bioRxivMolecular BiologyPreprint
Spatial organization of myofibroblastic and complement-secreting CAFs in neuroendocrine tumors
Helvijs NiedraiD, Marta Līva SpriņģeiD, Katrine Tiltina, Raitis PečulisiD, Rihards SaksisiD, Jurijs Nazarovs, Arturs Ozoliņš, Sofija Vilisova, Natalja Senterjakova, Aija Geriņa-Bērziņa, Ilze KonrādeiD, Aldis PuķītisiD, Vita RovīteiD
Abstract
Neuroendocrine tumors are graded and classified largely by tumor cell-intrinsic features, yet the stromal microenvironment remains poorly defined across anatomical sites. We applied near single-cell spatial transcriptomics augmented with cell segmentation to eight treatment-naive neuroendocrine tumor primary tissues from pancreas, colon, appendix, and bile duct to build a spatially resolved stromal reference. Integration of stromal-enriched cell polygons identified ten transcriptional states shared across tumors, with a minority of niche-restricted clusters mapping to tumor/stroma and stroma/non-tumor boundaries. Within the shared fibroblast compartment, program scoring resolved four cancer-associated fibroblast states. Myofibroblastic and complement-secretory states dominated across samples, whereas inflammatory and antigen-presenting programs were consistently detected but weaker. Spatial mapping in desmoplastic tumors showed myofibroblastic fibroblasts enriched in collagen-dense regions, while complement-secretory fibroblasts localized preferentially to tumor-adjacent stromal interfaces. Pseudobulk differential expression and gene set enrichment analyses supported extracellular matrix remodeling in myofibroblastic fibroblasts and complement cascade activation in complement-secretory fibroblasts. Together, these findings demonstrate that anatomically distinct neuroendocrine tumors share a conserved yet spatially segregated stromal architecture, characterized by dominant matrix-producing and complement-enriched fibroblast states.
- organism: human
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 0
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