HDC/histamine signaling axis drives macrophage reprogramming to promote angiogenesis in the hindlimb ischemia mice
Histamine is catalyzed by histidine decarboxylase (HDC), which plays important roles in many physiological and pathological processes, but its role in angiogenesis has not been thoroughly clarified. Here we report that HDC is highly expressed in Ly6C+macrophages, rather than in endothelial cells using Hdc-GFP transgenic mice with hindlimb ischemia (HLI) mouse model. Given the whole-process promoting effect of macrophages on angiogenesis, a cluster of HDC+CXCR2+ macrophages have been identified by single-cell sequencing technology in ischemic tissue. The inactivation of HDC leads to a lack of histamine and pro-angiogenic factor production in macrophages, inducing a harsh inflammatory microenvironment that is not conducive to the interaction between macrophages and endothelial cells. Moreover, HA-DA@histamine hydrogel has been designed and demonstrated to safely treat ischemic injury by modulating inflammation and angiogenesis. These data highlight the critical roles of HDC/histamine signaling in macrophage differentiation, angiogenesis, and muscle regeneration in the early stage of HLI. Overall design: The total RNA of muscles 3 days after surgery from WT and Hdc-/- mice was extracted using TRIzol reagent (Takara) for RNA-seq analysis. The quality of raw sequencing reads post initial quality control (version 0.20.1) was evaluated using FastQC (version 0.11.9), followed by quantification of gene expression levels through alignment to the reference genome using HISAT2 (version 2.1.0) and analysis with HTSeq-count (version 0.11.2). Clean reads were mapped to the GRCm39 mouse reference genome using STAR (version 2.7.11b) to generate the expression count matrix. The counts matrix was converted into FPKM values to normalize the RNA-Seq data for gene length and sequencing depth. The FPKM matrix was imported into R and analyzed using the DESeq2 package (version 1.22.2)) to normalize the data and estimate dispersion. Determinants of DEG were those with a p-value < 0.05 and a log2 fold change > 1. Ultimately, the analysis of KEGG pathway enrichment was conducted using the cluster Profiler package (version 4.13.0) in R.