Back

Muscle-Specific ECM Fibers Made with Anchored Cell Sheet Engineering Support Tissue Regeneration in Rat Models of Volumetric Muscle Loss

Shahin-Shamsabadi, A.; Cappuccitti, J.

2024-12-17 bioengineering
10.1101/2024.12.15.628541 bioRxiv
Show abstract

Volumetric muscle loss (VML) represents a critical unmet need in regenerative medicine, with no established standard of care. This study introduces a novel therapeutic strategy using tissue-specific skeletal muscle extracellular matrix (ECM) fibers fabricated using scaffold-free Anchored Cell Sheet Engineering technology. These engineered fibers replicate the native ECM composition and microarchitecture of skeletal muscle, incorporating essential structural and basement membrane proteins. In a rat VML model, engineered ECM fibers demonstrated a promising regenerative capacity compared to commercial porcine-derived small intestine submucosa (SIS) ECM. Over an 8-week period, the engineered fibers preserved muscle volume and weight, regulated inflammatory and fibrotic responses, and promoted vascularization. In contrast, SIS was rapidly degraded by week 4 and associated with excessive fibrotic response. Force recovery in the muscles treated with engineered ECM fibers was lower at the 8-week time point (77% compared to 91% in the control group), but histological and immunohistochemical analyses revealed newly formed, dispersed muscle fibers exclusively within the repaired muscle tissue treated with engineered ECM fibers. Importantly, only in cases where engineered ECM fibers were used, muscle weight was preserved, resulting in similar normalized force-to-weight recovery across all groups (87% in the test group vs. 88% in the control group). The histological analyses further demonstrated ongoing tissue remodeling, indicative of sustained regeneration, in contrast to the premature fibrotic healing observed in the other groups. A novel quantitative image analysis workflow using a custom Python script, enabled objective assessment of spatial tissue heterogeneity through histology and immunohistochemistry images, setting a new standard for tissue regeneration analysis. These findings establish engineered tissue-specific ECM fibers as a transformative approach for VML treatment and lay the groundwork for translation to clinical applications.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Advanced Healthcare Materials
71 papers in training set
Top 0.1%
18.5%
2
Biomaterials
78 papers in training set
Top 0.1%
10.4%
3
npj Regenerative Medicine
21 papers in training set
Top 0.1%
10.4%
4
Advanced Functional Materials
41 papers in training set
Top 0.5%
6.3%
5
Acta Biomaterialia
85 papers in training set
Top 0.2%
4.1%
6
Biofabrication
32 papers in training set
Top 0.2%
3.9%
50% of probability mass above
7
Stem Cell Research & Therapy
30 papers in training set
Top 0.1%
3.8%
8
Bioactive Materials
18 papers in training set
Top 0.2%
3.6%
9
Bioengineering & Translational Medicine
21 papers in training set
Top 0.2%
3.2%
10
Tissue Engineering Part A
15 papers in training set
Top 0.1%
3.1%
11
Journal of Biomedical Materials Research Part A
18 papers in training set
Top 0.1%
2.7%
12
Advanced Science
249 papers in training set
Top 7%
2.6%
13
Scientific Reports
3102 papers in training set
Top 51%
2.1%
14
Biomaterials Advances
20 papers in training set
Top 0.3%
1.8%
15
Annals of Biomedical Engineering
34 papers in training set
Top 0.7%
1.7%
16
Frontiers in Bioengineering and Biotechnology
88 papers in training set
Top 2%
1.3%
17
Nature Communications
4913 papers in training set
Top 56%
1.2%
18
Biomaterials Science
21 papers in training set
Top 0.4%
1.2%
19
ACS Biomaterials Science & Engineering
37 papers in training set
Top 0.9%
0.9%
20
Advanced Materials
53 papers in training set
Top 2%
0.8%
21
Materials Today Bio
18 papers in training set
Top 0.6%
0.7%
22
Molecular Therapy
71 papers in training set
Top 3%
0.7%
23
Advanced Materials Interfaces
10 papers in training set
Top 0.4%
0.7%
24
Cytotherapy
14 papers in training set
Top 0.4%
0.7%
25
PLOS ONE
4510 papers in training set
Top 70%
0.7%
26
Cell Reports Medicine
140 papers in training set
Top 9%
0.6%
27
Communications Biology
886 papers in training set
Top 29%
0.6%