Table_2_Predicting and Promoting Human Bone Marrow MSC Chondrogenesis by Way of TGFβ Receptor Profiles: Toward Personalized Medicine.docx
The use of human mesenchymal stromal cells (hMSCs) for cartilage regeneration has been hampered by the inherent donor variation of primary monolayer expanded cells. Although CD markers are typically used to characterize cell populations, there is no correlation between CD marker profile and functional outcomes. Therefore, we aimed to discover novel predictive MSC chondrogenesis markers. The chondrogenic potential of primary human bone marrow MSCs (hBMSCs) over multiple passages was assessed by standard pellet culture. We confirmed that the ratio of TGFβ-RI/TGFβ-RII at the time of cell recovery from the tissue culture plastic reliably predicted chondrogenic potential. Furthermore, it is possible to prospectively characterize any human BMSC cell population as responders or non-responders with respect to chondrogenic differentiation potential. Transient increase of the ratio with siRNA knockdown of TGFβ-RII reproducibly recovered the chondrogenic differentiation ability of non-responsive MSCs. Together this offers an opportunity to produce a more functionally characterized cell population for use in autologous cartilage repair therapies.
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References
- https://doi.org//10.3324/haematol.13740
- https://doi.org//10.4049/jimmunol.0803991
- https://doi.org//10.1186/ar1833
- https://doi.org//10.1016/j.ebiom.2015.12.020
- https://doi.org//10.1002/jbmr.5650110911
- https://doi.org//10.1074/jbc.274.6.3672
- https://doi.org//10.1016/j.yjmcc.2013.10.017
- https://doi.org//10.1089/scd.2016.0308
- https://doi.org//10.1016/j.joca.2015.11.018
- https://doi.org//10.1371/journal.pone.0146124
- https://doi.org//10.1038/srep36655
- https://doi.org//10.1002/stem.2691
- https://doi.org//10.1016/S1359-6101(02)00004-7
- https://doi.org//10.1089/ten.2006.12.2141
- https://doi.org//10.1089/ten.tea.2010.0043
- https://doi.org//10.1006/meth.2001.1262
- https://doi.org//10.1089/ten.tea.2014.0096
- https://doi.org//10.1038/nrm3434
- https://doi.org//10.22203/eCM.v034a14
- https://doi.org//10.1111/j.1474-9728.2004.00127.x
- https://doi.org//10.1093/emboj/16.17.5353
- https://doi.org//10.1007/s00441-013-1740-5
- https://doi.org//10.1016/j.stemcr.2016.05.011
- https://doi.org//10.1007/s00109-006-0097-6
- https://doi.org//10.1016/S0092-8674(03)00432-X
- https://doi.org//10.1007/s00441-003-0762-9
- https://doi.org//10.22203/eCM.v024a00
- https://doi.org//10.1089/scd.2007.0049
- https://doi.org//10.2106/00004623-199404000-00013
- https://doi.org//10.1089/ten.tea.2007.0266
- https://doi.org//10.2106/00004623-199812000-00004
- https://doi.org//10.1155/2018/4180857
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