Multicolor flow cytometric analysis method for skeletal muscle myeloid cells based on unsupervised automatic dimensionality reduction combined with manual gating
10.12206/j.issn.2097-2024.202404077
- VernacularTitle:基于无监督自动降维分析与手动圈门联用的骨骼肌髓系细胞多色流式分析方法
- Author:
Qi CAO
1
;
JiaBao ZHANG
1
;
Pei WANG
1
Author Information
1. Department of Pharmacology, School of Pharmacy, Naval Medical University, Shanghai 200433, China.
- Publication Type:Originalarticles
- Keywords:
muscle regeneration;
multicolor flow cytometer;
bioinformatics;
myeloid cells
- From:
Journal of Pharmaceutical Practice and Service
2025;43(7):325-328
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the analytical methods combining multicolor flow cytometry with bioinformatics techniques for analyzing myeloid cells in the gastrocnemius of mice after muscle injury, and provide an experimental foundation for the study of muscle regeneration mechanisms. Methods Immune cells were collected from single-cell suspensions of mouse gastrocnemius using multicolor flow cytometry, and data were analyzed by the t-distributed stochastic neighbor embedding（t-SNE）algorithm supplemented by manual gating techniques. The tSNE algorithm was used in multicolor flow cytometry to guide the setting of manual gates, optimize the identification and classification of cell populations. Results Compared to the sham surgery group, the proportions of dendritic cells, granulocytes, macrophages, and monocytes at the site of muscle injury in the model group significantly increased, with the increasing in monocytes being particularly notable. Conclusion The application of the tSNE algorithm combined with manual gating techniques in multicolor flow cytometry demonstrated that this method could effectively guide the setting of manual gates and enhance the efficiency of distinguishing immune cell types. Through this combined technology, the function and subtypes of myeloid cells in the mouse gastrocnemius could be analyzed more accurately.
