Jinze Li,1– 3,* Chao Chang,1– 3,* Yanqiu Li,4 Shengyu Cui,1– 3 Jun Bai,1– 3 Can Zhang,5 Xinyu Wang,6 Kang Li,1– 3 Fengzeng Jian1– 3 

1Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Spine Center, China International Neuroscience Institute (CHINA-INI), Beijing, People’s Republic of China; 3Lab of Spinal Cord Injury and Functional Reconstruction, China International Neuroscience Institute (CHINA-INI), Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China; 5Department of Neurosurgery, The First Hospital of Hebei Medical University, Shijiazhuang, People’s Republic of China; 6Baylor College of Medicine, Houston, Tx, USA

*These authors contributed equally to this work

Correspondence: Fengzeng Jian, Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of China, Email jianfengzeng@xwh.ccmu.edu.cn

Background: Spinal cord injury (SCI) initiates secondary inflammatory processes that exacerbate tissue damage, with neutrophil extracellular traps (NETs) playing a significant role in amplifying these cascades. This study aimed to explore the temporal dynamics and key regulatory genes of NET formation in SCI to identify therapeutic targets.
Methods: We integrated two transcriptomic datasets from the GEO database to identify differentially expressed NETs-related genes (NRGs) in SCI. WGCNA identified SCI-related modules, while GSVA assessed NET formation dynamics. Publicly available single-cell RNA sequencing data from the GEO database determined cell-specific expression patterns of key NRGs. Findings were validated through immunofluorescence, Western blot, and qPCR in a mouse SCI model. Regulatory networks were constructed, and potential therapeutic compounds were predicted using DSigDB and molecular docking.
Results: We identified seven key NRGs (Casp1, Ccl3, Fcgr2b, Itgam, Itgb2, Tlr2, Tlr4) in SCI. GSVA revealed peak NET score at day 1 post-injury, with attenuation at days 3 and 7. Single-cell transcriptome analysis demonstrated predominant expression of these key genes in neutrophils during the acute phase, most prominently at 1 day post-injury, which coincides with the most pronounced neutrophil infiltration. Immunofluorescence and Western blot analyses confirmed significantly elevated NET formation at 1 day post-SCI. qPCR verified the expression of all key NRGs. Regulatory network analysis identified CHD1 as an important transcription factor governing NET formation, while miRNA-mRNA network construction revealed sophisticated post-transcriptional regulation mechanisms. Drug prediction analysis identified atorvastatin as a promising therapeutic candidate with strong binding affinity to multiple key NET-related proteins.
Conclusion: Our study provides insights into the temporal dynamics and molecular mechanisms of NET formation after SCI, identifying potential therapeutic targets to mitigate neutrophil-mediated secondary injury and improve functional outcomes.

Keywords: spinal cord injury, neutrophil extracellular traps, bioinformatics, neuroinflammation, single-cell RNA sequencing, atorvastatin, regulatory network