Mapping the spatial atlas of the human bone tissue integrating spatial and single-cell transcriptomics

Weiqiang Lin; Yisu Li; Chuan Qiu; Binghao Zou; Yun Gong; Xiao Zhang; Di Tian; William Sherman; Fernando Sanchez; Di Wu; Kuan-Jui Su; Xinyi Xiao; Zhe Luo; Qing Tian; Yiping Chen; Hui Shen; Hongwen Deng

doi:10.1093/nar/gkae1298

. 2025 Jan 16;53(2):gkae1298. doi: 10.1093/nar/gkae1298

Mapping the spatial atlas of the human bone tissue integrating spatial and single-cell transcriptomics

Weiqiang Lin ^1,², Yisu Li ^2,², Chuan Qiu ³, Binghao Zou ⁴, Yun Gong ⁵, Xiao Zhang ⁶, Di Tian ⁷, William Sherman ⁸, Fernando Sanchez ⁹, Di Wu ¹⁰, Kuan-Jui Su ¹¹, Xinyi Xiao ¹², Zhe Luo ¹³, Qing Tian ¹⁴, Yiping Chen ¹⁵, Hui Shen ¹⁶, Hongwen Deng ^17,^✉

¹ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

² Department of Cell and Molecular Biology, School of Science and Engineering, Tulane University, 6823 St. Charles Avenue, Uptown, New Orleans, LA 70118, USA

³ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

⁴ Department of Structural and Cellular Biology, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA

⁵ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

⁶ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

⁷ The Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA

⁸ Department of Orthopaedic Surgery, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA

⁹ Department of Orthopaedic Surgery, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA

¹⁰ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

¹¹ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

¹² Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

¹³ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

¹⁴ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

¹⁵ Department of Cell and Molecular Biology, School of Science and Engineering, Tulane University, 6823 St. Charles Avenue, Uptown, New Orleans, LA 70118, USA

¹⁶ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

¹⁷ Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA

^✉

To whom correspondence should be addressed. Tel: +1 504 988 1310; Email: hdeng2@tulane.edu

The first two authors should be regarded as Joint First Authors.

PMCID: PMC11736439 PMID: 39817519

Abstract

Bone is a multifaceted tissue requiring orchestrated interplays of diverse cells within specialized microenvironments. Although significant progress has been made in understanding cellular and molecular mechanisms of component cells of bone, revealing their spatial organization and interactions in native bone tissue microenvironment is crucial for advancing precision medicine, as they govern fundamental signaling pathways and functional dependencies among various bone cells. In this study, we present the first integrative high-resolution map of human bone and bone marrow, using spatial and single-cell transcriptomics profiling from femoral tissue. This multi-modal approach discovered a novel bone formation-specialized niche enriched with osteoblastic lineage cells and fibroblasts and unveiled critical cell–cell communications and co-localization patterns between osteoblastic lineage cells and other cells. Furthermore, we discovered a novel spatial gradient of cellular composition, gene expression and signaling pathway activities radiating from the trabecular bone. This comprehensive atlas delineates the intricate bone cellular architecture and illuminates key molecular processes and dependencies among cells that coordinate bone metabolism. In sum, our study provides an essential reference for the field of bone biology and lays the foundation for advanced mechanistic studies and precision medicine approaches in bone-related disorders.

Graphical Abstract

Introduction

The skeletal system is crucial to the human body, providing structural support and playing a vital role in various physiological functions (1,2). As the global population ages, the prevalence of bone-related disorders, such as osteoporosis and osteoarthritis, is escalating, imposing a substantial burden on healthcare systems worldwide (3–5). Osteoporosis leads to approximately 37 million fragility fractures annually, severely impacting the quality of life (6–8), while osteoarthritis now affects over 590 million people, a 132.2% increase since 1990 (9). Therefore, understanding the molecular mechanisms that regulate bone metabolism under both physiological and pathological conditions is essential for advancing precision medicine in bone disorders.

Over the past decades, bone biology studies have made significant progress in intricate regulatory networks governing bone development (10,11), repair (10) and remodeling (10,11). For instance, several studies have demonstrated that the interactions among various bone cells, including osteoblasts, osteoblast precursors and osteoclasts, are pivotal in the processes of bone formation (12,13), maintenance (13,14) and remodeling (15,16). Furthermore, investigations into bone-related diseases such as osteoporosis have elucidated how aberrant osteocyte function and alterations in bone tissue microenvironment impact bone mineral density and structure, leading to bone health problems (17). These findings provided valuable insights into bone biology and established a theoretical framework for the prevention and treatment of skeletal-related diseases.

However, despite these advances, the intricate molecular mechanisms orchestrating bone homeostasis, repair and reconstruction are far from clear. These processes rely on the spatially coordinated efforts of various cell types within specific microenvironments (18,19). For instance, bone fracture healing involves a series of events from inflammatory cell activation to new bone formation, each stage necessitating close coordination and cooperation among different cell types in a spatial context (20,21). Similarly, bone modeling and remodeling depend on the delicate interplay between various bone cell types, particularly osteoblasts and osteoclasts, which must interact harmoniously within their microenvironment (15,22).

While single-cell RNA sequencing (scRNA-seq) technologies have revolutionized our understanding of cellular heterogeneity, cellular differentiation and cellular interactions within bone tissues (23–28), they inherently lack the ability to preserve the original spatial information of cells. This spatial context is pivotal for comprehending the precise functions of cells within tissues and how they interact with their surrounding microenvironment (29). The emergence of spatial transcriptomics has introduced new technical approaches to overcome this limitation (30). A recent study (31) applying spatial transcriptomics and scRNA-seq in mouse femurs has provided an endogenous, in vivo context for skeletal stem and progenitor cells within their niche, enabling the localization of cell subtypes to specific bone regions and mapping signaling gradients within the marrow cavity.

However, despite these advances in animal models, there is a critical gap in applying these technologies to human bone to fully characterize the bone marrow microenvironment. This research gap highlights the urgent need for studies that extend the application of spatial transcriptomics to fully mineralized human bone tissues. Such studies are essential to provide unprecedented insights into the complex architecture of the human bone microenvironment, offering a deeper understanding of bone biology and potentially revolutionizing our approach to bone-related diseases.

In this study, we have for the first time characterized the multi-modal landscape of the human bone microenvironment within the femoral head, employing a powerful combination of 10X Visium spatial transcriptomics and single-cell transcriptome assays. By integrating spatial transcriptomics and scRNA-seq data, we achieved high-resolution mapping of gene expression patterns, cellular compositions and signaling pathway activities within their native spatial context. We discovered a specialized bone formation niche enriched with osteoblastic lineage cells and fibroblasts, and characterized complex intercellular interactions and illuminated the spatial intercellular dependencies that potentially govern bone formation. Notably, we revealed an innovative spatial gradient originating from trabecular bone, demonstrating systematic variations in gene expression, cellular compositions, and signaling pathway activities. This finding provided initial evidence of regional specificity in human bone tissue dynamics, offering a novel perspective on the human bone microenvironment. Furthermore, the methodological framework applied in this study opens a new paradigm for future in-depth investigations into the bone microenvironment across various skeletal disorders.

Materials and methods

Ethics

This study was approved by the Institutional Review Board (IRB) of Tulane University. The human femoral head specimen was obtained from a 42-year-old African American male patient diagnosed with osteoarthritis, who underwent hip replacement surgery at Tulane Lakesides Hospital. The study adhered to all ethical guidelines outlined in the code of conduct for responsible human tissue use in the United States. Additionally, informed consent was obtained from the patient prior to the collection of the human femoral head specimen.

Bone tissue processing

During the hip replacement surgery, the human femoral head sample was collected and temporarily stored at 4°C before being transferred to the wet laboratory at Tulane School of Medicine. A sample, measuring approximately 1 cm × 1 cm × 0.5 cm, was excised from a location close to the central proximal epiphysis area of the femoral head and immediately fixed in 10% formalin and placed on the shaker at 4°C for 48 h, during which the reagent was changed every 12 h. Subsequently, the sample was rinsed with sterile phosphate buffered saline (PBS) for 5 min each and transferred into sterile 50 ml centrifuge tubes for decalcification in 14% EDTA (Sigma-Aldrich, 324 503) (pH 7.5) for 3 weeks (±7 days) on a shaker at 4°C, with fresh reagent change every 3 days until the sample was easily penetrated by 28G needles. The decalcification solution was made sterile and RNase-free. The decalcified sample was dehydrated by immersion in increasing concentrations of ethanol from 30% to 100%, with two overnight immersions at 70% and 100% ethanol concentrations. The dehydrated sample was then transferred to a 1:1 mixture of xylene and eventually cleared with 100% xylene. The sample was embedded in low-melting paraffin for 3 h at 60°C with reagent change every 1 h and embedded afterward and stored for future use.

Spatial gene expression assay

Spatial transcriptomics profiling was conducted following Visium CytAssist Spatial Gene Expression for formalin-fixed paraffin-embedded (FFPE) protocol (Demonstrated Protocol CG000520, 10X Genomics Inc., Pleasanton, CA). Briefly, the tissue section was prepared as per the Visium CytAssist Spatial Gene Expression for FFPE—Tissue Preparation Guide (Demonstrated Protocol CG000518). Following deparaffinization, the section was stained with hematoxylin and eosin (H&E), imaged and decoverslipped, followed by hematoxylin destaining and decrosslinking (Demonstrated Protocol CG000520). The glass slide with tissue section was processed with a Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression slide with a 0.42 cm² capture area, followed by probe extension, library construction and sequencing on an Illumina NextSeq 2000 system (Illumina Inc., San Diego, CA).

scRNA-seq data processing

To identify the major cell lineages in the human femoral head, we re-analyzed our scRNA-seq dataset from four human femoral head samples (32) (Supplementary Table S1) using Seurat (33) (v4.3.0).

The dataset went through identical quality control processing. We discarded cells (i) in the top or the bottom 2% quantile and (ii) in which >10% of the transcripts were attributed to mitochondrial genes. Count matrices were log-normalized for downstream analyses using a scaling factor of 10 000. Dimensionality reduction was performed on the collection of the top 2000 most variable genes that were shared with most of the samples using principal component analysis (PCA). A shared nearest neighbor (SNN) graph was built with the first 20 principal components using Seurat’s FindNeighbors, and the cells were clustered with a Louvain algorithm with FindClusters. Cluster markers were identified with Wilcoxon tests as implemented in Seurat’s FindAllMarkers function. A UMAP was created with Seurat’s RunUMAP function using the first two principal components to visualize all cells in a two-dimensional embedding. Major cell-type markers were estimated by performing differential expression analysis between different cell clusters.

Spatial transcriptomics data processing

Data preprocessing and alignment were performed using 10X Genomics Space Ranger (v2.1.1) with the reference human genome GRCh38. Pre-processed and aligned was used as initial input for spatial transcriptomics analysis with Seurat (v4.3.0). For data quality control, spots with <100 unique molecular identifiers (UMIs) were filtered out. Individual count matrices were normalized with SCTransform (34).

Several benchmarking studies have demonstrated that the cell2location method achieves top performance in both accuracy and robustness for cell composition estimation (35–37). So cell2location (38) (v0.1.3) was used to calculate cell-type abundance for each spot. Reference expression signatures of major cell types were estimated using regular negative binomial regressions and our scRNA-seq derived atlas. The hierarchical Bayesian model was used to perform deconvolution. Additionally, for each spot, we calculated cell-type proportions using the cell-type-specific abundance estimations. Cell-type compositions of each spot were calculated by the sweep function.

Spatial niche identification

To identify groups of spots in the sample that shared similar gene expression patterns, we used SCTransform for data transformation and clustered spots into niches using Seurat (v4.3.0). Within the same niche, different spots are likely to consist of similar cell types or cells in a similar state, reflecting a shared cellular environment or function. We regarded them as molecular niches, reflecting the underlying transcriptomic landscape.

Additionally, to complement the repertoire of niches identified with gene expression patterns, we also performed clustering on the estimated cell-type proportions of each spot. We named these groups as cell-type niches. The scran package (v1.30.2) was employed, with its buildSNNGraph function used to construct the nearest neighbor graph. Subsequently, the cluster_louvain function from the igraph (39) package (v2.0.2.9011) was utilized to perform the clustering process.

Differential gene expression analysis and pathway enrichment analysis between niches

Differential gene expression analysis between molecular niches (niche 1 and niche 2) was performed by using Seurat’s (v4.3.0) FindMarkers function. Genes with adjusted P-value (adj. P-value) ≤ 0.05 and avg_log2FC ≥ 0.1 were considered as significantly upregulated genes; genes with adj. P-value ≤ 0.05 and avg_log2FC ≤ -0.1 were considered as significantly downregulated genes.

Seurat’s (v4.3.0) FindAllMarkers function was used to identify the differential expression genes and genes with adj. P-value ≤ 0.05 were considered for pathway enrichment analysis. clusterProfiler (40) ’s (v4.11.0.2) enrichGO function was leveraged to find enriched pathways among niches.

Pathway signaling activity analysis of scRNA-seq and spatial transcriptomics data

PROGENy (41,42) is a footprint-based method that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway-responsive genes for humans and mice. The pathway activity score was calculated with progeny’s (v1.17.3) progeny function using the 500 most responsive genes per pathway. Average pathway activity scores were calculated for different cell types or niches. For each spot, we used PROGENy’s model matrix and decoupleR’s mlm method (43) to estimate signaling pathway activities by using the top 500 genes of each transcriptional footprint and the SCTransform normalized data.

Cell–cell communication analysis

To estimate ligand–receptor interactions between the different cell types, we used CellPhoneDB (44) v2 to infer ligand–receptor pairs between cells on processed scRNA-seq data. The significant cell–cell interactions were selected with P-value ≤ 0.05. Next, we employed the CrossTalkeR (45) to prioritize the identified ligand–receptor pairs based on their biological relevance and statistical significance. For spatial transcriptomics data, to evaluate the spatial colocalization of ligand–receptor pairs, the spatial connectivity of the network, comprising only connections between spots with both high expression of the ligand and the receptor, was estimated. Then, the Earth mover’s distance based on the degree distribution of the network was used to quantify its spatial connectivity (46).

Cell enrichment analysis

To determine cell type enrichment in niches, we used multimodal intersection analysis (47), which uses the hypergeometric cumulative distribution to determine the statistical significance of the overlap between cell type-specific gene sets and tissue region-specific gene sets. The intersection between all genes in the spatial transcriptomics count matrix and all genes in the scRNA-seq count matrix was used as the gene background to calculate the P-value. For each spot, the AddModuleScore function was employed to evaluate the enrichment level of cell types, utilizing the list of highly differentially expressed genes, derived from the differential expression analysis of scRNA-seq data. Highly differentially expressed genes were defined as those with over 0.5 difference in the percentages of cells expressing the gene between two groups.

Cell dependency analysis

The multiview intercellular spatial modeling framework (48) is an explainable machine learning framework for knowledge extraction and analysis of spatially resolved data, which takes into account both the cell-type proportion of each spot and their spatial location information within the tissue to infer the importance of cell-type relationships. Utilizing the multiview intercellular spatial modeling framework, we estimated the importance of each principal cell type’s abundance in accounting for the presence of other dominant cell types within spots. Cell-type proportions estimated by cell2location were adaptively integrated into the multiview intercellular spatial modeling framework. The resultant amalgamated and normalized significance was construed as indicative of cell-type dependencies within various spatial contexts, reflecting either colocalization or reciprocal inclusion. It is crucial to note, however, that such interactions do not insinuate causal relationships. To connect tissue structures to tissue functions, we employed the multiview intercellular spatial modeling framework to interpret the dispersion of PROGENy pathway activities, as measured by standardized scores.

spatialDistance analysis

For the spatialDistance analysis, we manually selected spots covering the trabecular bone region, guided by the H&E image as a reference. The Euclidean distances between the marginal spots within the trabecular bone and all other spots were calculated using scaled spatial coordinates. These distance measures were subsequently utilized to examine the gene expression levels, cell type score distributions and signaling pathway activities relative to the tissue depth within the femoral head. Density over distance plots, coupled with locally estimated scatterplot smoothing (LOESS) regression, were employed to visualize and model the spatial gradients of these molecular and cellular features as a function of the distance from the trabecular bone surface. This approach enabled the characterization of spatial transcriptomics and cellular landscapes, unveiling potential gradients or zonation patterns within the bone microenvironment.

Immunohistochemical analysis

The paraffin-embedded bone tissue was sectioned at 4 μm and deparaffinized in xylene and rehydrated in decreasing ethanol concentrations. Antigen retrieval was performed using Diva Decloaker (Biocare Medical, SKU: DV2004) at 60°C overnight. The section was then blocked with 3% hydrogen peroxide for 15 min and Background Sniper (Biocare Medical, SKU: BS966) for 10 min at room temperature to reduce nonspecific binding. Phospho-SMAD3 (Ser423/425) (#9520, Cell Signaling) was applied to the section at 1:100 dilution and incubated overnight at 4°C. The section was washed with TBS Wash Buffer (Biocare Medical, SKU: TWB945M) and incubated with ImmPRESS® HRP Horse Anti-Rabbit IgG Polymer (Vector Laboratories, MP-7401) for 30 min. Betazoid DAB Chromogen Kit (Biocare Medical, SKU: BDB2004) was used as the chromogen for visualization, and the section was counterstained with hematoxylin. Finally, the section was dehydrated, cleared and mounted with a coverslip. The intensity (H-score) of immunohistochemical staining was quantified using the open-source image analysis software QuPath v0.5.1 (49). Tissue sections were imported into QuPath, and regions of interest (ROIs) were randomly selected. Positive-stained cells were automatically detected using QuPath’s ‘positive cell detection’ tool. QuPath assigned each detected cell a staining intensity score, from 0 (negative) to 3 (strongly positive). The H-score of each ROI was then automatically calculated using the following formula: 3 × percentage of strongly staining nuclei + 2 × percentage of moderately staining nuclei + percentage of weakly staining nuclei, which results in a score range of 0 to 300 (50,51).

Immunofluorescence analysis

Results

Multi-modal map of human bone

In this study, we employed an integrative analysis strategy by combining spatial transcriptomics data with scRNA-seq data derived from the human femur, mapping the bone microenvironment at an unprecedented spatial and molecular resolution (Figure 1A and B). We collected a human femoral head sample from an osteoarthritis patient and processed it for spatial transcriptomics profiling. One section from an osteoarthritis patient was used for spatial transcriptomics profiling using the 10X Visium CytAssist Spatial Gene Expression for FFPE assay. The final library was sequenced on the Illumina NextSeq 2000 system (‘Materials and methods’ section). After data quality control, we obtained a total of 2947 spots with an average of 264 genes per spot (Figure 1C). This spatial transcriptomics dataset was integrated with a scRNA-seq dataset of femoral head samples from four osteoarthritis patients, comprising a total of 26 574 cells, with an average of 1035 genes per cell.

Figure 1. — Spatial multi-modal profiling of human bone. (A) Sampling region (see ‘Materials and methods’ section). (B) Data modalities. ST, spatial transcriptomics. (C) ST feature plots. nFeature, the number of unique genes per spot; nCount, the number of unique transcripts per spot. (D) Uniform manifold approximation and projection (UMAP) of scRNA-seq data from four samples (n = 26 574) and average marker gene expression after z-score transformation. Colors along the bottom correspond to the cell types. MP, mononuclear phagocyte; pDC, plasmacytoid dendritic cell. (E–G) Characterization of spatial transcriptomics data: gene expression (E), cell enrichment (F) and pathway activity (G). Prob, probability. Lo, low. Hi, high.

Based on cell clustering analysis of the scRNA-seq dataset, we identified nine major cell types, including mesenchymal lineage (MesLin) cell, B cell, endothelial cell, granulocyte, T/NK cell, mononuclear phagocyte (MP), erythrocyte, plasmacytoid dendritic cell (pDC) and progenitor cell (Figure 1D). We conducted differential gene expression analysis between MesLin and other types of cells on scRNA-seq data to obtain a list of the differentially expressed genes (Supplementary Table S2). Subsequently, cell enrichment analysis was performed to evaluate the MesLin enrichment level at each spot using spatial transcriptomics data and the differentially expressed gene list. Additionally, we characterized the expression level of the RUNX2 gene, which plays a pivotal role in skeletal development and osteoblast differentiation (52–55), on each spot. We observed that most spots exhibiting high expression of the RUNX2 gene were closely situated adjacent to trabecular bone (Figure 1E). Next, we estimated signaling pathway activities with PROGENy for each spot based on the spatial gene expression data. By comparing spatially localized pathway activities with the estimated cellular abundance per spot, we were able to establish connections between spatial cell composition and cellular function. For example, we observed the enrichment of vascular endothelial growth factor (VEGF) and transforming growth factor-β (TGFβ) signaling activities near the trabeculae, and TGFβ signaling activities were prominently active in areas abundant in MesLin (Figure 1F and G). This aligns with previous studies indicating the involvement of VEGF and TGFβ pathways in bone formation (56) and regeneration processes (56–58). Conversely, in other areas, we noted that there were relatively higher activities in nuclear factor kappa B (NFκB) and tumor necrosis factor α (TNFα) signaling pathways (Figure 1G). Previous studies have associated the NFκB and TNFα pathways with inflammatory responses (59,60), cell proliferation (61) and bone resorption (62) in various contexts. Corroborating these findings, we observed the formation of osteoclasts at locations with high NFκB and TNFα signaling pathways activities in the H&E stained image (Figure 1G).

Spatial organization of molecular niches in human bone tissue

To investigate the spatial organization of the bone tissue, we conducted unsupervised clustering of spots based on their spatial transcriptomics profiles and identified two clusters, which we defined as molecular niches (Figure 2A and ‘Materials and methods’ section). We hypothesized that different niches contribute to specific structural and functional modules within the bone tissue, thereby offering insights into the complex organization of the skeletal microenvironment. Notably, niche 2 is predominantly distributed around the trabecular bone (Figure 2B). We then performed differential gene expression analysis between niche 2 and niche 1 (Figure 2C and Supplementary Table S3). Remarkably, several genes that were known to be important for collagen synthesis, bone formation, maintenance and repair, such as COL1A1 (63,64), SPP1 (65,66), MMP9 (67,68) and SPARC (69) were significantly upregulated in niche 2. The elevated expression of these key bone-related genes in niche 2 suggested that this niche may be specialized for maintaining bone tissue homeostasis and facilitating regenerative processes. In contrast, niche 1 exhibited high expression levels of genes associated with immune function, such as CD74 (70–72), IGHA1 (73,74) and IGHM (75). The enrichment of these immune-related genes in niche 1 implied its potential involvement in immunomodulatory processes within the bone microenvironment. Corroborating these findings, the results of pathway enrichment analysis also showed that pathways related to bone development such as mesenchymal cell differentiation, bone remodeling and bone mineralization were enriched in niche 2, whereas immune-related pathways such as humoral immune response, regulation of apoptotic signaling pathway and leukocyte mediated immunity were mainly enriched in niche 1 (Figure 2D).

Figure 2. — Characterization of tissue organization utilizing spatial transcriptomics data. (A) Diagram of molecular niche definition. Each spot may contain multiple cells or various types of cells. Niches are identified based on the gene expression matrix within each spot. Spots that are grouped into the same cluster through clustering analysis are considered to have similar cellular compositions, defining a molecular niche. The cells depicted in spot 1 are the OstLin cells and fibroblasts identified in our study. Fibro, Fibroblast. (B) UMAP of spots based on spatial transcriptomics data (left) and spatial distribution of niches (right). (C) Volcano plot of differential gene expression among niches. (D) Pathway enrichment analysis of differentially expressed genes. (E) Cell enrichment analysis of niches. (F) Visualization and cell enrichment score comparison of MesLin cells in the bone tissue. (G) PROGENy pathway activities across different cell types. (H) PROGENy pathway activities across different niches.

To unveil the cellular composition within identified niches, we employed multimodal intersection analysis, a powerful approach that enables the delineation of constituent cell types. The result suggested that niche 2 was enriched with MesLin cells which, upon further cell subtype analysis, were found to comprise both osteoblastic lineage (OstLin) cell and fibroblast populations (Figure 2E and Supplementary Figure S1A). Examination of H&E-stained tissue section revealed the abundant distribution of osteoblasts along the surface of the trabeculae, aligning with the spatial distribution pattern of niche 2 (Figure 2F). To further validate this observation, we quantified the differences in MesLin cells enrichment scores for each spot between niche 1 and niche 2. We observed that MesLin cells were significantly less abundant in niche 1 compared to niche 2 (Figure 2F). To infer the signaling pathway activity of each cell type and niche, we used PROGENy in combination with decoupleR on both scRNA-seq and spatial transcriptomics data. The findings revealed high activity of the TGFβ pathway within OstLin and fibroblast cells (Figure 2G). Consistently, this pathway also exhibited greater activity within the MesLin-enriched niche 2 compared to niche 1. Additionally, niche 2 demonstrated relatively elevated activity in both the phosphoinositide 3-kinase (PI3K) and VEGF signaling pathways compared to niche 1 (Figure 2H). Notably, TGFβ pathway activity was high in endothelial cells and pDCs (Figure 2G), which were predominantly enriched in niche 1 according to the cell enrichment result (Figure 2E). However, the overall TGFβ pathway activity in niche 1 appears low (Figure 2H). This discrepancy arises because the cell enrichment result reflected the likelihood of cell types being present in niche 1 rather than their proportional abundance. Since niche 1 contained a heterogeneous mix of cell types, including many with low or negligible TGFβ pathway activity, such as B cells, the cumulative TGFβ signal in niche 1 was reduced compared to the localized high activity observed in specific cell types.

Mapping cell–cell communication and exploring cell dependency network in bone tissue

To further elucidate the cellular interactions within bone tissue, we conducted sub-clustering of the MP group and the T/NK group using scRNA-seq data. We identified T cells and NK cells from the T/NK cells group, and monocytes and macrophages from the MP group based on their classical cell marker genes (Supplementary Figure S1B and C). Our analysis indicated a pronounced spatial association between OstLin cells and fibroblasts, suggesting an increased likelihood of communication between these spatially correlated cell types. We employed CellPhoneDB on scRNA-seq data to delve into the intricate cell–cell communication network among distinct cell types within bone tissue. We observed conspicuous interactions between OstLin cells and fibroblasts, endothelial cells and monocytes (Figure 3A). Thereafter, the CrossTalkeR was employed to prioritize and visualize the ligand–receptor pairs obtained from CellPhoneDB to study the crosstalk of fibroblasts, endothelial cells, and monocytes with OstLin cells. Two ligand–receptor pairs, VIM-CD44 and TIMP1-CD63, showed high strength of relationships (quantified by LR scores) between fibroblasts, endothelial cells and monocytes individually with OstLin cells (Figure 3B and Supplementary Figure S2A and B). Subsequently, we employed SpaGene (46) to identify the co-expression patterns of VIM-CD44 and TIMP1-CD63 within the spatial transcriptomic data. Remarkably, a greater proportion of spatial locations exhibiting co-expression of these ligand–receptor pairs were associated with niche 2, and the difference in co-expression strength between the two groups was statistically significant (Figures 2B and 3C-F).

Figure 3. — Dissecting cell–cell communication across spatial tissue. (A) Cell–cell communication network. Edge richness represents the number of ligand–receptor interactions. Macro, Macrophage. Granu, granulocyte. (B) Sankey plots of top 50 ligand–receptor interactions between fibroblasts and OstLin. (C and E) Visualization of selected ligand–receptor interactions for spatial transcriptomics data. Left is the relative expression of the ligand and the receptor; right is the interaction strength. (D and F) LR score comparison of selected ligand–receptor interactions in molecular niches.

As each spatial transcriptomics spot encompasses a collective of cells, we employed cell2location and annotated scRNA-seq data to estimate the cell-type compositions of each spatial transcriptomics spot. We then used unsupervised clustering methods to generate a set of cell-type niches based on the estimated cell-type composition on each spot (Figure 4A and ‘Materials and methods’ section). We found that cell-type niche 3 generally matched with molecular niche 2 on spatial distribution (Figures 2B and 4B) and cell-type composition (Figures 2E and 4C). Cell-type niche 3 was enriched with MesLin cells (Figure 4C), consistent with the cell types prominently contributing to molecular niche 2 (Figure 2E). Additionally, both niches demonstrate a similar spatial distribution near the trabecular bone, as shown in Figure 2B for molecular niche 2 and Figure 4B for cell-type niche 3. We subsequently performed cell dependency analysis to identify potential dependencies between cells within spots. We observed that the abundance and presence of fibroblasts, B cells, endothelial cells and pDCs were highly predictable by that of OstLin cells. Notably, OstLin cells showed high dependencies on fibroblasts, which were highly co-enriched in molecular niche 2, cell-type niche 2 and cell-type niche 3 (Figures 2E, 4C and D). To validate the spatial relationship between fibroblasts and OstLin cells revealed within the bone marrow niche, we employed the immunofluorescence staining experiment. Fibroblasts were defined by the presence of vimentin (VIM+) without co-occurrence of RUNX2 (VIM+/RUNX2−), while OstLin cells were identified by RUNX2 positivity (RUNX2+), including co-occurrence of VIM (Figure 4E). We observed that RUNX2+ cells that have not migrated to the trabecular bone were often found near VIM+/RUNX2− cells, supporting the spatial co-localization suggested by spatial transcriptomics data, and hinting a potential interaction between fibroblasts and OstLin cells during osteoblast differentiation and migration. To link the tissue organization to function, we investigated the spatial dependencies between signaling pathways and cell types, and among signaling pathways. We found that TGFβ signaling was colocalized with most cells, especially fibroblasts, endothelial cells and OstLin cells, which were largely consistent with the results of our pathway enrichment analysis on the scRNA-seq dataset (Figures 2G and 4F). The modeled importance of colocalized pathways highlighted the relationships between NFκB and TNFα signaling, revealing a spatial distribution that is mutually inclusive (Figures 1G and 4G).

Figure 4. — Characterization of tissue organization inferred from cell abundance estimated from cell2location. (A) Diagram of cell composition niche definition. Each spot may contain multiple cells of one type or multiple types. Spots that are grouped into the same cluster through clustering analysis based on the estimated cell proportion matrix are considered to have similar cellular compositions, defining a cell-type niche. The cells depicted in spot 1 are the OstLin cells and fibroblasts identified in our study. Fibro, Fibroblast. (B) UMAP of spots based on spatial transcriptomics data (left) and spatial distribution of niches (right). (C) Cell enrichment analysis of niches. (D) The degree of intercellular dependence within a spot. (E) Immunofluorescence staining of fibroblasts and OstLin cells with VIM and RUNX2 antibodies. (F) Co-localization of cell types and signaling pathways within a spot. (G) Co-localization of signaling pathways within a spot.

Multifaceted gradients: cellular composition, gene expression and pathway activity dynamics in bone microenvironment

To extensively study potential correlations of cell composition, gene expression patterns, and signaling pathway activity with spatial structure, we performed spatialDistance analysis on spatial transcriptomics data. Firstly, we manually selected the spots that covered the trabecular bone based on the H&E-stained image as a reference. The Euclidean distance from marginal spots of trabecular bone and other spots was calculated using scaled spatial coordinates (Figure 5A). RUNX2 is a pivotal transcription factor that primarily orchestrates osteoblast differentiation and bone matrix deposition by finely regulating the expression of diverse bone-related genes (52,53). We observed that as the distance from the bone trabeculae increased, there was a concurrent decrease in the average expression of RUNX2 (Figure 5B). To further validate this finding, we conducted the immunofluorescence staining experiment. The results demonstrated a distinct pattern of RUNX2 expression in relation to trabecular bone structures: (i) At the edges of trabecular bone, we observed high fluorescence intensity for RUNX2. (ii) In regions distant from trabecular bone, the fluorescence intensity of RUNX2 was notably reduced (Figure 5C). Similarly, we found that the abundance of OstLin cells gradually decreased with increasing distance from the trabeculae (Figure 5D). In contrast, immune cells exhibited an opposite trend, with B cells showing a marked increase in proportion with increasing distance from the trabeculae, while other immune cells also displayed an increasing trend, though less pronounced compared to B cells (Figure 5E and Supplementary Figure S3A–F). In order to connect the function to spatial structure, we used spatialDistance and PROGENy to elucidate the signaling pathway activities on the spatial region. We found a trend of decreasing the activities of PI3K, TGFβ and VEGF pathways with the increasing distance from the bone trabeculae (Figure 5F). The PI3K, TGFβ and VEGF pathways are commonly associated with bone formation (56,76,77) and bone remodeling (57,58,77) processes in bone tissue. The previous study on mice also observed a similar tendency in the TGFβ pathway (31). Remarkably, the activities of the NFκB and TNFα pathways initially escalated and then modestly receded with increasing distance from the bone trabeculae, exhibiting a congruent fluctuation pattern (Figure 5G), which aligned with our earlier co-localization analysis of these pathways (Figure 4G). TGFβ ligands bind to TGFβ receptors, which phosphorylate receptor-regulated SMAD (R-SMAD) proteins, such as SMAD2 and SMAD3. Phosphorylated SMAD2/3 (p-SMAD2/3) then form complexes with SMAD4, translocating into the nucleus and regulating the transcription of target genes (78). The presence of p-SMAD2/3 indicates the activity of the TGFβ/SMAD signaling pathway. Immunohistochemistry was used to validate the activity pattern of the TGFβ signaling pathway in bone tissue. Consistent with our spatial transcriptome, there is a distinct spatial distribution of TGFβ pathway activity within the bone. Specifically, p-SMAD2/3 was most intense in the peri-trabecular regions, suggesting high TGFβ signaling activity in the cells, such as osteoblast, that are adjacent to the trabecular bone surface (Figure 5H). Increased distance from bone trabeculae may reduce the stimuli associated with bone remodeling, resulting in decreased activity of these pathways.

Figure 5. — Characterization of spatial signaling gradient domain. (A) Gradient heatmap of distance from bone trabeculae. (B) Variation of average *RUNX2* expression with distance gradient. (C) Immunofluorescence staining of RUNX2. (D and E) Variation of cell-type proportion with distance gradient. (F and G) Variation of pathway activities with distance gradient. (H) Immunohistochemical staining of TGFβ activation (p-SMAD3) and comparison of p-SMAD3 intensity.

Discussion

In the structural assembly of complex tissue like the femoral head, coordinating interactions between various cell types is imperative for maintaining skeletal integrity and the physiological processes it supports. Leveraging the power of single-cell technologies, we can identify molecular heterogeneity and communication alterations of the different cell types that emerge in pathological conditions, which is critical for the fundamental understanding of osteological disease processes. Yet, the comprehensive understanding of these cellular interplays within their spatial milieu remains elusive without a proper context of their physical locales within the tissue architecture. A recent landmark study (79) utilized scRNA-seq and CODEX imaging to map the biogeography of human bone marrow, identifying diverse mesenchymal stem cell subtypes and characterizing their spatial distribution and functional roles within hematopoietic niches in situ. While CODEX provides high-resolution imaging of protein expression with cell-level spatial localization, its scope is inherently constrained by the limited panel of predefined markers that can be simultaneously imaged. In our study, we extended the analytical landscape to delineate a spatial map of the bone microenvironment in the human femoral head by integrating spatial transcriptomics with insights from scRNA-seq data. This map illuminated a systematic description of the spatial structure and biological functions within the human bone microenvironment, unveiling how the distinct types of cells in the femoral head interact spatially to synchronize the complex activities of bone maintenance and remodeling.

Our computational framework enhances the resolution of spatial transcriptomics, allowing for the inference of cell-type compositions and pinpointing of specific signaling pathway activities in bone microenvironment. The spatially resolved data highlight a nuanced pattern of cellular processes unfolding across different regions within the femoral head. Particularly, we identified distinct molecular niches within the bone tissue, which exhibited distinct gene expression profiles and pathway activities. The molecular niche 2 was localized around the trabecular bone and enriched with fibroblasts and OstLin cells with elevated activities in pathways involved in bone development, such as the TGFβ, PI3K and VEGF signaling pathways. In contrast, molecular niche 1 was associated with increased immune-related pathways such as humoral immune response, regulation of apoptotic signaling pathway and leukocyte mediated immunity. It highlighted the intricate coordination and interaction among cellular processes within the bone microenvironment. This spatial compartmentalization of cellular functions is crucial for understanding the complex interplay between different cell types in maintaining bone homeostasis and driving pathological processes during bone-related diseases. Notably, we observed low VEGF pathway activity in the MesLin cluster within the scRNA-seq data. Consistent with previous evidence that VEGF-mediated signaling is pivotal for osteogenic differentiation, the low VEGF pathway activity observed in our MesLin cluster likely reflected both the osteoporotic condition (80) and aging-associated functional decline of mesenchymal lineage cells (81).

In the analysis of scRNA-seq data, we elucidated the cell–cell communication networks within the bone microenvironment and identified prominent interactions between OstLin cells and fibroblasts, endothelial cells and monocytes, highlighting the importance of these cellular interactions in bone tissue homeostasis and remodeling. We identified two ligand–receptor pairs with relatively high LR scores related to skeletogenesis and bone repair, VIM-CD44 and TIMP1-CD63. These two ligand–receptor pairs are shared among fibroblasts, endothelial cells and monocytes with OstLin cells, which were found to be spatially co-expressed. VIM is a cytoskeletal protein and CD44 is a transmembrane glycoprotein, both of which are involved in regulating cell–cell and cell–matrix interactions (82,83). In intercellular communication, fibroblasts, endothelial cells and monocytes through the expression of VIM and CD44, may directly influence OstLin cells by facilitating their adhesion, signal transduction and differentiation. Similarly, TIMP1, as a metalloproteinase inhibitor, restricts bone resorption by inhibiting matrix metalloproteinases (MMPs) activity (84), while CD63, another transmembrane glycoprotein, is involved in cell migration, signaling and mineralization (85–87). The TIMP1-CD63 pair likely plays a pivotal role in maintaining the dynamic balance of bone remodeling by modulating MMPs activity and affecting osteoblast functions. It provided novel insights into how these processes are spatially organized and potentially regulated. Cell dependency analysis and immunofluorescence analysis revealed a notable spatial relationship between fibroblasts and OstLin cells, substantiating previous coculture experiments observed that fibroblasts tended to grow around the osteoblast population (88). The observed spatial colocalization, combined with existing research findings, supports the hypothesis that fibroblasts potentially play a key role in the migration and differentiation of osteoblastic lineage cells. These findings highlight the potential importance of these cellular interactions in regulating processes like cell adhesion, migration and signaling, which are crucial for bone tissue formation, maintenance and repair. Our results also delineated the intricate spatial co-localization and interdependencies among cells, cell-signaling pathways, and signaling pathways themselves in bone microenvironment, which are instrumental for bone remodeling.

Furthermore, spatial transcriptomics analysis revealed a striking spatial gradient in the bone tissue, where the proximity to the bone trabeculae positively correlated with the enrichment of OstLin cells but negatively correlated with the distribution of immune cells. This spatial pattern is underpinned by the differential expression of the key transcription factor RUNX2, which decreases in abundance with increasing distance from the trabeculae, in line with the declining OstLin cells. Similarly, several bone remodeling-related pathways, such as PI3K, TGFβ and VEGF pathways, exhibited gradually decreased activities with increasing distance from the trabeculae. The observed decrease in VEGF signaling pathway activity with increasing distance from the trabeculae was influenced by the limited availability of VEGF receptors (FLT1 and KDR), despite an increase in VEGFA expression distance from the trabeculae (Supplementary Figure S4A and B), which was likely driven by hypoxia-induced factors in adipose-rich marrow regions (89,90) (Supplementary Figure S4C). This trend might also reflect the higher vascularization and remodeling activity near the trabeculae, where VEGF signaling could play a more active role in supporting osteogenesis (91), bone resorption (92) and neovascularization (93) within the bone remodeling compartment. Conversely, pathways linked to inflammation, like the NFκB and TNFα pathways, showed an opposing trend of increasing activity farther from the trabeculae. These findings not only elucidated the intricate spatial organization of bone tissue but also provided important insights into the underlying regulatory mechanisms that govern bone homeostasis, suggesting a potential influence of the local trabecular microenvironment on cellular composition, transcriptional programs, and signaling pathway activities in bone.

There are some limitations in our study. Firstly, the unique attributes of bone tissue and current limitations in sequencing technology pose significant challenges to comprehensive analysis of the bone microenvironment. Due to these constraints, our study was limited to a single human bone sample and unable to capture certain cell types crucial to bone biology, such as osteocytes, adipocytes and osteoclasts. These limitations may affect our overall understanding of the bone tissue microenvironment, including specific cell functions and interactions. Despite these challenges, our research demonstrates the potential of spatial transcriptomics in bone research. Future studies should focus on overcoming these technical hurdles by optimizing experimental procedures for hard tissues and increasing sample size. Such advancements will be crucial for expanding our understanding of bone biology and validating the findings of this preliminary study. Secondly, the femoral head samples utilized in this study were obtained from patients diagnosed with osteoarthritis, we cannot rule out the possibility that our findings may not accurately represent the spatial cellular atlas of the femoral head from healthy adults. However, it is important to note that our study also lays a valuable foundation for future comparative studies aimed at elucidating the potential relationship between osteoarthritis and the internal microenvironment of the femoral head. Thirdly, this study’s single-cell and spatial transcriptomics samples were derived from different racial backgrounds. This may impact our ability to conduct more in-depth investigations specific to particular racial groups and obtain more race-related findings. However, this racial diversity in the sample sources has also emerged as an advantage for our study. By leveraging omics data from different races, we potentially uncovered findings that are more broadly generalizable across diverse populations. This broader perspective could prove invaluable in uncovering fundamental mechanisms and core regulatory processes that may be conserved across diverse racial backgrounds.

Overall, we envision that the spatial multi-modal atlas of human bone provided in this study lays a foundational reference for future investigations aiming to elucidate intricate interconnections among cellular compositions, gene expression patterns, signaling pathway activities and their spatial interplay within the bone microenvironment that dictate bone physiology and pathology. Moreover, it equips researchers with a blueprint to probe the interplay between cellular phenotypes, molecular signatures and bone microarchitecture, fostering a deeper understanding of skeletal diseases and uncovering new therapeutic targets.

Supplementary Material

gkae1298_Supplemental_File

gkae1298_supplemental_file.pdf^{(7.5MB, pdf)}

Acknowledgements

Author contributions: W.L. designed computational analysis, analyzed and interpreted the data, and wrote the manuscript. Y.L., Y.G., X.Z. and D.W. performed the bone tissue processing and revised the bone tissue processing section of Materials and methods in the manuscript. D.T. performed the spatial gene expression assay. B.Z. and X.X. carried out the immunohistochemistry experiment and revised the immunohistochemical analysis section of Materials and methods in the manuscript. Y.L., B.Z. and X.X. carried out the immunofluorescence experiment and revised the immunofluorescence analysis section of Materials and methods in the manuscript. H.D., H.S., C.Q., D.T. and Y.C. edited and commented on the manuscript. Y.G., Z.L., Q.T., W.S. and F.S. organized patient tissue collection and biobanking and consent from the patients. K.S. handled data storage and sharing. H.D. conceived, initiated, laid the foundation and obtained funding for the study. All authors read and approved the manuscript.

Contributor Information

Weiqiang Lin, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Yisu Li, Department of Cell and Molecular Biology, School of Science and Engineering, Tulane University, 6823 St. Charles Avenue, Uptown, New Orleans, LA 70118, USA.

Chuan Qiu, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Binghao Zou, Department of Structural and Cellular Biology, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA.

Yun Gong, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Xiao Zhang, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Di Tian, The Molecular Pathology Laboratory, Department of Pathology and Laboratory Medicine, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA.

William Sherman, Department of Orthopaedic Surgery, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA.

Fernando Sanchez, Department of Orthopaedic Surgery, School of Medicine, Tulane University, 1430 Tulane Avenue, Downtown, New Orleans, LA 70112, USA.

Di Wu, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Kuan-Jui Su, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Xinyi Xiao, Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Zhe Luo, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Qing Tian, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Yiping Chen, Department of Cell and Molecular Biology, School of Science and Engineering, Tulane University, 6823 St. Charles Avenue, Uptown, New Orleans, LA 70118, USA.

Hui Shen, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Hongwen Deng, Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, 1440 Canal Street, Downtown, New Orleans, LA 70112, USA.

Data availability

scRNA-seq data applied in this paper is available in the Gene Expression Omnibus (GEO) database under accession number GSE169396. Spatial transcriptomics data applied in this paper is also available in the GEO database under accession number GSE284089.

Supplementary data

Supplementary Data are available at NAR Online.

Funding

National Institutes of Health [R01AR069055, U19AG055373, R01AG061917, P20GM109036 (in part)]. Funding for open access charge: Aron Family Endowment.

Conflict of interest statement. None declared.

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Data Availability Statement

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PERMALINK

Mapping the spatial atlas of the human bone tissue integrating spatial and single-cell transcriptomics

Weiqiang Lin

Yisu Li

Chuan Qiu

Binghao Zou

Yun Gong

Xiao Zhang

Di Tian

William Sherman

Fernando Sanchez

Di Wu

Kuan-Jui Su

Xinyi Xiao

Zhe Luo

Qing Tian

Yiping Chen

Hui Shen

Hongwen Deng

Abstract

Graphical Abstract

Graphical Abstract.

Introduction

Materials and methods

Ethics

Bone tissue processing

Spatial gene expression assay

scRNA-seq data processing

Spatial transcriptomics data processing

Spatial niche identification

Differential gene expression analysis and pathway enrichment analysis between niches

Pathway signaling activity analysis of scRNA-seq and spatial transcriptomics data

Cell–cell communication analysis

Cell enrichment analysis

Cell dependency analysis

spatialDistance analysis

Immunohistochemical analysis

Immunofluorescence analysis

Results

Multi-modal map of human bone

Figure 1.

Spatial organization of molecular niches in human bone tissue

Figure 2.

Mapping cell–cell communication and exploring cell dependency network in bone tissue

Figure 3.

Figure 4.

Multifaceted gradients: cellular composition, gene expression and pathway activity dynamics in bone microenvironment

Figure 5.

Discussion

Supplementary Material

Acknowledgements

Contributor Information

Data availability

Supplementary data

Funding

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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