Back

Electrospun Fiber Surface Roughness Modulates Human Monocyte-Derived Macrophage Phenotype

Alemifar, A.; Robinson, J.; Burnette, K.; Jandres, B.; Hurt, S.; Tse, H.

2024-09-03 bioengineering
10.1101/2024.08.30.610568 bioRxiv
Show abstract

Injuries to fibrous connective tissues have very little capacity for self-renewal and exhibit poor healing after injury. Phenotypic shifts in macrophages play a vital role in mediating the healing response, creating an opportunity to design immunomodulatory biomaterials which control macrophage polarization and promote regeneration. In this study, electrospun poly(-caprolactone) fibers with increasing surface roughness (SR) were produced by increasing relative humidity and inducing vapor-induced phase separation during the electrospinning process. The impact of surface roughness on macrophage phenotype was assessed using human monocyte-derived macrophages in vitro and in vivo using B6.Cg-Tg(Csf1r-EGFP)1Hume/J (MacGreen) mice. In vitro experiments showed that macrophages cultured on mesh with increasing SR exhibited decreased release of both pro- and anti-inflammatory cytokines potentially driven by increased protein adsorption and biophysical impacts on the cells. Further, increasing SR led to an increase in the expression of the pro-regenerative cell surface marker CD206 relative to the pro-inflammatory marker CD80. Mesh with increasing SR were implanted subcutaneously in MacGreen mice, again showing an increase in the ratio of cells expressing CD206 to those expressing CD80 visualized by immunofluorescence. SR on implanted biomaterials is sufficient to drive macrophage polarization, demonstrating a simple feature to include in biomaterial design to control innate immunity. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/610568v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1b40283org.highwire.dtl.DTLVardef@1110307org.highwire.dtl.DTLVardef@1ddc63eorg.highwire.dtl.DTLVardef@1021581_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.