Aligned nanofiber material supports cell growth and increases osteogenesis in canine adipose-derived mesenchymal stem cells in vitro.
Sony PandeyKusum RathoreJed JohnsonMaria CekanovaPublished in: Journal of biomedical materials research. Part A (2018)
Tissue engineering shows great promise for the treatment of degenerative diseases, including bone repair. Polymer nanofibers provide a three-dimensional (3-D) scaffold for attachment and growth of mesenchymal stem cells. Increasing evidence supports that fiber alignment on scaffolds plays a major role in the viability and differentiation of stem cells. We compared the cell viability of canine adipose tissue-derived mesenchymal stem cells (cADMSCs) cultured in the aligned- (NanoAligned™) and random- (NanoECM™) oriented polycaprolactone (PCL) nanofiber-coated plates to control polystyrene tissue culture plates using a proliferation assay. Ability of the plates to induce differentiation of cADMSCs into osteocytes, adipocytes, and neurons was evaluated based on expression of the osteocyte markers, COL1A1 and osterix; adipocyte markers PPARγ2 and LPL; and neuronal marker nestin using RT-PCR. Proliferation results demonstrated that aligned-oriented PCL nanofiber-coated plates were more suitable substrate for cADMSCs after 7 days in culture compared to random-oriented PCL nanofiber-coated or control plates. Additionally, we demonstrated that both 3-D PCL nanofiber-coated plates were a better scaffold for cADMSCs differentiation into osteocytes compared to control plates. In conclusion, our results confirm that PCL nanofiber is a suitable tissue engineering material for use in regenerative medicine for canine patients in vivo. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1780-1788, 2018.
Keyphrases
- tissue engineering
- adipose tissue
- mesenchymal stem cells
- stem cells
- insulin resistance
- end stage renal disease
- poor prognosis
- signaling pathway
- chronic kidney disease
- ejection fraction
- high fat diet
- umbilical cord
- spinal cord
- prognostic factors
- bone marrow
- fatty acid
- patient reported outcomes
- body composition
- metabolic syndrome
- machine learning
- long non coding rna
- artificial intelligence
- binding protein