Single-cell RNA-sequencing profiles reveal the developmental landscape of hawthorn leaves

Single-cell RNA-sequencing profiles reveal the developmental landscape of hawthorn leaves | 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 Research Article Single-cell RNA-sequencing profiles reveal the developmental landscape of hawthorn leaves Guigang Zhao, Xien Wu, Baozheng Wang, Xiaolu Li, Junjuan Zheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8423721/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Hawthorn ( Crataegus spp.) is a valuable genus of medicinal and edible plants in the Rosaceae family that are rich in bioactive compounds. Despite the availability of chromosome‑level genomes and bulk transcriptomes for Crataegus species, the leaf cellular composition, developmental trajectories, and cell type-specific expression of biosynthetic pathways remain unexplored at single‑cell resolution. Here, we applied complementary protoplast‑based single‑cell RNA sequencing (scRNA‑seq) and nucleus‑based snRNA‑seq to construct the first single‑cell atlas of hawthorn leaves. We optimized isolation protocols for two Crataegus species ( C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder), generated four single‑cell transcriptomic libraries across platforms, and profiled 32,292 high‑quality cells to explore the developmental landscape of hawthorn leaves. The cells were clustered into sixteen groups that we annotated into nine major leaf cell types (mesophyll, pavement, guard, xylem, metaxylem, phloem parenchyma, companion, sieve element, and meristematic cells), revealing extensive cellular heterogeneity and candidate marker genes. Pseudotime reconstruction revealed branched developmental trajectories, and cell‑type‑resolved profiling revealed the cell‑specific expression of flavonoid biosynthetic genes. This single‑cell atlas lays a foundation for mechanistic investigations of tissue‑specific metabolite biosynthesis and offers a cellular framework to guide future functional studies and trait improvement breeding in hawthorn. Crataegus pinnatifida var. major N. E. Br. Crataegus scabrifolia (Franch.) Rehder leaf development single‑cell transcriptomics cell types gene expression characteristics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Multicellular organisms rely on the division of labor across cell types, with cells acting as modular building blocks that generate heterogeneity and contribute to the formation of complex organs through differentiation during development [ 1 , 2 ] . Although individual cells in multicellular organisms share the same genomic DNA, their functions differ owing to the influence of specific gene expression programs [ 1 , 3 ] . Traditional bulk-level analyses often obscure differences between individual cells. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) enable clustering and classification of cells within complex tissues or organs, revealing unique expression profiles and functional characteristics of distinct cell populations [ 2 , 4 – 7 ] . These emerging technologies thus facilitate celltype identification and the elucidation of gene regulatory networks. With continuous advances in sequencing technologies and sample preparation methods, single-cell transcriptomics is now widely applied not only to studies of cellular heterogeneity in animals and microorganisms but also to plant research [ 4 , 8 – 10 ] . To date, single-cell transcriptomics has been applied to different tissues and organs of several plant species (e.g., Arabidopsis [ 6 ] , strawberry [ 11 ] , rice [ 12 ] , maize [ 13 ] , peanut [ 14 ] , poplar [ 15 ] , and tea [ 16 ] ), providing important support for the construction of single-cell atlases, developmental trajectory analyses, and the elucidation of gene regulatory networks. However, the relatively few research reports on the application of single-cell transcriptomics in nonmodel plants can be attributed primarily to several challenges. These limitations include the lack of reliable specific marker genes for cell type identification, the variability in plant cell size, and bottlenecks in protoplast isolation efficiency, which have limited their application in these plants [ 16 ] . The genus Crataegus (hawthorn), a flowering shrub or tree in the Rosaceae family that is widely distributed across the temperate regions of the Northern Hemisphere, is a significant medicinal and edible plant with a long history of use in health maintenance in China [ 17 , 18 ] . Both cultivated varieties and wild species (such as Crataegus chungtienensis) possess considerable nutritional and therapeutic value [ 19 ] . Hawthorn leaves and fruits are rich in bioactive compounds, including flavonoids, triterpenoids, organic acids, phenols, and procyanidins, which underpin their traditional medicinal applications [ 17 , 20 – 22 ] . These compounds exhibit a range of pharmacological effects, including antihypertensive, lipid-lowering, cardiotonic, digestive-stimulating, appetite-enhancing, antimicrobial, and anticancer properties [ 23 – 25 ] . Various dosage forms of hawthorn-based drugs have demonstrated proven therapeutic efficacy and are commonly used to treat conditions related to the digestive, cardiovascular, cerebrovascular, and reproductive systems [ 23 ] . In recent years, with the rapid development of high-throughput sequencing technologies, high-quality, chromosome-level genomes of multiple Crataegus species have been sequenced. For example, high-quality, chromosome-level genomes have been published for the hawthorn cultivar C. pinnatifida ‘Qiu Jinxing' [ 17 ] , the yellow-peel, hard-seeded C. pinnatifida ‘Jinruyi’ [ 26 ] , the red-peel, soft-seeded C. pinnatifida var. major 'Ruanzi' [ 26 ] , and C. scabrifolia [ 27 ] . These assemblies have elucidated the genetic traits and metabolic potential of hawthorn species, providing important molecular evidence for research in pharmacology and botany. Moreover, transcriptomic studies on Crataegus species have provided important insights into the quality traits of hawthorn fruits and the mechanisms underlying their formation. For example, by comparing the gene expression profiles of hawthorn varieties with those of soft-endocarp and hard-endocarp varieties, a study revealed differentially expressed genes associated with the formation of the soft endocarp [ 28 ] . Furthermore, a comprehensive analysis of the transcriptomes and metabolomes of different hawthorn varieties at the mature stage revealed several candidate genes related to fruit flavor, fruit peel color and seed hardness [ 19 , 26 ] . These studies not only provide key genetic information for understanding the molecular mechanisms behind the development of hawthorn fruit quality but also facilitate the development of hawthorn resources, the exploration of functional metabolites, and advancements in breeding improvements. However, despite chromosome-level genome and bulk transcriptome analyses, the composition of leaf cell types, developmental trajectories, and the specific expression of biosynthetic pathways for bioactive metabolites in hawthorn remain unexplored at single-cell resolution. In this study, we applied scRNA-seq and snRNA-seq to cultivated hawthorn leaves to construct a single-cell atlas, identify major leaf tissues, reconstruct developmental trajectories, and resolve the cell-type–specific expression of flavonoid biosynthesis genes. We optimized the protoplast and nucleus isolation protocols for two Crataegus species ( C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder) and generated four libraries across different platforms. Using Arabidopsis homologs, we annotated nine major leaf cell types (mesophyll cells, pavement cells, guard cells, xylem cells, metaxylem cells, phloem parenchyma cells, companion cells, sieve element cells, and meristematic cells) and identified novel cell type–specific marker candidates. Pseudotime analysis reconstructed dynamic gene expression programs during leaf development. Cell-type–resolved profiling revealed that a portion of flavonoid pathway genes are transcribed in leaves and display cell type–specific expression, providing a cellular framework for future studies on tissue-specific metabolite biosynthesis in hawthorn. 2. Materials and methods 2.1 Plant materials and growth conditions Crataegus pinnatifida var. major N. E. Br. and Crataegus scabrifolia (Franch.) Rehder were grown at the Kunming Institute of Botany, Chinese Academy of Sciences. We collected two leaf samples from each type of hawthorn tree for experimental purposes. Samples 1 and 3 were collected from C. pinnatifida var. major N. E. Br., while Samples 2 and 4 were collected from C. scabrifolia (Franch.) Rehder. Fresh, tender leaves were used for all single-cell/nuclear RNA-seq experiments. The collected leaf material was cut into approximately 1 mm fragments and placed in an ice-cold dissociation pretreatment buffer (20 mM KCl, 10 mM CaCl₂, 0.1% bovine serum albumin, 20 mM MES, 0.6 M mannitol). Ticao Zhang undertook the formal identification of the plant material used in our study, and the voucher specimens (YUNCM20230189) were deposited in the Herbarium of Yunnan University of Chinese Medicine (YUNCM). All samples were collected following the necessary permissions obtained from the Kunming Institute of Botany to ensure compliance with local, national, and international regulations regarding plant research. The voucher specimens (YUNCM20230189) were deposited in the Herbarium of Yunnan University of Chinese Medicine (YUNCM). 2.2 Protoplast isolation In our study, Protoplasts were isolated from cleaned hawthorn leaf material for Samples 1 and 2 via an enzymatic digestion method [ 16 ] , with modifications to improve efficiency. Five milliliters of enzymatic digestion solution (20 mM KCl, 10 mM CaCl₂, 0.1% bovine serum albumin, 20 mM MES, 0.6 M mannitol, 1% cellulase R10, 0.5% snailase, 0.5% macerozyme R-10) was added to the cleaned plant material fragments. The mixture was incubated at 25°C on a shaker at 40 rpm for 4–5 h to release protoplasts from the leaves. The protoplasts were filtered through a 40 µm cell sieve, centrifuged at 200 × g for 3 minutes, and washed with 2 mL of washing solution (5 mM KCl, 125 mM CaCl₂, 154 mM NaCl, 0.1% bovine serum albumin, 20 mM MES). An aliquot was stained with trypan blue and analyzed on a LUNA-FL Cell Counter to determine protoplast count and viability. 2.3 Nuclei isolation For Samples 3 and 4, hawthorn leaf nuclei were isolated following a published protocol [ 29 ] , with slight modifications. Briefly, 1 mL of ice-cold NIB buffer (20 mM HEPES, pH 8.0, 250 mM sucrose, 1 mM MgCl₂, 5 mM KCl, 0.25% Triton X-100, 40% glycerol, 0.5 mM spermine, 0.5 mM spermidine, 0.1% 2-ME) was added to the cleaned plant material fragments, and the tissue was gently chopped with a razor for 5 min until it could be pipetted ,with 1 mL wide-bore tip. The homogenate was filtered through a 40 µm cell sieve into a chilled 50 mL conical tube. The filtrate was collected and washed with the remaining buffer to recover the nuclei. The mixture was incubated on ice for 10 min, centrifuged at 50 × g for 5 min at 4°C to remove bulk impurities, and then centrifuged at 1,100 × g for 10 min at 4°C. After the supernatant was removed, the nuclei were resuspended in 500 µL of NIB buffer, and the quality and count of the nuclei were validated via the Countstar Rigel S6 system with AOPI staining. 2.4 snRNA-seq/scRNA-seq library construction and sequencing Single protoplasts were captured via the BD Rhapsody HT single-cell analysis system, and scRNA libraries were constructed via the WTA Amplification Kit (BD). For snRNA library construction, single nuclei were captured via a 10X Genomics GemCode single-cell instrument with the Chromium Next GEM Single Cell 3' Reagent Kit (v3.1). The snRNA-seq and scRNA-seq libraries were constructed according to the protocols of each platform. Briefly, single protoplasts or nuclei were encapsulated in droplets or wells via HT Xpress chips (BD Rhapsody) or Chromium Next GEM Chip (10X Genomics) to create independent environments. mRNA was captured via magnetic beads, followed by protoplast/nuclear lysis, reverse transcription, cDNA amplification, purification, and conversion into single-cell/nuclear RNA-seq libraries with barcode sequences. Indexed sequencing libraries were prepared following the Illumina user guide, with each library assigned a unique index. The library concentration was measured via a Qubit 3.0, and sequencing was performed on the Illumina NovaSeq 6000 System in PE150 mode. 2.5 Data filter, dimensionality reduction The snRNA-seq and scRNA-seq data for the four wild-type replicates from this study were processed individually via 10X Genomics Cell Ranger (version 8.0.1) and BD Rhapsody CWL-runner software (version 2.2.1). These pipelines include processing of raw fastq data to align reads and generate gene-cell matrices. After the low-quality barcode and UMI data were filtered out, the processed data were aligned to the C. scabrifolia (Franch.) Rehder genome [ 27 ] . The snRNA-seq/scRNA-seq gene-cell matrices for each sample were imported into Seurat (v5.2.1) for downstream analysis. When filtering the data, the parameter "nFeature_RNA" was set between 500 and 6000, the percentage of mitochondrial genes was less than 5% and the percentage of chloroplast genes was less than 10%. Each dataset was subsequently normalized and integrated via the IntegrateData function. Principal component analysis (PCA) was performed to reduce the dimensionality of the integrated single-cell matrices, and the top 20 PCs were selected as inputs for UMAP and t-SNE visualization and clustering analysis. We used the FindClusters method for cell clustering, with the resolution value determined by comparing different values, which was set to 0.4. The 'FindAllMarkers' function was then used to identify differentially expressed genes (DEGs) for each cluster, with parameters set to min.pct = 0.25 and logfc.threshold = 0.25. On the basis of the DEGs identified between the clusters, the integrated single-cell RNA data were assigned to the corresponding cell populations. 2.6 Marker gene and cell type identification To identify the various cell populations in Hawthorn leaf tissues, we utilized marker genes specific to each cell type. First, we aligned the cell type marker genes reported in Arabidopsis ( A. thaliana ) with their homologous counterparts in Hawthorn. Differential gene expression analysis was performed on the integrated and clustered single-cell RNA data, with a focus on genes upregulated in each population. Known tissue-specific markers from A. thaliana were used as references, and cell types were predicted and manually cross-checked via the PlantCellMarker and scPlantDB databases [ 30 , 31 ] . This approach enabled the annotation and identification of cell types in Hawthorn leaf tissues. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the upregulated genes to validate the molecular functions of the gene in the cell populations. Following cell population identification, we explored cell-specific marker genes in Hawthorn leaf tissue. The selection criterion included a log fold change (logFC) threshold of 0.25, requiring that at least 25% of the cells in each cluster expressed the target gene. The top 20 genes in each cluster, ranked by their average log2 fold change (avg_log2FC), were selected as potential marker genes. 2.7 Pseudotime analysis To explore gene expression changes and the determine of cell fate during the development of hawthorn leaves, we constructed a single-cell pseudotime differentiation trajectory via Monocle (version 2.30.1) on the basis of the expression matrix of all cell populations. The first step involved filtering out genes with low expressed genes, and creating a structured datasets (lowerDetectionLimit = 0.5). The size factors and dispersion of each gene were estimated, and genes expressed in at least 10 cells were selected. Genes were identified as exhibiting significant differential expression across various cell populations (qval < 0.01). These genes, which show significant variability, are crucial for constructing cell trajectories and analyzing cell states. We utilized the plot_ordering_genes function to visualize the ordered genes defined in the single-cell trajectory analysis, demonstrating how they varied across different cellular states or orders. The dimensionality of the data was reduced to two components (max_components = 2, method = ‘DDRTree’) to facilitate further analysis. We employed the orderCells() function to order the cells on the basis of their gene expression profiles and set the root_state argument to specify the meristematic cells as the starting point of the trajectory. The resulting cell trajectory was then visualized via the plot_cell_trajectory function in Monocle. To analyze the branches in the differentiation trajectory, branch expression analysis modeling (BEAM) was used to identify genes that were dependent on pseudotime or specific branches. These branch-dependent genes were visualized via the plot_genes_branched_heatmap function, providing insight into the gene expression changes across different branches. 3. Results 3.1 Single-cell RNA-seq generates comparable transcriptome datasets The isolation of protoplasts and nuclei is fundamental for single-cell transcriptome research and analysis. In this study, we optimized the preparation protocols for the isolation of protoplasts and nuclei from the leaves of two Crataegus species ( C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder) and constructed four libraries following the protocols of the BD Rhapsody HT and the 10X Genomics GemCode single-cell platform (Figure 1a). To assess the sequencing data quality of each library, several parameters, including total reads, gene count, and cell count, were evaluated. A total of 574,493,457 and 544,520,935 reads were obtained from C. pinnatifida var. major N. E. Br. through scRNA-seq and snRNA-seq, respectively. After filtering out the low-quality cells and genes with abnormal expression were filtered out, 7,147 protoplasts and 10,674 nuclei were obtained, with an average of 914 and 716 genes detected per cell, respectively. For C. scabrifolia (Franch.) Rehder, scRNA-seq and snRNA-seq obtained a total of 597,928,880 and 562,537,244 reads, respectively. After filtering out the low-quality cells and genes with abnormal expression, 4,463 protoplasts and 10,008 nuclei were obtained, with an average of 937 and 2,005 genes detected per cell, respectively. To cluster distinct cell populations, we integrated four filtered single-cell datasets from different samples using Canonical Correlation Analysis (CCA). After integration, an unbiased, unsupervised clustering was performed on the transcriptomes of 32,292 cells using the Seurat software package. The transcriptomes from the integrated data were then plotted in two dimensions using tSNE, revealing clustering results with the cells grouped into sixteen distinct clusters (Figure 1b; Supplemental Figure 1). We further compared the single-cell data from different methods (protoplast and nuclei) and species ( C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder) via t-distributed stochastic neighbor embedding (t-SNE), which revealed largely overlapping cell distributions and a comparable proportion of cells in each cluster across all the libraries (Figure 1c-d). 3.2 Identification of Main Leaf Tissues: Mesophyll, Vasculature, and Epidermis in Hawthorn Leaves To explore the cellular heterogeneity in hawthorn leaves, we performed gene expression profiling on sixteen distinct cell clusters, and identified 6,024 differentially expressed genes (DEGs) (Supplemental Table S1). As no cell-specific marker genes for hawthorn have been reported, we used homologous marker genes from Arabidopsis to determine the corresponding cell types in hawthorn leaves (Supplemental Table S2). We characterized several distinct tissue types: mesophyll cells (MCs), pavement cells (PCs), xylem (XY), phloem parenchyma (PP), metaxylem (MXY), guard cells (GCs), companion cells (CCs), sieve elements (SEs), and meristematic cells (MERs) in hawthorn leaves (Figure 2a, Supplemental Table S2). Both C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder presented the same set of cell types, with similar distribution trends across these species, indicating conserved cellular features in the hawthorn leaf structure (Figure 2c, Supplemental Table S3). Clusters 0 and 4, classified as mesophyll cells, had the highest proportion of cells. These clusters predominantly expressed marker genes involved in photosynthesis, such as ribulose bisphosphate carboxylase small chain (RBCS) and light-harvesting chlorophyll a-b binding protein ( LHCB ) (Supplemental Table S2, Figure 2b). Clusters 2, 7, and 11 were identified as epidermal cells, based on the basis of their specific expression of genes related to cuticular wax and suberin biosynthesis (Supplemental Table S2, Figure 2b). These genes include 3-ketoacyl-CoA synthase 10 ( FDH ), homeodomain glabrous2 ( HDG2 ), and protodermal factor 1-like gene (FACTOR1/PDF1). Specifically, Clusters 2 and 7 were classified as leaf pavement cells, whereas Cluster 11, a subset of epidermal cells, was designated as leaf guard cells, on the basis of the expression FAMA ( Crsca04aG0005700 ) and SCRM ( Crsca13aG0002900 ) marker genes during stomatal development. The identity of the phloem parenchyma and conducting cells was determined on the basis of transcript enrichment of highly expressed genes, such as protein argonaute 10-like ( Crsca06aG0101400 ), glyoxylase I 4 ( Crsca01aG0100700 ), auxin efflux carrier component 5 ( Crsca17aG0247600 ), and linoleate 9S-lipoxygenase 6 ( Crsca02aG0180900 ) (Supplemental Table S2, Figure 2b). These genes are expressed in Clusters 3, 10, 13, and 15. Clusters 3 and 10 were classified as phloem parenchyma, Cluster 13 as companion cells, and Cluster 15 as sieve elements. Zinc finger protein ZAT12-like ( Crsca05aG0093600 ), auxin-induced protein 22D ( Crsca07aG0086600 ), subtilisin-like protease SBT4.14 ( Crsca09aG0122400 ), expansin-A4-like ( Crsca16aG0137100 ), and probable beta-1,4-xylosyltransferase IRX10 ( Crsca07aG0156100 ) were specifically expressed in Clusters 1, 8, 9, 12, and 14 (Supplemental Table S2, Figure 2b). Clusters 9 and 14 correspond to metaxylem cells, whereas Clusters 1, 8, and 12 are classified as xylem cells. Additionally, Clusters 5 and 6 presented high meristematic activity and were identified as meristematic cells, with many cell cycle-related genes being enriched and highly expressed in these clusters (Supplemental Table S2). For example, kinesin-like protein KIN-12D ( Crsca03aG0118600 ) (Figure 2b), which is involved in microtubule movement, was highly expressed in these clusters, suggesting its role in the dynamic processes of cell division and growth within meristematic regions. 3.3 Characterization of Different Cell Populations and Discovery of Novel Marker Genes After identifying the major cell populations, we characterized their gene expression profiles and recalculated differentially expressed genes (DEGs) between cell types to identify novel marker genes. The number of upregulated DEGs ranged from 154 in phloem parenchyma populations to 567 in meristematic cells (Supplemental Table S4). Across the nine cell types, these DEGs presented distinct transcriptional profiles and associated biological processes (Figure 3, Supplemental Table S5). Mesophyll cells, located beneath the epidermal layers and serving as the principal sites of photosynthesis, presented distinct transcriptional profiles, with GO enrichment analysis revealing overrepresentation of biological processes associated with photosynthetic light reactions and the electron transport chain. The leaf epidermis functions as more than a physical barrier; it contributes to defense against biotic threats and regulates water loss and gas exchange. Accordingly, pavement cells were enriched in GO terms related to lipid metabolism and cuticle development and responses to osmotic stress, whereas guard cells were enriched in processes associated with potassium ion transport and proton transmembrane transport, reflecting their protective and stomatal roles under desiccation and environmental stress. The vascular tissue, which transports water and nutrients and supports long-distance signaling, comprises five distinct subpopulations with specialized transcriptional profiles. Phloem parenchyma cells were enriched in processes associated with secondary metabolite biosynthesis, stress responses, and water transport, whereas sieve elements were enriched in phloem development, autophagy, and responses to cellular component size and toxic compounds. The companion cells were enriched in processes related to osmotic and oxidative stress responses, xenobiotic metabolism, carbohydrate metabolism, and cytoplasmic translation, suggesting that they are metabolically active and stress responsive,which in line with their supportive roles in sieve elements. Xylem and metaxylem cells were enriched in GO categories linked to responses to high light and osmotic stress, steroid and fatty acid metabolism, and cell wall–associated metabolic processes, highlighting their involvement in water transport, structural support, and stress adaptation. Finally, meristematic cells presented upregulated expression of genes involved in mitotic spindle organization, microtubule-based movement, translational initiation, and chromatin and nucleosome organization, , which was consistent with their proliferative and developmental activities. The detailed GO enrichment results for all the cell populations are summarized in Supplemental Table S5. In addition, characterization of the chloroplast gene content across different cell populations revealed substantial differences, with mesophyll and meristematic cells exhibiting a significantly greater proportion of chloroplast-related genes than epidermal and vascular cell populations (Supplemental Figure 2, Supplemental Figure 3). Building on these cell-type specific functional signatures, we selected the top 20 upregulated DEGs for each population (ranked by avg_log2FC) and visualized their expression across the nine cell types via a heatmap (Figure 3). We highlight representative marker genes from each cluster that exemplify their specialized expression patterns, facilitating the identification of leaf cell types in hawthorn. In mesophyll cells, REDUCED CHLOROPLAST COVERAGE 2-like ( Crsca06aG0026300 ) and NADH dehydrogenase subunit F ( Crsca11aG0017000 ), which are required for chloroplast maintenance and electron transport, were highly expressed(Figure 3, Supplemental Figure 4a-b), which is consistent with the central role of mesophyll cells in photosynthesis and energy production. In pavement cells, a desiccation protectant protein (Crsca12aG0163100 ) and a very-long-chain aldehyde decarbonylase ( Crsca07aG0013600 ), involved in long-chain fatty acid biosynthesis and membrane protection, were specifically expressed (Figure 3, Supplemental Figure 4c-d), suggesting contributions to cuticle integrity and tolerance to water loss and osmotic stress. Guard cells specifically expressed two phosphoenolpyruvate carboxylases ( Crsca10aG0042800 and Crsca08aG0052200 ) (Figure 3, Supplemental Figure 4e-f), which participate in CO 2 assimilation and associated metabolic pathways, thereby optimizing photosynthesis under conditions of low CO 2 availability. In phloem parenchyma cells, genes encoding the TMV resistance protein N ( Crsca03aG0044200 ) and the putative disease resistance protein RGA3 ( Crsca11aG0044000 ) were specifically expressed (Figure 3, Supplemental Figure 4g-h), indicating a role in local defense responses within the vascular system. In sieve elements, SIEVE ELEMENT OCCLUSION B ( Crsca14aG0077000 ) and callose synthase 7-like ( Crsca13aG0129700 ) were specifically expressed (Figure 3, Supplemental Figure 4i-j), which is consistent with their involvement in sieve plate function and callose deposition to preserve phloem integrity and facilitate defense. Companion cells, which support sieve element function, specifically expressed PHLOEM PROTEIN 2-like ( Crsca02aG0095700 ) and glycine-rich protein A3-like ( Crsca09aG0081000 ) (Figure 3, Supplemental Figure 4k-l), underscoring their specialized roles in maintaining phloem structure and in the transport of nutrients and signaling molecules. In xylem cells, a DOMON/cytochrome b561 domain–containing protein ( Crsca03aG0331200 ), REF/SRPP-like protein ( Crsca17aG0206500 ), and 2-oxoglutarate-dependent dioxygenase 19-like protein ( Crsca02aG0293200 ) are highly expressed (Figure 3, Supplemental Figure 4m–o); together these redox- and cell wall–related proteins likely contribute to maintaining xylem integrity and modulating responses to oxidative and environmental stresses. In metaxylem cells, the enrichment of methionine metabolism and protein localization to the cell surface was reflected by the specific expression of probable rhamnogalacturonate lyase B ( Crsca01aG0251900 ), probable hexosyltransferase MUCI70 ( Crsca02aG0039900 ), and rhamnogalacturonan I rhamnosyltransferase 1 ( Crsca07aG0213100 ) (Figure 3, Supplemental Figure 4p–r), suggesting a specialized role in rhamnogalacturonan metabolism and cell wall remodeling during stress adaptation. Finally, meristematic cells, which are characterized by upregulated genes associated with microtubule organization, protein binding, and cytoskeletal movement, presented markedly higher expression of histone H1.2-like ( Crsca17aG0233000 ) and histone H1-like ( Crsca02aG0134300 ) than other populations did (Figure 3, Supplemental Figure 4 s-t), highlighting their roles in chromatin organization, cell division, and differentiation in actively proliferating meristematic tissues. 3.4 Pseudotime Analysis of Differentiation Trajectories in Hawthorn Leaf Development To better understand the developmental trajectories of hawthorn leaves, we performed pseudotime analysis, which enabled us to track the progression of cellular differentiation along a temporal axis. The trajectories revealed consistent developmental paths between C. pinnatifida var. major N. E. Br. and C. acerifolia , with nine cell types from both species mapped to the hawthorn leaf differentiation trajectories, demonstrating a convergence of differentiation pathways (Figure 4b, Figure 4d). The cellular distribution was visualized in reduced dimensions and colored by pseudotime progression, with cells transitioning from early to late stages of development. Five key branching points were identified within the differentiation paths, dividing the trajectories into 11 distinct developmental states, reflecting major shifts in cellular fate during leaf development (Figure 4a, Figure 4c). A total of 4,475 genes with dynamic expression changes were implicated in cell growth (Supplemental Table S6), providing insights for further investigation into leaf cell development. A heatmap was then generated on the basis of the top 50 most significantly regulated genes ranked by q-value from the pseudotime differential expression analysis (Figure 4f). We observed changes in the distribution and density of different cell types across pseudotime, with PC, XY, and PP being prominent in the early stages, followed by the emergence of MXY, GC, CC, and SE in mid-pseudotime, and finally, MC dominating in the late stages, highlighting their significant role in development advancement(Figure 6e). This analysis emphasizes the temporal changes and relationships among various cell types during hawthorn leaf development, offering a detailed transcriptional roadmap that identifies conserved regulatory modules. Building on these cell-type specific functional signatures, we selected the top 20 upregulated DEGs for each population (ranked by avg_log2FC) and visualized their expression across the nine cell types via a heatmap (Figure 3). We highlight representative marker genes from each cluster that exemplify their specialized expression patterns, facilitating the identification of leaf cell types in hawthorn. In mesophyll cells, REDUCED CHLOROPLAST COVERAGE 2-like ( Crsca06aG0026300 ) and NADH dehydrogenase subunit F ( Crsca11aG0017000 ), which are required for chloroplast maintenance and electron transport, were highly expressed(Figure 3, Supplemental Figure 4a-b), which is consistent with the central role of mesophyll cells in photosynthesis and energy production. In pavement cells, a desiccation protectant protein (Crsca12aG0163100 ) and a very-long-chain aldehyde decarbonylase ( Crsca07aG0013600 ), involved in long-chain fatty acid biosynthesis and membrane protection, were specifically expressed (Figure 3, Supplemental Figure 4c-d), suggesting contributions to cuticle integrity and tolerance to water loss and osmotic stress. Guard cells specifically expressed two phosphoenolpyruvate carboxylases ( Crsca10aG0042800 and Crsca08aG0052200 ) (Figure 3, Supplemental Figure 4e-f), which participate in CO 2 assimilation and associated metabolic pathways, thereby optimizing photosynthesis under conditions of low CO 2 availability. In phloem parenchyma cells, genes encoding the TMV resistance protein N ( Crsca03aG0044200 ) and the putative disease resistance protein RGA3 ( Crsca11aG0044000 ) were specifically expressed (Figure 3, Supplemental Figure 4g-h), indicating a role in local defense responses within the vascular system. In sieve elements, SIEVE ELEMENT OCCLUSION B ( Crsca14aG0077000 ) and callose synthase 7-like ( Crsca13aG0129700 ) were specifically expressed (Figure 3, Supplemental Figure 4i-j), which is consistent with their involvement in sieve plate function and callose deposition to preserve phloem integrity and facilitate defense. Companion cells, which support sieve element function, specifically expressed PHLOEM PROTEIN 2-like ( Crsca02aG0095700 ) and glycine-rich protein A3-like ( Crsca09aG0081000 ) (Figure 3, Supplemental Figure 4k-l), underscoring their specialized roles in maintaining phloem structure and in the transport of nutrients and signaling molecules. In xylem cells, a DOMON/cytochrome b561 domain–containing protein ( Crsca03aG0331200 ), REF/SRPP-like protein ( Crsca17aG0206500 ), and 2-oxoglutarate-dependent dioxygenase 19-like protein ( Crsca02aG0293200 ) are highly expressed (Figure 3, Supplemental Figure 4m–o); together these redox- and cell wall–related proteins likely contribute to maintaining xylem integrity and modulating responses to oxidative and environmental stresses. In metaxylem cells, the enrichment of methionine metabolism and protein localization to the cell surface was reflected by the specific expression of probable rhamnogalacturonate lyase B ( Crsca01aG0251900 ), probable hexosyltransferase MUCI70 ( Crsca02aG0039900 ), and rhamnogalacturonan I rhamnosyltransferase 1 ( Crsca07aG0213100 ) (Figure 3, Supplemental Figure 4p–r), suggesting a specialized role in rhamnogalacturonan metabolism and cell wall remodeling during stress adaptation. Finally, meristematic cells, which are characterized by upregulated genes associated with microtubule organization, protein binding, and cytoskeletal movement, presented markedly higher expression of histone H1.2-like ( Crsca17aG0233000 ) and histone H1-like ( Crsca02aG0134300 ) than other populations did (Figure 3, Supplemental Figure 4 s-t), highlighting their roles in chromatin organization, cell division, and differentiation in actively proliferating meristematic tissues. 3.4 Pseudotime Analysis of Differentiation Trajectories in Hawthorn Leaf Development To better understand the developmental trajectories of hawthorn leaves, we performed pseudotime analysis, which enabled us to track the progression of cellular differentiation along a temporal axis. The trajectories revealed consistent developmental paths between C. pinnatifida var. major N. E. Br. and C. acerifolia , with nine cell types from both species mapped to the hawthorn leaf differentiation trajectories, demonstrating a convergence of differentiation pathways (Figure 4b, Figure 4d). The cellular distribution was visualized in reduced dimensions and colored by pseudotime progression, with cells transitioning from early to late stages of development. Five key branching points were identified within the differentiation paths, dividing the trajectories into 11 distinct developmental states, reflecting major shifts in cellular fate during leaf development (Figure 4a, Figure 4c). A total of 4,475 genes with dynamic expression changes were implicated in cell growth (Supplemental Table S6), providing insights for further investigation into leaf cell development. A heatmap was then generated on the basis of the top 50 most significantly regulated genes ranked by q-value from the pseudotime differential expression analysis (Figure 4f). We observed changes in the distribution and density of different cell types across pseudotime, with PC, XY, and PP being prominent in the early stages, followed by the emergence of MXY, GC, CC, and SE in mid-pseudotime, and finally, MC dominating in the late stages, highlighting their significant role in development advancement(Figure 6e). This analysis emphasizes the temporal changes and relationships among various cell types during hawthorn leaf development, offering a detailed transcriptional roadmap that identifies conserved regulatory modules. 3.5 Cell specificity of genes related to the biosynthesis of flavonoids in hawthorn leaves during leaf development To further investigate whether flavonoid biosynthesis is cell-specific during leaf development, we analyzed the expression of related genes in various cell types. The results revealed that genes involved in flavonoid formation presented similar expression patterns in the leaves of both C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder, indicating consistency in gene expression between the two species (Figure 5, Supplemental Table S6). Previous studies have reported the amplification of flavonoid biosynthesis genes in the C. pinnatifida var. major N. E. Br. genome; however, in our single-cell RNA sequencing analysis, most of these candidate genes were not detected. Only 14 genes related to flavonoid biosynthesis exhibited detectable expression(Figure 5a). Flavonoid biosynthesis begins with phenylalanine, which is converted into precursor compounds by enzymes such as phenylalanine ammonia-lyase ( PAL ), cinnamate 4-hydroxylase, and 4-coumarate-CoA ligase(Figure 5b). PAL ( Crsca12aG0117800 ) was highly expressed in both the PC and PP cells of C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder. Similarly, 4-Coumarate-CoA ligase ( 4CL , Crsca07aG0047300 and Crsca08aG0053500 ) were highly expressed in the MXY cells of both species. The Expression of 4CL was also observed in the GC and PC cells of C. pinnatifida var. major N. E. Br., but not in the corresponding cells of C. scabrifolia (Franch.) Rehder. Chalcone synthase ( CHS ) and chalcone isomerase ( CHI ) are key enzymes in flavonoid biosynthesis(Figure 5b). CHS catalyzes the formation of the chalcone backbone for all flavonoids, whereas CHI converts chalcones into naringenin. CHS ( Crsca04aG0218800 ) was highly expressed in XY cells of both species, with detectable expression also found in the MC and MXY cells of C. scabrifolia (Franch.) Rehder. CHI ( Crsca01aG0098200 , Crsca09aG0185300 ) was highly expressed in MER cells of both species, whereas another CHI gene ( Crsca12aG0102700 ) was expressed in PP cells. As a major metabolite, naringenin enters the flavonoid biosynthesis pathway and undergoes various modifications to generate different flavonoid compounds(Figure 5b). Flavanone 3-hydroxylase ( F3H , Crsca09aG0185700 ), which catalyzes the conversion of naringenin to dihydrokaempferol, was highly expressed in GC and PP cells of both species, with lower but still detectable expression in the MC, MER, and XY cells of C. pinnatifida var. major N. E. Br.. Dihydroflavonol 4-reductase ( DFR , Crsca10aG0223200 ), which is essential for the biosynthesis of compounds such as epigallocatechin and epicatechin, was highly expressed in MC cells of both species, with additional expression observed in MER and PP cells of C. pinnatifida var. major N. E. Br. and in MXY cells of C. scabrifolia (Franch.) Rehder. Isoflavone synthase ( IFS ), a key enzyme in the isoflavonoid biosynthesis pathway, catalyzes the structural rearrangement of naringenin, transferring the connection from C-2 to C-3 of the intermediate ring(Figure 5b). The IFS genes, including Crsca03aG0100400 (highly expressed in SE cells of both species), Crsca04aG0315000 and Crsca07aG0097100 (highly expressed in MER cells of both species), and Crsca13aG0040000 (highly expressed in PP cells of C. pinnatifida var. major N. E. Br.), exhibited distinct, cell-specific expression patterns across different cell types. Anthocyanidin synthase ( ANS , Crsca14aG0165700 ), which is expressed primarily in MC and PP cells, plays a crucial role in the further synthesis of compounds such as Procyanidin B2. These results demonstrate that different homologous genes of an enzyme are transcribed in distinct cell types, highlighting the heterogeneity of cell types and the spatiotemporal expression of genes involved in secondary metabolism at different stages of hawthorn leaf development. 4. Discussion Plant leaves are heterogeneous organs consisting of epidermal cells, mesophyll cells, vascular cells and other cell types with different functions [ 32 , 33 ] . In plant single‑cell transcriptomics, two commonly used strategies are protoplast‑based scRNA‑seq and nucleus‑based snRNA‑seq. Here, we present the first integrated single‑cell and single‑nucleus transcriptomic atlas of hawthorn leaves, profiling 32,292 high‑quality cells from two Crataegus species and resolving nine principal leaf cell types across sixteen transcriptional clusters. Leveraging the high sensitivity and resolution of scRNA‑seq and snRNA‑seq, we distinguished subpopulations within the same cell types that are often indistinguishable by bulk RNA‑seq [ 32 , 34 ] . Accurate cell annotation remains a central challenge for single-cell studies in non‑model plants [ 16 ] . To annotate the hawthorn cell types, we used homologous marker genes from Arabidopsis and compared the chloroplast gene content across different cell populations. The dominance of the mesophyll clusters is supported by strong expression of canonical mesophyll markers (e.g., RBCS [ 35 – 38 ] , LHCB [ 32 , 36 , 39 ] , and PSAB [ 36 , 39 , 40 ] ), which is consistent with the central role of mesophyll cells in carbon fixation and light harvesting and reinforces the validity of our cell‑type assignments. Epidermal clusters express marker genes involved in cuticle/suberin biosynthesis (e.g., FDH [ 35 , 36 , 38 , 41 , 42 ] , HDG2 [ 43 , 44 ] , PDF1 [ 44 – 46 ] ), consistent with the protective and water-retention roles of the epidermis. Guard cell identity is corroborated by the stomatal development markers FAMA [ 35 , 47 ] and SCRM [ 35 , 48 ] , suggesting intact regulatory modules for stomatal differentiation in hawthorn. Vasculature-related clusters show enrichment of transport and signaling associated marker genes (e.g., AGO10 [ 36 , 44 ] , GLYI4 [ 49 , 50 ] , FP3 [ 36 , 39 , 49 ] , TBL34 [ 50 , 51 ] , ATGUT1 [ 50 , 52 ] ), aligning with roles in photoassimilate translocation and long-distance signaling. Moreover, GO enrichment analysis revealed that mesophyll and meristematic clusters are significantly enriched for chloroplast‑related terms, providing additional evidence that our cell‑type annotations accurately reflect photosynthetic and developmental identities. Furthermore, cluster reproducibility was validated across two Crataegus species: we observed the same set of cell types in both C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder with broadly similar distribution patterns. This cross‑species concordance supports conserved cellular organization in hawthorn leaves and increases confidence in the accuracy of our cell type annotations. Protoplasting requires enzymatic removal of the cell wall to release intact cells, and because different cell types may have distinct osmotic tolerances, inadequate osmotic conditions can cause protoplast rupture and the selective loss of fragile cell types [ 39 ] . In addition, enzymatic digestion itself can trigger stress responses in protoplasts, leading to transcriptional changes that deviate from their native in vivo states and potentially confound downstream single‑cell transcriptomic analyses [ 53 ] . In leaf tissues, mesophyll cells typically predominate and contain abundant chlorophyll, which produces strong autofluorescence that can confound fluorescence‑based assays, including protoplast viability measurements [ 54 ] . Similarly, snRNA‑seq is less affected by protoplasting‑induced bias but predominantly captures nuclear transcripts and therefore typically detects fewer genes per nucleus than does whole‑cell scRNA‑seq [ 55 ] . In our study, we combined both approaches to balance their complementary strengths: snRNA‑seq mitigated the loss of osmotically fragile cell types, whereas scRNA‑seq improved cytoplasmic transcript detection. Integrating data from both modalities and confirming cluster reproducibility across two Crataegus species allowed us to assemble a more complete and robust single‑cell transcriptomic atlas for hawthorn leaves. The development of plant leaves to their final form entails coordinated cell proliferation, expansion, and differentiation [ 56 – 58 ] . Cell proliferation is concentrated in meristematic regions, which are characterized by active division and the potential to develop into multiple cell types [ 59 ] . In our pseudotime reconstruction, meristematic cells occupy the earliest positions of the trajectory. The analysis revealed branched differentiation pathways with five major branch points and 11 discrete developmental states, reflecting ordered transitions from proliferative meristematic cells to specialized epidermal, vascular, and photosynthetic lineages. Pseudotime analysis indicated that pavement cells and vascular progenitors are enriched at early stages, followed by the emergence of guard cells, companion cells, and sieve elements, culminating in mesophyll cell dominance. These results align with classical models of leaf organogenesis and suggest conserved developmental programs across C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder. Previous studies in Arabidopsis have shown that during development chloroplasts assume different roles depending on the functions of differentiated cells and can influence both cell proliferation and cell expansion [ 60 ] . In the epidermal lineage, chloroplasts are present during early epidermal development but mature epidermal cells lose photosynthetic activity upon differentiation [ 61 , 62 ] . Consistent with these findings, we observed high expression of chloroplast-associated genes in both meristematic cells and mesophyll cells. These findings suggest that chloroplast-related processes contribute to cell type-specific developmental programs in these hawthorn species and provide a theoretical basis for further elucidation of the mechanisms underlying plant leaf development. Cell‑type‑resolved analysis revealed spatial partitioning of flavonoid biosynthetic genes in hawthorn leaves, indicating that flavonoid production is organized across multiple cell types rather than confined to single cells. We detected a limited subset of annotated pathway genes at transcriptional resolution, each with a distinct localization: PAL transcripts were enriched in pavement and phloem parenchyma cells; CHS was predominantly expressed in xylem‑associated cells; DFR was most abundant in mesophyll; and IFS family members were partitioned among sieve elements, meristematic cells, and phloem parenchyma. This pattern indicates the potential for intercellular metabolite channeling, whereby precursors and intermediates are produced, transferred or exchanged across neighboring cell types instead of being sequentially processed within a single cell. The observation that only a fraction of annotated pathway genes were transcriptionally detected likely reflects biological factors such as developmental-stage or condition-specific expression and low basal transcript levels; accordingly, the absence of detection in our dataset should be interpreted with caution and validated by targeted methods (e.g. in situ hybridization or higher-coverage spatial transcriptomics). Flavonoids are ubiquitous phenolic secondary metabolites involved in development, ripening, stress tolerance (biotic and abiotic), and organismal interactions. Their biosynthesis is regulated not only by structural enzymes and transcription factors but also by epigenetic regulators such as long noncoding RNAs (lncRNAss and microRNAs (miRNAs) [ 63 ] . Comparative analysis of single-cell transcriptomes from two hawthorn leaf types supports a model in which flavonoid biosynthesis is a multicellular, spatially organized process involving cell-specific enzyme complements, intercellular metabolite transfer, and localized regulation; future studies should validate the cellular localizations, trace metabolite flows, and identify cell-specific transcription factors, lncRNAs, and miRNAs to reconstruct the spatial organization and regulatory logic of this pathway and to inform strategies for manipulating flavonoid composition for improved stress resistance or nutritional value. Although combining scRNA‑seq and snRNA‑seq improved cellular coverage and reduced some preparation artifacts, important limitations of this study remain and should be addressed in future work. Protoplast isolation can induce stress‑responsive transcriptional changes, whereas snRNA‑seq—although it mitigates this artifact—captures a transcriptome biased toward nuclear RNAs; therefore, each method has inherent biases. All the samples were collected at a single developmental stage under a single set of environmental conditions, which restricts our ability to capture the cell‑type dynamics and transcriptional programs that occur during other developmental stages or under varying environmental cues; as a result, temporal and condition specific gene expression may be underrepresented. In addition, single‑cell transcriptomics measures mRNA abundance but does not directly demonstrate protein expression, enzyme activity, or metabolite production, so transcript‑level associations require orthogonal validation to establish functional relevance. To address these limitations, future studies should expand spatiotemporal sampling and integrate orthogonal assays: collect leaves across multiple developmental stages (e.g., young/expanding, mature, senescing) and under various environmental conditions (e.g., light regimes, drought or salinity, temperature treatments) to construct a comprehensive spatiotemporal atlas; apply spatial transcriptomics to localize candidate genes in situ; and combine proteomics, translation‑level assays (e.g., mass spectrometry, Ribo‑seq) and metabolomics to validate protein expression, translational regulation, and metabolite accumulation. These combined efforts will strengthen functional inference and mechanistic interpretation. They will yield a more complete, functionally validated view of cell‑type heterogeneity, developmental dynamics, and the transcriptional–biochemical networks underlying secondary metabolite formation in hawthorn leaves. 5. Conclusions Our findings reveal extensive cellular heterogeneity in hawthorn leaves, categorizing cells into sixteen distinct groups corresponding to nine major leaf cell types: mesophyll, pavement, guard, xylem, metaxylem, phloem parenchyma, companion, sieve element, and meristematic cells. Through differential expression and functional enrichment analyses, we identified specialized transcriptional programs and novel marker genes for each cell type. We also established consistent developmental trajectories of hawthorn leaf differentiation, demonstrating convergence between C. pinnatifida var. major N. E. Br. and C. scabrifolia (Franch.) Rehder in their cellular progression. Furthermore, mapping flavonoid biosynthetic gene expression onto our single-cell atlas revealed distinct cell-type specificity in key enzymes such as PAL, 4CL, CHS, CHI, F3H, DFR, IFS , and ANS . These findings provide insights into tissue-specific secondary metabolism during leaf development, establishing a robust single-cell transcriptomic framework for hawthorn leaves that highlights developmental conservation across species, reveals the cellular partitioning of flavonoid biosynthetic machinery, and sets the stage for functional validation and applied manipulation of metabolite biosynthesis in this economically and medicinally important genus. Declarations Ethics approval and consent to participate: This manuscript is original research and has not been published or submitted in other journals. Consent for publication All authors listed have read the complete manuscript and have approved submission of the paper. Availability of data and material: The datasets generated and processed during the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE315448(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE315448). Other datasets that support the conclusions of this article are included within the article and its supplementary files. Competing interests: The authors declare no competing interests. Funding: This study was supported by grants from Strategic Priority Research Program of the Chinese Academy of Sciences (XDB1230000), National Natural Science Foundation of China (32260094, 32570434), and Yunnan Fundamental Research Projects (202501AS070177). Author Contributions: TCZ, GDL and ZLD designed the research project; GGZ, XEW, BZW and XLL generated molecular data; GGZ, GGZ and JJZ performed data analyses; GGZ wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements: Special thanks to the Genome Center of Biodiversity of Kunming Institute of Zoology of Chinese Academy of Sciences for providing platform support. References Wang X, Huang H, Jiang S, et al. A single-cell multi-omics atlas of rice[J]. Nature. 2025;644(8077):722–30. Yu X, Liu Z, Sun X. 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Table S4: Summary of upregulated differentially expressed genes across hawthorn leaf cell types. Table S5: Gene Ontology enrichment results for upregulated DEGs across hawthorn leaf cell types. Table S6: Differential expression analysis of genes with dynamic expression changes during hawthorn leaf cell development. Table S7: Average gene expression across hawthorn leaf cell-type and species combinations. SupplementalFig.1.tif Figure 1: Spearman correlation heatmap of average gene expression across clusters. SupplementalFig.2.tif Figure 2: Comparison of Chloroplast Content Across Tissue Types. SupplementalFig.3.tif Figure 3: Distribution of Chloroplast Gene Expression Ratios Across Clusters. SupplementalFig.4.tif Figure 4: Expression Pattern Visualization Using t-SNE in Various Leaf Tissue Cell Populations. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Editor invited by journal 05 Jan, 2026 Submission checks completed at journal 04 Jan, 2026 First submitted to journal 04 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8423721","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571779669,"identity":"ae56fe47-80c4-4cdd-ade8-29d7731a6303","order_by":0,"name":"Guigang Zhao","email":"","orcid":"","institution":"College of Chinese Material Medica, Yunnan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guigang","middleName":"","lastName":"Zhao","suffix":""},{"id":571779670,"identity":"79b3f8f8-d201-4e94-89b1-e80534852432","order_by":1,"name":"Xien Wu","email":"","orcid":"","institution":"Yunnan Key Laboratory of 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07:48:40","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172330,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/7ce7e7a2d33f4e580d4825eb.html"},{"id":100029876,"identity":"a111bdad-e442-4f10-bf4d-4477de8a1e27","added_by":"auto","created_at":"2026-01-12 09:21:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":127643,"visible":true,"origin":"","legend":"\u003cp\u003eIsolation and uniform manifold approximation and projection (UMAP) clustering analysis of single-cell transcriptomes from hawthorn leaves. (A) Workflow used for snRNA-seq and scRNA-seq to obtain transcriptomes from individual hawthorn leaf cells. (B) UMAP projection plots showing the dimensional reduction of integrated single-cell transcriptomes from four libraries. (C) UMAP projection showing the cell distributions of the protoplast and nucleus single-cell transcriptomes. (D) UMAP projection showing the cell distribution of single-cell transcriptomes from \u003cem\u003eC. pinnatifida\u003c/em\u003evar. \u003cem\u003emajor \u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/486652c7ccc3f74a2f5e07a9.png"},{"id":100029877,"identity":"431e2fa8-9c46-4451-b8ff-31cbe58b120e","added_by":"auto","created_at":"2026-01-12 09:21:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123142,"visible":true,"origin":"","legend":"\u003cp\u003eAnnotation of Hawthorn leaf cell types via cluster-specific expression profiles of canonical marker genes. (A) t-SNE projection visualizing the annotation results of 16 clusters from 32,292 single cells obtained from hawthorn leaves, categorized into 9 main cell types: mesophyll cells (MC), pavement cells (PC), xylem (XY), phloem parenchyma (PP), metaxylem (MXY), guard cells (GC), companion cells (CC), sieve elements (SE), and meristematic cells (MER). (B) Violin plots showing the distribution of normalized expression for representative genes across the annotated clusters defined in panel A. (C) Distribution of cell counts by cell type for two\u003cem\u003e Crataegus\u003c/em\u003e species. The x-axis displays the nine identified cell types, whereas the y-axis shows the corresponding cell counts. The blue bars represent \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor \u003c/em\u003eN. E. Br., and the orange bars represent \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Both \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor \u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder exhibit the same set of cell types, with similar distribution trends across these species.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/1a7b6bdbf4eec7ee604bb8f7.png"},{"id":100029880,"identity":"ae76e41d-0d43-4222-94eb-690ac3e4f333","added_by":"auto","created_at":"2026-01-12 09:21:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94639,"visible":true,"origin":"","legend":"\u003cp\u003eCell type–specific marker genes and their associated biological processes across nine leaf cell populations. Heatmap showing the expression patterns of selected marker genes across nine leaf cell populations, including mesophyll cells (MC), xylem cells (XY), phloem parenchyma cells (PP), metaxylem cells (MXY), meristematic cells (MER), companion cells (CC), guard cells (GC), sieve elements (SE), and pavement cells (PC). For each population, the top differentially expressed genes (DEGs) were selected and their normalized expression levels are represented as Z‑scores (color scale, right). The line plots on the left depict the average expression profile of the corresponding gene set across the nine cell types. Representative enriched Gene Ontology (GO) biological processes associated with each gene set are listed on the right, highlighting functional specializations such as photosynthesis and electron transport in mesophyll cells, responses to high light intensity and osmotic stress in xylem cells, acetyl‑CoA and sterol metabolism in pavement cells, flavonoid and long‑chain fatty acid biosynthesis in phloem parenchyma cells, protein–DNA complex organization and cytoplasmic translation in meristematic cells, protein localization to the cell surface and methionine metabolism in metaxylem cells, karrikin response and ion transmembrane transport in guard cells, and stress- and size‑regulation–related processes in sieve elements.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/bc2e5c0eeb2c7bfe9406a771.png"},{"id":100363067,"identity":"2f8965f7-4b39-4827-8f1f-2aa16e1d13d3","added_by":"auto","created_at":"2026-01-16 07:48:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":99892,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopmental trajectories and expression dynamics in hawthornleaf cell differentiation\u003c/p\u003e\n\u003cp\u003e(a) Pseudotemporal trajectory: Cellular distribution in reduced dimensions colored by pseudotime progression (color ranging from dark to light represents early to late stages). (b) Species-specific patterning: Comparative mapping of \u003cem\u003eC. pinnatifida\u003c/em\u003evar. \u003cem\u003emajor \u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder within the latent space. (c) State stratification: Cellular clusters classified into 11 discrete developmental states. (d) Cell-type annotation: Major cell types highlighted, including mesophyll cells (MCs), companion cells (PCs), meristematic cells (MERs), guard cells (GCs), sieve elements (SEs), xylem (XY), phloem parenchyma (PPs), metaxylem (MXY), and companion cells (CCs). (e) Density distribution: Frequency of cell types across pseudotime. (f) Heatmap showing the dynamic expression patterns of the top 50 marker genes selected from pseudotime analysis, with a color scale representing normalized expression intensity (deep blue: low; red: high).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/22d1e7684a131908e16bf348.png"},{"id":100029881,"identity":"9d9dffab-1c5b-461e-b2e9-23528a845e17","added_by":"auto","created_at":"2026-01-12 09:21:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":130091,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies-specific flavonoid pathway regulation in hawthorn leaves\u003c/p\u003e\n\u003cp\u003e(a) Cell type-specific gene expression: Heatmap of flavonoid biosynthesis gene expression across vascular cell types in \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor \u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Genes are annotated with IDs and enzyme abbreviations. (b) Flavonoid biosynthesis pathway: Metabolic route from phenylalanine to specialized compounds.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/98cd76a80c319274287b03f8.png"},{"id":100406282,"identity":"914461f3-c17c-44ce-b80d-60ede890b0c1","added_by":"auto","created_at":"2026-01-16 12:59:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1820274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/0568e259-9301-4f2a-9af4-cf4c2651f6ea.pdf"},{"id":100363017,"identity":"cf31948d-5c77-447b-a027-c7c2c170a016","added_by":"auto","created_at":"2026-01-16 07:48:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2849193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable S1: Summary of 6,024 differentially expressed genes identified across sixteen cell clusters in hawthorn leaves. Table S2: Maker Gene Information Table used for cell subset annotation. Table S3: Distribution of integrated cell types across hawthorn leaf samples, showing conserved cell-type composition and similar abundance trends among species. Table S4: Summary of upregulated differentially expressed genes across hawthorn leaf cell types. Table S5: Gene Ontology enrichment results for upregulated DEGs across hawthorn leaf cell types. Table S6: Differential expression analysis of genes with dynamic expression changes during hawthorn leaf cell development. Table S7: Average gene expression across hawthorn leaf cell-type and species combinations.\u0026nbsp;\u003c/p\u003e","description":"","filename":"SupplementalTableS1S7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/813458fed191637754ab1ad1.xlsx"},{"id":100029903,"identity":"3f363113-44ab-4d1b-9fff-c07c1fc7b8f0","added_by":"auto","created_at":"2026-01-12 09:21:28","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18629448,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1: Spearman correlation heatmap of average gene expression across clusters.\u003c/p\u003e","description":"","filename":"SupplementalFig.1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/bb37a2f152072c8bcad310f5.tif"},{"id":100363100,"identity":"60a580e0-ca20-4c2c-aeca-b07c4b4c7910","added_by":"auto","created_at":"2026-01-16 07:48:53","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":32541644,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: Comparison of Chloroplast Content Across Tissue Types.\u003c/p\u003e","description":"","filename":"SupplementalFig.2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/fadf6e2dce87ad1602bab09a.tif"},{"id":100029904,"identity":"f6119398-ba31-428a-ac6d-65f2fa9f0956","added_by":"auto","created_at":"2026-01-12 09:21:28","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18555100,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3: Distribution of Chloroplast Gene Expression Ratios Across Clusters.\u003c/p\u003e","description":"","filename":"SupplementalFig.3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/c67aa007b4b8266fd56c2f7a.tif"},{"id":100029905,"identity":"d5c38543-6e5d-4fd9-a713-2129e13798e3","added_by":"auto","created_at":"2026-01-12 09:21:29","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":28522732,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: Expression Pattern Visualization Using t-SNE in Various Leaf Tissue Cell Populations.\u003c/p\u003e","description":"","filename":"SupplementalFig.4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8423721/v1/ce2e398745cd2d72ae14c94e.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell RNA-sequencing profiles reveal the developmental landscape of hawthorn leaves","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMulticellular organisms rely on the division of labor across cell types, with cells acting as modular building blocks that generate heterogeneity and contribute to the formation of complex organs through differentiation during development\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although individual cells in multicellular organisms share the same genomic DNA, their functions differ owing to the influence of specific gene expression programs\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Traditional bulk-level analyses often obscure differences between individual cells. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) enable clustering and classification of cells within complex tissues or organs, revealing unique expression profiles and functional characteristics of distinct cell populations\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. These emerging technologies thus facilitate celltype identification and the elucidation of gene regulatory networks. With continuous advances in sequencing technologies and sample preparation methods, single-cell transcriptomics is now widely applied not only to studies of cellular heterogeneity in animals and microorganisms but also to plant research\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. To date, single-cell transcriptomics has been applied to different tissues and organs of several plant species (e.g., Arabidopsis\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, strawberry\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, rice\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, maize\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, peanut\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, poplar\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, and tea\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e), providing important support for the construction of single-cell atlases, developmental trajectory analyses, and the elucidation of gene regulatory networks. However, the relatively few research reports on the application of single-cell transcriptomics in nonmodel plants can be attributed primarily to several challenges. These limitations include the lack of reliable specific marker genes for cell type identification, the variability in plant cell size, and bottlenecks in protoplast isolation efficiency, which have limited their application in these plants\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe genus \u003cem\u003eCrataegus\u003c/em\u003e (hawthorn), a flowering shrub or tree in the Rosaceae family that is widely distributed across the temperate regions of the Northern Hemisphere, is a significant medicinal and edible plant with a long history of use in health maintenance in China\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Both cultivated varieties and wild species (such as Crataegus chungtienensis) possess considerable nutritional and therapeutic value\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Hawthorn leaves and fruits are rich in bioactive compounds, including flavonoids, triterpenoids, organic acids, phenols, and procyanidins, which underpin their traditional medicinal applications\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. These compounds exhibit a range of pharmacological effects, including antihypertensive, lipid-lowering, cardiotonic, digestive-stimulating, appetite-enhancing, antimicrobial, and anticancer properties\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Various dosage forms of hawthorn-based drugs have demonstrated proven therapeutic efficacy and are commonly used to treat conditions related to the digestive, cardiovascular, cerebrovascular, and reproductive systems\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In recent years, with the rapid development of high-throughput sequencing technologies, high-quality, chromosome-level genomes of multiple \u003cem\u003eCrataegus\u003c/em\u003e species have been sequenced. For example, high-quality, chromosome-level genomes have been published for the hawthorn cultivar \u003cem\u003eC. pinnatifida\u003c/em\u003e \u0026lsquo;Qiu Jinxing'\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, the yellow-peel, hard-seeded \u003cem\u003eC. pinnatifida\u003c/em\u003e \u0026lsquo;Jinruyi\u0026rsquo;\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, the red-peel, soft-seeded \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e 'Ruanzi'\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, and \u003cem\u003eC. scabrifolia\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. These assemblies have elucidated the genetic traits and metabolic potential of hawthorn species, providing important molecular evidence for research in pharmacology and botany. Moreover, transcriptomic studies on \u003cem\u003eCrataegus\u003c/em\u003e species have provided important insights into the quality traits of hawthorn fruits and the mechanisms underlying their formation. For example, by comparing the gene expression profiles of hawthorn varieties with those of soft-endocarp and hard-endocarp varieties, a study revealed differentially expressed genes associated with the formation of the soft endocarp\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Furthermore, a comprehensive analysis of the transcriptomes and metabolomes of different hawthorn varieties at the mature stage revealed several candidate genes related to fruit flavor, fruit peel color and seed hardness\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. These studies not only provide key genetic information for understanding the molecular mechanisms behind the development of hawthorn fruit quality but also facilitate the development of hawthorn resources, the exploration of functional metabolites, and advancements in breeding improvements. However, despite chromosome-level genome and bulk transcriptome analyses, the composition of leaf cell types, developmental trajectories, and the specific expression of biosynthetic pathways for bioactive metabolites in hawthorn remain unexplored at single-cell resolution.\u003c/p\u003e \u003cp\u003eIn this study, we applied scRNA-seq and snRNA-seq to cultivated hawthorn leaves to construct a single-cell atlas, identify major leaf tissues, reconstruct developmental trajectories, and resolve the cell-type\u0026ndash;specific expression of flavonoid biosynthesis genes. We optimized the protoplast and nucleus isolation protocols for two \u003cem\u003eCrataegus\u003c/em\u003e species (\u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder) and generated four libraries across different platforms. Using Arabidopsis homologs, we annotated nine major leaf cell types (mesophyll cells, pavement cells, guard cells, xylem cells, metaxylem cells, phloem parenchyma cells, companion cells, sieve element cells, and meristematic cells) and identified novel cell type\u0026ndash;specific marker candidates. Pseudotime analysis reconstructed dynamic gene expression programs during leaf development. Cell-type\u0026ndash;resolved profiling revealed that a portion of flavonoid pathway genes are transcribed in leaves and display cell type\u0026ndash;specific expression, providing a cellular framework for future studies on tissue-specific metabolite biosynthesis in hawthorn.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials and growth conditions\u003c/h2\u003e \u003cp\u003e\u003cem\u003eCrataegus pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eCrataegus scabrifolia\u003c/em\u003e (Franch.) Rehder were grown at the Kunming Institute of Botany, Chinese Academy of Sciences. We collected two leaf samples from each type of hawthorn tree for experimental purposes. Samples 1 and 3 were collected from \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br., while Samples 2 and 4 were collected from \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Fresh, tender leaves were used for all single-cell/nuclear RNA-seq experiments. The collected leaf material was cut into approximately 1 mm fragments and placed in an ice-cold dissociation pretreatment buffer (20 mM KCl, 10 mM CaCl₂, 0.1% bovine serum albumin, 20 mM MES, 0.6 M mannitol). Ticao Zhang undertook the formal identification of the plant material used in our study, and the voucher specimens (YUNCM20230189) were deposited in the Herbarium of Yunnan University of Chinese Medicine (YUNCM). All samples were collected following the necessary permissions obtained from the Kunming Institute of Botany to ensure compliance with local, national, and international regulations regarding plant research.\u003c/p\u003e \u003cp\u003eThe voucher specimens (YUNCM20230189) were deposited in the Herbarium of Yunnan University of Chinese Medicine (YUNCM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Protoplast isolation\u003c/h2\u003e \u003cp\u003eIn our study, Protoplasts were isolated from cleaned hawthorn leaf material for Samples 1 and 2 via an enzymatic digestion method\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, with modifications to improve efficiency. Five milliliters of enzymatic digestion solution (20 mM KCl, 10 mM CaCl₂, 0.1% bovine serum albumin, 20 mM MES, 0.6 M mannitol, 1% cellulase R10, 0.5% snailase, 0.5% macerozyme R-10) was added to the cleaned plant material fragments. The mixture was incubated at 25\u0026deg;C on a shaker at 40 rpm for 4\u0026ndash;5 h to release protoplasts from the leaves. The protoplasts were filtered through a 40 \u0026micro;m cell sieve, centrifuged at 200 \u0026times; g for 3 minutes, and washed with 2 mL of washing solution (5 mM KCl, 125 mM CaCl₂, 154 mM NaCl, 0.1% bovine serum albumin, 20 mM MES). An aliquot was stained with trypan blue and analyzed on a LUNA-FL Cell Counter to determine protoplast count and viability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nuclei isolation\u003c/h2\u003e \u003cp\u003eFor Samples 3 and 4, hawthorn leaf nuclei were isolated following a published protocol\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, with slight modifications. Briefly, 1 mL of ice-cold NIB buffer (20 mM HEPES, pH 8.0, 250 mM sucrose, 1 mM MgCl₂, 5 mM KCl, 0.25% Triton X-100, 40% glycerol, 0.5 mM spermine, 0.5 mM spermidine, 0.1% 2-ME) was added to the cleaned plant material fragments, and the tissue was gently chopped with a razor for 5 min until it could be pipetted ,with 1 mL wide-bore tip. The homogenate was filtered through a 40 \u0026micro;m cell sieve into a chilled 50 mL conical tube. The filtrate was collected and washed with the remaining buffer to recover the nuclei. The mixture was incubated on ice for 10 min, centrifuged at 50 \u0026times; g for 5 min at 4\u0026deg;C to remove bulk impurities, and then centrifuged at 1,100 \u0026times; g for 10 min at 4\u0026deg;C. After the supernatant was removed, the nuclei were resuspended in 500 \u0026micro;L of NIB buffer, and the quality and count of the nuclei were validated via the Countstar Rigel S6 system with AOPI staining.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 snRNA-seq/scRNA-seq library construction and sequencing\u003c/h2\u003e \u003cp\u003eSingle protoplasts were captured via the BD Rhapsody HT single-cell analysis system, and scRNA libraries were constructed via the WTA Amplification Kit (BD). For snRNA library construction, single nuclei were captured via a 10X Genomics GemCode single-cell instrument with the Chromium Next GEM Single Cell 3' Reagent Kit (v3.1). The snRNA-seq and scRNA-seq libraries were constructed according to the protocols of each platform. Briefly, single protoplasts or nuclei were encapsulated in droplets or wells via HT Xpress chips (BD Rhapsody) or Chromium Next GEM Chip (10X Genomics) to create independent environments. mRNA was captured via magnetic beads, followed by protoplast/nuclear lysis, reverse transcription, cDNA amplification, purification, and conversion into single-cell/nuclear RNA-seq libraries with barcode sequences. Indexed sequencing libraries were prepared following the Illumina user guide, with each library assigned a unique index. The library concentration was measured via a Qubit 3.0, and sequencing was performed on the Illumina NovaSeq 6000 System in PE150 mode.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data filter, dimensionality reduction\u003c/h2\u003e \u003cp\u003eThe snRNA-seq and scRNA-seq data for the four wild-type replicates from this study were processed individually via 10X Genomics Cell Ranger (version 8.0.1) and BD Rhapsody CWL-runner software (version 2.2.1). These pipelines include processing of raw fastq data to align reads and generate gene-cell matrices. After the low-quality barcode and UMI data were filtered out, the processed data were aligned to the \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder genome\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The snRNA-seq/scRNA-seq gene-cell matrices for each sample were imported into Seurat (v5.2.1) for downstream analysis. When filtering the data, the parameter \"nFeature_RNA\" was set between 500 and 6000, the percentage of mitochondrial genes was less than 5% and the percentage of chloroplast genes was less than 10%. Each dataset was subsequently normalized and integrated via the IntegrateData function. Principal component analysis (PCA) was performed to reduce the dimensionality of the integrated single-cell matrices, and the top 20 PCs were selected as inputs for UMAP and t-SNE visualization and clustering analysis. We used the FindClusters method for cell clustering, with the resolution value determined by comparing different values, which was set to 0.4. The 'FindAllMarkers' function was then used to identify differentially expressed genes (DEGs) for each cluster, with parameters set to min.pct\u0026thinsp;=\u0026thinsp;0.25 and logfc.threshold\u0026thinsp;=\u0026thinsp;0.25. On the basis of the DEGs identified between the clusters, the integrated single-cell RNA data were assigned to the corresponding cell populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Marker gene and cell type identification\u003c/h2\u003e \u003cp\u003eTo identify the various cell populations in Hawthorn leaf tissues, we utilized marker genes specific to each cell type. First, we aligned the cell type marker genes reported in Arabidopsis (\u003cem\u003eA. thaliana\u003c/em\u003e) with their homologous counterparts in Hawthorn. Differential gene expression analysis was performed on the integrated and clustered single-cell RNA data, with a focus on genes upregulated in each population. Known tissue-specific markers from \u003cem\u003eA. thaliana\u003c/em\u003e were used as references, and cell types were predicted and manually cross-checked via the PlantCellMarker and scPlantDB databases\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. This approach enabled the annotation and identification of cell types in Hawthorn leaf tissues. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the upregulated genes to validate the molecular functions of the gene in the cell populations. Following cell population identification, we explored cell-specific marker genes in Hawthorn leaf tissue. The selection criterion included a log fold change (logFC) threshold of 0.25, requiring that at least 25% of the cells in each cluster expressed the target gene. The top 20 genes in each cluster, ranked by their average log2 fold change (avg_log2FC), were selected as potential marker genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Pseudotime analysis\u003c/h2\u003e \u003cp\u003eTo explore gene expression changes and the determine of cell fate during the development of hawthorn leaves, we constructed a single-cell pseudotime differentiation trajectory via Monocle (version 2.30.1) on the basis of the expression matrix of all cell populations. The first step involved filtering out genes with low expressed genes, and creating a structured datasets (lowerDetectionLimit\u0026thinsp;=\u0026thinsp;0.5). The size factors and dispersion of each gene were estimated, and genes expressed in at least 10 cells were selected. Genes were identified as exhibiting significant differential expression across various cell populations (qval\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These genes, which show significant variability, are crucial for constructing cell trajectories and analyzing cell states. We utilized the plot_ordering_genes function to visualize the ordered genes defined in the single-cell trajectory analysis, demonstrating how they varied across different cellular states or orders. The dimensionality of the data was reduced to two components (max_components\u0026thinsp;=\u0026thinsp;2, method = \u0026lsquo;DDRTree\u0026rsquo;) to facilitate further analysis. We employed the orderCells() function to order the cells on the basis of their gene expression profiles and set the root_state argument to specify the meristematic cells as the starting point of the trajectory. The resulting cell trajectory was then visualized via the plot_cell_trajectory function in Monocle. To analyze the branches in the differentiation trajectory, branch expression analysis modeling (BEAM) was used to identify genes that were dependent on pseudotime or specific branches. These branch-dependent genes were visualized via the plot_genes_branched_heatmap function, providing insight into the gene expression changes across different branches.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 \u0026nbsp;Single-cell RNA-seq generates comparable transcriptome datasets\u003c/h2\u003e\n\u003cp\u003eThe isolation of protoplasts and nuclei is fundamental for single-cell transcriptome research and analysis. In this study, we optimized the preparation protocols for the isolation of protoplasts and nuclei from the leaves of two \u003cem\u003eCrataegus\u003c/em\u003e species (\u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder) and constructed four libraries following the protocols of the BD Rhapsody HT and the 10X Genomics GemCode single-cell platform (Figure 1a). To assess the sequencing data quality of each library, several parameters, including total reads, gene count, and cell count, were evaluated. A total of 574,493,457 and 544,520,935 reads were obtained from \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. through scRNA-seq and snRNA-seq, respectively. After filtering out the low-quality cells and genes with abnormal expression were filtered out, 7,147 protoplasts and 10,674 nuclei were obtained, with an average of 914 and 716 genes detected per cell, respectively. For \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder, scRNA-seq and snRNA-seq obtained a total of 597,928,880 and 562,537,244 reads, respectively. After filtering out the low-quality cells and genes with abnormal expression, 4,463 protoplasts and 10,008 nuclei were obtained, with an average of 937 and 2,005 genes detected per cell, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo cluster distinct cell populations, we integrated four filtered single-cell datasets from different samples using Canonical Correlation Analysis (CCA). After integration, an unbiased, unsupervised clustering was performed on the transcriptomes of 32,292 cells using the Seurat software package. The transcriptomes from the integrated data were then plotted in two dimensions using tSNE, revealing clustering results with the cells grouped into sixteen distinct clusters (Figure 1b; Supplemental Figure 1). We further compared the single-cell data from different methods (protoplast and nuclei) and species (\u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder) via t-distributed stochastic neighbor embedding (t-SNE), which revealed largely overlapping cell distributions and a comparable proportion of cells in each cluster across all the libraries (Figure 1c-d).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 \u0026nbsp;Identification of Main Leaf Tissues: Mesophyll, Vasculature, and Epidermis in Hawthorn Leaves\u003c/h2\u003e\n\u003cp\u003eTo explore the cellular heterogeneity in hawthorn leaves, we performed gene expression profiling on sixteen distinct cell clusters, and identified 6,024 differentially expressed genes (DEGs) (Supplemental Table S1). As no cell-specific marker genes for hawthorn have been reported, we used homologous marker genes from Arabidopsis to determine the corresponding cell types in hawthorn leaves (Supplemental Table S2). We characterized several distinct tissue types: mesophyll cells (MCs), pavement cells (PCs), xylem (XY), phloem parenchyma (PP), metaxylem (MXY), guard cells (GCs), companion cells (CCs), sieve elements (SEs), and meristematic cells (MERs) in hawthorn leaves (Figure 2a, Supplemental Table S2). Both \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder presented the same set of cell types, with similar distribution trends across these species, indicating conserved cellular features in the hawthorn leaf structure (Figure 2c, Supplemental Table S3). Clusters 0 and 4, classified as mesophyll cells, had the highest proportion of cells. These clusters predominantly expressed marker genes involved in photosynthesis, such as ribulose bisphosphate carboxylase small chain (RBCS) and light-harvesting chlorophyll a-b binding protein (\u003cem\u003eLHCB\u003c/em\u003e) (Supplemental Table S2, Figure 2b). Clusters 2, 7, and 11 were identified as epidermal cells, based on the basis of their specific expression of genes related to cuticular wax and suberin biosynthesis (Supplemental Table S2, Figure 2b). These genes include 3-ketoacyl-CoA synthase 10 (\u003cem\u003eFDH\u003c/em\u003e), homeodomain glabrous2 (\u003cem\u003eHDG2\u003c/em\u003e), and protodermal factor 1-like gene (FACTOR1/PDF1). Specifically, Clusters 2 and 7 were classified as leaf pavement cells, whereas Cluster 11, a subset of epidermal cells, was designated as leaf guard cells, on the basis of the expression \u003cem\u003eFAMA\u003c/em\u003e (\u003cem\u003eCrsca04aG0005700\u003c/em\u003e) and \u003cem\u003eSCRM\u0026nbsp;\u003c/em\u003e(\u003cem\u003eCrsca13aG0002900\u003c/em\u003e) marker genes during stomatal development. The identity of the phloem parenchyma and conducting cells was determined on the basis of transcript enrichment of highly expressed genes, such as protein argonaute 10-like (\u003cem\u003eCrsca06aG0101400\u003c/em\u003e), glyoxylase I 4 (\u003cem\u003eCrsca01aG0100700\u003c/em\u003e), auxin efflux carrier component 5 (\u003cem\u003eCrsca17aG0247600\u003c/em\u003e), and linoleate 9S-lipoxygenase 6 (\u003cem\u003eCrsca02aG0180900\u003c/em\u003e) (Supplemental Table S2, Figure 2b). These genes are expressed in Clusters 3, 10, 13, and 15. Clusters 3 and 10 were classified as phloem parenchyma, Cluster 13 as companion cells, and Cluster 15 as sieve elements. Zinc finger protein ZAT12-like (\u003cem\u003eCrsca05aG0093600\u003c/em\u003e), auxin-induced protein 22D (\u003cem\u003eCrsca07aG0086600\u003c/em\u003e), subtilisin-like protease SBT4.14 (\u003cem\u003eCrsca09aG0122400\u003c/em\u003e), expansin-A4-like (\u003cem\u003eCrsca16aG0137100\u003c/em\u003e), and probable beta-1,4-xylosyltransferase \u003cem\u003eIRX10\u003c/em\u003e (\u003cem\u003eCrsca07aG0156100\u003c/em\u003e) were specifically expressed in Clusters 1, 8, 9, 12, and 14 (Supplemental Table S2, Figure 2b). Clusters 9 and 14 correspond to metaxylem cells, whereas Clusters 1, 8, and 12 are classified as xylem cells. Additionally, Clusters 5 and 6 presented high meristematic activity and were identified as meristematic cells, with many cell cycle-related genes being enriched and highly expressed in these clusters (Supplemental Table S2). For example, kinesin-like protein \u003cem\u003eKIN-12D\u003c/em\u003e (\u003cem\u003eCrsca03aG0118600\u003c/em\u003e) (Figure 2b), which is involved in microtubule movement, was highly expressed in these clusters, suggesting its role in the dynamic processes of cell division and growth within meristematic regions.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.3 \u0026nbsp;Characterization of Different Cell Populations and Discovery of Novel Marker Genes\u003c/h2\u003e\n\u003cp\u003eAfter identifying the major cell populations, we characterized their gene expression profiles and recalculated differentially expressed genes (DEGs) between cell types to identify novel marker genes. The number of upregulated DEGs ranged from 154 in phloem parenchyma populations to 567 in meristematic cells (Supplemental Table S4). Across the nine cell types, these DEGs presented distinct transcriptional profiles and associated biological processes (Figure 3, Supplemental Table S5). Mesophyll cells, located beneath the epidermal layers and serving as the principal sites of photosynthesis, presented distinct transcriptional profiles, with GO enrichment analysis revealing overrepresentation of biological processes associated with photosynthetic light reactions and the electron transport chain. The leaf epidermis functions as more than a physical barrier; it contributes to defense against biotic threats and regulates water loss and gas exchange. Accordingly, pavement cells were enriched in GO terms related to lipid metabolism and cuticle development and responses to osmotic stress, whereas guard cells were enriched in processes associated with potassium ion transport and proton transmembrane transport, reflecting their protective and stomatal roles under desiccation and environmental stress. The vascular tissue, which transports water and nutrients and supports long-distance signaling, comprises five distinct subpopulations with specialized transcriptional profiles. Phloem parenchyma cells were enriched in processes associated with secondary metabolite biosynthesis, stress responses, and water transport, whereas sieve elements were enriched in phloem development, autophagy, and responses to cellular component size and toxic compounds. The companion cells were enriched in processes related to osmotic and oxidative stress responses, xenobiotic metabolism, carbohydrate metabolism, and cytoplasmic translation, suggesting that they are metabolically active and stress responsive,which in line with their supportive roles in sieve elements. Xylem and metaxylem cells were enriched in GO categories linked to responses to high light and osmotic stress, steroid and fatty acid metabolism, and cell wall\u0026ndash;associated metabolic processes, highlighting their involvement in water transport, structural support, and stress adaptation. Finally, meristematic cells presented upregulated expression of genes involved in mitotic spindle organization, microtubule-based movement, translational initiation, and chromatin and nucleosome organization, , which was consistent with their proliferative and developmental activities. The detailed GO enrichment results for all the cell populations are summarized in Supplemental Table S5. In addition, characterization of the chloroplast gene content across different cell populations revealed substantial differences, with mesophyll and meristematic cells exhibiting a significantly greater proportion of chloroplast-related genes than epidermal and vascular cell populations (Supplemental Figure 2, Supplemental Figure 3).\u003c/p\u003e\n\u003cp\u003eBuilding on these cell-type specific functional signatures, we selected the top 20 upregulated DEGs for each population (ranked by avg_log2FC) and visualized their expression across the nine cell types via a heatmap (Figure 3). We highlight representative marker genes from each cluster that exemplify their specialized expression patterns, facilitating the identification of leaf cell types in hawthorn. In mesophyll cells, REDUCED CHLOROPLAST COVERAGE 2-like (\u003cem\u003eCrsca06aG0026300\u003c/em\u003e) and NADH dehydrogenase subunit F (\u003cem\u003eCrsca11aG0017000\u003c/em\u003e), which are required for chloroplast maintenance and electron transport, were highly expressed(Figure 3, Supplemental Figure 4a-b), which is consistent with the central role of mesophyll cells in photosynthesis and energy production. In pavement cells, a desiccation protectant protein \u003cem\u003e(Crsca12aG0163100\u003c/em\u003e) and a very-long-chain aldehyde decarbonylase (\u003cem\u003eCrsca07aG0013600\u003c/em\u003e), involved in long-chain fatty acid biosynthesis and membrane protection, were specifically expressed (Figure 3, Supplemental Figure 4c-d), suggesting contributions to cuticle integrity and tolerance to water loss and osmotic stress. Guard cells specifically expressed two phosphoenolpyruvate carboxylases (\u003cem\u003eCrsca10aG0042800\u003c/em\u003e and \u003cem\u003eCrsca08aG0052200\u003c/em\u003e) (Figure 3, Supplemental Figure 4e-f), which participate in CO\u003csub\u003e2\u003c/sub\u003e assimilation and associated metabolic pathways, thereby optimizing photosynthesis under conditions of low CO\u003csub\u003e2\u003c/sub\u003e availability. In phloem parenchyma cells, genes encoding the TMV resistance protein N (\u003cem\u003eCrsca03aG0044200\u003c/em\u003e) and the putative disease resistance protein \u003cem\u003eRGA3\u003c/em\u003e (\u003cem\u003eCrsca11aG0044000\u003c/em\u003e) were specifically expressed (Figure 3, Supplemental Figure 4g-h), indicating a role in local defense responses within the vascular system. In sieve elements, SIEVE ELEMENT OCCLUSION B (\u003cem\u003eCrsca14aG0077000\u003c/em\u003e) and callose synthase 7-like (\u003cem\u003eCrsca13aG0129700\u003c/em\u003e) were specifically expressed (Figure 3, Supplemental Figure 4i-j), which is consistent with their involvement in sieve plate function and callose deposition to preserve phloem integrity and facilitate defense. Companion cells, which support sieve element function, specifically expressed PHLOEM PROTEIN 2-like (\u003cem\u003eCrsca02aG0095700\u003c/em\u003e) and glycine-rich protein A3-like (\u003cem\u003eCrsca09aG0081000\u003c/em\u003e) (Figure 3, Supplemental Figure 4k-l), underscoring their specialized roles in maintaining phloem structure and in the transport of nutrients and signaling molecules. In xylem cells, a DOMON/cytochrome b561 domain\u0026ndash;containing protein (\u003cem\u003eCrsca03aG0331200\u003c/em\u003e), REF/SRPP-like protein (\u003cem\u003eCrsca17aG0206500\u003c/em\u003e), and 2-oxoglutarate-dependent dioxygenase 19-like protein (\u003cem\u003eCrsca02aG0293200\u003c/em\u003e) are highly expressed (Figure 3, Supplemental Figure 4m\u0026ndash;o); together these redox- and cell wall\u0026ndash;related proteins likely contribute to maintaining xylem integrity and modulating responses to oxidative and environmental stresses. In metaxylem cells, the enrichment of methionine metabolism and protein localization to the cell surface was reflected by the specific expression of probable rhamnogalacturonate lyase B (\u003cem\u003eCrsca01aG0251900\u003c/em\u003e), probable hexosyltransferase MUCI70 (\u003cem\u003eCrsca02aG0039900\u003c/em\u003e), and rhamnogalacturonan I rhamnosyltransferase 1 (\u003cem\u003eCrsca07aG0213100\u003c/em\u003e) (Figure 3, Supplemental Figure 4p\u0026ndash;r), suggesting a specialized role in rhamnogalacturonan metabolism and cell wall remodeling during stress adaptation. Finally, meristematic cells, which are characterized by upregulated genes associated with microtubule organization, protein binding, and cytoskeletal movement, presented markedly higher expression of histone H1.2-like (\u003cem\u003eCrsca17aG0233000\u003c/em\u003e) and histone H1-like (\u003cem\u003eCrsca02aG0134300\u003c/em\u003e) than other populations did (Figure 3, Supplemental Figure 4 s-t), highlighting their roles in chromatin organization, cell division, and differentiation in actively proliferating meristematic tissues.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.4 \u0026nbsp;Pseudotime Analysis of Differentiation Trajectories in Hawthorn Leaf Development\u003c/h2\u003e\n\u003cp\u003eTo better understand the developmental trajectories of hawthorn leaves, we performed pseudotime analysis, which enabled us to track the progression of cellular differentiation along a temporal axis. The trajectories revealed consistent developmental paths between \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. and \u003cem\u003eC. acerifolia\u003c/em\u003e, with nine cell types from both species mapped to the hawthorn leaf differentiation trajectories, demonstrating a convergence of differentiation pathways (Figure 4b, Figure 4d). The cellular distribution was visualized in reduced dimensions and colored by pseudotime progression, with cells transitioning from early to late stages of development. Five key branching points were identified within the differentiation paths, dividing the trajectories into 11 distinct developmental states, reflecting major shifts in cellular fate during leaf development (Figure 4a, Figure 4c). A total of 4,475 genes with dynamic expression changes were implicated in cell growth (Supplemental Table S6), providing insights for further investigation into leaf cell development. A heatmap was then generated on the \u0026nbsp;basis of the top 50 most significantly regulated genes ranked by q-value from the pseudotime differential expression analysis (Figure 4f). We observed changes in the distribution and density of different cell types across pseudotime, with PC, XY, and PP being prominent in the early stages, followed by the emergence of MXY, GC, CC, and SE in mid-pseudotime, and finally, MC dominating in the late stages, highlighting their significant role in development advancement(Figure 6e). This analysis emphasizes the temporal changes and relationships among various cell types during hawthorn leaf development, offering a detailed transcriptional roadmap that identifies conserved regulatory modules.\u003c/p\u003e\n\u003cp\u003eBuilding on these cell-type specific functional signatures, we selected the top 20 upregulated DEGs for each population (ranked by avg_log2FC) and visualized their expression across the nine cell types via a heatmap (Figure 3). We highlight representative marker genes from each cluster that exemplify their specialized expression patterns, facilitating the identification of leaf cell types in hawthorn. In mesophyll cells, REDUCED CHLOROPLAST COVERAGE 2-like (\u003cem\u003eCrsca06aG0026300\u003c/em\u003e) and NADH dehydrogenase subunit F (\u003cem\u003eCrsca11aG0017000\u003c/em\u003e), which are required for chloroplast maintenance and electron transport, were highly expressed(Figure 3, Supplemental Figure 4a-b), which is consistent with the central role of mesophyll cells in photosynthesis and energy production. In pavement cells, a desiccation protectant protein \u003cem\u003e(Crsca12aG0163100\u003c/em\u003e) and a very-long-chain aldehyde decarbonylase (\u003cem\u003eCrsca07aG0013600\u003c/em\u003e), involved in long-chain fatty acid biosynthesis and membrane protection, were specifically expressed (Figure 3, Supplemental Figure 4c-d), suggesting contributions to cuticle integrity and tolerance to water loss and osmotic stress. Guard cells specifically expressed two phosphoenolpyruvate carboxylases (\u003cem\u003eCrsca10aG0042800\u003c/em\u003e and \u003cem\u003eCrsca08aG0052200\u003c/em\u003e) (Figure 3, Supplemental Figure 4e-f), which participate in CO\u003csub\u003e2\u003c/sub\u003e assimilation and associated metabolic pathways, thereby optimizing photosynthesis under conditions of low CO\u003csub\u003e2\u003c/sub\u003e availability. In phloem parenchyma cells, genes encoding the TMV resistance protein N (\u003cem\u003eCrsca03aG0044200\u003c/em\u003e) and the putative disease resistance protein \u003cem\u003eRGA3\u003c/em\u003e (\u003cem\u003eCrsca11aG0044000\u003c/em\u003e) were specifically expressed (Figure 3, Supplemental Figure 4g-h), indicating a role in local defense responses within the vascular system. In sieve elements, SIEVE ELEMENT OCCLUSION B (\u003cem\u003eCrsca14aG0077000\u003c/em\u003e) and callose synthase 7-like (\u003cem\u003eCrsca13aG0129700\u003c/em\u003e) were specifically expressed (Figure 3, Supplemental Figure 4i-j), which is consistent with their involvement in sieve plate function and callose deposition to preserve phloem integrity and facilitate defense. Companion cells, which support sieve element function, specifically expressed PHLOEM PROTEIN 2-like (\u003cem\u003eCrsca02aG0095700\u003c/em\u003e) and glycine-rich protein A3-like (\u003cem\u003eCrsca09aG0081000\u003c/em\u003e) (Figure 3, Supplemental Figure 4k-l), underscoring their specialized roles in maintaining phloem structure and in the transport of nutrients and signaling molecules. In xylem cells, a DOMON/cytochrome b561 domain\u0026ndash;containing protein (\u003cem\u003eCrsca03aG0331200\u003c/em\u003e), REF/SRPP-like protein (\u003cem\u003eCrsca17aG0206500\u003c/em\u003e), and 2-oxoglutarate-dependent dioxygenase 19-like protein (\u003cem\u003eCrsca02aG0293200\u003c/em\u003e) are highly expressed (Figure 3, Supplemental Figure 4m\u0026ndash;o); together these redox- and cell wall\u0026ndash;related proteins likely contribute to maintaining xylem integrity and modulating responses to oxidative and environmental stresses. In metaxylem cells, the enrichment of methionine metabolism and protein localization to the cell surface was reflected by the specific expression of probable rhamnogalacturonate lyase B (\u003cem\u003eCrsca01aG0251900\u003c/em\u003e), probable hexosyltransferase MUCI70 (\u003cem\u003eCrsca02aG0039900\u003c/em\u003e), and rhamnogalacturonan I rhamnosyltransferase 1 (\u003cem\u003eCrsca07aG0213100\u003c/em\u003e) (Figure 3, Supplemental Figure 4p\u0026ndash;r), suggesting a specialized role in rhamnogalacturonan metabolism and cell wall remodeling during stress adaptation. Finally, meristematic cells, which are characterized by upregulated genes associated with microtubule organization, protein binding, and cytoskeletal movement, presented markedly higher expression of histone H1.2-like (\u003cem\u003eCrsca17aG0233000\u003c/em\u003e) and histone H1-like (\u003cem\u003eCrsca02aG0134300\u003c/em\u003e) than other populations did (Figure 3, Supplemental Figure 4 s-t), highlighting their roles in chromatin organization, cell division, and differentiation in actively proliferating meristematic tissues.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.4 \u0026nbsp;Pseudotime Analysis of Differentiation Trajectories in Hawthorn Leaf Development\u003c/h2\u003e\n\u003cp\u003eTo better understand the developmental trajectories of hawthorn leaves, we performed pseudotime analysis, which enabled us to track the progression of cellular differentiation along a temporal axis. The trajectories revealed consistent developmental paths between \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. and \u003cem\u003eC. acerifolia\u003c/em\u003e, with nine cell types from both species mapped to the hawthorn leaf differentiation trajectories, demonstrating a convergence of differentiation pathways (Figure 4b, Figure 4d). The cellular distribution was visualized in reduced dimensions and colored by pseudotime progression, with cells transitioning from early to late stages of development. Five key branching points were identified within the differentiation paths, dividing the trajectories into 11 distinct developmental states, reflecting major shifts in cellular fate during leaf development (Figure 4a, Figure 4c). A total of 4,475 genes with dynamic expression changes were implicated in cell growth (Supplemental Table S6), providing insights for further investigation into leaf cell development. A heatmap was then generated on the \u0026nbsp;basis of the top 50 most significantly regulated genes ranked by q-value from the pseudotime differential expression analysis (Figure 4f). We observed changes in the distribution and density of different cell types across pseudotime, with PC, XY, and PP being prominent in the early stages, followed by the emergence of MXY, GC, CC, and SE in mid-pseudotime, and finally, MC dominating in the late stages, highlighting their significant role in development advancement(Figure 6e). This analysis emphasizes the temporal changes and relationships among various cell types during hawthorn leaf development, offering a detailed transcriptional roadmap that identifies conserved regulatory modules.\u003c/p\u003e\n\u003ch2\u003e3.5 \u0026nbsp;Cell specificity of genes related to the biosynthesis of flavonoids in hawthorn leaves during leaf development\u003c/h2\u003e\n\u003cp\u003eTo further investigate whether flavonoid biosynthesis is cell-specific during leaf development, we analyzed the expression of related genes in various cell types. The results revealed that genes involved in flavonoid formation presented similar expression patterns in the leaves of both \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder, indicating consistency in gene expression between the two species (Figure 5, Supplemental Table S6). Previous studies have reported the amplification of flavonoid biosynthesis genes in the \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. genome; however, in our single-cell RNA sequencing analysis, most of these candidate genes were not detected. Only 14 genes related to flavonoid biosynthesis exhibited detectable expression(Figure 5a).\u0026nbsp;Flavonoid biosynthesis begins with phenylalanine, which is converted into precursor compounds by enzymes such as phenylalanine ammonia-lyase (\u003cem\u003ePAL\u003c/em\u003e), cinnamate 4-hydroxylase, and 4-coumarate-CoA ligase(Figure 5b). \u003cem\u003ePAL\u003c/em\u003e (\u003cem\u003eCrsca12aG0117800\u003c/em\u003e) was highly expressed in both the PC and PP cells of \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Similarly, 4-Coumarate-CoA ligase (\u003cem\u003e4CL\u003c/em\u003e, \u003cem\u003eCrsca07aG0047300\u003c/em\u003e and \u003cem\u003eCrsca08aG0053500\u003c/em\u003e) were highly expressed in the MXY cells of both species. The Expression of \u003cem\u003e4CL\u003c/em\u003e was also observed in the GC and PC cells of \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br., but not in the corresponding cells of \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Chalcone synthase (\u003cem\u003eCHS\u003c/em\u003e) and chalcone isomerase (\u003cem\u003eCHI\u003c/em\u003e) are key enzymes in flavonoid biosynthesis(Figure 5b). \u003cem\u003eCHS\u003c/em\u003e catalyzes the formation of the chalcone backbone for all flavonoids, whereas \u003cem\u003eCHI\u003c/em\u003e converts chalcones into naringenin. \u003cem\u003eCHS\u003c/em\u003e (\u003cem\u003eCrsca04aG0218800\u003c/em\u003e) was highly expressed in XY cells of both species, with detectable expression also found in the MC and MXY cells of \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. \u003cem\u003eCHI\u003c/em\u003e (\u003cem\u003eCrsca01aG0098200\u003c/em\u003e, \u003cem\u003eCrsca09aG0185300\u003c/em\u003e) was highly expressed in MER cells of both species, whereas another \u003cem\u003eCHI\u003c/em\u003e gene (\u003cem\u003eCrsca12aG0102700\u003c/em\u003e) was expressed in PP cells. As a major metabolite, naringenin enters the flavonoid biosynthesis pathway and undergoes various modifications to generate different flavonoid compounds(Figure 5b). Flavanone 3-hydroxylase (\u003cem\u003eF3H\u003c/em\u003e, \u003cem\u003eCrsca09aG0185700\u003c/em\u003e), which catalyzes the conversion of naringenin to dihydrokaempferol, was highly expressed in GC and PP cells of both species, with lower but still detectable expression in the MC, MER, and XY cells of \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br.. Dihydroflavonol 4-reductase (\u003cem\u003eDFR\u003c/em\u003e, \u003cem\u003eCrsca10aG0223200\u003c/em\u003e), which is essential for the biosynthesis of compounds such as epigallocatechin and epicatechin, was highly expressed in MC cells of both species, with additional expression observed in MER and PP cells of \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br.\u003cem\u003e\u0026nbsp;\u003c/em\u003eand in MXY cells of \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Isoflavone synthase (\u003cem\u003eIFS\u003c/em\u003e), a key enzyme in the isoflavonoid biosynthesis pathway, catalyzes the structural rearrangement of naringenin, transferring the connection from C-2 to C-3 of the intermediate ring(Figure 5b). The\u003cem\u003e\u0026nbsp;IFS\u003c/em\u003e genes, including \u003cem\u003eCrsca03aG0100400\u003c/em\u003e (highly expressed in SE cells of both species), \u003cem\u003eCrsca04aG0315000\u003c/em\u003e and \u003cem\u003eCrsca07aG0097100\u003c/em\u003e (highly expressed in MER cells of both species), and \u003cem\u003eCrsca13aG0040000\u003c/em\u003e (highly expressed in PP cells of \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u0026nbsp;\u003c/em\u003eN. E. Br.), exhibited distinct, cell-specific expression patterns across different cell types. Anthocyanidin synthase (\u003cem\u003eANS\u003c/em\u003e, \u003cem\u003eCrsca14aG0165700\u003c/em\u003e), which is expressed primarily in MC and PP cells, plays a crucial role in the further synthesis of compounds such as Procyanidin B2. These results demonstrate that different homologous genes of an enzyme are transcribed in distinct cell types, highlighting the heterogeneity of cell types and the spatiotemporal expression of genes involved in secondary metabolism at different stages of hawthorn leaf development.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePlant leaves are heterogeneous organs consisting of epidermal cells, mesophyll cells, vascular cells and other cell types with different functions\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In plant single‑cell transcriptomics, two commonly used strategies are protoplast‑based scRNA‑seq and nucleus‑based snRNA‑seq.\u0026nbsp;Here, we present the first integrated single‑cell and single‑nucleus transcriptomic atlas of hawthorn leaves, profiling 32,292 high‑quality cells from two \u003cem\u003eCrataegus\u003c/em\u003e species and resolving nine principal leaf cell types across sixteen transcriptional clusters. Leveraging the high sensitivity and resolution of scRNA‑seq and snRNA‑seq, we distinguished subpopulations within the same cell types that are often indistinguishable by bulk RNA‑seq\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Accurate cell annotation remains a central challenge for single-cell studies in non‑model plants\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. To annotate the hawthorn cell types, we used homologous marker genes from Arabidopsis and compared the chloroplast gene content across different cell populations. The dominance of the mesophyll clusters is supported by strong expression of canonical mesophyll markers (e.g., \u003cem\u003eRBCS\u003c/em\u003e\u003csup\u003e[\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eLHCB\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, and \u003cem\u003ePSAB\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e), which is consistent with the central role of mesophyll cells in carbon fixation and light harvesting and reinforces the validity of our cell‑type assignments. Epidermal clusters express marker genes involved in cuticle/suberin biosynthesis (e.g., \u003cem\u003eFDH\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eHDG2\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003ePDF1\u003c/em\u003e\u003csup\u003e[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e), consistent with the protective and water-retention roles of the epidermis. Guard cell identity is corroborated by the stomatal development markers \u003cem\u003eFAMA\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e and \u003cem\u003eSCRM\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, suggesting intact regulatory modules for stomatal differentiation in hawthorn. Vasculature-related clusters show enrichment of transport and signaling associated marker genes (e.g., \u003cem\u003eAGO10\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eGLYI4\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eFP3\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eTBL34\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e, \u003cem\u003eATGUT1\u003c/em\u003e\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e), aligning with roles in photoassimilate translocation and long-distance signaling. Moreover, GO enrichment analysis revealed that mesophyll and meristematic clusters are significantly enriched for chloroplast‑related terms, providing additional evidence that our cell‑type annotations accurately reflect photosynthetic and developmental identities. Furthermore, cluster reproducibility was validated across two \u003cem\u003eCrataegus\u003c/em\u003e species: we observed the same set of cell types in both \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder with broadly similar distribution patterns. This cross‑species concordance supports conserved cellular organization in hawthorn leaves and increases confidence in the accuracy of our cell type annotations.\u003c/p\u003e \u003cp\u003eProtoplasting requires enzymatic removal of the cell wall to release intact cells, and because different cell types may have distinct osmotic tolerances, inadequate osmotic conditions can cause protoplast rupture and the selective loss of fragile cell types\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In addition, enzymatic digestion itself can trigger stress responses in protoplasts, leading to transcriptional changes that deviate from their native in vivo states and potentially confound downstream single‑cell transcriptomic analyses\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. In leaf tissues, mesophyll cells typically predominate and contain abundant chlorophyll, which produces strong autofluorescence that can confound fluorescence‑based assays, including protoplast viability measurements\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Similarly, snRNA‑seq is less affected by protoplasting‑induced bias but predominantly captures nuclear transcripts and therefore typically detects fewer genes per nucleus than does whole‑cell scRNA‑seq\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. In our study, we combined both approaches to balance their complementary strengths: snRNA‑seq mitigated the loss of osmotically fragile cell types, whereas scRNA‑seq improved cytoplasmic transcript detection. Integrating data from both modalities and confirming cluster reproducibility across two \u003cem\u003eCrataegus\u003c/em\u003e species allowed us to assemble a more complete and robust single‑cell transcriptomic atlas for hawthorn leaves.\u003c/p\u003e \u003cp\u003eThe development of plant leaves to their final form entails coordinated cell proliferation, expansion, and differentiation\u003csup\u003e[\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Cell proliferation is concentrated in meristematic regions, which are characterized by active division and the potential to develop into multiple cell types\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our pseudotime reconstruction, meristematic cells occupy the earliest positions of the trajectory. The analysis revealed branched differentiation pathways with five major branch points and 11 discrete developmental states, reflecting ordered transitions from proliferative meristematic cells to specialized epidermal, vascular, and photosynthetic lineages. Pseudotime analysis indicated that pavement cells and vascular progenitors are enriched at early stages, followed by the emergence of guard cells, companion cells, and sieve elements, culminating in mesophyll cell dominance. These results align with classical models of leaf organogenesis and suggest conserved developmental programs across \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder. Previous studies in Arabidopsis have shown that during development chloroplasts assume different roles depending on the functions of differentiated cells and can influence both cell proliferation and cell expansion\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. In the epidermal lineage, chloroplasts are present during early epidermal development but mature epidermal cells lose photosynthetic activity upon differentiation\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Consistent with these findings, we observed high expression of chloroplast-associated genes in both meristematic cells and mesophyll cells. These findings suggest that chloroplast-related processes contribute to cell type-specific developmental programs in these hawthorn species and provide a theoretical basis for further elucidation of the mechanisms underlying plant leaf development.\u003c/p\u003e \u003cp\u003eCell‑type‑resolved analysis revealed spatial partitioning of flavonoid biosynthetic genes in hawthorn leaves, indicating that flavonoid production is organized across multiple cell types rather than confined to single cells. We detected a limited subset of annotated pathway genes at transcriptional resolution, each with a distinct localization: \u003cem\u003ePAL\u003c/em\u003e transcripts were enriched in pavement and phloem parenchyma cells; \u003cem\u003eCHS\u003c/em\u003e was predominantly expressed in xylem‑associated cells; \u003cem\u003eDFR\u003c/em\u003e was most abundant in mesophyll; and \u003cem\u003eIFS\u003c/em\u003e family members were partitioned among sieve elements, meristematic cells, and phloem parenchyma. This pattern indicates the potential for intercellular metabolite channeling, whereby precursors and intermediates are produced, transferred or exchanged across neighboring cell types instead of being sequentially processed within a single cell. The observation that only a fraction of annotated pathway genes were transcriptionally detected likely reflects biological factors such as developmental-stage or condition-specific expression and low basal transcript levels; accordingly, the absence of detection in our dataset should be interpreted with caution and validated by targeted methods (e.g. in situ hybridization or higher-coverage spatial transcriptomics). Flavonoids are ubiquitous phenolic secondary metabolites involved in development, ripening, stress tolerance (biotic and abiotic), and organismal interactions. Their biosynthesis is regulated not only by structural enzymes and transcription factors but also by epigenetic regulators such as long noncoding RNAs (lncRNAss and microRNAs (miRNAs)\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. Comparative analysis of single-cell transcriptomes from two hawthorn leaf types supports a model in which flavonoid biosynthesis is a multicellular, spatially organized process involving cell-specific enzyme complements, intercellular metabolite transfer, and localized regulation; future studies should validate the cellular localizations, trace metabolite flows, and identify cell-specific transcription factors, lncRNAs, and miRNAs to reconstruct the spatial organization and regulatory logic of this pathway and to inform strategies for manipulating flavonoid composition for improved stress resistance or nutritional value.\u003c/p\u003e \u003cp\u003eAlthough combining scRNA‑seq and snRNA‑seq improved cellular coverage and reduced some preparation artifacts, important limitations of this study remain and should be addressed in future work. Protoplast isolation can induce stress‑responsive transcriptional changes, whereas snRNA‑seq\u0026mdash;although it mitigates this artifact\u0026mdash;captures a transcriptome biased toward nuclear RNAs; therefore, each method has inherent biases. All the samples were collected at a single developmental stage under a single set of environmental conditions, which restricts our ability to capture the cell‑type dynamics and transcriptional programs that occur during other developmental stages or under varying environmental cues; as a result, temporal and condition specific gene expression may be underrepresented. In addition, single‑cell transcriptomics measures mRNA abundance but does not directly demonstrate protein expression, enzyme activity, or metabolite production, so transcript‑level associations require orthogonal validation to establish functional relevance. To address these limitations, future studies should expand spatiotemporal sampling and integrate orthogonal assays: collect leaves across multiple developmental stages (e.g., young/expanding, mature, senescing) and under various environmental conditions (e.g., light regimes, drought or salinity, temperature treatments) to construct a comprehensive spatiotemporal atlas; apply spatial transcriptomics to localize candidate genes in situ; and combine proteomics, translation‑level assays (e.g., mass spectrometry, Ribo‑seq) and metabolomics to validate protein expression, translational regulation, and metabolite accumulation. These combined efforts will strengthen functional inference and mechanistic interpretation. They will yield a more complete, functionally validated view of cell‑type heterogeneity, developmental dynamics, and the transcriptional\u0026ndash;biochemical networks underlying secondary metabolite formation in hawthorn leaves.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOur findings reveal extensive cellular heterogeneity in hawthorn leaves, categorizing cells into sixteen distinct groups corresponding to nine major leaf cell types: mesophyll, pavement, guard, xylem, metaxylem, phloem parenchyma, companion, sieve element, and meristematic cells. Through differential expression and functional enrichment analyses, we identified specialized transcriptional programs and novel marker genes for each cell type. We also established consistent developmental trajectories of hawthorn leaf differentiation, demonstrating convergence between \u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder in their cellular progression. Furthermore, mapping flavonoid biosynthetic gene expression onto our single-cell atlas revealed distinct cell-type specificity in key enzymes such as \u003cem\u003ePAL, 4CL, CHS, CHI, F3H, DFR, IFS\u003c/em\u003e, and \u003cem\u003eANS\u003c/em\u003e. These findings provide insights into tissue-specific secondary metabolism during leaf development, establishing a robust single-cell transcriptomic framework for hawthorn leaves that highlights developmental conservation across species, reveals the cellular partitioning of flavonoid biosynthetic machinery, and sets the stage for functional validation and applied manipulation of metabolite biosynthesis in this economically and medicinally important genus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript is original research and has not been published or submitted in other journals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors listed have read the complete manuscript and have approved submission of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and processed during the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE315448(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE315448). Other datasets that support the conclusions of this article are included within the article and its supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from Strategic Priority Research Program of the Chinese Academy of Sciences (XDB1230000), National Natural Science Foundation of China (32260094, 32570434), and Yunnan Fundamental Research Projects (202501AS070177).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTCZ, GDL and ZLD designed the research project; GGZ, XEW, BZW and XLL generated molecular data; GGZ, GGZ and JJZ performed data analyses; GGZ wrote the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecial thanks to the Genome Center of Biodiversity of Kunming Institute of Zoology of Chinese Academy of Sciences for providing platform support.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang X, Huang H, Jiang S, et al. A single-cell multi-omics atlas of rice[J]. Nature. 2025;644(8077):722\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Liu Z, Sun X. Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives[J]. Plant Commun. 2023;4(3):100508.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. F ST. Plant Single-Cell/Nucleus RNA-seq Workflow[J]. 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Food Chem. 2022;383:132531.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crataegus pinnatifida var. major N. E. Br., Crataegus scabrifolia (Franch.) Rehder, leaf development, single‑cell transcriptomics , cell types, gene expression characteristics","lastPublishedDoi":"10.21203/rs.3.rs-8423721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8423721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHawthorn (\u003cem\u003eCrataegus\u003c/em\u003e spp.) is a valuable genus of medicinal and edible plants in the Rosaceae family that are rich in bioactive compounds. Despite the availability of chromosome‑level genomes and bulk transcriptomes for \u003cem\u003eCrataegus\u003c/em\u003e species, the leaf cellular composition, developmental trajectories, and cell type-specific expression of biosynthetic pathways remain unexplored at single‑cell resolution. Here, we applied complementary protoplast‑based single‑cell RNA sequencing (scRNA‑seq) and nucleus‑based snRNA‑seq to construct the first single‑cell atlas of hawthorn leaves. We optimized isolation protocols for two \u003cem\u003eCrataegus\u003c/em\u003e species (\u003cem\u003eC. pinnatifida\u003c/em\u003e var. \u003cem\u003emajor\u003c/em\u003e N. E. Br. and \u003cem\u003eC. scabrifolia\u003c/em\u003e (Franch.) Rehder), generated four single‑cell transcriptomic libraries across platforms, and profiled 32,292 high‑quality cells to explore the developmental landscape of hawthorn leaves. The cells were clustered into sixteen groups that we annotated into nine major leaf cell types (mesophyll, pavement, guard, xylem, metaxylem, phloem parenchyma, companion, sieve element, and meristematic cells), revealing extensive cellular heterogeneity and candidate marker genes. Pseudotime reconstruction revealed branched developmental trajectories, and cell‑type‑resolved profiling revealed the cell‑specific expression of flavonoid biosynthetic genes. This single‑cell atlas lays a foundation for mechanistic investigations of tissue‑specific metabolite biosynthesis and offers a cellular framework to guide future functional studies and trait improvement breeding in hawthorn.\u003c/p\u003e","manuscriptTitle":"Single-cell RNA-sequencing profiles reveal the developmental landscape of hawthorn leaves","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 09:21:23","doi":"10.21203/rs.3.rs-8423721/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-13T08:16:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T10:54:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69895894904065983535357043384318112078","date":"2026-02-06T09:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166229891773862851616284018980052025648","date":"2026-02-04T13:37:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T09:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142890699284878871390400602751761825804","date":"2026-01-13T11:44:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T09:44:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T01:26:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T10:08:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-04T07:01:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2026-01-04T06:53:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed171f4e-0b80-42c6-91b1-2e294afb71cf","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T10:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 09:21:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8423721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8423721","identity":"rs-8423721","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}