The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Comparative Study of Injury Models for Studying Muscle Regeneration in Mice
Open accessAbstract
BACKGROUND: A longstanding goal in regenerative medicine is to reconstitute functional tissues or organs after injury or disease. Attention has focused on the identification and relative contribution of tissue specific stem cells to the regeneration process. Relatively little is known about how the physiological process is regulated by other tissue constituents. Numerous injury models are used to investigate tissue regeneration, however, these models are often poorly understood. Specifically, for skeletal muscle regeneration several models are reported in the literature, yet the relative impact on muscle physiology and the distinct cells types have not been extensively characterised. METHODS: We have used transgenic Tg:Pax7nGFP and Flk1GFP/+ mouse models to respectively count the number of muscle stem (satellite) cells (SC) and number/shape of vessels by confocal microscopy. We performed histological and immunostainings to assess the differences in the key regeneration steps. Infiltration of immune cells, chemokines and cytokines production was assessed in vivo by Luminex®. RESULTS: We compared the 4 most commonly used injury models i.e. freeze injury (FI), barium chloride (BaCl2), notexin (NTX) and cardiotoxin (CTX). The FI was the most damaging. In this model, up to 96% of the SCs are destroyed with their surrounding environment (basal lamina and vasculature) leaving a "dead zone" devoid of viable cells. The regeneration process itself is fulfilled in all 4 models with virtually no fibrosis 28 days post-injury, except in the FI model. Inflammatory cells return to basal levels in the CTX, BaCl2 but still significantly high 1-month post-injury in the FI and NTX models. Interestingly the number of SC returned to normal only in the FI, 1-month post-injury, with SCs that are still cycling up to 3-months after the induction of the injury in the other models. CONCLUSIONS: Our studies show that the nature of the injury model should be chosen carefully depending on the experimental design and desired outcome. Although in all models the muscle regenerates completely, the trajectories of the regenerative process vary considerably. Furthermore, we show that histological parameters are not wholly sufficient to declare that regeneration is complete as molecular alterations (e.g. cycling SCs, cytokines) could have a major persistent impact.
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Arnold, L., Henry, A., Poron, F., Baba-Amer, Y., van Rooijen, N., Plonquet, A., Gherardi, R. K., & Chazaud, B. (2007) · The Journal of Experimental Medicine · doi:10.1084/jem.20070075 · Cited by 2,002
Asymmetric self-renewal and commitment of satellite stem cells in muscle
Kuang, S., Kuroda, K., Le Grand, F., & Rudnicki, M. A. (2007) · Cell · doi:10.1016/j.cell.2007.03.044 · Cited by 1,449
Collins, C. A., Olsen, I., Zammit, P. S., Heslop, L., Petrie, A., Partridge, T. A., & Morgan, J. E. (2005) · Cell · doi:10.1016/j.cell.2005.05.010 · Cited by 1,445
Murphy, M. M., Lawson, J. A., Mathew, S. J., Hutcheson, D. A., & Kardon, G. (2011) · Development · doi:10.1242/dev.064162 · Cited by 1,249
Lepper, C., Partridge, T. A., & Fan, C.-M. (2011) · Development · doi:10.1242/dev.067595 · Cited by 1,105
Vascular and Neurogenic Rejuvenation of the Aging Mouse Brain by Young Systemic Factors
Katsimpardi, L., Litterman, N. K., Schein, P. A., Miller, C. M., Loffredo, F. S., Wojtkiewicz, G. R., Chen, J. W., Lee, R. T., Wagers, A. J., & Rubin, L. L. (2014) · Science · doi:10.1126/science.1251141 · Cited by 1,037
Pax7-expressing satellite cells are indispensable for adult skeletal muscle regeneration
Sambasivan, R., Yao, R., Kissenpfennig, A., Van Wittenberghe, L., Paldi, A., Gayraud-Morel, B., Guenou, H., Malissen, B., Tajbakhsh, S., & Galy, A. (2011) · Development · doi:10.1242/dev.067587 · Cited by 939
Self-renewal and expansion of single transplanted muscle stem cells
Sacco, A., Doyonnas, R., Kraft, P., Vitorovic, S., & Blau, H. M. (2008) · Nature · doi:10.1038/nature07384 · Cited by 901
GDF11 increases with age and inhibits skeletal muscle regeneration
Egerman, M. A., Cadena, S. M., Gilbert, J. A., Meyer, A., Nelson, H. N., Swalley, S. E., Mallozzi, C., Jacobi, C., Jennings, L. L., Clay, I., et al. (2015) · Cell Metabolism · doi:10.1016/j.cmet.2015.05.010 · Cited by 527
Rocheteau, P., Gayraud-Morel, B., Siegl-Cachedenier, I., Blasco, M. A., & Tajbakhsh, S. (2012) · Cell · doi:10.1016/j.cell.2011.11.049 · Cited by 510
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Tidball, J. G. (2017) · Nature Reviews Immunology · doi:10.1038/nri.2016.150 · Cited by 740
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Dissecting Murine Muscle Stem Cell Aging through Regeneration Using Integrative Genomic Analysis
Shcherbina, A., Larouche, J., Fraczek, P., Yang, B. A., Brown, L. A., Markworth, J. F., Chung, C. H., Khaliq, M., de Silva, K., Choi, J. J., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107964 · Cited by 76
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Evano, B., Gill, D., Hernando-Herraez, I., Comai, G., Stubbs, T. M., Commere, P.-H., Reik, W., & Tajbakhsh, S. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1009022 · Cited by 40
Self-citationJensen, J. B., Dollerup, O. L., Møller, A. B., Billeskov, T. B., Dalbram, E., Chubanava, S., Damgaard, M. V., Dellinger, R. W., Trošt, K., Moritz, T., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.158314 · Cited by 39
Demonbreun, A. R., Fallon, K. S., Oosterbaan, C. C., Vaught, L. A., Reiser, N. L., Bogdanovic, E., Velez, M. P., Salamone, I. M., Page, P. G. T., Hadhazy, M., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abf0376 · Cited by 32
Bencze, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms24010362 · Cited by 20
Park, K.-S., Kim, H. J., Hwang, J. T., & Ko, B. S. (2020) · Journal of Functional Foods · doi:10.1016/j.jff.2020.104109 · Cited by 8
CD34 Regulates the Skeletal Muscle Response to Hypoxia
Pagé, M., Maheux, C., Langlois, A., Brassard, J., Bernatchez, É., Martineau, S., Henry, C., Beaulieu, M.-J., Bossé, Y., Morissette, M. C., et al. (2019) · Journal of Muscle Research and Cell Motility · doi:10.1007/s10974-019-09525-x · Cited by 7
Recent Developments in Mouse Trauma Research Models: A Mini-Review
Gihring, A., Gärtner, F., Schirmer, M., Wabitsch, M., & Knippschild, U. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.866617 · Cited by 6
Transient hypoxia followed by progressive reoxygenation is required for muscle repair
Quétin, M., Der Vartanian, A., Dubois, C., Berthier, J., Ledoux, M., Michineau, S., Drayton-Libotte, B., Prola, A., Sotiropoulos, A., Relaix, F., et al. (2026) · EMBO Reports · doi:10.1038/s44319-025-00679-z · Cited by 1
Metadata
- Authors
- Hardy, D., Besnard, A., Latil, M., Jouvion, G., Briand, D., Thépenier, C., Pascal, Q., Guguin, A., Gayraud-Morel, B., Cavaillon, J.-M., Tajbakhsh, S., Rocheteau, P., & Chrétien, F.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-01-25
- Volume
- 11
- Issue
- 1
- Pages
- e0147198
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2267893954
- Cited by
- 509
- References
- 15
- FWCI
- 18.66
- Type
- journal article
- Language
- en
- Open access
- gold
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