Reconstructing the Developmental Trajectory of Adipocytes in Human Adipose Tissue Using Single-Cell RNA Sequencing

# Reconstructing the Developmental Trajectory of Adipocytes in Human Adipose Tissue Using Single-Cell RNA Sequencing
Source: [https://arxiv.org/abs/2606.27657](https://arxiv.org/abs/2606.27657)
[View PDF](https://arxiv.org/pdf/2606.27657)

> Abstract:Obesity is a global health crisis associated with metabolic disorders such as type 2 diabetes and cardiovascular disease\. This study employed single\-cell RNA sequencing to reconstruct the developmental trajectory of human adipocytes from adipose tissue samples\. Our analysis identified 15 transcriptionally distinct cell clusters, including 7 transitional states, revealing the dynamic process of adipocyte differentiation\. We detected 16 functionally active signaling pathways mediating cellular communication between adipocytes and their progenitors\. Among these, insulin\-like growth factor \(IGF\) and fibroblast growth factor \(FGF\) pathways emerged as the most prominent networks, showing consistent activity across differentiation stages \(p<0\.05\)\. The study revealed depot\-specific differences, with visceral adipocytes undergoing additional extracellular matrix remodeling absent in subcutaneous differentiation\. Spatial analysis further showed that IGF signaling was particularly active in perivascular niches, while FGF activity dominated in mature adipocyte zones\. These results provide the first comprehensive map of human adipocyte development, highlighting IGF and FGF pathways as potential therapeutic targets\. The identified signaling networks offer new insights for developing interventions to promote healthy adipose expansion or inhibit pathological fat accumulation\. This work advances our fundamental understanding of adipose tissue biology while providing clinically relevant data for metabolic disorder treatments\.

## Submission history

From: Humasak Simanjuntak \[[view email](https://arxiv.org/show-email/3f8aa7de/2606.27657)\] **\[v1\]**Fri, 26 Jun 2026 02:27:16 UTC \(8,585 KB\)