Characterization of Zymosan-Modulated Neutrophils With Neuroregenerative Properties

Abstract

Recent studies using advanced techniques such as single cell RNA sequencing (scRNAseq), high parameter flow cytometry, and proteomics reveal that neutrophils are more heterogeneous than previously appreciated. Unique subsets have been identified in the context of bacterial and parasitic infections, cancer, and tissue injury and repair. The characteristics of infiltrating neutrophils differ depending on the nature of the inflammation-inciting stimulus, the stage of the inflammatory response, as well as the tissue microenvironment in which they accumulate. We previously described a new subpopulation of immature Ly6G^low neutrophils that accumulate in the peritoneal cavity 3 days following intraperitoneal (i.p.) administration of the fungal cell wall extract, zymosan. These neutrophils express markers of alternative activation and possess neuroprotective/regenerative properties. In addition to inducing neurite outgrowth of explanted neurons, they enhance neuronal survival and axon regeneration in vivo following traumatic injury to the optic nerve or spinal cord. In contrast, the majority of neutrophils that accumulate in the peritoneal fluid 4 hours following i.p. zymosan injection (4h NΦ) have features of conventional, mature Ly6G^hi neutrophils and lack neuroprotective or neuroregenerative properties. In the current study, we expand upon on our previously published observations by performing a granular, in-depth analysis of these i.p. zymosan-modulated neutrophil populations using scRNAseq and high parameter flow cytometry. We also analyze cell lysates of each neutrophil population by liquid chromatography/mass spectrometry. Circulating blood neutrophils, harvested from naive mice, are analyzed in parallel as a control. When samples were pooled from all three groups, scRNAseq revealed 11 distinct neutrophil clusters. Pathway analyses demonstrated that 3d NΦ upregulate genes involved in tissue development and wound healing, while 4h NΦ upregulate genes involved in cytokine production and perpetuation of the immune response. Proteomics analysis revealed that 3d NΦ and 4h NΦ also express distinct protein signatures. Adding to our earlier findings, 3d NΦ expressed a number of neuroprotective/neuroregenerative candidate proteins that may contribute to their biological functions. Collectively, the data generated by the current study add to the growing literature on neutrophil heterogeneity and functional sub-specialization and might provide new insights in elucidating the mechanisms of action of pro-regenerative, neuroprotective neutrophil subsets.

Keywords: heterogeneity; neutrophils; proteomics; regeneration; transcriptomics.

Figure 1

Flow cytometric characterization of neutrophils. (A) t-SNE plot of naïve blood, 4 hour, and 3 day i.p. zymosan stimulated neutrophils showing unique clusters for each neutrophil type. Heat map overlay of protein expression focusing on (B) Ly6G, (C) CD101, (D) CD14, (E) CD206, and (F) IL-4Rα in each of the clusters.

Figure 2

scRNA seq characterization of neutrophils. (A) scRNA seq of purified blood, 4 hour, and 3 day neutrophils unbiased cluster formation based on differentially expressed genes in each cluster. (B) Heat map of the top 5 cluster defining genes for each cluster. (C) Overlay of neutrophil type on the defined UMAP clusters from (A) showing the location of blood neutrophils, 4 hour, and 3 day i.p zymosan stimulated neutrophils. (D) Pie charts illustrating the percentage of total neutrophils in each cluster for blood, 4 hour, and 3 day neutrophils. (E) UMAP clustering analysis demonstrating the cluster distribution of all cells from each neutrophil group (blood, 4 hour, and 3 day) within the total cellular analysis of all cell groups combined.

Figure 3

Single cell RNA sequencing cluster evaluation. (A) Heat map of the top 100 differentially expressed genes that define the clusters with the highest percentage of cells (0, 1, 2, 3, 4, and 5) with select genes displayed. (B) Heat map showing relative gene expression of genes associated with maturation and activation state across neutrophil populations within the clusters with the highest percentage of 3 day neutrophils.

Figure 4

Pseudotime analysis of neutrophil clusters. (A) Pseudotime analysis. (B) Pseudotime UMAP with overlay of original clusters as defined in Figure 2A. (C) Distribution of each neutrophil population within the pseudotime UMAP.

Figure 5

Neutrophil gene expression is unique and diverse. (A) Venn diagram of unique and overlapping genes between the 3 different neutrophil types. (B) Selected GO biological process pathway comparison based on differential expression of genes uniquely upregulated in each cell type. (C) Heat map of gene expression with select genes displayed from the cytokine production GO biological process pathway. (D) Heat map of gene expression with select genes displayed from the wound healing GO biological process pathway. (E) Heat map of gene expression from the insulin like growth factor GO signaling pathway between neutrophil subsets.

Figure 6

Mass spectrometry proteomic comparison of i.p. zymosan stimulated 4 hour and 3 day neutrophils (n = 3 mice per group). (A) Volcano plot illustrating differential protein expression between 4 hour and 3 day i.p. zymosan stimulation neutrophils. (B) Heat map of differentially expressed proteins between 4 hour and 3 day neutrophils as represented by row z-score. (C) Top differentially expressed GO biological process pathways based on proteins with increased expression in the 3 day neutrophils compared to 4 hour neutrophils. (D–F) Heat maps of selected GO biological process pathways, illustrating expression of proteins between 4 hour and 3 day neutrophils in the (D) inflammatory response pathway, (E) response to wounding pathway, and the (F) regulation of cellular catabolic process pathway.