Introduction Lupus nephritis (LN) is characterized by significant heterogeneity and a complex pathophysiology, which traditional methods struggle to fully resolve. Advanced multi-omics approaches are essential to disentangle its cellular and molecular drivers. Methods We employed an integrative strategy combining single-cell RNA sequencing (scRNA-seq) profiling of LN biopsies with large-scale bulk RNA-seq cohorts. We applied non-negative matrix factorization (NMF) to scRNA-seq data to define robust immune meta-programs and utilized CellChat to decode cell-cell communication networks. Leveraging these insights to overcome sample size limitations, we prioritized key pathways and developed 399 machine learning predictive models using bulk transcriptomics, validated on independent cohorts. Results ScRNA-seq analysis revealed a distinct cellular landscape, including a rare population of plasmacytoid dendritic cells (pDCs) and an expanded population of CD56dimCD16 + natural killer (NK) cells expressing high levels of IFN-γ and perforin, suggesting a role in inflammatory pathology. Macrophage subpopulation CM2 emerged as a central pro-inflammatory hub, potentially driving fibrosis via autocrine signaling and epithelial activation. We observed reduced Treg-B cell interactions, suggesting a regulatory collapse. Our machine learning models, based on innate immunity, circadian rhythms, apoptosis, and NF-κB signaling, achieved high diagnostic accuracy (AUC = 0.929 for innate immunity). Hub genes, including CYBB , CSF2RB , and IRF8 , were confirmed to be upregulated in LN and correlated with clinical severity in external validation datasets. Molecular docking simulations suggested a potential structural basis for CYBB-dexamethasone interaction, providing a hypothesis for future verification. Discussion This study identifies CM2 macrophages and dysregulated pDC-NK axes as key drivers of LN. By bridging cellular interactomes with clinical predictive modeling, we provide a robust roadmap for precision detection and identifying potential therapeutic targets in LN.

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide, characterized by increasing incidence rates and challenging prognoses. This study integrates single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics to unravel the complex molecular and structural landscape of HCC, focusing on the identification of mitochondrial-related genes (MitRGs) and their pivotal role in disease progression. Utilizing scRNA-seq and bulk RNA-seq data, we performed a comprehensive differential expression analysis to highlight MitRGs. A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. Among the genes analyzed, FLAD1 emerged as significantly upregulated in HCC tissues, correlating with advanced disease stages and poorer patient outcomes, and exhibited exceptional diagnostic accuracy with an AUC of 0.962. Functional enrichment analyses revealed that high FLAD1 expression is involved in crucial biological processes like copper ion detoxification and heme complex assembly. Interaction networks further elucidated the connection between FLAD1 and critical HCC pathways, with its expression levels negatively correlated with key immune effector cells such as CD8+ T cells and DCs. Spatial transcriptomics analysis provided a structural basis for this immune exclusion, demonstrating that an intact tumor capsule can function as a physical barrier that fosters an immune-exempt microenvironment. This analysis also validated FLAD1 upregulation within the spatial context of the tumor. Additionally, DNA methylation analysis indicated a hypomethylation pattern in the FLAD1 promoter region, likely contributing to its overexpression in HCC. Validation of FLAD1 protein levels in an in-house cohort via Western blotting further confirmed these findings. Collectively, our integrative study highlights the utility of MitRGs as potential biomarkers and positions FLAD1 as a dual prognostic and therapeutic target linked to the structural and immune landscape of HCC.

BackgroundUveal melanoma (UVM) is a form of eye cancer with a poor prognosis, particularly in metastatic patients. This study aimed to elucidate the cellular heterogeneity within UVM and identify prognostic biomarkers.MethodsWe performed single-cell RNA sequencing (scRNA-seq) on primary and metastatic UVM samples. A UVM-specific gene signature was constructed using LASSO regression and validated via ROC curve analysis in the TCGA-UVM and GSE84976 cohorts. AlphaFold 3 was used to predict the 3D structures of key proteins. T-cell populations were analyzed using pseudotime trajectory mapping and interaction network visualization. CRISPR-Cas9 screening analysis was conducted to identify hub genes and cytokine pathways that may serve as therapeutic targets. Additionally, we constructed the Dictionary of Immune Responses to Cytokines at single-cell resolution to evaluate cytokine signatures.ResultsScRNA-seq revealed five major cell types within UVMs and subdivided them into seven distinct subtypes. Cytokine signaling analysis revealed differential expression of cytokine signaling in immune-related genes (CSIRGs) across these subtypes in primary and metastatic tumors. The UVM-specific gene signature demonstrated high predictive accuracy in ROC curve analysis and was associated with overall survival in Kaplan–Meier survival analyses. Additionally, AlphaFold 3 predicted the 3D structures of key proteins with high confidence. T-cell population analysis revealed complex developmental pathways and interaction networks in UVM. Myeloid-derived suppressor cells (MDSCs) were found to be increased in metastatic UVM, correlating with the enrichment of GM-CSF. CRISPR-Cas9 screening analysis identified hub genes and cytokine pathways with low gene effect scores across cell lines, indicating their potential importance in UVM.ConclusionThis study identified critical cellular subtypes and prognostic biomarkers in UVM, shedding light on targeted therapies. The insights into cytokine signaling and T-cell dynamics within the UVM microenvironment provide a foundation for developing personalized therapeutic strategies to improve patient outcomes.

To better understand the complexity of the tumor microenvironment and to identify novel treatment strategies for uveal melanoma (UVM), we analyzed single-cell RNA sequencing (scRNA-seq) data from eight primary UVM eye tissues and three metastatic UVMs in the liver (GSE139829). We integrated this with bulk RNA-seq data from UVM patients derived from TCGA-UVM and GSE84976 cohorts. Our study focused on cytokine signaling in immune-related genes (CSIRGs), revealing distinct cellular compositions, intercellular interactions, and prognostic implications in UVM. We identified 137 cytokine signaling-related genes in UVM, with ISG20 significantly upregulated and linked to advanced stages and poor prognosis. Recognized for immune regulation, ISG20 emerged as a key survival predictor and therapeutic target from a risk model using Cox regression and LASSO, effectively categorizing patients by survival risk. Furthermore, differential drug response analysis revealed distinct sensitivities to drugs between the risk groups. Immune infiltration analysis showed a diverse immune landscape, potentially influencing response to immunotherapy. Structural prediction using AlphaFold 2 technology and molecular docking analyses revealed interactions between ISG20 and the therapeutic candidate decitabine. This study combines scRNA-seq analysis with structural biology approaches to unravel the molecular complexity of UVM, emphasizing the prognostic and therapeutic relevance of CSIRGs, particularly ISG20.

IntroductionType 2 diabetes (T2D) is characterized by insulin resistance and chronic inflammation, with macrophages playing a crucial role in pancreatic islet dysfunction. This study explored the intersection of macrophage-specific gene expression and abnormal blood monovalent inorganic cation concentration-related genes (ABRGs) in T2D patients via single-cell RNA sequencing (scRNA-seq) and machine learning to identify key genes and potential therapeutic targets.MethodsScRNA-seq data from the pancreatic islet cells of 27 nondiabetic (ND) patients and 17 T2D patients were analyzed to identify differentially expressed genes (DEGs) in macrophages. These DEGs were intersected with ABRGs to identify hub genes. Machine learning models were developed to predict T2D, and structural predictions of the hub proteins were performed. PPI networks and regulatory networks involving transcription factors (TFs) and miRNAs were also analyzed. Correlations between hub ABRGs and immune cell infiltration, as well as cytokine responses, were examined via ssGSEA and immune response enrichment analysis (IREA).ResultsSixteen overlapping hub ABRGs, including ATP1A1, CACNA1D, and CLDN10, were identified. The GBM model demonstrated high predictive accuracy, with an AUC of 0.988. Correlation analysis revealed significant relationships between the hub genes and the infiltration of immune cells, particularly macrophages. Cytokine enrichment analysis revealed that macrophages in T2D exhibit a distinct signature of cytokines, including IL15, IFNα1, IFNβ, and IL17F. PPI networks highlighted significant interactions among the hub genes. Regulatory network analysis revealed that STAT3 is a central TF and that miRNAs such as hsa-mir-1-3p are critical regulators.DiscussionThis study highlights the central roles of macrophages and ABRGs in T2D, identifying novel genes and regulatory networks that contribute to disease progression. The integration of scRNA-seq and machine learning provides valuable insights and potential therapeutic targets for T2D.

BackgroundIslet transplantation is a promising treatment for type 1 diabetes that aims to restore insulin production and improve glucose control, but long-term graft survival remains a challenge due to immune rejection.MethodsScRNA-seq data from syngeneic and allogeneic islet transplantation grafts were obtained from GSE198865. Seurat was used for filtering and clustering, and UMAP was used for dimension reduction. Differentially expressed genes were analyzed between syngeneic and allogeneic islet transplantation grafts. Gene set variation analysis (GSVA) was performed on the HALLMARK gene sets from MSigDB. Monocle 2 was used to reconstruct differentiation trajectories, and cytokine signature enrichment analysis was used to compare cytokine responses between syngeneic and allogeneic grafts.ResultsThree distinct macrophage clusters (Mø-C1, Mø-C2, and Mø-C3) were identified, revealing complex interactions and regulatory mechanisms within macrophage populations. The significant activation of macrophages in allogeneic transplants was marked by the upregulation of allograft rejection-related genes and pathways involved in inflammatory and interferon responses. GSVA revealed eight pathways significantly upregulated in the Mø-C2 cluster. Trajectory analysis revealed that Mø-C3 serves as a common progenitor, branching into Mø-C1 and Mø-C2. Cytokine signature enrichment analysis revealed significant differences in cytokine responses, highlighting the distinct immunological environments created by syngeneic and allogeneic grafts.ConclusionThis study significantly advances the understanding of macrophage roles within the context of islet transplantation by revealing the interactions between immune pathways and cellular fate processes. The findings highlight potential therapeutic targets for enhancing graft survival and function, emphasizing the importance of understanding the immunological aspects of transplant acceptance and longevity.

IntroductionLupus nephritis (LN), a severe complication of systemic lupus erythematosus (SLE), presents significant challenges in patient management and treatment outcomes. The identification of novel LN-related biomarkers and therapeutic targets is critical to enhancing treatment outcomes and prognosis for patients.MethodsIn this study, we analyzed single-cell expression data from LN (n=21) and healthy controls (n=3). A total of 143 differentially expressed genes were identified between the LN and control groups. Then, proteomics analysis of LN patients (n=9) and control (SLE patients without LN, n=11) revealed 55 differentially expressed genes among patients with LN and control group. We further utilizes protein-protein interaction network and functional enrichment analyses to elucidate the pivotal role of COL6A3 in key signaling pathways. Its diagnostic value is evaluate through its correlation with disease progression and renal function metrics, as well as Receiver Operating Characteristic Curve (ROC) analysis. Additionally, immunohistochemistry and qPCR experiments were performed to validate the expression of COL6A3 in LN.ResultsBy comparison of single-cell and proteomics data, we discovered that COL6A3 is significantly upregulated, highlighting it as a critical biomarker of LN. Our findings emphasize the substantial involvement of COL6A3 in the pathogenesis of LN, particularly noting its expression in mesangial cells. Through comprehensive protein-protein interaction network and functional enrichment analyses, we uncovered the pivotal role of COL6A3 in key signaling pathways including integrin-mediated signaling pathways, collagen-activated signaling pathways, and ECM-receptor interaction, suggesting potential therapeutic targets. The diagnostic utility is confirmed by its correlation with disease progression and renal function metrics of the glomerular filtration rate. ROC analysis further validates the diagnostic value of COL6A3, with the area under the ROC values of 0.879 in the in-house cohort, and 0.802 and 0.915 in tubular and glomerular external cohort samples, respectively. Furthermore, immunohistochemistry and qPCR experiments were consistent with those obtained from the single-cell RNA sequencing and proteomics studies. DiscussionThese results proved that COL6A3 is a promising biomarker and therapeutic target, advancing personalized medicine strategies for LN.

BackgroundProliferative diabetic retinopathy (PDR), a major cause of blindness, is characterized by complex pathogenesis. This study integrates single-cell RNA sequencing (scRNA-seq), Non-negative Matrix Factorization (NMF), machine learning, and AlphaFold 2 methods to explore the molecular level of PDR.MethodsWe analyzed scRNA-seq data from PDR patients and healthy controls to identify distinct cellular subtypes and gene expression patterns. NMF was used to define specific transcriptional programs in PDR. The oxidative stress-related genes (ORGs) identified within Meta-Program 1 were utilized to construct a predictive model using twelve machine learning algorithms. Furthermore, we employed AlphaFold 2 for the prediction of protein structures, complementing this with molecular docking to validate the structural foundation of potential therapeutic targets. We also analyzed protein−protein interaction (PPI) networks and the interplay among key ORGs.ResultsOur scRNA-seq analysis revealed five major cell types and 14 subcell types in PDR patients, with significant differences in gene expression compared to those in controls. We identified three key meta-programs underscoring the role of microglia in the pathogenesis of PDR. Three critical ORGs (ALKBH1, PSIP1, and ATP13A2) were identified, with the best-performing predictive model demonstrating high accuracy (AUC of 0.989 in the training cohort and 0.833 in the validation cohort). Moreover, AlphaFold 2 predictions combined with molecular docking revealed that resveratrol has a strong affinity for ALKBH1, indicating its potential as a targeted therapeutic agent. PPI network analysis, revealed a complex network of interactions among the hub ORGs and other genes, suggesting a collective role in PDR pathogenesis.ConclusionThis study provides insights into the cellular and molecular aspects of PDR, identifying potential biomarkers and therapeutic targets using advanced technological approaches.

Lupus nephritis (LN), a complex complication of systemic lupus erythematosus, requires in-depth cellular and molecular analysis for advanced treatment strategies, including mRNA vaccine development. In this study, we analyzed single-cell RNA sequencing data from 24 LN patients and 10 healthy controls, supplemented by bulk RNA-seq data from additional LN patients and controls. By applying non-negative matrix factorization (NMF), we identified four distinct leukocyte meta-programs in LN, highlighting diverse immune functions and potential mRNA vaccine targets. Utilizing 12 machine learning algorithms, we developed 417 predictive models incorporating gene sets linked to key biological pathways, such as MTOR signaling, autophagy, Toll-like receptor, and adaptive immunity pathways. These models were instrumental in identifying potential targets for mRNA vaccine development. Our functional network analysis further revealed intricate gene interactions, providing novel insights into the molecular basis of LN. Additionally, we validated the mRNA expression levels of potential vaccine targets across multiple cohorts and correlated them with clinical parameters such as the glomerular filtration rate (GFR) and pathological stage. This study represents a significant advance in LN research by merging single-cell genomics with the precision of NMF and machine learning, broadening our understanding of LN at the cellular and molecular levels. More importantly, our findings shed light on the development of targeted mRNA vaccines, offering new possibilities for diagnostics and therapeutics for this complex autoimmune disease.

Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.

Islet transplantation is a promising therapy for diabetes treatment. However, the molecular underpinnings governing the immune response, particularly T-cell dynamics in syngeneic and allogeneic transplant settings, remain poorly understood. Understanding these T cell dynamics is crucial for enhancing graft acceptance and managing diabetes treatment more effectively. This study aimed to elucidate the molecular mechanisms, gene expression differences, biological pathway alterations, and intercellular communication patterns among T-cell subpopulations after syngeneic and allogeneic islet transplantation. Using single-cell RNA sequencing, we analyzed cellular heterogeneity and gene expression profiles using the Seurat package for quality control and dimensionality reduction through t-SNE. Differentially expressed genes (DEGs) were analyzed among different T cell subtypes. GSEA was conducted utilizing the HALLMARK gene sets from MSigDB, while CellChat was used to infer and visualize cell-cell communication networks. Our findings revealed genetic variations within T-cell subpopulations between syngeneic and allogeneic islet transplants. We identified significant DEGs across these conditions, highlighting molecular discrepancies that may underpin rejection or other immune responses. GSEA indicated activation of the interferon-alpha response in memory T cells and suppression in CD4+ helper and γδ T cells, whereas TNFα signaling via NFκB was particularly active in regulatory T cells, γδ T cells, proliferating T cells, and activated CD8+ T cells. CellChat analysis revealed complex communication patterns within T-cell subsets, notably between proliferating T cells and activated CD8+ T cells. In conclusion, our study provides a comprehensive molecular landscape of T-cell diversity in islet transplantation. The insights into specific gene upregulation in xenotransplants suggest potential targets for improving graft tolerance. The differential pathway activation across T-cell subsets underscores their distinct roles in immune responses posttransplantation.

Islet transplantation to treat the late stage of type 1 diabetic patient (T1DM) has recently made inspiring success in clinical trials. However, most patients experience a decline in islet graft function in one to three years due to immune rejection. Although the mechanisms of immune cells, including macrophages, dendritic cells (DCs), neutrophils, natural killer cells (NKs), B cells, and T cells, that mediate immune rejection have been investigated, the overall characteristics of immune infiltrates in islet allografts and syngeneic grafts remain unclear. Single-cell RNA sequencing (scRNA-seq) has provided us with new opportunities to study the complexity of the immune microenvironment in islet transplants. In the present study, we used scRNA-seq to comprehensively analyze the immune heterogeneity in the mouse model of islet transplantation. Our data revealed T lymphocytes and myeloid cells as the main immune components of grafts 7 days post-islet transplantation, especially in allografts. Moreover, our results indicated that allogeneic islet cells were transformed into antigen-presenting cell-like cells with highly expressed MHC class I molecules and genes involved in MHC class I-mediated antigen presentation. This transformation may dramatically facilitate the interaction with cytotoxic CD8+ T cells and promote the destruction of islet allografts. Our study provides insight into the transcriptomics and diverse microenvironment of islet grafts and their impacts on immune rejection.

Pig islet xenotransplantation is a potential treatment for patients with type 1 diabetes. Current efforts are focused on identifying the optimal pig islet source and overcoming the immunological barrier. The optimal age of the pig donors remains controversial since both adult and neonatal pig islets have advantages. Isolation of adult islets using GMP grade collagenase has significantly improved the quantity and quality of adult islets, but neonatal islets can be isolated at a much lower cost. Certain culture media and coculture with mesenchymal stromal cells facilitate neonatal islet maturation and function. Genetic modification in pigs affords a promising strategy to prevent rejection. Deletion of expression of the three known carbohydrate xenoantigens (Gal, Neu5Gc, Sda) will certainly be beneficial in pig organ transplantation in humans, but this is not yet proven in islet transplantation, though the challenge of the ‘4th xenoantigen’ may prove problematic in nonhuman primate models. Blockade of the CD40/CD154 costimulation pathway leads to long-term islet graft survival (of up to 965 days). Anti-CD40mAbs have already been applied in phase II clinical trials of islet allo transplantation. Fc region-modified anti-CD154mAbs successfully prevent the thrombotic complications reported previously. In this review, we discuss (I) the optimal age of the islet-source pig, (ii) progress in genetic modification of pigs, (iii) the immunosuppressive regimen for pig islet xenotransplantation, and (iv) the reduction in the instant blood-mediated inflammatory reaction.

Pig to human xenotransplantation is considered to be a possible approach to alleviate the shortage of human allografts. Porcine endogenous retrovirus (PERV) is the most significant pathogen in xenotransplantation. We screened for pigs that consistently did not transmit human-tropic replication competent PERVs (HTRC PERVs), namely, non-transmitting pigs. Then, we conducted whole-genome resequencing and full-length transcriptome sequencing to further investigate the sequence characteristics of one non-transmitting pig. Using in vitro transmission assays, we found 5 (out of 105) pigs of the Chinese Wuzhishan minipig inbred line that did not transmit PERV to human cells, i.e., non-transmitting pigs. Whole-genome resequencing and full-length transcriptome sequencing of one non-transmitting pig showed that all of the pol genes were defective at both the genome and transcript levels. We speculate that the defective PERV pol genes in this pig might be attributable to the long-term inbreeding process. This discovery is promising for the development of a strain of highly homozygous and genetically stable pigs with defective PERV pol genes as a source animal species for xenotransplantation.

Type 1 diabetes mellitus (T1DM) is caused by autoimmune destruction of pancreatic β cells, which results in little or no insulin production. Islet transplantation plays an important role in the treatment of T1DM, with the improved glycometabolic control, the reduced progression of complications, the reduction of hypoglycemic episodes when compared with traditional insulin therapy. The results of phase III clinical trial also demonstrated the safety and efficacy of islet allotransplantation in T1DM. However, the shortage of pancreas donors limits its widespread use. Animals as a source of islets such as the pig offer an alternative choice. Because the architecture of the pig pancreas is different from the islets of mice or humans, the pig islet isolation procedure is still challenging. Since the translation of alternative porcine islet sources (xenogeneic) to the clinical setting for treating T1DM through cellular transplantation is of great importance, a cost-effective, standardized, and reproducible protocol for isolating porcine islets is urgently needed. This manuscript describes a simplified and cost-effective method to isolate and purify adult porcine islets based on the previous protocols that have successfully transplanted porcine islets to non-human primates. This will be a beginners guide without the use of specialized equipment such as a COBE 2991 Cell Processor.

Islet transplantation is poised to play an important role in the treatment of type 1 diabetes mellitus (T1DM). However, there are several challenges limiting its widespread use, including the instant blood-mediated inflammatory reaction, hypoxic/ischemic injury, and the immune response. Mesenchymal stem/stromal cells (MSCs) are known to exert regenerative, immunoregulatory, angiogenic, and metabolic properties. Here, we review recent reports on the application of MSCs in islet allo- and xenotransplantation. We also document the clinical trials that have been undertaken or are currently underway, relating to the co-transplantation of islets and MSCs. Increasing evidence indicates that co-transplantation of MSCs prolongs islet graft survival by locally secreted protective factors that reduce immune reactivity and promote vascularization, cell survival, and regeneration. MSC therapy may be a promising option for islet transplantation in patients with T1DM.

Background. Type 1 diabetes (T1DM) is a chronic autoimmune disease characterized by T-cell-mediated destruction of insulin-producing beta cells. Evidence shows that patients with T1DM and mice used in specific diabetic models both exhibit changes in their intestinal microbiota and dysregulated microbiota contributes to the pathogenesis of T1DM. Islet transplantation (Tx) is poised to play an important role in the treatment of T1DM. However, whether treatment of T1DM with islet Tx can rescue dysregulated microbiota remains unclear. Methods. In this study, we induced diabetic C57BL/6 mice with streptozotocin. Then treatment with either insulin administration, or homogenic or allogenic islet Tx was performed to the diabetic mice. Total DNA was isolated from fecal pellets and high-throughput 16S rRNA sequencing was used to investigate intestinal microbiota composition. Results. The overall microbial diversity was comparable between control (nonstreptozotocin treated) and diabetic mice. Our results showed the ratio of the Bacteroidetes: Firmicutes between nondiabetic and diabetic mice was significant different. Treatment with islet Tx or insulin partially corrects the dysregulated bacterial composition. At the genus level, Bacteroides, Odoribacter, and Alistipes were associated with the progression and treatment efficacy of the disease, which may be used as a biomarker to predict curative effect of treatment for patients with T1DM. Conclusions. Collectively, our results indicate that diabetic mice show changed microbiota composition and that treatment with insulin and islet Tx can partially correct the dysregulated microbiota.

Introduction Diabetic retinopathy (DR) is one of the most prevalent microvascular complications of diabetes mellitus. Guidelines for DR screening in different countries vary greatly, including fundus photography, slit-lamp biomicroscopy, indirect ophthalmoscopy, Optical Coherence Tomography (OCT), OCT-A and Fundus Fluorescein Angiography (FFA). Two-field non-mydriatic fundus photography (NMFP) is an effective screening method due to its low cost and less time-consuming process. However, it is controversial due to the sensitivity and specificity of two-field NMFP. This review intends to evaluate the performance of the two-field NMFP in diagnosing DR and helps clinicians determine the most optimal screening method. Methods and analysis Two reviewers will independently search on the Medline, Embase, Cochrane databases, ProQuest, Opengrey, Chinese National Knowledge Infrastructure, Wanfang Data, VIP China Science and Technology Journal Database, Chinese BioMedical Literature Database, ISRCTN, ClinicalTrials.gov and the WHO ICTRP to identify relevant studies. There is no restriction posed on the language of the study. Included studies focus on the performance of two-field NMFP in detecting DR in diabetes patients. Analysis and evaluation of the studies will be examined by two reviewers independently using the Quality Assessment for Diagnostic Accuracy Studies-2 tool and later evaluated using the Population, Intervention, Comparison, Outcome, Study design criteria. A random-effect model will calculate the diagnostic indicators, including the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic OR, area under the curve and 95% CIs. We will also develop a summary receiver operating characteristic curve. We anticipate analysing subgroups according to the factors, which may lead to heterogeneity, including DR levels of patients, the reference standards, camera models, the interpretation criteria. The data will be analysed by STATA software. This study was registered with PROSPERO. Ethics and dissemination This review will analyse the published data. Patients/the public were not involved in this research. The results of this study will be published in peer-reviewed journals. PROSPERO registration number CRD42020203608.