{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T06:23:35Z","timestamp":1787207015548,"version":"build-2736575974"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T00:00:00Z","timestamp":1787184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetic nephropathy (DN) is the leading cause of end-stage renal disease worldwide and is characterized by progressive glomerulosclerosis, tubulointerstitial fibrosis, and a chronic inflammatory microenvironment. Although macrophage infiltration and immune dysregulation are recognized as central pathological features, the cell-type-specific transcriptional programs that drive inflammatory amplification in DN remain incompletely understood, particularly at single-cell resolution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify macrophage-associated transcriptional biomarkers and delineate intercellular communication networks in DN using an integrated workflow combining urine single-cell RNA sequencing, multi-algorithm machine learning, and tissue-level experimental validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Urine scRNA-seq data from 17 healthy-control libraries (GSE157640 and GSE165396) and 8 diabetic nephropathy (DN) libraries (GSE266146) were processed using Seurat and integrated using Harmony. IL6-JAK-STAT3 pathway activity was quantified using AUCell. The GSE30122 cohort was stratified by disease status and randomly divided into training and held-out internal validation sets. Candidate-gene selection using LASSO, random forest, and Boruta was performed exclusively in the training set, and the discriminatory performance of the consensus genes was subsequently evaluated in the held-out internal validation set using receiver-operating-characteristic analysis. Cell-cell communication was computationally inferred using CellChat. Renal CD36 expression and its spatial association with F4\/80-positive macrophages were assessed in an STZ-induced mouse model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Single-cell analysis identified major epithelial and immune cell populations in the urine of patients with DN and controls. IL6-JAK-STAT3 pathway activity was significantly elevated in DN, with macrophages showing the highest enrichment. Machine learning identified CD36 as a consensus biomarker with an AUC of 0.894 in the external validation cohort. CD36 was predominantly expressed in macrophages, and CellChat analysis predicted inflammatory and profibrotic ligand\u2013receptor communication patterns associated with CD36-detected macrophages. In DN mice, CD36 was significantly upregulated at both the mRNA and protein levels and co-localized with F4\/80-positive macrophages in renal tissue.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CD36 represents a macrophage-enriched candidate molecular marker associated with an IL6\u2013JAK\u2013STAT3-high transcriptional state and predicted intercellular communication patterns in DN. These findings support further investigation of CD36, although its clinical utility and therapeutic relevance require independent clinical and functional validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1868468","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T05:26:29Z","timestamp":1787203589000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated single-cell transcriptomic and machine-learning analyses identify a CD36-associated macrophage state in diabetic nephropathy"],"prefix":"10.3389","volume":"17","author":[{"given":"Xin","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Nephrology, Ganzhou People\u2019s Hospital","place":["Ganzhou, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangying","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","place":["Nanchang, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Nephrology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","place":["Nanchang, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gao","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanchang University Fuzhou Medical College","place":["Fuzhou, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jilan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Ganzhou People\u2019s Hospital","place":["Ganzhou, Jiangxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Nephrology, Ganzhou 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