Programmed nanozyme hydrogel enabling spatiotemporal modulation of wound healing achieves skin regeneration after biofilm infection

Abstract

Skin regeneration after wound healing is challenging, especially following infection. Wound repair is a staged yet continuous program, necessitating distinct therapeutic approaches at each stage. Regulation of infection-induced excessive reactive oxygen species (ROS) represents a strategy. Thus, this study employs a therapeutic program involving ROS-responsive nanozyme release, ROS-generation, and ROS-scavenging to achieve dynamic modulation of wound microenvironment. Furthermore, by leveraging the physicochemical properties of the hydrogel to match healing requirements, both macroscopic and microscopic programmed treatment were achieved. In vitro studies confirmed that the treatment reprograms the infected microenvironment by attenuating lipopolysaccharide (LPS)/ ROS-driven inflammation, promoting M2 macrophage polarization, and suppressing myofibroblast over-activation, establishing coordinated control over “infection-inflammation-fibrosis”. In vivo results demonstrated that skin regeneration was achieved through advancing inflammation-to-proliferation phase transition temporally and by spatially guiding the healing direction. To further understand the spatial skin regeneration, a novel analysis named the ‘Patch Repair Division Method’ was reported to showcase the differences in the spatial structure between scar and regenerative area after the treatment. The altered healing orientation further resulted in more organized dermal architecture, enhanced hair follicle neogenesis, and improved vascularization. Collectively, these effects enabled the biofilm-infected wounds to achieve skin regeneration instead of scar formation.

Graphical abstract:

Supplementary Information: The online version contains supplementary material available at 10.1186/s12951-025-03773-5.

Keywords: Hydrogel; Infected wounds; Nanozyme; Reactive oxygen species; Skin regeneration.

Scheme 1

Schematic illustration of fabrication and therapeutics PBPA hydrogel. a) Fabrication process of PBPA; b) Therapeutic process of PBPA hydrogel includes I: photothermal biofilm destruction, II: responsive antibacterial nanozyme release for ROS level control and antibacterial, III: inflammation modulation to promote anti-inflammation M2 macrophage polarization, IV: fibroblast fate modulation to orientate collagen arrangement for skin regeneration

Fig. 1

Characterization, and mechanical properties of PDA@Ag and PBPA hydrogel. a) SEM of PDA and PDA@AgNP, scale bar = 400 nm; b) TEM of PDA and PDA@AgNP, scale bar = 110 nm; c) Plasticity of PBPA2 hydrogel, scale bar = 1 cm; d) Frequency sweep test (from 0.1 to 10 Hz) at 37 °C of PB and PBP; e) Frequency sweep test (from 0.1 to 10 Hz) at 37 °C of PBPA1 and PBPA2; f) Quantitative average storage modulus at 1 Hz of the hydrogels, n = 3; g) Compression test with stress-strain curve from 0% to 80% of different hydrogel; h) strain from 5% to 15% of compression test of different hydrogel; i) Elasticity modulus of different hydrogels in the 5–15% strain range of compression curves, n = 3; j) SEM images of different hydrogels, scale bar = 5 μm; k) The pore size of the hydrogel with different components, n = 50. l) injection of PBPA2 hydrogel, scale bar = 1 cm; m) viscosity measurements of PBPA2 with increasing shear rate at a fixed frequency of 1 Hz under continues stress; n) Rheological properties of the hydrogel in strain amplitude sweep (γ = 0.1–1000%) at 1 Hz; o) Rheological property of PBPA2 at low-1% and high-50% strains; p) Dynamic hydrogen and B-O bond of PBPA; q) π-π stacking of PDA@AgNP. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 2

Adhesiveness, hemostatic, fluid absorption, and swelling ability of the hydrogel. a) Possible adhesive mechanisms of PBPA hydrogel to tissue. b) Application of PBPA2 hydrogel when bending joint, scale bar = 1 cm; c) Application of PBPA2 hydrogel on porcine skin, scale bar = 1 cm; d) Force-displacement curves of different hydrogels-bonded porcine skin; e) Quantitative adhesive strength of the hydrogels calculated from the force-displacement curves, n = 3; f) Schematic figure of the lap shear adhesive test using porcine skin and the hydrogel; g) Representative images of the hemostatic test using rat liver, scale bar = 1 cm; h) Quantitative blood loss, n = 3; i) Representative images of different hydrogels emerged in PBS at different time points, size of small square = 5 mm*5 mm; j) Quantitative swelling analysis of different hydrogels from 0–60 min, n = 3; k) Quantitative swelling analysis of different hydrogels from 0–24 h, n = 3; Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 3

ROS-depend intelligent delivery and ROS-scavenger properties of the hydrogels. a) Schematic image of ROS-depend degradation and cargo-release of the PBPA hydrogel; b) Hydrogel degradation rate in 500 µM H₂O₂ environment, n = 3; c) Hydrogel degradation rate in 1 mg/ml lysozyme, n = 3; d) Representative images of DPPH and NBT test of different hydrogel; e) Intracellular ROS-scavenger performance of different hydrogels by DCFH-DA test on RAW264.7 cell line under H₂O₂ stimulation, scale bar = 200 μm; f) Flowcytometry of DCFH-DA labeled RAW264.7 cell in fluorescein isothiocyanate FITC-A channel on different hydrogels; g) Representative images of HaCaT migration under H₂O₂ environment treating with different hydrogels, scale bar = 100 μm; h) Quantitative analysis of HaCaT migration, comparison was made to C group, n = 3; i) Representative images of live/dead staining of HaCaT and NIH3T3 cell under H₂O₂ environment treating with or without PBPA2 hydrogel, scale bar = 50 μm; j) Quantitative analysis of live/dead staining of HaCaT under H₂O₂ environment treating with different hydrogels, n = 3; k) Quantitative analysis of live/dead staining of NIH3T3 under H₂O₂ environment treating with different hydrogels, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 4

Photothermal effect, artificial triggered biofilm disruption, and antibacterial effect of different hydrogels. a) Representative images of PBPA2 hydrogels under different power of 808 nm NIR; b) Quantitative analysis of the photothermal effect of PBPA2 hydrogels under different power of 808 nm NIR; c) Quantitative analysis of the photothermal effect of different hydrogels under 2 W/cm² 808 nm NIR; d) Photothermal stability under repeated on-and-off 808 nm NIR of different hydrogels; e) PCE of different hydrogels, n = 3; f) Photothermal stability of TGA curves of different hydrogels; g) Representative images of colonization of MRSA and E. coli under different hydrogel treatments; h) Quantitative MRSA bacterial viability from colonization after different hydrogel treatments, n = 3; i) Quantitative E. coli bacterial viability from colonization after different hydrogel treatments, n = 3; j) Representative SEM images of MRSA and E. coli under different hydrogel treatments, scale bar = 500 nm, red arrow indicates the destruction; k) Representative images of the immature biofilm inhibition and mature biofilm destruction under different hydrogel treatments stained by crystal violet; l) Quantitative analysis of immature MRSA biofilm inhibition after different hydrogel treatments, n = 3; m) Quantitative analysis of immature E. coli biofilm inhibition after different hydrogel treatments, n = 3; n) Quantitative analysis of mature E. coli biofilm destruction after different hydrogel treatments, n = 3; o) Quantitative analysis of mature MRSA biofilm destruction after different hydrogel treatments, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 5

In vitro anti-inflammation effect and biocompatibility of different hydrogels. a) Immunofluorescence staining of CD86, CD206, and iNOS on RAW 264.7 under different treatment, scale bar = 100 μm; b) Relative intensity of CD86 based on immunofluorescence staining, n = 3; c) Relative intensity of CD206 based on immunofluorescence staining, n = 3; d) Relative intensity of iNOS based on immunofluorescence staining, n = 3; e) Flow cytometry analysis of CD206 and CD86 on RAW 264.7 under different treatment; f) Immunofluorescence staining of α-SMA on NIH3T3 under different treatment, scale bar = 100 μm; g) Relative intensity of α-SMA based on immunofluorescence staining, n = 3; h) Quantitative analysis and representative images of the hemolysis test after treating with different hydrogels, n = 3; i) Representative images of live/dead staining of RAW264.7, HaCaT, and NIH3T3 treated with PBPA2 hydrogel, scale bar = 50 μm; j) RAW264.7, k) HaCaT, and l) NIH3T3 live/dead staining quantitative analysis after treating with different hydrogels, n = 3; m) RAW264.7, n) HaCaT, and o) NIH3T3 MTT test quantitative analysis after treating different hydrogels at corresponding time point, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 6

In vivo application of different treatment for biofilm-infected wound. a) Schematic illustration of the procedure for biofilm infection model establishment and programmed treatment. b) Representative infrared images of the photothermal effect of PBPA2 hydrogel on mice; c) Quantitative analysis of wound healing rate under different treatments, n = 3; d) Representative images on different time points of biofilm-infected wounds treated with different treatments, scale bar = 4 mm; e) Schematic images on different time points of biofilm-infected wounds treated with different treatments; f) Representative images of colonization of bacteria on wound exudate at determined time point after different treatments; g) Quantitative analysis of corresponding bacterial colonization, n = 3; h) White blood cell counting of blood samples from mice treated with different treatments, n = 3; i) Neutrophil counting of blood samples from mice treated with different treatments, n = 3; j) Monocyte counting of blood samples from mice treated with different treatments, n = 3; k) Eosinophil counting of blood samples from mice treated with different treatments, n = 3; l) Lymphocyte counting of blood samples from mice treated with different treatments, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 7

H&E and Masson’s staining of skin section for evaluation of skin regeneration. a) H&E staining of skin on D7 and D14 in different treatment group, scale bar of whole section = 1 mm, scale bar of enlarged part = 100 μm; b) Wound length on D7 calculated through H&E staining, n = 3; c) Scar length on D14 calculated through H&E staining, n = 3; d) Dermal thickness on D7 calculated through H&E staining, n = 3; e) Epidermal thickness on D14 calculated through H&E staining, n = 3; f) Masson’s staining on D7 and D14 in different treatment group with fractal images, scale bar of whole section = 1 mm, scale bar of enlarged part = 100 μm; g) Collagen volume fraction surrounding wounds of D7 calculated through Masson’s staining, n = 3; h) Fractal dimension calculated on wound area of D7 through Masson’s staining, n = 3; i) Lacunarity calculated on wound area of D7 through Masson’s staining, n = 3; j) Collagen volume fraction surrounding scars of D14 calculated through Masson’s staining, n = 3; k) Fractal dimension calculated on scar area of D14 through Masson’s staining, n = 3; l) Lacunarity calculated on scar area of D14 through Masson’s staining, n = 3; m) Healing orientation pattern difference among groups (orange indicates “bottom-to-top mobilized fascia scar filling mode”, green indicates “peripheral-to-central normal tissue creeping repair mode”). Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 8

Picro-Sirius Red staining and healing orientation differences among three distinct healing parts. a) Schematic illustration of wound healing and the three different local healing area (a: Superficial regeneration-indicating full regeneration above dermal area; b: Superficial scar-indicating scar formation above dermal area; c: Deep scar-indicating scar formation below dermal area); b) Representative images of Picro-Sirius Red staining and orientation images on D14 in different treatment groups compare to normal skin based on the three different healing area, scale bar = 50 μm; c) Collagen I/III ratio of superficial regeneration area, n = 3; d) Collagen I/III ratio of superficial scar area, n = 3; e) Collagen I/III ratio of deep scar area, n = 3; f) Orientation distribution of different treatment groups compare to normal skin based on the three different healing area; g) Maximal single orientation frequency among different groups in three distinct regions, n = 3; h) Frequency difference between two different orientations among different groups in three distinct regions, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 9

Immunofluorescence and immunohistochemistry staining of skin section under different treatment. a) Immunohistochemistry staining of Ki67 (area within yellow lines indicates epidermis), scale bar = 100 μm; b) Positive area of Ki67 based on immunohistochemistry staining, n = 3; c) Immunohistochemistry staining of iNOS, scale bar = 100 μm; d) Positive area of iNOS based on immunohistochemistry staining, n = 3; e) Immunofluorescence staining of CD86 and CD206 (area within yellow lines indicates epidermis), upper scale bar = 200 μm, lower scale bar = 100 μm; f) Ratio of CD86/CD206 based on immunofluorescence staining, n = 3; g) Relative intensity of CD86 and CD206 based on immunofluorescence staining, n = 3; h) Immunohistochemistry staining of TNF-α, scale bar = 100 μm; i) Positive area of TNF-α based on immunohistochemistry staining, n = 3; j) Immunohistochemistry staining of TGF-β on D14, scale bar = 100 μm; k) Positive area of TGF-β based on immunohistochemistry staining, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05

Fig. 10

Evaluation of skin appendages regeneration and fibrotic scar formation after healing orientation change. a) Immunofluorescence staining of DAPI, vimentin, and α-SMA in deeper dermis on D14, scale bar = 50 μm; b) Immunofluorescence colocalization analysis of vimentin and α-SMA in control group; c) Immunofluorescence colocalization analysis of vimentin and α-SMA in PB group; d) Immunofluorescence colocalization analysis of vimentin and α-SMA in PBPA2 group; e) Immunofluorescence colocalization analysis of vimentin and α-SMA in PBPA2 group; f) Colocalization index calculated through vimentin and α-SMA immunofluorescence staining in different groups, black line indicates threshold of Pearson’s ratio which is 0.5, red line indicates threshold of Overlaps ratio which is 0.6, n = 3; g) Immunochemistry staining of CD31 on D14, black arrows indicate blood vessels, scale bar = 100 μm; j) Blood vessel number per field calculated on immunochemistry staining, n = 3; i) Skin appendages regeneration in different treatment groups on H&E staining, scale bar = 100 μm; j) Number of skin appendages on D14 per field calculated on H&E staining, n = 3; k) Area of hair regeneration on D14 of mice, n = 3; l) Immunofluorescence staining of DAPI, β-catenin, and α-SMA in superficial dermis on D14, scale bar = 50 μm; m) Immunofluorescence colocalization analysis of β-catenin and α-SMA in control group; n) Immunofluorescence colocalization analysis of β-catenin and α-SMA in PBPA2 + L group; o) Immunofluorescence colocalization analysis of β-catenin and α-SMA in normal skin; p) Colocalization index calculated through β-catenin and α-SMA immunofluorescence staining in different groups, black line indicates threshold of Pearson’s ratio which is 0.5, red line indicates threshold of Overlaps ratio which is 0.6, n = 3. Data represented mean ± SD. **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p < 0.05