An infusible extracellular matrix biomaterial improves survival in a model of severe systemic inflammation

Figures

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  Fig. 1. Biodistribution of iECM in animal model of MODS. a Overview of the timeline for biodistribution assessment in healthy animals with no treatment, healthy animals treated with iECM, LPS-dosed animals receiving an IV injection of trilysine, and LPS-dosed animals receiving an IV injection of iECM. b Brightfield and Licor imaging at 785 nm to visualize vivotag-750 tagged iECM and trilysine. c Signal intensity of vivotag-750 tagged iECM and trilysine was quantified and compared across each organ. Significance was determined using an unpaired t-test assuming individual variance for each organ. P-values were adjusted using the Holm–Šídák method. *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

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  Fig. 2. Vessel permeability is lowered in the lung with iECM treatment. a Overview of the animal timeline used to quantify vessel permeability. b Representative images of VT-750 albumin of lungs from animals treated with iECM or saline compared to healthy control lungs injected with albumin. c Quantification of fluorescently labeled albumin in lungs of saline (red) and iECM (blue) treated animals. Statistical significance was determined using a two-tailed, unpaired t-test. *p < 0.05.

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  Fig. 3. Overview of methods used to determine iECM efficacy in MODS model. a Overview of the animal model timeline. Animals were treated with a dual dose of LPS as described previously and then treated with saline or iECM. Survival was monitored over a 30-h time period. At 30 h, animals that survived were harvested for analysis. b A total of n = 112 animals (n = 47 for iECM, n = 65 for saline) were utilized for analysis and incorporated for survival analysis. Survival curve analysis was performed using the Log-Rank (Mantel–Cox) test. c In addition to survival analysis, blood/plasma, brain, heart, kidney, and lung tissue were harvested and processed for protein analysis using a multikine ELISA system and transcriptomic analysis using the Nanostring nCounter platform. Lung tissue was further processed for a 22-color spectral flow cytometry panel. Error bars = standard error of the mean.

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  Fig. 4. Quantification of inflammatory cytokines. Cytokine levels were assessed in plasma and tissue lysate from saline (red, n = 6) and iECM (blue, n = 6) treated animals using a 13-plex multiplex ELISA array. a IL-6, b IFN-γ, c IL-1, d IL-1b, e GM-CSF, f TNF-a, g IL-12 p70, h IL-17, i IL-23. Significance was determined using an unpaired t-test with individual variance assumption for each row to accommodate for inherent differences in baseline tissue expression of each cytokine. Data is mean ± standard deviation. *p < 0.05, **p < 0.01. LOD is the limit of detection, indicating levels fell below the detectable levels of the assay.

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  Fig. 5. Volcano plot of differentially expressed genes in iECM-treated animals versus saline. Nanostring gene expression analysis was performed on samples from a blood, b lung, c brain, d heart and e kidney. An n = 5–6 was used per tissue. Nanostring analysis was performed using Rosalind to determine log fold change and p-values for each gene. Differentially expressed genes that were significant (p < 0.05) and had an absolute fold change >1.01 are colored red, while nonsignificant genes that were differentially expressed are shown in blue. f Genes that were commonly differentially expressed (>3 tissues) were plotted together to show trends in downregulated genes. Size of dots correlates to −log10(pvalue) in gene expression, while color correlates to the log2 fold change in gene expression in iECM versus saline-treated animals.

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  Fig. 6. Downregulated immune signaling pathways in tissues of iECM-treated animals compared to saline. Enrichment analysis was performed using EnrichR to determine significantly downregulated immune signaling pathways from downregulated genes in each tissue. Genes, as well as their log2 fold change in common pathways, were incorporated to show crosstalk across immune signaling. a Blood, b Lung, c Brain, d Heart, e Kidney. f To identify commonly downregulated pathways, the top 10 significantly differentially expressed signaling pathways in all tissues were compared. P-values were adjusted using the Benjamini–Hochberg method. Color = −log10(padj) and dot size = odds ratio.

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  Fig. 7. Flow cytometry and single-cell analysis of immune cells in the lung of animals treated with iECM or saline. a UMAP of live CD45+ Cells isolated from saline (red) and iECM (blue) animals. b UMAP showing cells classified by gating strategy. c Percentages of major cell populations in lung of saline vs iECM-treated animals. d Unsupervised clustering of all CD45+ cells using flowSOM analysis. e Relative marker expression of CD45+ immune cells in cluster 2. f Comparison of levels of gated CD11b- Ly6C+ cells in lungs of saline (red) vs iECM (blue) treated animals based on relative expression of markers in cluster two of FlowSOM-derived clusters. g UMAP of macrophages as determined using the gating strategy outlined in Supplementary Fig. 8. h Percentages of inflammatory macrophage phenotypes in saline vs iECM-treated animals based on expression of inflammatory markers MHCII and CD86. i UMAP of neutrophil clusters isolated from lung scRNA sequencing data. j Heatmap comparing expression of top 20 differentially expressed genes across identified neutrophil clusters. Significance was determined using a two-tailed unpaired t-test. n = 4 per group. *p < 0.05.

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  Fig. 8. Kidney sc-RNAseq results demonstrate alterations in proximal tubule phenotype with iECM treatment. a UMAP of captured cell types in kidney tissue. b UMAP of proximal tubule cells in iECM and saline-treated animals. c Heatmap of top 20 differentially expressed genes within proximal tubule clusters. d FeaturePlot blend of Defb1 and Cxcl10 expression in proximal tubule samples showing colocalization of Defb1 and Cxcl10 expression in clusters unique to saline-treated animals.