{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T06:39:49Z","timestamp":1776321589690,"version":"3.50.1"},"posted":{"date-parts":[[2025,11,21]]},"group-title":"Bioinformatics","reference-count":47,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene\u2013protein linkage or cell-level barcode pairing, which is often unavailable.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, i.e., convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>\n                    Source code is accessible at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/azizilab\/ARCADIA_public\">https:\/\/github.com\/azizilab\/ARCADIA_public<\/jats:ext-link>\n                    . Reproducibility scripts and data are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/azizilab\/arcadia_reproducibility\">https:\/\/github.com\/azizilab\/arcadia_reproducibility<\/jats:ext-link>\n                    .\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.11.20.689521","type":"posted-content","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:25:11Z","timestamp":1763727911000},"source":"Crossref","is-referenced-by-count":1,"title":["ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics"],"prefix":"10.64898","author":[{"given":"Bar","family":"Rozenman","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Hoffer-Hawlik","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Columbia University, New York, NY, USA"},{"name":"Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Djedjos","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elham","family":"Azizi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Columbia University, New York, NY, USA"},{"name":"Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA"},{"name":"Department of Computer Science, Columbia University, New York, NY, USA"},{"name":"Data Science Institute, Columbia University, New York, NY, USA"},{"name":"Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026031900201553000_2025.11.20.689521v2.1","unstructured":"Patrik L. St\u00e5hl et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science, 2016."},{"key":"2026031900201553000_2025.11.20.689521v2.2","doi-asserted-by":"crossref","unstructured":"E. Armingol et al. Deciphering cell\u2013cell interactions and communication from gene expression. Nat Rev Genet, 2021.","DOI":"10.1038\/s41576-020-00292-x"},{"key":"2026031900201553000_2025.11.20.689521v2.3","doi-asserted-by":"crossref","unstructured":"E. Lundberg et al. Spatial proteomics: a powerful discovery tool for cell biology. Nat Rev Mol Cell Biol, 2019.","DOI":"10.1038\/s41580-018-0094-y"},{"key":"2026031900201553000_2025.11.20.689521v2.4","unstructured":"Itay Tirosh et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell rna-seq. Science, 2016."},{"key":"2026031900201553000_2025.11.20.689521v2.5","doi-asserted-by":"crossref","unstructured":"E. Azizi et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell, 2018.","DOI":"10.1101\/221994"},{"key":"2026031900201553000_2025.11.20.689521v2.6","unstructured":"Lingting Shi et al. Spatiotemporal single-cell analysis reveals t cell clonal dynamics& bioRxiv, 2025."},{"key":"2026031900201553000_2025.11.20.689521v2.7","doi-asserted-by":"crossref","unstructured":"Yury Goltsev et al. Deep profiling of mouse splenic architecture with codex multiplexed imaging. Cell, 2018.","DOI":"10.2139\/ssrn.3155542"},{"key":"2026031900201553000_2025.11.20.689521v2.8","doi-asserted-by":"crossref","unstructured":"L. Keren et al. Mibi-tof: A multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv, 2019.","DOI":"10.1126\/sciadv.aax5851"},{"key":"2026031900201553000_2025.11.20.689521v2.9","doi-asserted-by":"crossref","unstructured":"C. Giesen et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods, 2014.","DOI":"10.1038\/nmeth.2869"},{"key":"2026031900201553000_2025.11.20.689521v2.10","doi-asserted-by":"crossref","unstructured":"S. He et al. High-plex imaging of rna and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol, 2022.","DOI":"10.1038\/s41587-022-01483-z"},{"key":"2026031900201553000_2025.11.20.689521v2.11","doi-asserted-by":"crossref","unstructured":"Romain Lopez et al. Deep generative modeling for single-cell transcriptomics. Nature Methods, 2018.","DOI":"10.1038\/s41592-018-0229-2"},{"key":"2026031900201553000_2025.11.20.689521v2.12","unstructured":"K.E. Wu et al. Babel enables cross-modality translation between multiomic profiles at single-cell resolution. Proc. Natl. Acad. Sci. U.S.A., 2021."},{"key":"2026031900201553000_2025.11.20.689521v2.13","doi-asserted-by":"crossref","unstructured":"J. Zhao et al. Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets. 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The covariance environment defines cellular niches for spatial inference. Nat Biotechnol, 2025.","DOI":"10.1038\/s41587-024-02193-4"},{"key":"2026031900201553000_2025.11.20.689521v2.18","doi-asserted-by":"crossref","unstructured":"Z. He et al. Mosaic integration and knowledge transfer of single-cell multimodal data with midas. Nat Biotechnol, 2024.","DOI":"10.1038\/s41587-023-02040-y"},{"key":"2026031900201553000_2025.11.20.689521v2.19","doi-asserted-by":"crossref","unstructured":"R. Argelaguet et al. Computational principles and challenges in single-cell data integration. Nat Biotechnol, 2021.","DOI":"10.1038\/s41587-021-00895-7"},{"key":"2026031900201553000_2025.11.20.689521v2.20","doi-asserted-by":"crossref","unstructured":"Y. Xu et al. Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward. 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Journal of Machine Learning Research, 2023."},{"key":"2026031900201553000_2025.11.20.689521v2.34","unstructured":"Yuhan Hao et al. Integrated analysis of multimodal single-cell data. Cell, 2021."},{"key":"2026031900201553000_2025.11.20.689521v2.35","doi-asserted-by":"crossref","unstructured":"J. Kennedy-Darling et al. Highly multiplexed tissue imaging using repeated oligonucleotide exchange reaction. Eur. J. Immunol., 2021.","DOI":"10.1002\/EJI.202048891\/v2\/response1"},{"key":"2026031900201553000_2025.11.20.689521v2.36","unstructured":"Hamish W. King et al. Single-cell analysis of human b cell maturation predicts how antibody class switching shapes selection dynamics. Sci Immunol, 2021."},{"key":"2026031900201553000_2025.11.20.689521v2.37","unstructured":"Hamish W. King et al. Integrated single-cell transcriptomics and epigenomics reveals strong germinal center\u2013associated etiology of autoimmune risk loci. Sci Immunol, 2021."},{"key":"2026031900201553000_2025.11.20.689521v2.38","doi-asserted-by":"crossref","unstructured":"Maren B\u00fcttner et al. A test metric for assessing single-cell rna-seq batch correction. Nature Methods, 2019.","DOI":"10.1038\/s41592-018-0254-1"},{"key":"2026031900201553000_2025.11.20.689521v2.39","unstructured":"Cornelis Joost van Rijsbergen . Information Retrieval. Butterworths, 1979."},{"key":"2026031900201553000_2025.11.20.689521v2.40","doi-asserted-by":"crossref","unstructured":"Lawrence Hubert et al. Comparing partitions. Journal of Classification, 1985.","DOI":"10.1007\/BF01908075"},{"key":"2026031900201553000_2025.11.20.689521v2.41","doi-asserted-by":"crossref","unstructured":"Ilya Korsunsky et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nature Methods, 2019.","DOI":"10.1101\/461954"},{"key":"2026031900201553000_2025.11.20.689521v2.42","doi-asserted-by":"crossref","unstructured":"Peter J. Rousseeuw . Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 1987.","DOI":"10.1016\/0377-0427(87)90125-7"},{"key":"2026031900201553000_2025.11.20.689521v2.43","doi-asserted-by":"crossref","unstructured":"Domenick E. Kennedy et al. Compartments and connections within the germinal center. Frontiers in Immunology, 2021.","DOI":"10.3389\/fimmu.2021.659151"},{"key":"2026031900201553000_2025.11.20.689521v2.44","doi-asserted-by":"crossref","unstructured":"U. Klein et al. Germinal centres: role in b-cell physiology and malignancy. Nat Rev Immunol, 2008.","DOI":"10.1038\/nri2217"},{"key":"2026031900201553000_2025.11.20.689521v2.45","doi-asserted-by":"crossref","unstructured":"N. De Silva et al. Dynamics of b cells in germinal centres. Nat Rev Immunol, 2015.","DOI":"10.1038\/nri3804"},{"key":"2026031900201553000_2025.11.20.689521v2.46","doi-asserted-by":"crossref","unstructured":"S. Crotty . Follicular helper cd4 t cells (tfh). Annu Rev Immunol, 2011.","DOI":"10.1146\/annurev-immunol-031210-101400"},{"key":"2026031900201553000_2025.11.20.689521v2.47","doi-asserted-by":"crossref","unstructured":"CH. Koh et al. Cd8 t-cell subsets: heterogeneity, functions, and therapeutic potential. 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