Long-read single-cell RNA sequencing analysis of key genes and isoforms during corneal wound healing in cynomolgus monkeys

Long-read single-cell RNA sequencing analysis of key genes and isoforms during corneal wound healing in cynomolgus monkeys | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Long-read single-cell RNA sequencing analysis of key genes and isoforms during corneal wound healing in cynomolgus monkeys Zhuo-Xing Shi, Hu Chen, Qi Dai, Ming Zhou, Yuan-Xia Yang, Zhi-Bo Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5232061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The repair of corneal damage is essential for maintaining clear vision. Upon corneal epithelial injury, cells at the corneal limbus initiate complex processes such as migration, extracellular matrix remodeling, and proliferation. However, the transcriptional heterogeneity of limbal cell populations during these stages remains understudied. In this study, we used high-throughput long-read single-cell RNA sequencing to analyze five major cell types in the corneal limbus of cynomolgus monkeys at three time points: before injury, and one and three days post-injury. These cell types include terminally differentiated corneal epithelial cells (TDCE), basal cells (BC), transit-amplifying cells (TAC), limbal stem cells (LSC), and conjunctival cells (CC). We identified key regulatory genes and RNA isoforms involved in cell migration, proliferation, and differentiation, including IGF2 , FN1 , LAMC2 , ITGB1 , ITGAV , and keratins ( KRT3 , KRT12 , and KRT6A ). Our findings reveal the critical roles of LSC and BC in corneal repair and provide new insights into the transcriptional landscape during epithelial healing. Biological sciences/Cell biology Biological sciences/Developmental biology/Self-renewal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The corneal epithelium comprises 5–7 layers of squamous epithelial cells 1 , essential for maintaining visual health, protecting the eye from external damage, and adapting to environmental changes 2 , 3 , 4 , 5 . As a self-renewing tissue, corneal epithelial cells are continuously replenished through the proliferation, migration, and differentiation of stem cells located in the corneal limbus, ensuring clear vision 6 , 7 , 8 , 9 . However, physical or chemical damage to the cornea can compromise its barrier function, posing serious risks to visual health. Fortunately, the corneal epithelium has a remarkable ability to repair itself, restoring epithelial integrity and corneal clarity through a dynamic healing process. Corneal epithelial injury repair involves multiple cellular activities, including cell migration 10 , 11 , extracellular matrix reconstruction 12 , 13 , 14 , 15 , 16 , adhesion, proliferation, and differentiation 17 , 18 , 19 . After injury, peri-wound epithelial cells reorganize and migrate towards the wound, forming a monolayer that covers the damaged area. These cells then proliferate, stratify, and differentiate, ultimately reestablishing the corneal epithelium's original multilayered structure 20 . To better understand the molecular mechanisms underlying corneal injury repair, full-length RNA sequencing technologies, such as Nanopore and PacBio, are invaluable 21 , 22 . These methods allow for the detection of alternative splicing events and transcriptional modifications, both of which play critical roles in regulating gene expression during the repair process 23 . By capturing complete transcripts, full-length RNA sequencing uncovers complex RNA variants and regulatory elements that are often missed by short-read methods, making it an indispensable tool for studying the repair mechanisms of both corneal and skin injuries 24 . The corneal injury repair process involves dynamic changes across multiple cell types, including corneal limbal stem cells, and epithelial cells, and basal cells. Single-cell RNA sequencing (scRNA-seq) offers valuable insights into the transcriptional heterogeneity of these cells during injury repair. Although scRNA-seq data exist for mouse 25 , cynomolgus monkeys 26 , 27 , and human 28 , 29 , 30 , 31 corneal tissues under steady-state or pathological conditions, comprehensive studies on the dynamic regulatory processes involved in corneal injury repair remain limited. In a previous study, we developed HIT-scISOseq 26 , achieving over 99.99% accuracy and specificity for cellular barcode detection using long-read single-cell RNA sequencing of resting-state corneal limbal tissues from cynomolgus monkeys. HIT-scISOseq enables the concatenation of multiple single-cell cDNAs up to ~ 6 kb, generating over 10 million full-length non-concatemer (FLNC) reads using the PacBio Sequel II SMRT Cell 8M. Our HIT-scISOseq data revealed an average of 2,407 unique molecular identifiers (UMIs) and 1,096 genes across 2,214 resting-state corneal limbal cells, aligning with high-depth short-read sequencing results (13,740 UMIs and 2,804 genes) while also identifying cell type-specific RNA isoforms. To date, no studies have applied long-read single-cell RNA sequencing to corneal injury repair. Leveraging HIT-scISOseq for single-cell detection of gene and RNA isoform expression, we analyzed cellular changes during corneal injury repair by sequencing corneal limbal tissues from cynomolgus monkeys at 0, 1, and 3 days post-injury. Our analysis identified LSC and BC as key players in the repair process among the five corneal epithelial subpopulations. Post-injury, cell migration was driven by interactions with the extracellular matrix, while proliferation and differentiation were initially inhibited but significantly increased after the third day. These findings indicate that corneal epithelial healing is regulated by a combination of growth factors, extracellular matrix components, integrins, cytoskeletal genes, and their specific RNA isoforms. Results HIT-scISOseq based single-cell gene expression landscape of the corneal limbus To investigate the dynamic changes in cell types and gene regulation during corneal injury repair, we established a corneal injury model in cynomolgus monkeys. Corneal limbal tissue samples were collected before injury (0d) and at 1 and 3 days post-injury (1d and 3d), with two biological replicates (s1 and s2) for each time point (Supplementary Fig. S1A). After dissociating the cells, we employed the 10× Genomics Chromium platform for single-cell capture and full-length cDNA synthesis. The resulting full-length cDNAs were processed using the HIT-scISOseq method and sequenced on a Sequel II SMRT Cell 8M chip (Fig. 1 A). A statistical analysis of the long-read single-cell RNA sequencing data revealed that each sample generated approximately 10.8–14.8 million full-length non-concatemer (FLNC) reads (Supplementary Tables 1–4). After quality control and batch effect removal, 7,895 corneal epithelial cells were selected from a total of 12,800 cells for downstream analysis (Supplementary Tables 5–6, Supplementary Data 1). On average, these cells exhibited 3,443 unique molecular identifiers (UMIs) and 1,468 genes detected per cell at the gene level (Supplementary Table 5, Supplementary Fig. S1B). At the RNA isoform level, primarily focusing on isoforms of annotated genes (see Methods for details), each cell showed an average of 2,205 UMIs and 1,051 isoforms (Supplementary Table 6, Supplementary Fig. S1C). Using unsupervised clustering, we classified the 7,891 cells into five distinct clusters with unique transcriptional profiles at the gene level (Fig. 1 B, Supplementary Fig. S1D). Based on known marker genes (Fig. 1 C, Supplementary Fig. S2A, Supplementary Data 2), we identified five major cell types: corneal epithelial terminally differentiated cells (TDCE, KRT3 + 32 , KRT12 + 33 , KRT24 + 29 ), basal cells (BC, ITGB1 + 34 , ITGA6 + 35 , COL17A1 + 36, 37 ), corneal limbal stem cells (LSC, KRT15 + 37, 38 , S100A2 + 29, 39 ), conjunctival cells (CC, KRT19 + 39, 40 , S100A8 + and S100A9 + 41 ), and transit-amplifying cells (TAC, MKI67 + 42 , TOP2A + 43 , CDK1 + 44 , CENPF + 43 cell cycle genes). At the isoform level, clustering similarly identified five corresponding cell clusters (Fig. 1 B, Supplementary Fig. S1E, Supplementary Fig. S2B, Supplementary Data 2), highlighting RNA isoforms highly expressed in each cell type (Fig. 1 C). Analysis across the three time points showed no significant sample-specific clustering, suggesting clustering reflects physiological differences rather than technical variation (Supplementary Fig. S3A-B). We also quantified cell type composition at both the gene and isoform levels (Fig. 1 D). To explore cell-type-specific changes in gene expression during corneal injury repair, we compared transcriptional profiles at 0d, 1d, and 3d, identifying differentially expressed genes (DEGs) in TDCE, BC, LSC, CC, and TAC. A total of 522 up-regulated and 467 down-regulated genes were differentially expressed in at least one cell type during the repair process. Notably, 43.6% of the DEGs (213 up-regulated and 218 down-regulated) were shared among at least two cell types, while the rest were cell-type-specific (Fig. 1EF, Supplementary Data 3). LSC-specific DEGs comprised 65.9% of al l cell-type-specific DEGs, with 284 identified. Among these, 21.5% (107) were up-regulated in LSC at 1d compared to 0d, and 12.8% (64) were down-regulated at 3d compared to 1d, indicating that LSC undergo the most significant transcriptional changes, highlighting their critical role in the early stages of corneal injury repair (Supplementary Fig. S4A). We next performed GO and KEGG enrichment analyses on cell-type-specific DEGs (Fig. 1 G, Supplementary Fig. S4B). For up-regulated genes, GO terms like “epithelium development” and “keratinocyte differentiation” were enriched in TDCE and BC, along with cell migration-related terms such as “regulation of cell migration” and “regulation of locomotion”. BC also showed enrichments for “anatomical structure formation involved in morphogenesis” and “regulation of organelle organization”, similar to LSC. CC were enriched for inflammatory response-related terms, including “inflammatory response”, “positive regulation of cell death”, “apoptotic signaling pathway”, “granulocyte migration”, and “neutrophil migration”. Down-regulated genes during the early repair phase were associated with “response to oxygen-containing compounds”, “cellular response to stimuli”, and “cellular metabolic regulation”. (Fig. 1 G, Supplementary Data 4). KEGG enrichment analysis across all cell types highlighted key pathways in the repair process, such as the “PI3K-Akt signaling pathway” and “MAPK signaling pathway”, essential for cell survival, proliferation, differentiation, and apoptosis 45 , 46 . Pathways like “focal adhesion” and “ECM-receptor interaction” enriched in LSC and BC 47 , 48 , underscored their role in extracellular matrix interactions, suggesting their critical involvement in corneal injury repair (Supplementary Fig. S4B, Supplementary Data 4). In summary, corneal injury repair is a complex, multi-cellular process with LSC and BC playing pivotal roles. Further analyses will focus on genes and RNA isoforms involved in three key pathways: cell migration, proliferation, and differentiation. Key genes and RNA isoforms involved in LSC and BC migration during corneal wound healing Cell migration plays a critical role during the early phase of corneal injury repair 49 , where cells coordinate movement towards the damaged site to restore the epithelial layer. To explore the dynamics of cell migration, we conducted a comprehensive analysis of gene expression changes related to “actin”, “extracellular matrix components”, “matrix metalloproteinases (MMPs)”, and “membrane protein integrins” across different time points for each cell type. Our results showed that “actin” expression significantly increased in all cell types, with LSC and BC showing the most notable up-regulation between 1d and 3d post-injury (Fig. 2 A, Supplementary Data 5). Changes in “extracellular matrix components” and “integrin family members” were particularly evident in LSC and BC (Fig. 2 B-C, Supplementary Data 6–7), and LSC expressed key MMPs, such as MMP2 and MMP13 , involved in extracellular matrix degradation at 3d post-injury (Supplementary Fig. S5A, Supplementary Data 8). These results highlight LSC and BC as central to extracellular matrix remodeling and cell migration during corneal injury repair. To further explore migration-related gene modules in LSC and BC, we applied high-dimensional weighted gene co-expression network analysis (hdWGCNA) 50 , identifying 14 gene modules (Fig. 2 D, Supplementary Data 9). We quantified the number of genes and the distribution of DEGs within these modules (Supplementary Fig. S5B), conducted GO enrichment analysis for each module (Supplementary Fig. S5C, Supplementary Data 10), and identified four key modules-“turquoise”, “salmon”, “magenta”, and “yellow”-associated with cell migration. We identified 127 functional genes in LSC and 118 in BC by intersecting DEGs with these modules (Fig. 2 E). Protein-protein interaction (PPI) analysis of these genes using STRING 51 , 52 revealed hub genes involved in migration-related processes (Fig. 2 F, Supplementary Data 11). Enrichment analysis showed these genes were involved in pathways such as "ECM-receptor interaction" and "focal adhesion" (Supplementary Fig. S5D, Supplementary Data 12), underscoring the importance of cell-extracellular matrix interactions in promoting migration. The fibronectin-integrin (FI) system is crucial for cell attachment and migration during corneal repair 20 , 53 . In this process, the integrin α5β1 receptor ( ITGA5 / ITGB1 ) binds to fibronectin ( FN1 ), establishing stable connections between cells and the extracellular matrix while coordinating cell movement 54 , 55 . For effective migration, cells must detach from the fibronectin-extracellular matrix 56 , 57 , a process driven by proteolytic enzymes, with urokinase-type plasminogen activator (uPA, PLAU ) playing a key role 11 . While FN1 and ITGB1 were identified as key hub genes in LSC and BC (Fig. 2 F-G), PLAU was not classified as a functional gene in these cells. We examined the gene expression dynamics of key hub genes such as FN1 , ITGB1 , and PLAU across different time points and cell types, focusing on their top five isoforms (Fig. 2 H, Supplementary Fig. S5E). FN1 was consistently expressed in both LSC and BC at 3d post-injury, though isoform PB.7424.49( FN1 ~ NIC) showed minimal expression in BC. ITGB1 levels significantly increased over time in both cell types, with similar trends across its isoforms. PLAU was up-regulated starting at 1 day post-injury, particularly in BC, where isoform PB.62488.875( PLAU ~ NIC) showed consistent up-regulation and distinct splicing compared to other isoforms (Fig. 2 H). Structural analysis further revealed notable differences between PB.62488.875( PLAU ~ NIC) and the major isoform PB.62488.521( PLAU ~ NIC), suggesting diverse functional roles at different stages of corneal injury(Supplementary Fig. S6A, B). In summary, these findings highlight the dynamic interactions between cells and the extracellular matrix during early corneal repair, offering valuable insights into the molecular mechanisms that regulate cell migration and wound healing. Key genes and RNA isoforms involved in cell proliferation during corneal wound healing Upon corneal injury, nearby BC migrate to the wound site, forming a monolayer to cover the damaged area 58 , 59 , 60 , after which they proliferate and differentiate to restore the corneal epithelium 60 , 61 . In our study, we identified TAC clusters at both the gene and isoform levels, showing a close relationship with BC at the gene level and with limbal stem cells (LSC) at the isoform level (Fig. 3 A). We examined proliferation markers across cell types and assessed cell cycle status. TAC clusters specifically expressed proliferation markers, including CENPF , MKI67 , TOP2A , CDC20 , CDK1 , and CDKN3 42, 43, 44, 62, 63, 64, 65, 66 (Fig. 3 B). Cell cycle analysis revealed that all TAC cells were actively cycling, with 16.42% in the S phase and 83.58% in the G2/M phase. Additionally, 64.06% of LSC and 61.15% of BC also showed significant proliferative activity (Fig. 3 C). We further investigated the gene expression dynamics of proliferation markers such as TOP2A, MKI67, and CENPF, along with their isoform expression trends and splice variations. These markers exhibited an initial down-regulation followed by up-regulation at both the gene and isoform levels across the three time points post-injury (Fig. 3 D, Supplementary Fig. S6A). This suggests that on 1d, TAC, LSC, and BC were primarily involved in apoptotic or immune-inflammatory responses, with proliferation suppressed. Proliferative activity then increased, signaling a recovery phase. This dynamic expression shift marks a critical transition in cellular behavior during corneal injury repair (Fig. 3 D). The PI3K/AKT signaling pathway, essential for cell proliferation, apoptosis, and migration 45 , was prominently expressed in LSC, BC, and TAC (Fig. 3 E). We further analyzed differentially expressed PI3K/AKT-related genes in TAC, LSC, and BC clusters (Fig. 3 F). These genes included growth factors (e.g., IGF2 , AREG , PDGFA , and VEGFA ), integrins ( ITGA2 , ITGB4 , ITGAV , ITGB1 , ITGB6 , and ITGA6 ), and extracellular matrix components ( LAMA3 , LAMC2 , LAMB1 , FN1 , THBS1 , and COL1A1 ), which were up-regulated at 1d post-injury and down-regulated thereafter, reflecting an early reparative response (Fig. 3 F). Notably, IGF2 , a key factor in corneal cell proliferation, showed a down-regulation followed by an up-regulation in TAC. Isoforms PB.65680.21( IGF2 ~ NIC) and PB.65680.19( IGF2 ~ FSM) showed opposite expression trends in LSC, with PB.65680.21( IGF2 ~ NIC) specifically expressed in TAC and PB.65680.19( IGF2 ~ FSM) in BC on 1d (Fig. 3 G). Protein sequence comparison and structure prediction revealed significant differences between these isoforms (Fig. 4 A), highlighting the ability of HIT-scISOseq to capture isoform-level variations. We also analyzed read coverage for these isoforms, observing their distinct expression patterns in TAC (Fig. S4B, C). Structural predictions via AlphaFold2 showed notable differences between the two isoforms (Fig. 4 D). Additionally, the ITGAV/ITGB6 complex, which promotes epithelial proliferation, was highly expressed at 3d (Fig. 3 F), with isoform-level changes in ITGAV in TAC, suggesting functional divergence between isoforms (Fig. 3 G). The LAMC2 isoform, involved in PI3K/AKT-regulated proliferation, showed similar expression trends (Fig. 3 G). These insights enhance our understanding of the molecular mechanisms driving cell proliferation during early corneal repair. Key genes and RNA isoforms involved in cell differentiation during corneal wound healing To thoroughly investigate the transcriptional changes associated with corneal cell differentiation, we reconstructed the differentiation trajectories of LSC, BC, and TDCE using Monocle2, revealing a progression from LSC to TDCE with BC in between (Fig. 5 A), consistent with corneal epithelium development 67 , 68 , 69 . Notably, at the isoform level, the predictive power for BC differentiation was significantly enhanced, identifying a distinct branch from BC to TDCE, suggesting key roles for splice variants in cell fate decisions (Fig. 5 A, Supplementary Fig. S8A). The heatmap of pseudo-time-dependent genes revealed three clusters, each with distinct biological processes: epithelial development ("red"), protein metabolism ("green"), and cell-extracellular matrix interactions ("blue") (Fig. 5 B, Supplementary Data 13). At the isoform level, four clusters were identified, with similar GO terms, such as epithelial development, metabolic regulation, and extracellular matrix (Supplementary Fig. S8B, Supplementary Data 13). Keratinization is a vital aspect of cell differentiation, primarily occurring in the skin and various epithelial tissues. Through SCORE analysis of keratinization-related genes, we observed that the gene signature scores for LSC and BC initially decreased compared to TDCE, followed by a notable increase, indicating that cell differentiation in LSC and BC was initially inhibited post-injury, with differentiation activity significantly increasing after 1d to promote corneal healing (Fig. 5 C, Supplementary Data 14). Known corneal epithelial differentiation markers KRT3 and KRT12 70, 71 , alongside KRT6A -a novel candidate-showed increased expression with pseudo-time, underscoring their roles in corneal healing (Fig. 5 D). To further explore the regulation of LSC and BC differentiation post-injury, we identified three key genes- KRT7 , RBP1 , and PTN -from the “red” cluster at both gene and isoform levels. KRT7 , part of the keratin family, was significantly upregulated in LSC and BC by 3d, with isoforms showing similar expression trends and varying splice structures (Fig. 5 E). RBP1 , involved in vitamin A transport and critical for epithelial differentiation, was also expressed at 3d, with PB.34376.4( RBP1 ~ ISM) being the most prominent (Fig. 5 E). PTN , an autocrine factor associated with growth and differentiation 72 , 73 , 74 , had distinct isoform expression patterns in LSC (Fig. 5 E). Additionally, cytoskeleton-related genes, including TPM1 , TPM2 , PALLD , TAGLN , and VIM 75 , 76 , were differentially upregulated in LSC and BC by 3d post-injury, highlighting their importance in cell growth, differentiation, and injury response (Supplementary Fig. S8C). In summary, these findings provide new insights into the intricate gene regulation driving LSC and BC differentiation during the early phases of corneal injury repair. Discussion The advent of single-cell sequencing technologies has enabled detailed investigations of gene expression patterns in individual cells within tissues or organs 77 , 78 , 79 . In this study, we conducted a long-read scRNA-seq analysis of the corneal injury repair in cynomolgus monkeys, mapping cell migration, proliferation, and differentiation during healing. Previous human 28 , 80 and murine studies 81 , 82 focused on limbal epithelial stem cells in corneal homeostasis, but few addressed the heterogeneity of corneal cells or transcriptional profiles in injury repair. Our research explores transcriptional dynamics across three time points following injury, aiming to identify key cell types and the isoform profiles involved in healing. The corneal epithelium consists of five major cell types identified via long-read scRNA-seq: TDCE, BC, TAC, LSC, and CC. Notably, TAC displayed gene-level similarity to BC and isoform-level similarity to LSC, suggesting that TAC acts as an intermediate between LSC and BC during heightened cellular activity. Furthermore, shared gene expression between LSC and BC, particularly in extracellular matrix and regulatory processes, highlights their roles in migration, proliferation, and differentiation during injury repair. Corneal epithelial cell migration is regulated by factors including actin 10 , integrins 48 , 54 , ECM components 48 , the fibrinolytic system 11 , and MMPs 83 , 84 . During migration, cells polarize via actin-driven protrusions, while MMPs degrade the ECM to facilitate movement. ECM components like fibronectin and laminin provide scaffolding for migration, interacting with integrins to promote adhesion and signaling. Notably, research by Sugioka et al. has underscored the fibrinolytic system's critical role in this process 11 . Corneal epithelial cells migrate toward defects in an orderly manner, driven by actin dynamics, ECM-integrin-mediated adhesion, and cell-stromal detachment governed by the fibrinolytic system. We identified 14 hub genes in LSC and 12 in BC linked to migration, shedding light on their expression dynamics and isoform variations, potentially revealing biomarkers for corneal epithelial cell migration. As cells surrounding a corneal wound migrate to cover the defect, they initiate a cascade of proliferation and differentiation essential for healing. Previous studies have shown that LSC drive the formation of TAC through asymmetric division, providing TAC with heightened proliferative capacity. Research by Li et al. has further identified cell cycle-dependent genes as key markers of TAC, highlighting their critical role in expanding the corneal epithelial cell population during the healing process 43 . Our analysis revealed down-regulation followed by up-regulation of proliferation markers- TOP2A , MKI67 , and CENPF -within the 1d post-injury, indicating initial inhibition of proliferation. Genes like IGF2 , ITGAV , and LAMC2 , which are involved in the PI3K/AKT pathway, were identified as key regulators of corneal cell proliferation. We also characterized isoform-level differences of ITGAV and IGF2 , enhancing our understanding of the functional implications of alternative splicing during healing. In this study, we mapped the dynamic regulation of key genes and isoforms during the differentiation of LSC into TDCE using a pseudo-temporal trajectory model during the pre-healing phase. Our trajectory highlights the progression from the least differentiated LSC, through TAC, to TDCE (Fig. 5 ). Previous work by Kao identified KRT3 and KRT12 as markers of corneal epithelial differentiation. 70 . Notably, our findings show that differentiation in LSC and BC is inhibited within the 1d post-injury, emphasizing the roles of KRT3 , KRT12 , and KRT6A in this process. This is consistent with the literature suggesting that apoptosis dominates the initial response to corneal injury, with cell proliferation and differentiation suppressed at this stage. Additionally, we identified several regulatory genes, including KRT7 , RBP1 , PTN , and cytoskeleton-related genes, which showed increased expression by day three. This supports the notion that corneal epithelial differentiation is governed by a complex gene network. Using long-read single-cell RNA sequencing (scRNA-seq), we comprehensively analyzed the transcriptional landscape of corneal injury healing in cynomolgus monkeys. Our findings reveal key cell types, genes, and isoforms essential for migration, proliferation, and differentiation during healing, providing insights into their transcriptional regulation. While this study focused on annotated gene isoforms, future research could explore the regulatory roles of novel noncoding RNAs, such as transposable elements 85 , in injury repair. The use of long-read scRNA-seq also opens avenues for investigating alternative polyadenylation (APA) and RNA editing, offering a deeper understanding of gene regulation during corneal repair and advancing therapeutic strategies. Methods Long-read single-cell RNA sequencing The experimental design is summarized in Fig. 1 A. Briefly, we extracted corneal epithelial cells from the limbus on days 0, 1, and 3 post-injury in cynomolgus monkeys (two replicates per time point). Full-length cDNAs were generated using the 10× Genomics platform, followed by HIT-scISOseq for library construction and sequencing. Data processing and downstream analysis After acquiring the HIT-scISOseq data, we used the accompanying scISA-Tools software for data preprocessing, RNA isoform identification (only the annotated genes were retained as FSM: full splice match, ISM: incomplete splice match, NIC: novel in the catalog, and NNC: novel not in catalog isoforms), gene and isoform matrix generation. Subsequently, the count matrix was imported into the R package Seurat (version 4.3.0) 86 for further scRNA-seq data analysis. In this case, cells with fewer than 500 detected genes or more than 10% of mitochondrial genes, and genes expressed in fewer than 5 cells were excluded from downstream analysis. Each experimental sample was normalized using the “LogNormalize” method. To avoid batch effects between samples and experiments, a typical correlation analysis (CCA) method was used to exclude effects in data integration and to align samples. For cell clustering of Gene and Isoform count matrices, the first 2000 highly variable genes were used for principal component analysis (PCA). The first 10 PCs of the Gene and Isoform count matrices were clustered with a resolution of 0.15 by the graph-based shared nearest neighbor method (SNN) in the “FindClusters” function. Finally, five unsupervised cell clusters were obtained at both Gene level and Isoform level. The marker genes for each cluster were determined by the “FindAllMarkers” function, where |avg_logFC| > 1 and pvalue < 0.05 were used to filter the marker genes. Identification of damage repair-related DEGs We utilized the “FindMarkers” function in Seurat to identify differentially expressed genes (DEGs) associated with corneal epithelial injury healing between the 1- and 0-day groups after corneal epithelial injury (1/0d), the 3- and 1-day groups (3/1d), and the 3- and 0-day groups (3/0d). The logarithmic multiplicity of difference (log2FC) and p-value for each DEG were calculated by using the nonparametric two-sided Wilcoxon rank sum test. Here, |avg_log2FC| > 1 and pvalue < 0.05 were used to determine DEGs for each cell type of the final corneal epithelium. GO/KEGG Analysis GO/KEGG analyses were performed by the enrichGO function in Clusterprofiler (version 4.6.2) 87 , 88 and data were visualized using the R package ggplot2 for representative GO/KEGG terms (pvalue < 0.05). One of the selected annotated gene sets of Macaca fascicularis (AH107668) was obtained from AnnotationHub (version 3.6.0). Gene set scoring analysis The “AddModuleScore” function of the Seurat R package was used to calculate the characterization scores of the specified gene sets in a single cell and tested for significance using the bilateral Wilcoxon rank sum test. The gene sets used in this study were obtained from the MSigDB database ( https://www.gsea-msigdb.org/gsea/msigdb/ ). Co-expression network hdGCNA analysis High-dimensional weighted correlation network analysis (hdWGNCA) is a recently proposed novel algorithm applicable to scRNA-seq data, which allows for the construction of co-expression networks across multiple scales of cells and spatial hierarchies. We selected genes expressed in at least 5% of cells to construct hdWGCNA objects, which were then transformed into Metacells objects. We simulate the similarity between the co-expression network and the scale-free graph under different soft power thresholds by using the “TestSoftPowers” function, and construct the co-expression network by using the “ConstructNetwork” function to automatically select the soft power thresholds. The “ConstructNetwork” function is used to automatically select the soft power thresholds to construct the co-expression network. Use “PlotDendrogram” function and “GetModules” function to visualize the co-expression modules and obtain the module assignment table. Protein interaction analysis and visualization Protein interactions were searched through the STRING database ( https://cn.string-db.org/ ). Interaction results from STRING were imported into Cytoscape (version 3.9.1, https://cytoscape.org/ ) for visualization of interaction networks and identification of hub genes. Cell cycle analysis Based on the cell cycle-specific expression data, the “CellCycleScoring” function in Seurat was used to identify the cell cycle stage of individual cells. Cells were scored using G2/M and S-phase markers, and cells with neither G2/M nor S-phase markers were considered to be in G1 phase. Pseudotemporal trajectory analysis Single-cell pseudotemporal trajectories were generated by the R package Monocle2 88, 89 . We used the “newCellDataSet” function to construct monocle objects, “estimateSizeFactors” and “estimateDispersions” functions to preprocess the data, the “detectGenes” function to filter low-quality cells, and the “reduceDimension” function to remove the “DDRDimension” in the “newCellDataSet” function. Function to filter low quality cells and “reduceDimension” function to reduce the dimensionality of the data. Protein Sequence Comparison CLUSTALW ( https://www.genome.jp/tools-bin/clustalw ) was used to perform multiple sequence alignment analysis of the corresponding isoform protein sequences of genes. The results were imported into ENDscript (version 3.0) 90 for visualization. Protein structure prediction ESMFold 91 and AlphaFold2 92 were applied for protein structure prediction analysis. We imported the structure files (PBD format) into Pymol (version 2.5.7, https://pymol.org/2/ ) and dewatered, compared and visualized the protein structures in different isoforms. Declarations This experiment was conducted using two 4-year-old female cynomolgus monkeys provided by the Institutional Animal Care and Use Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University. All animal procedures were performed in strict compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the Animal Experiment Ethics Committee of Zhongshan Ophthalmic Center (Guangzhou, China; approval number: 2019-044). The experimental protocols were designed and implemented in accordance with the highest standards of animal welfare and ethical research practices. Data availability All sequencing data generated in this study are available at NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under the accession numbers GSE278797. The reference genome and gene annotation file (Macaca_fascicularis_5.0.99) were downloaded from Ensembl (https://ftp.ensembl.org/pub/release-99/fasta/macaca_fascicularis/). Code availability No additional unique code was generated in this manuscript. Acknowledgements We acknowledge financial support from the CAMS Innovation Fund for Medical Sciences (2019-I2M-5-005); National Natural Science Foundation of China (81721003, 42107148, 62172369); Special Support Plan for High Level Talents in Zhejiang Province (2021R52019). Author Contributions Statement Q.D., Y.F.Z., and Z.X.S. conceived and designed the project. M.Z., Y.X.Y., and S.D.C. performed the experimental validation; M.Z., Y.X.Y., S.D.C., and Y.F.Z. collected the monkey cornea samples; M.Z., Z.B.H., S.D.C., and Y.F.Z. performed single-cell sequencing experiments. C.H., M.Z., Z.B.H., and Z.X.S. performed the informatics analysis; C.H., M.Z., Z.B.H., and Z.X.S. coordinated data release and assisted with executing the pipeline. C.H., M.Z., and Z.X.S. wrote the manuscript and created the figures; Q.D., and Y.F.Z. advised the study and revised the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5232061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":421188392,"identity":"b3b21533-edb2-405c-b47d-e03eaf41e212","order_by":0,"name":"Zhuo-Xing Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACCRBhwMDAxt588MEHAxs74rXw8RxLNpxRkJZMpBYgkJPIMZPm+XCIsYGQDvnZPYaPeQru2LVJpCUb2xgcYGZgP3x0Az4tjHPOGBvOMHiW3Mbz+ODjHIM7fAw8aWk38GlhBrpH4oPB4WQ2dqAtOQbPmBkkeMzwamEDaUkAaWEA+sXC4DBjAyEtPFBb7Ng4gFoYiNEiIZFWDPTL4QQ2UCD3GKQlsxHyi/yM5I2Pef4ctpdvB0bljz82dvzsh4/h1QIDiQ1w3xGjHATsiVU4CkbBKBgFIxAAACwHRuNwntgLAAAAAElFTkSuQmCC","orcid":"","institution":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science","correspondingAuthor":true,"prefix":"","firstName":"Zhuo-Xing","middleName":"","lastName":"Shi","suffix":""},{"id":421188393,"identity":"dd8f389f-42e9-47af-9563-1afcd47e3879","order_by":1,"name":"Hu Chen","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Chen","suffix":""},{"id":421188394,"identity":"afb759a1-a5da-47c8-bffe-78466508d19d","order_by":2,"name":"Qi Dai","email":"","orcid":"","institution":"Zhejiang Sci-Tech University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Dai","suffix":""},{"id":421188395,"identity":"eb3f1c7b-8585-46b3-8623-cb2437ab6504","order_by":3,"name":"Ming Zhou","email":"","orcid":"","institution":"Zhongshan Ophthalmic Center","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Zhou","suffix":""},{"id":421188396,"identity":"9dfc8740-6dd4-42e8-9b4a-a6b6cb2cec42","order_by":4,"name":"Yuan-Xia Yang","email":"","orcid":"","institution":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science","correspondingAuthor":false,"prefix":"","firstName":"Yuan-Xia","middleName":"","lastName":"Yang","suffix":""},{"id":421188397,"identity":"2a601273-6bd8-431e-b485-b631125ae78c","order_by":5,"name":"Zhi-Bo Huang","email":"","orcid":"","institution":"South-Central Minzu University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Bo","middleName":"","lastName":"Huang","suffix":""},{"id":421188398,"identity":"6d99bb1a-ace4-4673-952e-576c46f05d63","order_by":6,"name":"Shida Chen","email":"","orcid":"","institution":"State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Shida","middleName":"","lastName":"Chen","suffix":""},{"id":421188399,"identity":"317f2040-4672-4e00-b470-7ecfd4a19afe","order_by":7,"name":"Yingfeng Zheng","email":"","orcid":"https://orcid.org/0000-0002-0914-7864","institution":"Sun Yat-sen University, Guangzhou, China.","correspondingAuthor":false,"prefix":"","firstName":"Yingfeng","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2024-10-09 11:20:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5232061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5232061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77742599,"identity":"00dfcea4-8944-45b0-8e38-4ac13a5cc1de","added_by":"auto","created_at":"2025-03-05 06:00:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":825244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in transcriptional profiles of different cell types during corneal injury healing in cynomolgus monkeys.\u003c/strong\u003e (A). Overview of the experimental workflow of this study. (B). Gene UMAP and Isoform UMAP plots showing four and five corneal cell types of C. elegans, respectively. (C). Gene level (top panel) and Isoform (bottom panel) levels of known representative cellular markers expressed between clusters. (D). Composition of cell types at the Gene level (above) and Isoform level (below). (E). Upset plot of celltype-specific up-regulated genes. (F). Upset plot of celltype-specific down-regulated genes. (G). GO analysis of cell-specific DEGs.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/908a16bc1e51e7cce767c0ef.png"},{"id":77742897,"identity":"db09104b-1bd8-4052-a462-9ced5f34f9fa","added_by":"auto","created_at":"2025-03-05 06:08:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":751781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of key cell types and genes in the pre-healing phase of corneal injury. \u003c/strong\u003e(A). Score analysis of actin gene sets associated with cell migration at different time points of cell types, score analysis of extracellular matrix gene sets (B), and score analysis of integrin complex gene sets (C). (D). Dendrogram identified 14 hdWGCNA modules in corneal cells. €. Wayne plots showing the intersection of differential and functional module genes in LSC and BC at three time points. (F). PPI interaction plots of functional genes in LSC (left panel) and BC (right panel). (G). Heatmaps showing expression changes of LSC (left panel) and BC (right panel) hub genes at different time points of cell types. (H). Cell migration key gene expression violin plots (left panel), Isoform expression trend fold plots (middle panel) and Isoform structure plots (right panel).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/72d9a0846a1467be99e5bcc3.png"},{"id":77742620,"identity":"57149cd8-b038-4bdd-87fe-47f034773cc2","added_by":"auto","created_at":"2025-03-05 06:00:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":640565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression changes of corneocyte proliferation-related genes. \u003c/strong\u003e(A). UMAP plot showing the clustering distribution of TAC at Gene level and Isoform level. (B). Expression of cell cycle genes in different cell types at the Gene level. (C). At Gene level, Seurat cell cycle score showing cell cycle status in different cell types. (D). Gene expression changes of the cell cycle gene \u003cem\u003eTOP2A\u003c/em\u003e. (E). UMAP plot showing the expression levels of genes related to PI3K/AKT signaling pathway. (F). Bubble plots showing the expression of PI3K-Akt signaling pathway related genes at different time points in LSC, TAC and BC. (G). Gene expression violin plots (left panel), Isoform expression trend fold plots (middle panel) and variable shear structure plots (right panel).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/7d885749fe77ea01608f85af.png"},{"id":77742601,"identity":"4e7fd764-e95a-4d77-9170-7f19a4517245","added_by":"auto","created_at":"2025-03-05 06:00:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":684844,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequence and protein structure analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIGF2\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e (A). Sequence comparison of different isoforms of \u003cem\u003eIGF2\u003c/em\u003e. (B). IGV view showing coverage of raw read lengths. (C). Expression violin plots of \u003cem\u003eIGF2\u003c/em\u003e isoforms at different time points. (D). Comparison of protein structures of \u003cem\u003eIGF2\u003c/em\u003e isoforms, white parts indicate concordance.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/fb7a89b0ad46973e953cfeb6.png"},{"id":77742603,"identity":"1b7566fc-4b93-4ca3-b876-b25aee019890","added_by":"auto","created_at":"2025-03-05 06:00:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":744260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMimetic temporal developmental trajectories reveal key genes and isoforms for corneal cell differentiation.\u003c/strong\u003e (A). LSC, BC, and TDCE cell types are arranged along the differentiation trajectory (top), and cells are colored by type. All cells of cell types are arranged along the differentiation trajectory (bottom), and cells are colored by Pseudotime from dark purple to light yellow. (B). Heatmap showing the expression of differentially expressed genes (vertical columns) sorted by Pseudotime (horizontal columns) that may be key genes driving cell differentiation. The right panel shows GO annotation entries for the corresponding clusters. (C). Score analysis of gene sets associated with keratinization at different time points of cell types. D. Kinetics plot showing the relative expression levels of \u003cem\u003eKRT3\u003c/em\u003e, \u003cem\u003eKRT12\u003c/em\u003e and \u003cem\u003eKRT6A\u003c/em\u003e within Pseudotime. (E). Gene expression violin plot (left panel), Isoform expression trend fold plot (middle panel) and variable shear structure plot (right panel).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/692cffa137b0209c10c0cbe6.png"},{"id":77743758,"identity":"5c9bd165-cc76-4b4d-a200-2258ed8d9f58","added_by":"auto","created_at":"2025-03-05 06:16:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4367260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/0c6b7bc4-b8b0-42df-aff6-48f6b42d01b7.pdf"},{"id":77742598,"identity":"e30f88d0-4d04-41af-ab33-0731d6514c79","added_by":"auto","created_at":"2025-03-05 06:00:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23455,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryMateriallegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/ee9e6acd4ba19055745e4e47.docx"},{"id":77742606,"identity":"0c9061cd-8936-44b7-8395-a36763db922b","added_by":"auto","created_at":"2025-03-05 06:00:08","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2271560,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Data1-14\u003c/p\u003e","description":"","filename":"SupplementaryData.zip","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/0c1544848061a7d90e7abd61.zip"},{"id":77742615,"identity":"dbeb3bfc-6d69-42f3-9e35-3a023d291020","added_by":"auto","created_at":"2025-03-05 06:00:08","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3619756,"visible":true,"origin":"","legend":"\u003cp\u003esupplemental file\u003c/p\u003e","description":"","filename":"supplementalfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5232061/v1/2d46a46ad749097b424750ae.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Long-read single-cell RNA sequencing analysis of key genes and isoforms during corneal wound healing in cynomolgus monkeys","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe corneal epithelium comprises 5\u0026ndash;7 layers of squamous epithelial cells\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, essential for maintaining visual health, protecting the eye from external damage, and adapting to environmental changes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a self-renewing tissue, corneal epithelial cells are continuously replenished through the proliferation, migration, and differentiation of stem cells located in the corneal limbus, ensuring clear vision\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, physical or chemical damage to the cornea can compromise its barrier function, posing serious risks to visual health. Fortunately, the corneal epithelium has a remarkable ability to repair itself, restoring epithelial integrity and corneal clarity through a dynamic healing process.\u003c/p\u003e \u003cp\u003eCorneal epithelial injury repair involves multiple cellular activities, including cell migration\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, extracellular matrix reconstruction\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, adhesion, proliferation, and differentiation\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. After injury, peri-wound epithelial cells reorganize and migrate towards the wound, forming a monolayer that covers the damaged area. These cells then proliferate, stratify, and differentiate, ultimately reestablishing the corneal epithelium's original multilayered structure\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To better understand the molecular mechanisms underlying corneal injury repair, full-length RNA sequencing technologies, such as Nanopore and PacBio, are invaluable \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These methods allow for the detection of alternative splicing events and transcriptional modifications, both of which play critical roles in regulating gene expression during the repair process \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. By capturing complete transcripts, full-length RNA sequencing uncovers complex RNA variants and regulatory elements that are often missed by short-read methods, making it an indispensable tool for studying the repair mechanisms of both corneal and skin injuries\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe corneal injury repair process involves dynamic changes across multiple cell types, including corneal limbal stem cells, and epithelial cells, and basal cells. Single-cell RNA sequencing (scRNA-seq) offers valuable insights into the transcriptional heterogeneity of these cells during injury repair. Although scRNA-seq data exist for mouse\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, cynomolgus monkeys\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and human\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e corneal tissues under steady-state or pathological conditions, comprehensive studies on the dynamic regulatory processes involved in corneal injury repair remain limited.\u003c/p\u003e \u003cp\u003eIn a previous study, we developed HIT-scISOseq\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, achieving over 99.99% accuracy and specificity for cellular barcode detection using long-read single-cell RNA sequencing of resting-state corneal limbal tissues from cynomolgus monkeys. HIT-scISOseq enables the concatenation of multiple single-cell cDNAs up to ~\u0026thinsp;6 kb, generating over 10\u0026nbsp;million full-length non-concatemer (FLNC) reads using the PacBio Sequel II SMRT Cell 8M. Our HIT-scISOseq data revealed an average of 2,407 unique molecular identifiers (UMIs) and 1,096 genes across 2,214 resting-state corneal limbal cells, aligning with high-depth short-read sequencing results (13,740 UMIs and 2,804 genes) while also identifying cell type-specific RNA isoforms. To date, no studies have applied long-read single-cell RNA sequencing to corneal injury repair.\u003c/p\u003e \u003cp\u003eLeveraging HIT-scISOseq for single-cell detection of gene and RNA isoform expression, we analyzed cellular changes during corneal injury repair by sequencing corneal limbal tissues from cynomolgus monkeys at 0, 1, and 3 days post-injury. Our analysis identified LSC and BC as key players in the repair process among the five corneal epithelial subpopulations. Post-injury, cell migration was driven by interactions with the extracellular matrix, while proliferation and differentiation were initially inhibited but significantly increased after the third day. These findings indicate that corneal epithelial healing is regulated by a combination of growth factors, extracellular matrix components, integrins, cytoskeletal genes, and their specific RNA isoforms.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHIT-scISOseq based single-cell gene expression landscape of the corneal limbus\u003c/h2\u003e \u003cp\u003eTo investigate the dynamic changes in cell types and gene regulation during corneal injury repair, we established a corneal injury model in cynomolgus monkeys. Corneal limbal tissue samples were collected before injury (0d) and at 1 and 3 days post-injury (1d and 3d), with two biological replicates (s1 and s2) for each time point (Supplementary Fig. S1A). After dissociating the cells, we employed the 10\u0026times; Genomics Chromium platform for single-cell capture and full-length cDNA synthesis. The resulting full-length cDNAs were processed using the HIT-scISOseq method and sequenced on a Sequel II SMRT Cell 8M chip (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA statistical analysis of the long-read single-cell RNA sequencing data revealed that each sample generated approximately 10.8\u0026ndash;14.8\u0026nbsp;million full-length non-concatemer (FLNC) reads (Supplementary Tables\u0026nbsp;1\u0026ndash;4). After quality control and batch effect removal, 7,895 corneal epithelial cells were selected from a total of 12,800 cells for downstream analysis (Supplementary Tables\u0026nbsp;5\u0026ndash;6, Supplementary Data 1). On average, these cells exhibited 3,443 unique molecular identifiers (UMIs) and 1,468 genes detected per cell at the gene level (Supplementary Table\u0026nbsp;5, Supplementary Fig. S1B). At the RNA isoform level, primarily focusing on isoforms of annotated genes (see Methods for details), each cell showed an average of 2,205 UMIs and 1,051 isoforms (Supplementary Table\u0026nbsp;6, Supplementary Fig. S1C).\u003c/p\u003e \u003cp\u003eUsing unsupervised clustering, we classified the 7,891 cells into five distinct clusters with unique transcriptional profiles at the gene level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Supplementary Fig. S1D). Based on known marker genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Supplementary Fig. S2A, Supplementary Data 2), we identified five major cell types: corneal epithelial terminally differentiated cells (TDCE, \u003cem\u003eKRT3\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e32\u003c/sup\u003e, \u003cem\u003eKRT12\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e33\u003c/sup\u003e, \u003cem\u003eKRT24\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e29\u003c/sup\u003e), basal cells (BC, \u003cem\u003eITGB1\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e34\u003c/sup\u003e, \u003cem\u003eITGA6\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e35\u003c/sup\u003e, \u003cem\u003eCOL17A1\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e36, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e), corneal limbal stem cells (LSC, \u003cem\u003eKRT15\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e37, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eS100A2\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e29, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e), conjunctival cells (CC, \u003cem\u003eKRT19\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e39, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eS100A8\u0026thinsp;+\u003c/em\u003e\u0026thinsp;and \u003cem\u003eS100A9\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e41\u003c/sup\u003e), and transit-amplifying cells (TAC, \u003cem\u003eMKI67\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e42\u003c/sup\u003e, \u003cem\u003eTOP2A\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e43\u003c/sup\u003e, \u003cem\u003eCDK1\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e44\u003c/sup\u003e, \u003cem\u003eCENPF\u0026thinsp;+\u003c/em\u003e\u0026thinsp;\u003csup\u003e43\u003c/sup\u003e cell cycle genes). At the isoform level, clustering similarly identified five corresponding cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Supplementary Fig. S1E, Supplementary Fig. S2B, Supplementary Data 2), highlighting RNA isoforms highly expressed in each cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Analysis across the three time points showed no significant sample-specific clustering, suggesting clustering reflects physiological differences rather than technical variation (Supplementary Fig. S3A-B). We also quantified cell type composition at both the gene and isoform levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo explore cell-type-specific changes in gene expression during corneal injury repair, we compared transcriptional profiles at 0d, 1d, and 3d, identifying differentially expressed genes (DEGs) in TDCE, BC, LSC, CC, and TAC. A total of 522 up-regulated and 467 down-regulated genes were differentially expressed in at least one cell type during the repair process. Notably, 43.6% of the DEGs (213 up-regulated and 218 down-regulated) were shared among at least two cell types, while the rest were cell-type-specific (Fig.\u0026nbsp;1EF, Supplementary Data 3). LSC-specific DEGs comprised 65.9% of al\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003el\u003c/span\u003e cell-type-specific DEGs, with 284 identified. Among these, 21.5% (107) were up-regulated in LSC at 1d compared to 0d, and 12.8% (64) were down-regulated at 3d compared to 1d, indicating that LSC undergo the most significant transcriptional changes, highlighting their critical role in the early stages of corneal injury repair (Supplementary Fig. S4A).\u003c/p\u003e \u003cp\u003eWe next performed GO and KEGG enrichment analyses on cell-type-specific DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, Supplementary Fig. S4B). For up-regulated genes, GO terms like \u0026ldquo;epithelium development\u0026rdquo; and \u0026ldquo;keratinocyte differentiation\u0026rdquo; were enriched in TDCE and BC, along with cell migration-related terms such as \u0026ldquo;regulation of cell migration\u0026rdquo; and \u0026ldquo;regulation of locomotion\u0026rdquo;. BC also showed enrichments for \u0026ldquo;anatomical structure formation involved in morphogenesis\u0026rdquo; and \u0026ldquo;regulation of organelle organization\u0026rdquo;, similar to LSC. CC were enriched for inflammatory response-related terms, including \u0026ldquo;inflammatory response\u0026rdquo;, \u0026ldquo;positive regulation of cell death\u0026rdquo;, \u0026ldquo;apoptotic signaling pathway\u0026rdquo;, \u0026ldquo;granulocyte migration\u0026rdquo;, and \u0026ldquo;neutrophil migration\u0026rdquo;.\u003c/p\u003e \u003cp\u003eDown-regulated genes during the early repair phase were associated with \u0026ldquo;response to oxygen-containing compounds\u0026rdquo;, \u0026ldquo;cellular response to stimuli\u0026rdquo;, and \u0026ldquo;cellular metabolic regulation\u0026rdquo;. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, Supplementary Data 4). KEGG enrichment analysis across all cell types highlighted key pathways in the repair process, such as the \u0026ldquo;PI3K-Akt signaling pathway\u0026rdquo; and \u0026ldquo;MAPK signaling pathway\u0026rdquo;, essential for cell survival, proliferation, differentiation, and apoptosis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Pathways like \u0026ldquo;focal adhesion\u0026rdquo; and \u0026ldquo;ECM-receptor interaction\u0026rdquo; enriched in LSC and BC\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, underscored their role in extracellular matrix interactions, suggesting their critical involvement in corneal injury repair (Supplementary Fig. S4B, Supplementary Data 4).\u003c/p\u003e \u003cp\u003eIn summary, corneal injury repair is a complex, multi-cellular process with LSC and BC playing pivotal roles. Further analyses will focus on genes and RNA isoforms involved in three key pathways: cell migration, proliferation, and differentiation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKey genes and RNA isoforms involved in LSC and BC migration during corneal wound healing\u003c/h3\u003e\n\u003cp\u003eCell migration plays a critical role during the early phase of corneal injury repair\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, where cells coordinate movement towards the damaged site to restore the epithelial layer. To explore the dynamics of cell migration, we conducted a comprehensive analysis of gene expression changes related to \u0026ldquo;actin\u0026rdquo;, \u0026ldquo;extracellular matrix components\u0026rdquo;, \u0026ldquo;matrix metalloproteinases (MMPs)\u0026rdquo;, and \u0026ldquo;membrane protein integrins\u0026rdquo; across different time points for each cell type. Our results showed that \u0026ldquo;actin\u0026rdquo; expression significantly increased in all cell types, with LSC and BC showing the most notable up-regulation between 1d and 3d post-injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Supplementary Data 5). Changes in \u0026ldquo;extracellular matrix components\u0026rdquo; and \u0026ldquo;integrin family members\u0026rdquo; were particularly evident in LSC and BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C, Supplementary Data 6\u0026ndash;7), and LSC expressed key MMPs, such as \u003cem\u003eMMP2\u003c/em\u003e and \u003cem\u003eMMP13\u003c/em\u003e, involved in extracellular matrix degradation at 3d post-injury (Supplementary Fig. S5A, Supplementary Data 8). These results highlight LSC and BC as central to extracellular matrix remodeling and cell migration during corneal injury repair.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore migration-related gene modules in LSC and BC, we applied high-dimensional weighted gene co-expression network analysis (hdWGCNA)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, identifying 14 gene modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, Supplementary Data 9). We quantified the number of genes and the distribution of DEGs within these modules (Supplementary Fig. S5B), conducted GO enrichment analysis for each module (Supplementary Fig. S5C, Supplementary Data 10), and identified four key modules-\u0026ldquo;turquoise\u0026rdquo;, \u0026ldquo;salmon\u0026rdquo;, \u0026ldquo;magenta\u0026rdquo;, and \u0026ldquo;yellow\u0026rdquo;-associated with cell migration. We identified 127 functional genes in LSC and 118 in BC by intersecting DEGs with these modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Protein-protein interaction (PPI) analysis of these genes using STRING\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003erevealed hub genes involved in migration-related processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, Supplementary Data 11). Enrichment analysis showed these genes were involved in pathways such as \"ECM-receptor interaction\" and \"focal adhesion\" (Supplementary Fig. S5D, Supplementary Data 12), underscoring the importance of cell-extracellular matrix interactions in promoting migration.\u003c/p\u003e \u003cp\u003eThe fibronectin-integrin (FI) system is crucial for cell attachment and migration during corneal repair\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. In this process, the integrin α5β1 receptor (\u003cem\u003eITGA5\u003c/em\u003e/\u003cem\u003eITGB1\u003c/em\u003e) binds to fibronectin (\u003cem\u003eFN1\u003c/em\u003e), establishing stable connections between cells and the extracellular matrix while coordinating cell movement\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. For effective migration, cells must detach from the fibronectin-extracellular matrix\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, a process driven by proteolytic enzymes, with urokinase-type plasminogen activator (uPA, \u003cem\u003ePLAU\u003c/em\u003e) playing a key role\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. While \u003cem\u003eFN1\u003c/em\u003e and \u003cem\u003eITGB1\u003c/em\u003e were identified as key hub genes in LSC and BC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-G), PLAU was not classified as a functional gene in these cells. We examined the gene expression dynamics of key hub genes such as \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, and \u003cem\u003ePLAU\u003c/em\u003e across different time points and cell types, focusing on their top five isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH, Supplementary Fig. S5E).\u003c/p\u003e \u003cp\u003e \u003cem\u003eFN1\u003c/em\u003e was consistently expressed in both LSC and BC at 3d post-injury, though isoform PB.7424.49(\u003cem\u003eFN1\u003c/em\u003e\u0026thinsp;~\u0026thinsp;NIC) showed minimal expression in BC. \u003cem\u003eITGB1\u003c/em\u003e levels significantly increased over time in both cell types, with similar trends across its isoforms. PLAU was up-regulated starting at 1 day post-injury, particularly in BC, where isoform PB.62488.875(\u003cem\u003ePLAU\u003c/em\u003e\u0026thinsp;~\u0026thinsp;NIC) showed consistent up-regulation and distinct splicing compared to other isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Structural analysis further revealed notable differences between PB.62488.875(\u003cem\u003ePLAU\u003c/em\u003e\u0026thinsp;~\u0026thinsp;NIC) and the major isoform PB.62488.521(\u003cem\u003ePLAU\u003c/em\u003e\u0026thinsp;~\u0026thinsp;NIC), suggesting diverse functional roles at different stages of corneal injury(Supplementary Fig. S6A, B).\u003c/p\u003e \u003cp\u003eIn summary, these findings highlight the dynamic interactions between cells and the extracellular matrix during early corneal repair, offering valuable insights into the molecular mechanisms that regulate cell migration and wound healing.\u003c/p\u003e\n\u003ch3\u003eKey genes and RNA isoforms involved in cell proliferation during corneal wound healing\u003c/h3\u003e\n\u003cp\u003eUpon corneal injury, nearby BC migrate to the wound site, forming a monolayer to cover the damaged area\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, after which they proliferate and differentiate to restore the corneal epithelium\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. In our study, we identified TAC clusters at both the gene and isoform levels, showing a close relationship with BC at the gene level and with limbal stem cells (LSC) at the isoform level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe examined proliferation markers across cell types and assessed cell cycle status. TAC clusters specifically expressed proliferation markers, including \u003cem\u003eCENPF\u003c/em\u003e, \u003cem\u003eMKI67\u003c/em\u003e, \u003cem\u003eTOP2A\u003c/em\u003e, \u003cem\u003eCDC20\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, and \u003cem\u003eCDKN3\u003c/em\u003e\u003csup\u003e42, 43, 44, 62, 63, 64, 65, 66\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Cell cycle analysis revealed that all TAC cells were actively cycling, with 16.42% in the S phase and 83.58% in the G2/M phase. Additionally, 64.06% of LSC and 61.15% of BC also showed significant proliferative activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eWe further investigated the gene expression dynamics of proliferation markers such as TOP2A, MKI67, and CENPF, along with their isoform expression trends and splice variations. These markers exhibited an initial down-regulation followed by up-regulation at both the gene and isoform levels across the three time points post-injury (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, Supplementary Fig. S6A). This suggests that on 1d, TAC, LSC, and BC were primarily involved in apoptotic or immune-inflammatory responses, with proliferation suppressed. Proliferative activity then increased, signaling a recovery phase. This dynamic expression shift marks a critical transition in cellular behavior during corneal injury repair (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eThe PI3K/AKT signaling pathway, essential for cell proliferation, apoptosis, and migration\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, was prominently expressed in LSC, BC, and TAC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). We further analyzed differentially expressed PI3K/AKT-related genes in TAC, LSC, and BC clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These genes included growth factors (e.g., \u003cem\u003eIGF2\u003c/em\u003e, \u003cem\u003eAREG\u003c/em\u003e, \u003cem\u003ePDGFA\u003c/em\u003e, and \u003cem\u003eVEGFA\u003c/em\u003e), integrins (\u003cem\u003eITGA2\u003c/em\u003e, \u003cem\u003eITGB4\u003c/em\u003e, \u003cem\u003eITGAV\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003eITGB6\u003c/em\u003e, and \u003cem\u003eITGA6\u003c/em\u003e), and extracellular matrix components (\u003cem\u003eLAMA3\u003c/em\u003e, \u003cem\u003eLAMC2\u003c/em\u003e, \u003cem\u003eLAMB1\u003c/em\u003e, \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eTHBS1\u003c/em\u003e, and \u003cem\u003eCOL1A1\u003c/em\u003e), which were up-regulated at 1d post-injury and down-regulated thereafter, reflecting an early reparative response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eNotably, \u003cem\u003eIGF2\u003c/em\u003e, a key factor in corneal cell proliferation, showed a down-regulation followed by an up-regulation in TAC. Isoforms PB.65680.21(\u003cem\u003eIGF2\u003c/em\u003e\u0026thinsp;~\u0026thinsp;NIC) and PB.65680.19(\u003cem\u003eIGF2\u003c/em\u003e\u0026thinsp;~\u0026thinsp;FSM) showed opposite expression trends in LSC, with PB.65680.21(\u003cem\u003eIGF2\u003c/em\u003e\u0026thinsp;~\u0026thinsp;NIC) specifically expressed in TAC and PB.65680.19(\u003cem\u003eIGF2\u003c/em\u003e\u0026thinsp;~\u0026thinsp;FSM) in BC on 1d (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Protein sequence comparison and structure prediction revealed significant differences between these isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), highlighting the ability of HIT-scISOseq to capture isoform-level variations. We also analyzed read coverage for these isoforms, observing their distinct expression patterns in TAC (Fig. S4B, C). Structural predictions via AlphaFold2 showed notable differences between the two isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the ITGAV/ITGB6 complex, which promotes epithelial proliferation, was highly expressed at 3d (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), with isoform-level changes in ITGAV in TAC, suggesting functional divergence between isoforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The LAMC2 isoform, involved in PI3K/AKT-regulated proliferation, showed similar expression trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). These insights enhance our understanding of the molecular mechanisms driving cell proliferation during early corneal repair.\u003c/p\u003e\n\u003ch3\u003eKey genes and RNA isoforms involved in cell differentiation during corneal wound healing\u003c/h3\u003e\n\u003cp\u003eTo thoroughly investigate the transcriptional changes associated with corneal cell differentiation, we reconstructed the differentiation trajectories of LSC, BC, and TDCE using Monocle2, revealing a progression from LSC to TDCE with BC in between (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), consistent with corneal epithelium development\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Notably, at the isoform level, the predictive power for BC differentiation was significantly enhanced, identifying a distinct branch from BC to TDCE, suggesting key roles for splice variants in cell fate decisions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary Fig. S8A).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heatmap of pseudo-time-dependent genes revealed three clusters, each with distinct biological processes: epithelial development (\"red\"), protein metabolism (\"green\"), and cell-extracellular matrix interactions (\"blue\") (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, Supplementary Data 13). At the isoform level, four clusters were identified, with similar GO terms, such as epithelial development, metabolic regulation, and extracellular matrix (Supplementary Fig. S8B, Supplementary Data 13).\u003c/p\u003e \u003cp\u003eKeratinization is a vital aspect of cell differentiation, primarily occurring in the skin and various epithelial tissues. Through SCORE analysis of keratinization-related genes, we observed that the gene signature scores for LSC and BC initially decreased compared to TDCE, followed by a notable increase, indicating that cell differentiation in LSC and BC was initially inhibited post-injury, with differentiation activity significantly increasing after 1d to promote corneal healing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Supplementary Data 14). Known corneal epithelial differentiation markers \u003cem\u003eKRT3\u003c/em\u003e and \u003cem\u003eKRT12\u003c/em\u003e\u003csup\u003e70, 71\u003c/sup\u003e, alongside \u003cem\u003eKRT6A\u003c/em\u003e-a novel candidate-showed increased expression with pseudo-time, underscoring their roles in corneal healing (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo further explore the regulation of LSC and BC differentiation post-injury, we identified three key genes-\u003cem\u003eKRT7\u003c/em\u003e, \u003cem\u003eRBP1\u003c/em\u003e, and \u003cem\u003ePTN\u003c/em\u003e-from the \u0026ldquo;red\u0026rdquo; cluster at both gene and isoform levels. \u003cem\u003eKRT7\u003c/em\u003e, part of the keratin family, was significantly upregulated in LSC and BC by 3d, with isoforms showing similar expression trends and varying splice structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). \u003cem\u003eRBP1\u003c/em\u003e, involved in vitamin A transport and critical for epithelial differentiation, was also expressed at 3d, with PB.34376.4(\u003cem\u003eRBP1\u003c/em\u003e\u0026thinsp;~\u0026thinsp;ISM) being the most prominent (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). \u003cem\u003ePTN\u003c/em\u003e, an autocrine factor associated with growth and differentiation\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, had distinct isoform expression patterns in LSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eAdditionally, cytoskeleton-related genes, including \u003cem\u003eTPM1\u003c/em\u003e, \u003cem\u003eTPM2\u003c/em\u003e, \u003cem\u003ePALLD\u003c/em\u003e, \u003cem\u003eTAGLN\u003c/em\u003e, and \u003cem\u003eVIM\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, were differentially upregulated in LSC and BC by 3d post-injury, highlighting their importance in cell growth, differentiation, and injury response (Supplementary Fig. S8C).\u003c/p\u003e \u003cp\u003eIn summary, these findings provide new insights into the intricate gene regulation driving LSC and BC differentiation during the early phases of corneal injury repair.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe advent of single-cell sequencing technologies has enabled detailed investigations of gene expression patterns in individual cells within tissues or organs\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. In this study, we conducted a long-read scRNA-seq analysis of the corneal injury repair in cynomolgus monkeys, mapping cell migration, proliferation, and differentiation during healing. Previous human\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e and murine studies\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e focused on limbal epithelial stem cells in corneal homeostasis, but few addressed the heterogeneity of corneal cells or transcriptional profiles in injury repair. Our research explores transcriptional dynamics across three time points following injury, aiming to identify key cell types and the isoform profiles involved in healing.\u003c/p\u003e \u003cp\u003eThe corneal epithelium consists of five major cell types identified via long-read scRNA-seq: TDCE, BC, TAC, LSC, and CC. Notably, TAC displayed gene-level similarity to BC and isoform-level similarity to LSC, suggesting that TAC acts as an intermediate between LSC and BC during heightened cellular activity. Furthermore, shared gene expression between LSC and BC, particularly in extracellular matrix and regulatory processes, highlights their roles in migration, proliferation, and differentiation during injury repair.\u003c/p\u003e \u003cp\u003eCorneal epithelial cell migration is regulated by factors including actin\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, integrins\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, ECM components\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, the fibrinolytic system\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and MMPs\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. During migration, cells polarize via actin-driven protrusions, while MMPs degrade the ECM to facilitate movement. ECM components like fibronectin and laminin provide scaffolding for migration, interacting with integrins to promote adhesion and signaling. Notably, research by Sugioka et al. has underscored the fibrinolytic system's critical role in this process\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Corneal epithelial cells migrate toward defects in an orderly manner, driven by actin dynamics, ECM-integrin-mediated adhesion, and cell-stromal detachment governed by the fibrinolytic system. We identified 14 hub genes in LSC and 12 in BC linked to migration, shedding light on their expression dynamics and isoform variations, potentially revealing biomarkers for corneal epithelial cell migration.\u003c/p\u003e \u003cp\u003eAs cells surrounding a corneal wound migrate to cover the defect, they initiate a cascade of proliferation and differentiation essential for healing. Previous studies have shown that LSC drive the formation of TAC through asymmetric division, providing TAC with heightened proliferative capacity. Research by Li et al. has further identified cell cycle-dependent genes as key markers of TAC, highlighting their critical role in expanding the corneal epithelial cell population during the healing process\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Our analysis revealed down-regulation followed by up-regulation of proliferation markers-\u003cem\u003eTOP2A\u003c/em\u003e, \u003cem\u003eMKI67\u003c/em\u003e, and \u003cem\u003eCENPF\u003c/em\u003e-within the 1d post-injury, indicating initial inhibition of proliferation. Genes like \u003cem\u003eIGF2\u003c/em\u003e, \u003cem\u003eITGAV\u003c/em\u003e, and \u003cem\u003eLAMC2\u003c/em\u003e, which are involved in the PI3K/AKT pathway, were identified as key regulators of corneal cell proliferation. We also characterized isoform-level differences of \u003cem\u003eITGAV\u003c/em\u003e and \u003cem\u003eIGF2\u003c/em\u003e, enhancing our understanding of the functional implications of alternative splicing during healing.\u003c/p\u003e \u003cp\u003eIn this study, we mapped the dynamic regulation of key genes and isoforms during the differentiation of LSC into TDCE using a pseudo-temporal trajectory model during the pre-healing phase. Our trajectory highlights the progression from the least differentiated LSC, through TAC, to TDCE (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Previous work by Kao identified \u003cem\u003eKRT3\u003c/em\u003e and \u003cem\u003eKRT12\u003c/em\u003e as markers of corneal epithelial differentiation.\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Notably, our findings show that differentiation in LSC and BC is inhibited within the 1d post-injury, emphasizing the roles of \u003cem\u003eKRT3\u003c/em\u003e, \u003cem\u003eKRT12\u003c/em\u003e, and \u003cem\u003eKRT6A\u003c/em\u003e in this process.\u003c/p\u003e \u003cp\u003eThis is consistent with the literature suggesting that apoptosis dominates the initial response to corneal injury, with cell proliferation and differentiation suppressed at this stage. Additionally, we identified several regulatory genes, including \u003cem\u003eKRT7\u003c/em\u003e, \u003cem\u003eRBP1\u003c/em\u003e, \u003cem\u003ePTN\u003c/em\u003e, and cytoskeleton-related genes, which showed increased expression by day three. This supports the notion that corneal epithelial differentiation is governed by a complex gene network.\u003c/p\u003e \u003cp\u003eUsing long-read single-cell RNA sequencing (scRNA-seq), we comprehensively analyzed the transcriptional landscape of corneal injury healing in cynomolgus monkeys. Our findings reveal key cell types, genes, and isoforms essential for migration, proliferation, and differentiation during healing, providing insights into their transcriptional regulation. While this study focused on annotated gene isoforms, future research could explore the regulatory roles of novel noncoding RNAs, such as transposable elements\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, in injury repair. The use of long-read scRNA-seq also opens avenues for investigating alternative polyadenylation (APA) and RNA editing, offering a deeper understanding of gene regulation during corneal repair and advancing therapeutic strategies.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eLong-read single-cell RNA sequencing\u003c/h2\u003e\u003cp\u003eThe experimental design is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. Briefly, we extracted corneal epithelial cells from the limbus on days 0, 1, and 3 post-injury in cynomolgus monkeys (two replicates per time point). Full-length cDNAs were generated using the 10× Genomics platform, followed by HIT-scISOseq for library construction and sequencing.\u003c/p\u003e\n\u003ch3\u003eData processing and downstream analysis\u003c/h3\u003e\n\u003cp\u003eAfter acquiring the HIT-scISOseq data, we used the accompanying scISA-Tools software for data preprocessing, RNA isoform identification (only the annotated genes were retained as FSM: full splice match, ISM: incomplete splice match, NIC: novel in the catalog, and NNC: novel not in catalog isoforms), gene and isoform matrix generation. Subsequently, the count matrix was imported into the R package Seurat (version 4.3.0)\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e for further scRNA-seq data analysis. In this case, cells with fewer than 500 detected genes or more than 10% of mitochondrial genes, and genes expressed in fewer than 5 cells were excluded from downstream analysis. Each experimental sample was normalized using the \u0026ldquo;LogNormalize\u0026rdquo; method. To avoid batch effects between samples and experiments, a typical correlation analysis (CCA) method was used to exclude effects in data integration and to align samples. For cell clustering of Gene and Isoform count matrices, the first 2000 highly variable genes were used for principal component analysis (PCA). The first 10 PCs of the Gene and Isoform count matrices were clustered with a resolution of 0.15 by the graph-based shared nearest neighbor method (SNN) in the \u0026ldquo;FindClusters\u0026rdquo; function. Finally, five unsupervised cell clusters were obtained at both Gene level and Isoform level. The marker genes for each cluster were determined by the \u0026ldquo;FindAllMarkers\u0026rdquo; function, where |avg_logFC| \u0026gt; 1 and pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used to filter the marker genes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of damage repair-related DEGs\u003c/h2\u003e \u003cp\u003eWe utilized the \u0026ldquo;FindMarkers\u0026rdquo; function in Seurat to identify differentially expressed genes (DEGs) associated with corneal epithelial injury healing between the 1- and 0-day groups after corneal epithelial injury (1/0d), the 3- and 1-day groups (3/1d), and the 3- and 0-day groups (3/0d). The logarithmic multiplicity of difference (log2FC) and p-value for each DEG were calculated by using the nonparametric two-sided Wilcoxon rank sum test. Here, |avg_log2FC| \u0026gt; 1 and pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used to determine DEGs for each cell type of the final corneal epithelium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGO/KEGG Analysis\u003c/h2\u003e \u003cp\u003eGO/KEGG analyses were performed by the enrichGO function in Clusterprofiler (version 4.6.2)\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e and data were visualized using the R package ggplot2 for representative GO/KEGG terms (pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05). One of the selected annotated gene sets of \u003cem\u003eMacaca fascicularis\u003c/em\u003e (AH107668) was obtained from AnnotationHub (version 3.6.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene set scoring analysis\u003c/h2\u003e \u003cp\u003eThe \u0026ldquo;AddModuleScore\u0026rdquo; function of the Seurat R package was used to calculate the characterization scores of the specified gene sets in a single cell and tested for significance using the bilateral Wilcoxon rank sum test. The gene sets used in this study were obtained from the MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCo-expression network hdGCNA analysis\u003c/h2\u003e \u003cp\u003eHigh-dimensional weighted correlation network analysis (hdWGNCA) is a recently proposed novel algorithm applicable to scRNA-seq data, which allows for the construction of co-expression networks across multiple scales of cells and spatial hierarchies. We selected genes expressed in at least 5% of cells to construct hdWGCNA objects, which were then transformed into Metacells objects. We simulate the similarity between the co-expression network and the scale-free graph under different soft power thresholds by using the \u0026ldquo;TestSoftPowers\u0026rdquo; function, and construct the co-expression network by using the \u0026ldquo;ConstructNetwork\u0026rdquo; function to automatically select the soft power thresholds. The \u0026ldquo;ConstructNetwork\u0026rdquo; function is used to automatically select the soft power thresholds to construct the co-expression network. Use \u0026ldquo;PlotDendrogram\u0026rdquo; function and \u0026ldquo;GetModules\u0026rdquo; function to visualize the co-expression modules and obtain the module assignment table.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProtein interaction analysis and visualization\u003c/h2\u003e \u003cp\u003eProtein interactions were searched through the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Interaction results from STRING were imported into Cytoscape (version 3.9.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for visualization of interaction networks and identification of hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell cycle analysis\u003c/h2\u003e \u003cp\u003eBased on the cell cycle-specific expression data, the \u0026ldquo;CellCycleScoring\u0026rdquo; function in Seurat was used to identify the cell cycle stage of individual cells. Cells were scored using G2/M and S-phase markers, and cells with neither G2/M nor S-phase markers were considered to be in G1 phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePseudotemporal trajectory analysis\u003c/h2\u003e \u003cp\u003eSingle-cell pseudotemporal trajectories were generated by the R package Monocle2\u003csup\u003e88, 89\u003c/sup\u003e. We used the \u0026ldquo;newCellDataSet\u0026rdquo; function to construct monocle objects, \u0026ldquo;estimateSizeFactors\u0026rdquo; and \u0026ldquo;estimateDispersions\u0026rdquo; functions to preprocess the data, the \u0026ldquo;detectGenes\u0026rdquo; function to filter low-quality cells, and the \u0026ldquo;reduceDimension\u0026rdquo; function to remove the \u0026ldquo;DDRDimension\u0026rdquo; in the \u0026ldquo;newCellDataSet\u0026rdquo; function. Function to filter low quality cells and \u0026ldquo;reduceDimension\u0026rdquo; function to reduce the dimensionality of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProtein Sequence Comparison\u003c/h2\u003e \u003cp\u003eCLUSTALW (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/tools-bin/clustalw\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/tools-bin/clustalw\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform multiple sequence alignment analysis of the corresponding isoform protein sequences of genes. The results were imported into ENDscript (version 3.0)\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e for visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eProtein structure prediction\u003c/h2\u003e \u003cp\u003eESMFold\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e and AlphaFold2\u003csup\u003e92\u003c/sup\u003e were applied for protein structure prediction analysis. We imported the structure files (PBD format) into Pymol (version 2.5.7, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/2/\u003c/span\u003e\u003cspan address=\"https://pymol.org/2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and dewatered, compared and visualized the protein structures in different isoforms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis experiment was conducted using two 4-year-old female cynomolgus monkeys provided by the Institutional Animal Care and Use Committee of Zhongshan Ophthalmic Center, Sun Yat-sen University. All animal procedures were performed in strict compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and were approved by the Animal Experiment Ethics Committee of Zhongshan Ophthalmic Center (Guangzhou, China; approval number: 2019-044). The experimental protocols were designed and implemented in accordance with the highest standards of animal welfare and ethical research practices.\u003cbr\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequencing data generated in this study are available at NCBI Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under the accession numbers GSE278797. The reference genome and gene annotation file (Macaca_fascicularis_5.0.99) were downloaded from Ensembl (https://ftp.ensembl.org/pub/release-99/fasta/macaca_fascicularis/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo additional unique code was generated in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge financial support from the CAMS Innovation Fund for Medical Sciences (2019-I2M-5-005); National Natural Science Foundation of China (81721003, 42107148, 62172369); Special Support Plan for High Level Talents in Zhejiang Province (2021R52019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eContributions\u0026nbsp;Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.D., Y.F.Z., and Z.X.S. conceived and designed the project. M.Z., Y.X.Y., and S.D.C. performed the experimental validation; M.Z., Y.X.Y., S.D.C., and Y.F.Z. collected the monkey cornea samples; M.Z., Z.B.H., S.D.C., and Y.F.Z. performed single-cell sequencing experiments. C.H., M.Z., Z.B.H., and Z.X.S. performed the informatics analysis; C.H., M.Z., Z.B.H., and Z.X.S. coordinated data release and assisted with executing the pipeline. C.H., M.Z., and Z.X.S. wrote the manuscript and created the figures; Q.D., and Y.F.Z. advised the study and revised the manuscript. All authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInterests\u0026nbsp;Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMasterton S, Ahearne M. 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Upon corneal epithelial injury, cells at the corneal limbus initiate complex processes such as migration, extracellular matrix remodeling, and proliferation. However, the transcriptional heterogeneity of limbal cell populations during these stages remains understudied. In this study, we used high-throughput long-read single-cell RNA sequencing to analyze five major cell types in the corneal limbus of cynomolgus monkeys at three time points: before injury, and one and three days post-injury. These cell types include terminally differentiated corneal epithelial cells (TDCE), basal cells (BC), transit-amplifying cells (TAC), limbal stem cells (LSC), and conjunctival cells (CC). We identified key regulatory genes and RNA isoforms involved in cell migration, proliferation, and differentiation, including \u003cem\u003eIGF2\u003c/em\u003e, \u003cem\u003eFN1\u003c/em\u003e, \u003cem\u003eLAMC2\u003c/em\u003e, \u003cem\u003eITGB1\u003c/em\u003e, \u003cem\u003eITGAV\u003c/em\u003e, and keratins (\u003cem\u003eKRT3\u003c/em\u003e, \u003cem\u003eKRT12\u003c/em\u003e, \u003cem\u003eand KRT6A\u003c/em\u003e). Our findings reveal the critical roles of LSC and BC in corneal repair and provide new insights into the transcriptional landscape during epithelial healing.\u003c/p\u003e","manuscriptTitle":"Long-read single-cell RNA sequencing analysis of key genes and isoforms during corneal wound healing in cynomolgus monkeys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-05 06:00:03","doi":"10.21203/rs.3.rs-5232061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3ab1ef6-f42a-4efa-84ba-2145e13d52e3","owner":[],"postedDate":"March 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44894991,"name":"Biological sciences/Cell biology"},{"id":44894992,"name":"Biological sciences/Developmental biology/Self-renewal"}],"tags":[],"updatedAt":"2025-03-05T06:00:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-05 06:00:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5232061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5232061","identity":"rs-5232061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}