Single-Cell RNA-Seq Reveals the Pseudo-temporal Dynamic Evolution Characteristics of ADSCs to Neuronal Differentiation

Cell Mol Neurobiol. 2024 Dec 11;45(1):5. doi: 10.1007/s10571-024-01524-y.

Abstract

Adipose-derived stromal cells (ADSCs) are commonly used in regenerative medicine, but the genetic features of their development into neuronal cells are unknown. This study used single-cell RNA sequencing (scRNA-seq) to reveal gene expression changes during ADSCs to neuronal differentiation. Sequencing of the ADSCs group, the prei-1d group, and the induction 1 h, 3 h, 5 h, 6 h, and 8 h groups was performed using the BD Rhapsody platform. Sequence data were analyzed using t-SNE, Monocle2, GO, and KEGG algorithms. Results showed that a total of 38,453 cells were collected, which were divided into 0-13 clusters. Monocle2 structured analysis revealed that ADSCs were located at the beginning of the trajectory, and the cells after 5 h of induction were mainly distributed at the end of the trajectory in branches 1 and 2. Up-regulated differentially expressed genes (DEGs) at 5 h after induction enriched GO items including cellular protein metabolism, cell adhesion, endocytosis, and cell migration. KEGG analysis showed that induced 6 h and 8 h groups mainly enriched pathways were oxidative phosphorylation, glutathione metabolism, and expression of Parkinson's disease-related genes. In conclusion, two distinct cell state mechanisms stimulate ADSCs to develop into mature neurons. ADSCs induced for 5 h had developed into mature neurons. Later, the differentiated cells undergo degenerative changes associated with senescence.

Keywords: Adipose-derived stromal cells; Genes; Neurons; Signaling pathways; Transcription factors.

MeSH terms

  • Adipose Tissue / cytology
  • Animals
  • Cell Differentiation* / genetics
  • Cells, Cultured
  • Humans
  • Mesenchymal Stem Cells / cytology
  • Mesenchymal Stem Cells / metabolism
  • Neurons* / cytology
  • Neurons* / metabolism
  • RNA-Seq
  • Single-Cell Analysis*
  • Single-Cell Gene Expression Analysis
  • Time Factors

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