Application of Single-cell Deep Learning in Elucidating the Mapping Relationship Between Visceral and Body Surface Inflammatory Patterns

Haixiang Huang; Bingbing Shen; Zhenwei Zhang; Jianming Yue; Lu Mei; Qiusheng Chen

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arXiv · 2503.17418

Application of Single-cell Deep Learning in Elucidating the Mapping Relationship Between Visceral and Body Surface Inflammatory Patterns

Haixiang Huang·Bingbing Shen·Zhenwei Zhang·Jianming Yue·Lu Mei·Qiusheng Chen

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Abstract

As a system of integrated homeostasis, life is susceptible to disruptions by visceral inflammation, which can disturb internal environment equilibrium. The role of body-spread subcutaneous fascia (scFascia) in this process is poorly understood. In the rat model of Salmonella-induced dysentery, scRNA-seq of scFascia and deep-learning analysis revealed Warburg-like metabolic reprogramming in macrophages (MPs) with reduced citrate cycle activity. Cd34+/Pdgfra+ telocytes (CPTCs) regulated MPs differentiation and proliferation via Wnt/Fgf signal, suggesting a pathological crosstalk pattern in the scFascia, herein termed the fascia-visceral inflammatory crosstalk pattern (FVICP). PySCENIC analysis indicated increased activity transcription factors Fosl1, Nfkb2, and Atf4, modulated by CPTCs signaling to MPs, downregulating aerobic respiration and upregulating cell cycle, DNA replication, and transcription. This study highlights scFascia's role in immunomodulation and metabolic reprogramming during visceral inflammation, underscoring its function in systemic homeostasis.

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Haixiang Huang, Bingbing Shen, Zhenwei Zhang, Jianming Yue, Lu Mei, Qiusheng Chen. 2025-03-21. Application of Single-cell Deep Learning in Elucidating the Mapping Relationship Between Visceral and Body Surface Inflammatory Patterns. https://arxiv.org/abs/2503.17418

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