Single-cell spatial transcriptomics to predict patient-specific drug responses in autoimmune diseases

Abstract

Background: Autoimmune diseases are highly heterogeneous, with unpredictable treatment outcomes that often result in prolonged morbidity. Conventional bulk transcriptomic approaches obscure cellular diversity and fail to capture the spatial microenvironment that drives drug responses. Aim: To identify spatial transcriptomic biomarkers that predict patient-specific therapeutic responses in autoimmune diseases. Methods: We applied single-cell spatial transcriptomics (scST) to patient-derived synovial tissue from rheumatoid arthritis (n = 12) and systemic lupus erythematosus (n = 8) to construct a high-resolution atlas of immune and stromal interactions during therapy. Results: By integrating scST with machine learning-based predictive modeling, we identified cell-state signatures that stratify patients into responders and non-responders before treatment initiation. Spatial colocalization of interferon gamma-responsive macrophages and C-X-C motif chemokine ligand 13-positive T follicular helper cells predicted resistance to Janus kinase inhibitors (AUC = 0.89). In contrast, enrichment of programmed cell death protein-1 in highly exhausted T cells adjacent to fibroblastic reticular cells improved response to tumor necrosis factor-alpha blockade (AUC = 0.92). Notably, extracellular matrix (ECM)-associated remodeling genes, including COL6A3 and FN1, emerged as critical determinants of microenvironmental drug sensitivity, highlighting the ECM as a therapeutic co-driver in autoimmunity. Validation in an independent cohort (n = 20) confirmed the predictive robustness of these spatial biomarkers. Conclusion: Our findings demonstrate that scST can resolve patient-specific immune niches and provide actionable biomarkers for precision immunotherapy. Relevance for patients: Beyond its immediate implications for rheumatology, this framework establishes spatial single-cell mapping as a predictive diagnostic platform for diverse autoimmune diseases, transforming treatment from trial-and-error to individualized therapeutic guidance.

Keywords

Spatial transcriptomics / Autoimmune diseases / Drug response prediction / Immune microenvironment / Precision medicine

References

[1] Liu W, He W, Li Y, et al. Inflammatory cell interactions in the rotator cuff microenvironment: insights from single-cell sequencing. Int J Genom. 2025; 2025:1-12. doi: 10.1155/ijog/6175946

[2] Karsdal M, Genovese F, Mortensen JH, Schuppan D, Nielsen MJ, Bay-Jensen AC, Leeming DJ, Christiansen C. Advances in extracellular matrix-associated diagnostics and therapeutics. J Clin Med. 2025; 14:1856. doi: 10.3390/jcm14061856

[3] Sun Z, Wang Z, Xu J, et al. Single-nucleus transcriptomics reveals subsets of degenerative myonuclei after rotator cuff tear-induced muscle atrophy. Cell Prolif. 2025; 58:e13420. doi: 10.1111/cpr.13763

[4] Sáez A, Herrero-Fernández B, Gómez-Bris R, Sánchez- Martínez H, González-Granado JM. Pathophysiology of inflammatory bowel disease: innate immune system. Int J Mol Sci. 2023; 24(2):1526. doi: 10.3390/ijms24021526

[5] Rose-John S, Jenkins BJ, Garbers C, Moll JM, Scheller J. Targeting IL-6 trans-signalling: past, present and prospects. Nat Rev Immunol. 2023; 23(10):666-681. doi: 10.1038/s41577-023-00856-y

[6] Mohamed AA, Ahmed AT, Al Abdulmonem W, et al. Interleukin-6 serves as a critical factor in various cancer progression and therapy. Med Oncol. 2024; 41(7):182. doi: 10.1007/s12032-024-02422-5

[7] Mishra AK, Abraham BM, Sahu KK, et al. Harms and contributors of leaving against medical advice in patients with infective endocarditis. J Patient Saf. 2022; 18(8):756-759. doi: 10.1097/PTS.0000000000001055

[8] Chen J, Larsson L, Swarbrick A, Lundeberg J. Spatial landscapes of cancers: insights and opportunities. Nature Reviews Clin Oncol. 2024; 21(9):660-674. doi: 10.1038/s41571-024-00926-7

[9] Tang X, Zhang Y, Zhang H, Zhang N, Dai Z, Cheng Q, Li Y. Single-cell sequencing: high-resolution analysis of cellular heterogeneity in autoimmune diseases. Clin Rev Allergy Immunol. 2024; 66(3):376-400. doi: 10.1007/s12016-024-09001-6

[10] Khalaf K, Hana D, Chou JT, Singh C, Mackiewicz A, Kaczmarek M. Aspects of the tumor microenvironment involved in immune resistance and drug resistance. Front Immunol. 2021; 12:656364. doi: 10.3389/fimmu.2021.656364

[11] Gulati GS, D’Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol. 2025; 26(1):11-31. doi: 10.1038/s41580-024-00768-2

[12] Ma Y, Shi W, Dong Y, Sun Y, Jin Q. Spatial multi-omics in Alzheimer’s disease: a multi-dimensional approach to understanding pathology and progression. Curr Issues Mol Biol. 2024; 46(5):4968-4990. doi: 10.3390/cimb46050298

[13] Yang H, Zhang Z, Li J, Wang K, Zhu W, Zeng Y. The Dual Role of B Cells in the Tumor Microenvironment: Implications for Cancer Immunology and Therapy. Int J Mol Sci. 2024; 25(21):11825. doi: 10.3390/ijms252111825

[14] Tsokos GC, Boulougoura A, Kasinath V, Endo Y, Abdi R, Li H. The immunoregulatory roles of non-haematopoietic cells in the kidney. Nat Rev Nephrol. 2024; 20(4):206-217. doi: 10.1038/s41581-023-00786-x

[15] Marozzi M, Parnigoni A, Negri A, et al. Inflammation, extracellular matrix remodeling, and proteostasis in tumor microenvironment. Int J Mol Sci. 2021; 22(15):8102. doi: 10.3390/ijms22158102

[16] Liu W, Wu Y, Hong Y, Zhang Z, Yue Y, Zhang J. Applications of machine learning in computational nanotechnology. Nanotechnology. 2022; 33(16):162501. doi: 10.1088/1361-6528/ac46d7

[17] Zhang S, Deshpande A, Verma BK, et al. Integration of clinical trial spatial multiomics analysis and virtual clinical trials enables immunotherapy response prediction and biomarker discovery. Cancer Res. 2024; 84(16):2734-2748. doi: 10.1158/0008-5472.CAN-24-0943

[18] Odunlami GJ, Ajibade A, Omotoso BA, et al. Clinical and laboratory profiles of systemic lupus erythematosus patients in a new rheumatology clinic in southwestern Nigeria. Rheumatology. 2024; 62(2):83-93. doi: 10.5114/reum/187208

[19] Snijders ML, Zajec M, Walter LA, et al. Cryo-Gel embedding compound for renal biopsy biobanking. Sci Rep. 2019; 9(1):15250. doi: 10.1038/s41598-019-51962-8

[20] LeSavage BL, Suhar RA, Broguiere N, Lutolf MP, Heilshorn SC. Next-generation cancer organoids. Nat Mater. 2022; 21(2):143-59. doi: 10.1038/s41563-021-01057-5

[21] Janesick A, Shelansky R, Gottscho AD, et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun. 2023; 14(1). doi: 10.1038/s41467-023-43458-x

[22] Chen JH, Nieman LT, Spurrell M, et al. Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy. Nat Immunol. 2024; 25(4):644-658. doi: 10.1038/s41590-024-01792-2

[23] Van Espen B, Prideaux EB, Wilson AR, et al. Laser Capture Microscopy RNA Sequencing for Topological Mapping of Synovial Pathology During Rheumatoid Arthritis. Arthritis Rheumatol. 2024; 76(8):1243-1251. doi: 10.1002/art.42853

[24] Li X, Sun H, Li H, et al. A single-cell RNA-sequencing analysis of distinct subsets of synovial macrophages in rheumatoid arthritis. DNA Cell Biol. 2023; 42(4):212-222. doi: 10.1089/dna.2022.0509

[25] Wan X, Xiao J, Tam SST, et al. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat Commun. 2023; 14(1):7848. doi: 10.1038/s41467-023-43629-w

[26] Li Z, Zhang B, Chan JJ, et al. An isoform-resolution transcriptomic atlas of colorectal cancer from long-read single-cell sequencing. Cell Genom. 2024; 4(9). doi: 10.1016/j.xgen.2024.100641

[27] Chen J, Zhu X, Liu H. A mutual neighbor-based clustering method and its medical applications. Comput Biol Med. 2022;150:106184. doi: 10.1016/j.compbiomed.2022.106184

[28] Kalhor K, Chen CJ, Lee HS, et al. Mapping human tissues with highly multiplexed RNA in situ hybridization. Nat Comm. 2024; 15(1):2511. doi: 10.1038/s41467-024-46437-y

[29] Davis-Marcisak EF, Deshpande A, Stein-O’Brien GL, et al. From bench to bedside: Single-cell analysis for cancer immunotherapy. Cancer Cell. 2021; 39(8):1062-1080. doi: 10.1016/j.ccell.2021.07.004

[30] Anastopoulo IN, Herczeg CK, Davis KN, Dixit AC. Multi-drug featurization and deep learning improve patient-specific predictions of adverse events. Int J Environ Res Public Health. 2021; 18(5):2600. doi: 10.3390/ijerph18052600

[31] Martin‐Gutierrez L, Peng J, Thompson NL, et al. Stratification of patients with Sjögren’s syndrome and patients with systemic lupus erythematosus according to two shared immune cell signatures, with potential therapeutic implications. Arthritis Rheumatol. 2021; 73(9):1626-1637. doi: 10.1002/art.41708

[32] Heidari P, Milan A. Combining K-fold cross validation with bayesian hyperparameter optimization for accuracy enhancement of land cover and land use classification. Sci Rep. 2025; 15(1):39758. doi: 10.1038/s41598-025-23336-w

[33] Wang C, Li T, Zhu J, et al. Single-cell transcriptome analysis profiles cellular and molecular alterations in aortic tissue from patients with Behçet’s syndrome. Rheumatology. 2025; 64(9):5037-5047. doi: 10.1093/rheumatology/keaf252

[34] Rahmatinejad Z, Dehghani T, Hoseini B, et al. A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department. Sci Rep. 2024; 14(1):3406. doi: 10.1038/s41598-024-54038-4

[35] Pietilä EA, Gonzalez-Molina J, Moyano-Galceran L, et al. Co-evolution of matrisome and adaptive adhesion dynamics drives ovarian cancer chemoresistance. Nat Comm. 2021; 12(1):3904. doi: 10.1038/s41467-021-24009-8

[36] Maher RE, Cytlak-Chaudhuri U, Aleem S, et al. The effect of highly effective modulator therapy on systemic inflammation in cystic fibrosis. medRxiv. Preprint posted online 2024. doi: 10.1101/2024.07.25.24310916

[37] Defard T, Laporte H, Ayan M, et al. A point cloud segmentation framework for image-based spatial transcriptomics. Comm Biol. 2024; 7(1):823. doi: 10.1038/s42003-024-06480-3

[38] Sah AK, Elshaikh RH, Shalabi MG, et al. Role of artificial intelligence and personalized medicine in enhancing HIV management and treatment outcomes. Life. 2025; 15(5):745. doi: 10.3390/life15050745

[39] Guo J, Wang S, Gao Q. An integrated overview of the immunosuppression features in the tumor microenvironment of pancreatic cancer. Front Immunol. 2023; 14:1258538. doi: 10.3389/fimmu.2023.1258538

[40] Liu Y, Sinjab A, Min J, et al. Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics. Cancer Cell. 2025; 43(5):905-24. doi: 10.1016/j.ccell.2025.03.004

[41] Jing SY, Liu D, Feng N, et al. Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma. Genome Med. 2024; 16(1):98. doi: 10.1186/s13073-024-01367-8

[42] Rodov A, Baniadam H, Zeiser R, et al. Towards the next generation of data‐driven therapeutics using spatially resolved single‐cell technologies and generative AI. Eur J Immunol. 2025; 55(2):e202451234. doi: 10.1002/eji.202451234

[43] Heller G, Fuereder T, Grandits AM, Wieser R. New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single-cell RNA sequencing and spatial transcriptomics. Oncol Res. 2023; 32(1):1-17. doi: 10.32604/or.2023.044774

[44] Zhang H, Cheng S, Xu Y. Malignant epithelial cell marker- driven risk signature enables precise stratification in esophageal cancer. Front Immunol. 2025; 16:1610991. doi: 10.3389/fimmu.2025.1610991

[45] Lösslein AK, Henneke P. Macrophage differentiation and metabolic adaptation in mycobacterial infections. Ann Rev Immunol. 2025; 43(1):423-450. doi: 10.1146/annurev-immunol-082323-120757

[46] Lee Y, Lee M, Shin Y, Kim K, Kim T. Spatial omics in clinical research: a comprehensive review of technologies and guidelines for applications. Int J Mol Sci. 2025; 26(9):3949. doi: 10.3390/ijms26093949

[47] Xue S, Zhu F, Wang C, Min W. stEnTrans:Transformer-Based Deep Learning for Spatial Transcriptomics Enhancement. In: Bioinformatics Research and Applications (Lecture Notes in Computer Science). Singapore: Springer Nature; 2024:63- 75. doi: 10.1007/978-981-97-5128-0_6

[48] Liu X, Peng T, Xu M, Lin S, Hu B, Chu T, Liu B, Xu Y, Ding W, Li L, Cao C. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. J Hematol Oncol. 2024; 17(1):72. doi: 10.1186/s13045-024-01596-9

[49] Jana S, Glabman RA, Koehne AL. Bridging the gap between histopathology and genomics: spotlighting spatial omics. Vet Pathol. 2025; 62(5):620-630. doi: 10.1177/03009858251322729