{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T01:58:58Z","timestamp":1785290338962,"version":"3.55.0"},"posted":{"date-parts":[[2026,7,21]]},"group-title":"Bioinformatics","reference-count":33,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.biorxiv.org\/about\/FAQ#license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,7,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Spatial transcriptomics (ST) enables high-resolution molecular profiling while preserving tissue architecture, creating new opportunities to investigate how disease-associated pathways are organized within tissues. However, existing analytical approaches largely focus on individual pathways or cell types and do not provide a unified framework for modeling spatially varying pathway interactions across tissue sections and anatomical planes. We present an integrative framework, Spatial Fingerprints Analytics (SFinx), that introduces the concept of a spatial fingerprint for representing patterns of molecular signatures, and Spatial Functional Data Analysis for spatial regression and mapping of localized pathway activity and pathway\u2013phenotype interactions in complex tissues. Applying SFinx to ST datasets on murine lupus nephritis, we reconstructed continuous spatial landscapes of pathway activity and disease-associated phenotypes across kidney sections. This approach identified anatomically restricted inflammatory domains characterized by coordinated activation of immune pathways and revealed substantial spatial heterogeneity in pathway crosstalk across renal compartments. Using generalized additive models with tensor-product splines, we quantified spatially varying associations between lupus nephritis and neutrophil activation pathways across tissue sections, uncovering regions with both positive and negative relationships that would be obscured by conventional bulk analyses. Multi-slice integration further demonstrated reproducible spatial interaction patterns while accounting for section-specific variability. Together, SFinx transforms mixed-spot transcriptomic measurements into interpretable spatial pathway landscapes and interaction maps, providing a general framework for identifying localized disease mechanisms. SFinx revealed previously unrecognized spatial organization of inflammatory signaling in lupus nephritis and presents a broadly applicable strategy for studying spatially coordinated biological processes in cancers, autoimmune and neurodegenerative diseases.<\/jats:p>","DOI":"10.64898\/2026.07.15.738836","type":"posted-content","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T18:20:20Z","timestamp":1784658020000},"source":"Crossref","is-referenced-by-count":0,"title":["An automated platform for spatial functional modeling and fingerprint analysis of tissue molecular landscapes"],"prefix":"10.64898","author":[{"given":"Morteza","family":"Hajihosseini","sequence":"first","affiliation":[{"name":"School of Public Health, University of Alberta, Edmonton, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Patino-Martinez","sequence":"additional","affiliation":[{"name":"Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health (NIAMS\/NIH), Bethesda, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Ghosal","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariana J.","family":"Kaplan","sequence":"additional","affiliation":[{"name":"Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health (NIAMS\/NIH), Bethesda, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3470-2345","authenticated-orcid":false,"given":"Saumyadipta","family":"Pyne","sequence":"additional","affiliation":[{"name":"Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health (NIAMS\/NIH), Bethesda, MD, USA"},{"name":"Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, USA"},{"name":"Health Analytics Network, Columbia, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026072818200725000_2026.07.15.738836v2.1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1038\/s42003-022-03175-5","article-title":"Deciphering tissue structure and function using spatial transcriptomics","volume":"5","year":"2022","journal-title":"Commun Biol"},{"key":"2026072818200725000_2026.07.15.738836v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.gpb.2022.10.001"},{"key":"2026072818200725000_2026.07.15.738836v2.3","doi-asserted-by":"publisher","DOI":"10.1007\/s10555-025-10304-z"},{"key":"2026072818200725000_2026.07.15.738836v2.4","doi-asserted-by":"crossref","first-page":"e405","DOI":"10.1002\/cpz1.405","article-title":"Analyzing Spatial Transcriptomics Data Using Giotto","volume":"2","year":"2022","journal-title":"Curr. 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