLUS
   chemiluminescent substrate (Thermo Fisher Scientific, Waltham, MA, USA)
   and quantified using ImageJ software, with protein levels normalized to
   β-actin.

RNA-seq and analysis

   RNA integrity was checked on a 1% agarose gel and quantified with
   NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA, USA). Samples
   with RIN > 7 were sequenced on Illumina HiSeq/Novaseq/MGI2000
   instruments (2 x 150 PE). Data were quality-checked, filtered, and
   aligned to the reference genome.

   This study utilized R 4.3.0 for bioinformatics analysis. The expression
   matrix was derived from raw sequencing data and analyzed using the
   “deseq2” package in R. The negative binomial regression model was
   fitted, and hypothesis testing was performed using the Wald test or
   likelihood ratio test to identify DEGs. Upregulated genes were selected
   based on the criteria of |log2FC| ≥ 1 and adjusted P < 0.05, while
   downregulated genes were selected based on |log2FC| ≤ -1 and adjusted P
   < 0.05. Volcano plot was generated using the “ggplot2” package, and
   heatmap was created with the “pheatmap” package. For Gene Ontology (GO)
   enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway enrichment analysis, we used the “ClusterProfiler” package in R
   software ([76]21).

   The raw sequence data reported in this paper have been deposited in the
   Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in
   National Genomics Data Center (Nucleic Acids Res 2021), China National
   Center for Bioinformation/Beijing Institute of Genomics, Chinese
   Academy of Sciences (GSA: HRA007945) that are publicly accessible at
   [77]https://ngdc.cncb.ac.cn/gsa.

Statistical analyses

   Data analysis was conducted using GraphPad Prism 8.0. T-tests and
   one-way ANOVA were employed for comparisons, with the Mann-Whitney test
   or Kruskal-Wallis test used when assumptions of normal distribution or
   homoscedasticity were not met. Mean ± SEM was reported, with P < 0.05
   considered significant.

Results

Downregulation of MerTK in circulating immune cells of patients with NPDR

   Following our inclusion and exclusion criteria, we selected the PBMCs
   of four patients with DM and five patients with NPDR for RNA-seq
   analysis ([78] Figure 1 ). This analysis revealed seven differentially
   expressed genes ([79] Figures 3A, B ), highlighting alterations in
   biological processes like neutrophil homeostasis and apoptotic cell
   clearance ([80] Figure 3C ). Notably, we observed a significant
   downregulation of MerTK in the NPDR group compared to the DM group
   (adjusted P = 0.02, |log2FC| = -1.232, [81]Figures 3A, D ). This
   reduction in MerTK expression at both transcriptional and translational
   levels was confirmed using qPCR, western blot and flow cytometry ([82]
   Figures 2A–E ), indicating a consistent decline of MerTK in PBMCs of
   patients with NPDR. However, no significant difference in MerTK
   expression was observed between the HD and DM groups ([83]
   Supplementary Figure 1 ).

Figure 3.

   [84]Figure 3
   [85]Open in a new tab

   RNA-sequencing reveals the different gens of NPDR compared to DM. The
   (A) volcano plot and (B) heatmap of differentially expressed genes
   between 4 DM and 5 NPDR samples, including 4 upregulated genes and 3
   downregulated genes. (C) GO and KEGG enrichment analysis of
   differentially expressed genes. MF: molecular function; CC: cellular
   component; GO: gene ontology; KEGG: kyoto encyclopedia of genes and
   genomes. (D) Overall MerTK comparison between 4 DM and 5 NPDR samples.
   The significance of the difference between the results was determined
   by Mann-Whitney test, *P<0.05.

Specific decline of MerTK expression in T cells of patients with NPDR

   Our analysis extended to various immune cell types to investigate MerTK
   expression differences. Using UMAP analysis, we identified nine
   distinct immune cell clusters ([86] Figures 4A, B ). MerTK expression
   was primarily noted in monocytes and MDSCs, with lesser amounts in NK
   and NKT cells, and minimal in T cells, including CD4+ and CD8+T cells,
   and the lowest in B cells ([87] Figures 4C, D ). In the NPDR group,
   significant reductions in MerTK expression were observed specifically
   in CD4+, CD8+, and NK T cells (P < 0.01, P < 0.05, P < 0.05,
   respectively) ([88] Figures 4E, F ). Conversely, monocytes, B cells, NK
   cells, and MDSCs did not show significant expression changes between DM
   and NPDR groups ([89] Figure 4G ).

Figure 4.

   [90]Figure 4
   [91]Open in a new tab

   Flow cytometry to demonstrate the expression of MerTK in immune cells.
   (A) UMAP maps of the fourteen patient cohorts (all n = 7) and
   identified nine clusters. (B) Dot plot showing the mean fluorescence
   intensity (MFI) of clusters. (C) UMAP to show the MFI of MerTK in each
   cluster. (D) Representative histograms of MerTK in T cells, CD4+T
   cells, CD8+T cells, NKT cells, Monocytes, MDSCs, NK cells and B cells.
   MFI of MerTK in immune cells, including (E) T cells and subtypes CD4+
   and CD8+T cells, (F) NKT cells (G) Monocytes, MDSCs, NK cells and B
   cells. The significance of the difference between the results was
   determined by unpaired T-tests and Mann-Whitney test. Data are
   presented as mean ± SEM, *P<0.05; **P<0.01.

Immune cell population changes in DM and NPDR

   Our study analyzed peripheral blood from 9 HDs, 15 patients with DM
   (without NPDR), and 18 patients with NPDR. We found significant
   alterations in the immune cell populations between the DM or NPDR
   groups and HDs, with no notable differences between DM and NPDR groups.
   Specifically, B cells increased significantly in both DM and NPDR
   groups compared to HDs, while NK cells showed a significant increase
   only in the DM group ([92] Figure 5A ). T cells, monocytes, low-density
   eosinophils, low-density neutrophils, NKT cells, and MDSCs did not
   significantly differ among the three groups ([93] Figures 5B, C ).
   Further analysis of T cell and monocyte subtypes revealed an increase
   only in CD4+T cells in the DM and NPDR groups, with no significant
   changes in CD8+T cells or in classical, non-classical, and intermediate
   monocytes ([94] Figures 5D, E ). These findings suggest that
   hyperglycemia-induced dysregulation of circulating immune cells may
   contribute to the onset of NPDR.

Figure 5.

   [95]Figure 5
   [96]Open in a new tab

   Characterization of immune cells in healthy donors (HD), patients with
   DM and patients with NPDR. The percentages of total single cells that
   are: (A) B cells and NK cells; (B) T cells and Monocytes; (C)
   Low-density eosinophils, Low-density neutrophils, NKT cells and MDSCs;
   (D) T cell subtypes including CD4+T cells and CD8+T cells; (E)
   Monocytes subtypes including Classical monocytes, Non-classical
   monocytes and Intermediate monocytes. n = 9 for HD, 15 for DM and 18
   for NPDR. The significance of the difference between the results was
   determined by one-way ANOVA and Kruskal-Wallis test. Results are
   presented as mean ± SEM, *P < 0.05, **P<0.01.

Discussion

   MerTK is known for its role in retinal pigment epithelium (RPE),
   mediating rapid phagocytosis and clearance of photoreceptor debris,
   with its dysfunction leading to retinal dystrophy and retinitis
   pigmentosa ([97]22–[98]24). Contrary to its well-documented presence in
   RPE cells, our study found MerTK to be significantly downregulated in
   the circulating T cells of patients with NPDR compared to those with
   DM, particularly in CD4+ and CD8+T cells, as well as NKT cells. Given
   that T cells are implicated in retinal infiltration during DR, our
   findings suggest a potential role for MerTK in modulating T cell
   functions, contributing to the progression from DM to NPDR.

   MerTK is a crucial cell surface receptor involved in the innate immune
   system, involved in the function of a variety of immune cells. One of
   the most well-known functions of MerTK in macrophages is mediating the
   clearance of apoptotic cells in vitro and vivo ([99]25). Cai et al.
   found that the signal from MerTK in macrophages can activate the
   ERK-dependent pathway and inhibit the phosphorylation of
   5-lipoxygenase, thus promoting the production of specialized
   proresolving mediators and promoting the regression of inflammation
   ([100]26). Meanwhile, TAM family kinases can suppress antitumor
   immunity and promote resistance to immune-checkpoint inhibitors
   ([101]27). In dendritic cells, MerTK regulates antigen presentation and
   immune activation, influencing initial activation and immune tolerance
   of CD8+T cells ([102]28). The research between MerTK with T cells is
   also very crucial. Previous research has indicated that MerTK
   negatively regulates T cell activation ([103]29) and serves as a
   co-stimulatory receptor on CD8+T cells ([104]30). Studies in pediatric
   T-cell acute lymphoblastic leukemia showed increased MerTK expression
   ([105]31), while inhibition of MerTK curtailed T-cell precursor
   expansion and induced apoptosis ([106]32, [107]33). These insights
   bolster our hypothesis that MerTK downregulation could enhance T cell
   activation and proliferation, intensifying T-cell-mediated immune
   responses and potentially hastening the transition from DM to DR.

   The relationship between DR and DM underscores a complex interplay of
   factors, including the vitreous environment ([108]11) and systemic
   inflammation ([109]10, [110]34) from prolonged hyperglycemia. Our
   results raise the question of whether changes in circulating T cells
   mirror alterations within the vitreous environment. The infiltration of
   these cells through the blood-retina barrier suggests their substantial
   influence on the vitreous immune response, either protective or harmful
   ([111]35). Furthermore, the common signaling pathways in blood and
   retinal T cells hint at a shared mechanism in their regulation.

   While the influence of immune cell-mediated inflammation on
   pathogenesis of DR is widely recognized, there is a lack of detailed
   quantitative analyses comparing immune cell profiles among patients
   with DM, those with NPDR, and HD ([112]36). Our study’s comparison of
   immune cell profiles among HD, DM, and NPDR groups revealed significant
   changes, particularly in immune cells like B cells, NK cells and T cell
   subtypes, suggesting their involvement in pathogenesis of DR.
   Interestingly, we observed increased NK and B cell activities in
   patients with DM, which might play a role in the early stages of DR
   ([113]37, [114]38). The adaptive immune response, represented by CD4+T
   cell, also showed significant increase, while CD8+ cell showed a
   decrease, aligning with literature indicating their varied involvement
   in progression of DR ([115]36). Follicular helper T cells, a
   subpopulation of CD4+T cells, have also been found to be elevated in
   patients with DR and diabetic mice, and inhibition of this population
   attenuates vascular inflammation and neovascularization ([116]39). Some
   studies have indicated that CD8+T cells can penetrate into retina and
   concentration significantly higher in vitreous and macular edema in
   patients with DR, which is associated with poor visual prognosis
   ([117]40, [118]41). These can further underline the significance of
   these cells in DR.

   Despite these findings, our study has limitations. We could not fully
   assess the impact of diabetes medication on PBMCs, with drugs like
   metformin known to modulate immune responses ([119]42, [120]43).
   Additionally, our patient cohort’s demographic skew towards
   postmenopausal women may influence the observed immune changes.
   Furthermore, it has been reported that the expression of MerTK in the
   lymphopoietic system is 59.3% higher in men than in women ([121]44). In
   our study, although there was no statistical difference in patient
   gender between the DM and NPDR groups, there were more men in the NPDR
   group. Therefore, it is necessary to investigate the changes in MerTK
   expression across different genders. Besides, future research should
   delve into MerTK’s functional role in T cells and its impact on
   development of NPDR.

   In conclusion, our study elucidates the nuanced differences in immune
   cell behavior between HD, DM, and NPDR groups, highlighting T cell
   dysregulation and MerTK downregulation as potential factors in
   pathogenesis of DR. These findings pave the way for further
   investigation into the role of immune cells in early development of DR.

Funding Statement

   The author(s) declare financial support was received for the research,
   authorship, and/or publication of this article. This work was supported
   by National Natural Science Foundation of China (Grant No. 8217060280
   to YL and No. 82071188 to YUL), Natural Science Foundation of Guangdong
   Province (Grant No. 2023A1515010376 to YZ), and Natural Science
   Foundation of Guangzhou Municipality, China (Grant No. 2023A03J0479 to
   YZ).

Data availability statement

   The datasets presented in this study can be found in online
   repositories. The names of the repository/repositories and accession
   number(s) can be found in the article/[122] Supplementary Material .

Ethics statement

   The studies involving humans were approved by Ethics Committee of
   Guangzhou First People’s Hospital. The studies were conducted in
   accordance with the local legislation and institutional requirements.
   Written informed consent for participation was not required from the
   participants or the participants’ legal guardians/next of kin because
   we explained verbally to participants and obtained their informed
   consent.

Author contributions

   SB: Data curation, Formal analysis, Investigation, Visualization,
   Writing – original draft. JL: Investigation, Methodology, Software,
   Visualization, Writing – original draft. XW: Investigation,
   Methodology, Software, Visualization, Writing – original draft. LZ:
   Methodology, Software, Visualization, Writing – original draft. ZZ:
   Investigation, Visualization, Writing – original draft, Methodology,
   Software. XS: Writing – original draft, Data curation. LH: Supervision,
   Validation, Writing – review & editing. YY: Supervision, Validation,
   Writing – review & editing. ZX: Supervision, Validation, Writing –
   review & editing. YUL: Validation, Writing – review & editing, Funding
   acquisition, Project administration. YL: Funding acquisition, Project
   administration, Resources, Writing – review & editing. YZ: Funding
   acquisition, Project administration, Resources, Writing – review &
   editing.

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Generative AI statement

   The author(s) declare that no Generative AI was used in the creation of
   this manuscript.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary Material for this article can be found online at:
   [123]https://www.frontiersin.org/articles/10.3389/fendo.2024.1509445/fu
   ll#supplementary-material
   [124]DataSheet1.pdf^ (192.4KB, pdf)

References