A spatially resolved whole-layers landscape of bladder cancer deciphers dynamic invasive progression

A spatially resolved whole-layers landscape of bladder cancer deciphers dynamic invasive progression | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A spatially resolved whole-layers landscape of bladder cancer deciphers dynamic invasive progression Wanhai Xu, Zhichao Tong, Di Wang, Yubo Zhao, Guixiang Xie, Hongjian Song, and 28 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5284291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cancer invasion is driven by complex interactions between tumor cells and tumor microenvironment (TME), which promote cancer cell plasticity and remodel the TME to support invasive behavior. In bladder cancer, invasion into the muscularis propria reduces the five-year survival rate to below 30%. Despite this clinical significance, the molecular mechanisms underlying bladder cancer invasion remain poorly understood. Current studies predominantly focus on the comparison between non-invasive and invasive tumor tissue, leaving dynamic TME variation largely unexplored. Leveraging the capabilities of spatial information, we integrated Stereo-seq spatial transcriptomics with single-nucleus RNA sequencing (snRNA-seq) on whole-layers bladder cancer specimens. These integrated datasets delineated a spatially resolved whole-layers landscape of bladder cancer at single cell resolution, elucidating the localization and function of principle cell types during the invasive process. We discovered a bladder cancer invasive leading structure characterized by EPCAM and KRT17 co-expressing, whose plasticity is induced and maintained through interactions with POSTN+ cancer associated fibroblasts (CAFs) and APOE+ macrophages. Additionally, we uncovered the specific spatial distribution of different CAF subtypes during bladder cancer progression, highlighting their roles in shaping distinct TME. Notably, we revealed a progressive increase in immunosuppressive states from superficial to muscle-invading bladder tumors. Our findings underscore the orchestrated dynamics of bladder cancer progression driven by intricate tumor-stroma interactions within the TME. These insights provide a framework for understanding invasive behavior in other muscle-invasive cancers, guiding future research into shared mechanisms of tumor progression and microenvironmental remodeling. Biological sciences/Cancer/Urological cancer/Bladder cancer Biological sciences/Cancer/Cancer microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files ExtandFigures.pdf SupplementaryData.pdf Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5284291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":369911264,"identity":"988d9b40-2158-4e8d-949f-f9bbfcb67fa7","order_by":0,"name":"Wanhai 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Beijing","correspondingAuthor":false,"prefix":"","firstName":"Fengpu","middleName":"","lastName":"Fan","suffix":""},{"id":369911275,"identity":"60943e84-bde9-4395-aa47-83968c3626e8","order_by":11,"name":"Zongzheng Yang","email":"","orcid":"","institution":"NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University Cancer Hospital, Harbin, China.","correspondingAuthor":false,"prefix":"","firstName":"Zongzheng","middleName":"","lastName":"Yang","suffix":""},{"id":369911276,"identity":"5aed7f6e-4f91-4c4e-9309-87f4ef08d0f6","order_by":12,"name":"Feng Wen","email":"","orcid":"","institution":"BGI Research, Beijing","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wen","suffix":""},{"id":369911277,"identity":"d66a0b85-d432-4521-bd48-a1945d5beda8","order_by":13,"name":"Hongxue 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Hou","email":"","orcid":"","institution":"Department of Medical Image Center, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Dayong","middleName":"","lastName":"Hou","suffix":""},{"id":369911281,"identity":"370f2070-f3e5-4270-a602-c64cc726c830","order_by":17,"name":"Zhenwei Zhang","email":"","orcid":"","institution":"NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Zhenwei","middleName":"","lastName":"Zhang","suffix":""},{"id":369911282,"identity":"6337d4c2-7876-4d08-8e23-eb23e516d859","order_by":18,"name":"Zhihao Yin","email":"","orcid":"","institution":"Department of Urology, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Yin","suffix":""},{"id":369911283,"identity":"4ae5ed9a-e07a-40a7-83c5-1d508d24222f","order_by":19,"name":"Ziyi Liu","email":"","orcid":"","institution":"Heilongjiang Provincial Key Laboratory of Basic Medical Sciences in Urology Cancer, Harbin Medical University Cancer Hospital, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Ziyi","middleName":"","lastName":"Liu","suffix":""},{"id":369911284,"identity":"50ca96bd-ed41-4f74-a6df-e9de4c7a5c9b","order_by":20,"name":"Jianwei Wang","email":"","orcid":"","institution":"Department of Urology, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Wang","suffix":""},{"id":369911285,"identity":"32310d3f-8e8d-4850-adad-be2d2f5c3232","order_by":21,"name":"Peng Zhang","email":"","orcid":"","institution":"Department of Urology, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhang","suffix":""},{"id":369911286,"identity":"153757b7-8dec-42ad-85dd-c0acff2bce77","order_by":22,"name":"Peng Dai","email":"","orcid":"","institution":"NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Dai","suffix":""},{"id":369911287,"identity":"418d98d2-97f6-4264-b867-6a73a5e43987","order_by":23,"name":"Changfu Li","email":"","orcid":"","institution":"Department of Urology, Harbin Medical University Cancer Hospital, Harbin, 150001, China","correspondingAuthor":false,"prefix":"","firstName":"Changfu","middleName":"","lastName":"Li","suffix":""},{"id":369911288,"identity":"ac6c3646-ba60-4312-acf3-6b54bac12b16","order_by":24,"name":"Lichen Teng","email":"","orcid":"","institution":"Department of Urology, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Lichen","middleName":"","lastName":"Teng","suffix":""},{"id":369911289,"identity":"072e516d-3034-4c6a-9783-7c56b8c7b5fe","order_by":25,"name":"Yangyang Xu","email":"","orcid":"","institution":"Department of Urology, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Yangyang","middleName":"","lastName":"Xu","suffix":""},{"id":369911290,"identity":"87cfaaec-9775-44a5-8d87-bed741f0432f","order_by":26,"name":"XiaoWei Hu","email":"","orcid":"","institution":"Department of Urogenital Medical Oncology, Harbin Medical University Cancer Hospital, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"XiaoWei","middleName":"","lastName":"Hu","suffix":""},{"id":369911291,"identity":"c1ce1a61-c625-40ef-8eb7-c7c8a0e5051b","order_by":27,"name":"Wentao Liu","email":"","orcid":"","institution":"Department of Urogenital Medical Oncology, Harbin Medical University Cancer Hospital, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Liu","suffix":""},{"id":369911292,"identity":"eedf7d44-ffa1-43de-bcaa-eb73c581ed46","order_by":28,"name":"Benedikt Ebner","email":"","orcid":"","institution":"Department of Urology, Ludwig-Maximilians-University, Munich, Germany","correspondingAuthor":false,"prefix":"","firstName":"Benedikt","middleName":"","lastName":"Ebner","suffix":""},{"id":369911293,"identity":"996341c0-646e-4166-a2b6-c942dbfaf45c","order_by":29,"name":"Roman Nawroth","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Roman","middleName":"","lastName":"Nawroth","suffix":""},{"id":369911294,"identity":"04cc0ecd-bab6-4422-bae7-1838357db2e1","order_by":30,"name":"Ziqing DENG","email":"","orcid":"https://orcid.org/0000-0001-8726-0160","institution":"BGI-Shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Ziqing","middleName":"","lastName":"DENG","suffix":""},{"id":369911295,"identity":"d8cdb924-dac1-4ebf-b130-02e419a21950","order_by":31,"name":"Ziqi Wang","email":"","orcid":"","institution":"NHC and CAMS Key Laboratory of Molecular Probe and Targeted Theranostics, Harbin Medical University Cancer Hospital, Harbin, 150001, China.","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Wang","suffix":""},{"id":369911296,"identity":"7fb4e408-78c5-4833-94d6-abb8e6033fd5","order_by":32,"name":"Liang Wu","email":"","orcid":"https://orcid.org/0000-0001-6259-261X","institution":"BGI Research,Shenzhen 518083, China","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Wu","suffix":""},{"id":369911297,"identity":"7a4ea29a-2772-40b2-8b16-797430b87615","order_by":33,"name":"Guibo Li","email":"","orcid":"https://orcid.org/0000-0002-6141-4931","institution":"BGI-Shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Guibo","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-17 16:31:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5284291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5284291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67429237,"identity":"0c2c6ccf-8efc-4e07-abf9-a6e964b055bf","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3025287,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive dynamic spatial architecture of bladder \u0026nbsp;cancer progression and spatial distribution of luminal/basal molecular \u0026nbsp;signatures \u0026nbsp;a, Schematic overview of the integrated spatial omics design and workflow. \u0026nbsp;The spatial transcriptomics (Stereo-seq, 18 samples) and single-nucleus \u0026nbsp;RNA sequencing (snRNA-seq, 26 samples) data were acquired from 22 \u0026nbsp;patients with bladder cancer to generate a spatially resolved landscape. All \u0026nbsp;samples were dissected into whole-layers bladder tissue sections, spanning \u0026nbsp;from the intravesical to extravesical regions, to capture the completed \u0026nbsp;tumor microenvironment (TME). Integrated spatial omics datasets were \u0026nbsp;analyzed to reveal dynamic interactions between invasive leader cells and \u0026nbsp;the TME. These findings were further validated in an independent cohort \u0026nbsp;of 82 patients with primary bladder cancer using multiplexed \u0026nbsp;immunofluorescence (mIF) staining, and in vivo validation was performed \u0026nbsp;using a spontaneous bladder cancer mouse model to represent temporal \u0026nbsp;progression. The directional compass indicates progression from the \u0026nbsp;intravesical to extravesical regions and is applied to all subsequent figures. ( Created in BioRender. BioRender.com/e89v702) b, Unified Manifold \u0026nbsp;Approximation and Projection (UMAP) visualization of the integrated \u0026nbsp;snRNA-seq data from 26 samples, showing the diverse cell types present \u0026nbsp;in the bladder cancer tissue. c, Dot plot illustrating the expression patterns \u0026nbsp;of canonical marker genes across each identified cell type in the integrated \u0026nbsp;snRNA-seq dataset. d, Pseudocolor plots depicting the spatial architecture \u0026nbsp;of cellular composition within the bladder cancer TME, shown for samples \u0026nbsp;HBCP3C and HBCP18. Dotted lines outline Epithelial Tumor (ET), \u0026nbsp;Invasive Tumor (IT), and Scattered Tumor (ST) regions. Adjacent \u0026nbsp;histological sections were stained with Hematoxylin and Eosin (H\u0026amp;E), \u0026nbsp;with expert pathologists marking Tumor, Muscle, Stroma, and Immune \u0026nbsp;areas. Scale bar, 500 μm. e, Quantitative visualization of the luminal and \u0026nbsp;basal gene set score calculated from spatial transcriptomics bin50 data, \u0026nbsp;illustrated with representative samples. Scale bar, 2 mm. f, Violin plots \u0026nbsp;comparing luminal and basal scores in ET, IT and ST tumor regions. \u0026nbsp;Statistical significance was assessed using t-tests. (****P ≤ 0.0001.)\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/7a1bc0cd36f7fc98f99dd4d6.png"},{"id":67429238,"identity":"36065b67-20fb-4f06-a6a4-de375b008d29","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2596969,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of spatial domain analysis and identification \u0026nbsp;of invasive leading structure and their correlation with survival a, Fifteen spatial domains (SDs) were identified from the integrated Stereo-seq and snRNA-seq datasets across all samples. Samples of HBCP3 were \u0026nbsp;displayed as they representing multiple stages of bladder cancer \u0026nbsp;progression. Six characteristic SDs enriched with tumor cells are shown \u0026nbsp;individually, with pseudocolors indicating the different SD types. The \u0026nbsp;boxed areas were identified as invasive bladder tumors for further \u0026nbsp;magnification in Fig. 2b. Scale bar, 2 mm. b, Enlarged view of the box \u0026nbsp;areas from panel a, depicting the spatial characteristics of microinvasive \u0026nbsp;foci from SD analysis of above 3 samples. The white arrows indicate SD14 \u0026nbsp;encircling SD3. Scale bar, 500 μm. c, Diagram illustrating the process of \u0026nbsp;computing spatial proximity between SDs. Heatmap showing spatial \u0026nbsp;proximity relationships between all SDs, which was calculated by cell \u0026nbsp;proportion within 30 μm spatial distance from margin cells of SDs. d, Sankey plot illustrating the composition of SDs within tumor regions. e-h, Cumulative box plots showing the distribution of luminal, basal, epithelial-mesenchymal transition (EMT), and cancer stemness scores across tumor \u0026nbsp;cells of different SDs. Stars indicate statistical significance based on t-tests. \u0026nbsp;(NS, not significant; P \u0026gt; 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, \u0026nbsp;****P ≤ 0.0001). i, Kaplan-Meier survival analysis illustrating the \u0026nbsp;association between SD marker gene set scores and patient survival \u0026nbsp;outcomes.\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/a6fde6356da1895e459f5ca9.png"},{"id":67429241,"identity":"99b33005-80b3-4c61-8ef5-6b1b96a4522f","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2940015,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of invasive leader cells characterized by \u0026nbsp;KRT17+ and EPCAM+ expression in dynamic bladder cancer \u0026nbsp;progression. a, Expression levels of identified top basal marker genes and KRT17 across \u0026nbsp;tumor cells of various SDs in all samples conducted with Stereo-seq. b, Quantitative visualization of KRT17 expression levels from spatial \u0026nbsp;transcriptomics bin50 data, illustrated with representative samples. The \u0026nbsp;direction indicated by the black arrow points toward the extravesical region. \u0026nbsp;Scale bar, 2 mm. c, d, Pseudo-temporal trajectory analysis of six tumor SDs. In panel c, the root of the trajectory path is positioned at the lower \u0026nbsp;left, while in panel d, the root of the trajectory tree is at the top. e, Branched \u0026nbsp;Expression Analysis Modeling (BEAM) results showing the two\u0002differentiation trajectories of tumor cells, with basal, luminal and \u0026nbsp;squamous marker genes visualized. f-m, Validation of KRT17 expression \u0026nbsp;in ET, IT, and ST regions using mIF in the validation cohort. In selected \u0026nbsp;sections, KRT17 and EPCAM were predominantly co-expressed at the \u0026nbsp;invasive leading edges during invasion, as indicated by white arrows. In \u0026nbsp;early-stage microinvasion foci, KRT17 was highly expressed with minimal \u0026nbsp;EPCAM expression, as highlighted by yellow arrows. EPCAM and KRT17 \u0026nbsp;stain tumor cells (green and purple, respectively), PDGFR marks \u0026nbsp;fibroblasts (red), and DAPI stains nuclei (blue). Scale bar, 50 μm.\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/a05def19346dec7c58b0d948.png"},{"id":67429239,"identity":"15533ec4-26ec-42ef-908a-76388fae922f","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1477793,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of intermediate bladder cancer cells with \u0026nbsp;dual luminal and basal traits, regulated by JUN/FOS and their \u0026nbsp;macroscopic spatial distribution. a, Scatter plot showing the distribution of basal and luminal characteristic \u0026nbsp;of tumor cells. X-axis is the subtraction of basal score and luminal score. \u0026nbsp;Y-axis is the maximum value of either basal or luminal score. Luminal \u0026nbsp;score, basal score, EPCAM expression and KRT17 expression are \u0026nbsp;quantitively colored on cell dots. b, Violin plots analyzing the differences \u0026nbsp;in basal and luminal scores and EPCAM and KRT17 expression among \u0026nbsp;identified luminal, intermediate, and basal phenotypes of tumor cells. Statistical significance is indicated based on t-tests. (****P ≤ 0.0001). c, \u0026nbsp;Box plot displaying the developmental potential of distinct tumor cell \u0026nbsp;phenotypes, analyzed using CytoTRACE2. d, Functional pathway \u0026nbsp;enrichment analysis of differentially expressed genes in the three different \u0026nbsp;tumor cell phenotypes. e, Analysis of transcription factor (TF) activity in \u0026nbsp;the three tumor cell phenotypes, quantified using the Regulon Specificity \u0026nbsp;Score (RSS) and Z-score. f, Pseudotime trajectories of various tumor cell \u0026nbsp;phenotypes. g, Heatmap showing continuous changes in key genes and \u0026nbsp;transcription factors expression during the differentiation pseudotime of \u0026nbsp;tumor cells. h, Validation of the macroscopic spatial distribution of \u0026nbsp;intermediate cells by KRT17 and EPCAM expression by mIF in the \u0026nbsp;validation cohort. The area indicated by the white arrow points toward the \u0026nbsp;tumor developmental region. EPCAM and KRT17 stain tumor cells (green \u0026nbsp;and purple, respectively), while DAPI stains nuclei (blue). Yellow scale bar, \u0026nbsp;2 mm; white scale bar, 100 μm.\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/4b76883065abdc0a371d93d7.png"},{"id":67429632,"identity":"9a3f16d8-4da5-4c43-8303-a41104a7e913","added_by":"auto","created_at":"2024-10-25 01:30:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1899546,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial heterogeneity of CAFs and the role of POSTN+ \u0026nbsp;myCAFs in promoting tumor invasion through spatial interactions a, UMAP visualization of 31,627 CAFs analyzed by snRNA-seq, with \u0026nbsp;clusters annotated using canonical markers as shown in panel b and panel \u0026nbsp;c. b, Dot plot illustrating the expression patterns of differentially expressed \u0026nbsp;genes in the integrated snRNA-seq data for each CAF subtype. c, Violin \u0026nbsp;plot analyzing the expression of muscle-related genes and inflammatory \u0026nbsp;CAF (iCAF) genes across different CAF subtypes. d, Analysis of \u0026nbsp;transcription factor activity in different CAF subtypes, which was \u0026nbsp;calculated by Area Under the Curve (AUC) of regulon. e, Pseudotime \u0026nbsp;trajectory analysis of CAFs based on UMAP coordinates, showing the \u0026nbsp;progression of cell states. f, Heatmap depicting the spatial proximity \u0026nbsp;between CAF subtypes and specific tumor SDs, calculated as the \u0026nbsp;proportion of each CAF subtype within a 30 μm distance from tumor cells \u0026nbsp;of the six SDs. g, Spatial maps depicting the distribution of IL6+ iCAFs, \u0026nbsp;SLC14A1+ myCAFs, FBXO32+ myCAFs, and POSTN+ myCAFs, \u0026nbsp;alongside corresponding spatial domain plots and the spatial expression of \u0026nbsp;key genes associated with each CAF subtype. The direction indicated by \u0026nbsp;the black arrow points toward the extravesical area. Scale bar, 2 mm. h, i, Validation of spatial TME of POSTN+ myCAFs and cancer cells by mIF in \u0026nbsp;the validation cohort. EPCAM and KRT17 stain tumor cells (green and \u0026nbsp;purple), while PDGFR and POSTN stain CAFs (red and yellow), with \u0026nbsp;DAPI staining nuclei (blue). Scale bar, 50 μm. j, Cell-cell communication \u0026nbsp;analysis between CAF subtypes as sender cells and tumor cells of SD3 and \u0026nbsp;SD14 as receiver cells. The left panel shows a dot plot illustrating the \u0026nbsp;expression patterns of ligands in each CAF subtype. The heatmap analyzes \u0026nbsp;the regulatory potential of predicted target genes in tumor cells and \u0026nbsp;prioritized ligands from CAFs. The bottom panel shows a dot plot \u0026nbsp;analyzing the expression levels of target genes in tumor cells of SD3 and \u0026nbsp;SD14. k, Regulatory network illustrating the interaction between POSTN as a ligand and FOS as a target gene. The red circle represents the ligand \u0026nbsp;in the sender cell, gray circles represent receptors or transcription factors \u0026nbsp;or cytokines, and the blue circle represents the downstream target gene in \u0026nbsp;the receiver cell. The width of the arrows indicates the strength of the \u0026nbsp;signaling pathway. l, Pseudotime trajectory analysis of myocytes in \u0026nbsp;relation to myCAF subtypes, highlighting the muscular origin of myCAFs.\u003c/p\u003e","description":"","filename":"Figures5.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/5ff7b92feb0d024e9c544ca1.png"},{"id":67429244,"identity":"07e62761-265c-4ca0-a57f-55a3c2d62d57","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2846882,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial immune diversity and functional shift of immune \u0026nbsp;cells from inflammation to exhaustion during bladder cancer \u0026nbsp;progression a-c, Violin plots demonstrating the proportions of T cells, B/Plasma cells, \u0026nbsp;and macrophages across different SDs in various samples. d, Spatial maps \u0026nbsp;highlighting the location association of immuno-enriched SDs, with the \u0026nbsp;direction indicated by the white arrow pointing toward SD4 and SD5, SD8, \u0026nbsp;and SD9. Yellow scale bar, 2mm；white scale bar，200 μm. e, f, Spatial \u0026nbsp;validation of specific immune cell markers by mIF in the validation cohort. \u0026nbsp;EPCAM stains tumor cells (green), CD4 and CD8 stain T cells (yellow and \u0026nbsp;red), CD20 stains B cells (purple), CD68 stains macrophages (orange), and \u0026nbsp;DAPI stains nuclei (blue). Scale bar, 50 μm. g-j, Box plots representing \u0026nbsp;functional gene set scores of immune cells across various SDs in the \u0026nbsp;Stereo-seq dataset. g) T cell exhaustion, h) T cell cytotoxicity, i) IFN\u0002response in macrophages, and j) parainflammation in macrophages. k, \u0026nbsp;UMAP visualization of all identified immune cell subtypes profiled by \u0026nbsp;snRNA-seq, showing their distribution across clusters. l, Dot plot \u0026nbsp;illustrating the expression patterns of differentially expressed genes in the \u0026nbsp;integrated snRNA-seq data for each immune cell type. m, Heatmap \u0026nbsp;depicting the spatial proximity between various immune cell subtypes and \u0026nbsp;specific tumor SDs, calculated as the proportion of each immune cell \u0026nbsp;subtype within a 30 μm distance from tumor cells of the 6 SDs. n-q, Box \u0026nbsp;plots showing the basal and luminal characteristics of tumor cells in \u0026nbsp;relation to different immune cell subtypes. n, o) Basal and luminal scores \u0026nbsp;of tumor cells within a 30 μm distance from different immune cell subtypes. \u0026nbsp;p, q) Basal and luminal scores of tumor cells at varying spatial distances \u0026nbsp;from APOE+ Macrophages. r, Cell communication analysis between \u0026nbsp;immune cell subtypes as sender cells and tumor cells from SD11, SD2, \u0026nbsp;SD10, and SD0 as receiver cells. The left panel shows a dot plot illustrating \u0026nbsp;the expression patterns of ligands in each immune cell type. The heatmap \u0026nbsp;analyzes the regulatory potential of predicted target genes in tumor cells \u0026nbsp;and prioritized ligands from immune cells. The bottom panel displays a dot \u0026nbsp;plot analyzing the expression levels of target genes in tumor cells of SD11, \u0026nbsp;SD2, SD10, and SD0. a-c, g-j, n-q, Statistical significance is indicated \u0026nbsp;based on t-tests. (**P ≤ 0.01, ***P ≤ 0.001, **P ≤ 0.0001).\u003c/p\u003e","description":"","filename":"Figures6.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/d9bcfdf6910b8d8f9eb6eb77.png"},{"id":67429245,"identity":"703a553e-4b28-4d5e-9eab-7d803db39281","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2351435,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic cancer cell phenotype plasticity evolution during \u0026nbsp;bladder cancer progression with in vivo model a, Timeline of the spontaneous temporal bladder cancer model. \u0026nbsp;Pseudocolor plots depict the spatial arrangement of cellular classifications \u0026nbsp;in four mouse samples collected at different time points. Histological \u0026nbsp;images were obtained from adjacent sections stained with H\u0026amp;E. Expert \u0026nbsp;pathologists identified specific regions, including the Normal Epithelial \u0026nbsp;region, Tumor region, Stroma region, and Muscle region. Scale bar, \u0026nbsp;500 μm. (Created in BioRender. BioRender.com/u64x945) b, c, \u0026nbsp;Abundance of luminal and basal characteristics, along with KRT17 and \u0026nbsp;EPCAM expression, derived from spatial transcriptomics bin50 data of the \u0026nbsp;four mouse bladder samples at different time points. d, H\u0026amp;E staining \u0026nbsp;indicating the tumor region in mouse bladders, with corresponding basal \u0026nbsp;scores and KRT17 expression profiles from spatial transcriptomics at \u0026nbsp;weeks 6 and 8. The black arrow highlights the invasive leading margin. \u0026nbsp;Scale bar, 100 μm. e, Summary illustration of the dynamic spatial-temporal \u0026nbsp;interactions between tumor cells and the TME, ultimately leading to \u0026nbsp;immune exhaustion and cancer cell invasion. This schematic encapsulates \u0026nbsp;the core findings of our study, emphasizing the complexity of tumor \u0026nbsp;progression and the crucial role of cellular plasticity and TME in driving \u0026nbsp;bladder cancer progression. (Created in BioRender. BioRender.com/n57a971)\u003c/p\u003e","description":"","filename":"Figures7.png","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/d05222253dbc3b8e9608a0a1.png"},{"id":71350856,"identity":"665a42d4-4f6b-4d6f-8534-720b88f04d8e","added_by":"auto","created_at":"2024-12-13 14:39:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2582111,"visible":true,"origin":"","legend":"","description":"","filename":"spatiallandscapebladdercancermanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1_covered_8b092a7f-98be-4d03-b915-68a9d940c0d5.pdf"},{"id":67429633,"identity":"d1891b4e-66ed-48c0-b8b6-cc26b53d17a9","added_by":"auto","created_at":"2024-10-25 01:30:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4915158,"visible":true,"origin":"","legend":"","description":"","filename":"ExtandFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/11156751f851f15f3156c926.pdf"},{"id":67429242,"identity":"a8def2b7-ee5b-4fee-8e0c-e9834be094fc","added_by":"auto","created_at":"2024-10-25 01:22:09","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1283441,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5284291/v1/efd7a3cbcd02a3f4e6a59834.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"A spatially resolved whole-layers landscape of bladder cancer deciphers dynamic invasive progression","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5284291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5284291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cancer invasion is driven by complex interactions between tumor cells and tumor microenvironment (TME), which promote cancer cell plasticity and remodel the TME to support invasive behavior. In bladder cancer, invasion into the muscularis propria reduces the five-year survival rate to below 30%. Despite this clinical significance, the molecular mechanisms underlying bladder cancer invasion remain poorly understood. Current studies predominantly focus on the comparison between non-invasive and invasive tumor tissue, leaving dynamic TME variation largely unexplored. Leveraging the capabilities of spatial information, we integrated Stereo-seq spatial transcriptomics with single-nucleus RNA sequencing (snRNA-seq) on whole-layers bladder cancer specimens. These integrated datasets delineated a spatially resolved whole-layers landscape of bladder cancer at single cell resolution, elucidating the localization and function of principle cell types during the invasive process. We discovered a bladder cancer invasive leading structure characterized by EPCAM and KRT17 co-expressing, whose plasticity is induced and maintained through interactions with POSTN+ cancer associated fibroblasts (CAFs) and APOE+ macrophages. Additionally, we uncovered the specific spatial distribution of different CAF subtypes during bladder cancer progression, highlighting their roles in shaping distinct TME. Notably, we revealed a progressive increase in immunosuppressive states from superficial to muscle-invading bladder tumors. Our findings underscore the orchestrated dynamics of bladder cancer progression driven by intricate tumor-stroma interactions within the TME. These insights provide a framework for understanding invasive behavior in other muscle-invasive cancers, guiding future research into shared mechanisms of tumor progression and microenvironmental remodeling.","manuscriptTitle":"A spatially resolved whole-layers landscape of bladder cancer deciphers dynamic invasive progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-25 01:22:05","doi":"10.21203/rs.3.rs-5284291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b945573-85c9-471b-a7c6-ff3cafcea25a","owner":[],"postedDate":"October 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39366342,"name":"Biological sciences/Cancer/Urological cancer/Bladder cancer"},{"id":39366343,"name":"Biological sciences/Cancer/Cancer microenvironment"}],"tags":[],"updatedAt":"2024-12-13T14:31:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-25 01:22:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5284291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5284291","identity":"rs-5284291","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}