• 2018-07
  • 2018-10
  • 2018-11
  • br Results br Discussion The first


    Discussion The first objective of this study was the definition of an optimal protocol for MSC isolation. We intentionally focused on BM and AT, as ack1 inhibitor these are the most accessible tissues for MSC recovery. Current protocols for AT-MSC isolation are almost universally based on tissue digestion; for the BM, instead, we selected the two most frequently used methods, namely isolation based on MSC adhesive properties (BM-MSCs) (Soleimani and Nadri, 2009) and density gradient centrifugation, followed by immunodepletion (iBM-MSCs) (Sung et al., 2008). Despite presenting a similar behavior in cell culture, the three MSC types showed a different therapeutic potential once injected in vivo in a CLI model, reducing necrosis and inflammation and stimulating muscle regeneration, although at a different extent. They also prompted the formation of functional arterioles, in accordance with previous reports (Cho et al., 2009; Iwase et al., 2005). Interestingly, this was better accomplished by BM-MSCs, which abundantly secreted soluble cytokines involved in vessel remodeling and stabilization (such as PDGF-β) and induced the migration of vascular SMCs. This contradicts the findings of Kim et al., who attributed a higher angiogenic and therapeutic potential to AT-MSCs in comparison to MSCs of BM origin (Kim et al., 2007). A few differences can explain this discrepancy. First, their BM-MSCs were purified by density gradient centrifugation, while our results clearly indicate that the therapeutic potential strictly depends on the isolation protocol. Second, they transplanted human ack1 inhibitor into nude mice, which did not allow for the assessment of the immune-modulatory action of MSCs. Although multiple evidence points toward vascular endothelial growth factor (Vegf) as the main angiogenic cytokine secreted by MSCs (Kinnaird et al., 2004), we did not observe significant differences in Vegf expression among the three MSC types. Instead, the major differences occurred in genes involved in SMC recruitment and matrix remodeling (Tgf-β, Pdgf-β, and Mmp9), which are two essential events for the proper maturation of arterial vessels (Zacchigna et al., 2008). Can this arteriogenic effect be recapitulated by the delivery of the MSC supernatant? Although not reaching statistical significance, probably due to the relatively low number of animals analyzed, the repeated injection of CM from the three MSC types resulted in a trend toward improvement, fully consistent with the results observed upon cell transplantation. This supports the conclusion that the MSC secretome could recapitulate the effect of MSC injection (Ranganath et al., 2012), but its effectiveness is most likely dampened by the short half-life of its molecules. These results leave at least two open questions. First, CM and MSC transplantation were not performed on the same set of animals, and thus we cannot exclude interanimal variability when comparing the efficacy of the two treatments. Second, we did not investigate whether the injection of higher CM amounts or the administration of more frequent doses would result in a better outcome. In conclusion, our findings indicate that BM-MSCs are the most effective MSC type to improve perfusion and functional recovery after hindlimb ischemia. In contrast to initial studies supporting MSC plasticity (Quevedo et al., 2009), these cells were not able to transdifferentiate into either endothelial cells or SMCs while exerting their effect through a paracrine mechanism.
    Experimental Procedures
    Author Contributions
    Introduction Several efforts have been reported in the emerging field of structural phenotyping for the integration of image acquisition, processing, and analysis to assess the response of cells and tissues to various challenges (Eliceiri et al., 2012). All of these methodologies are predicated on the assumption that cell shape is an important indicator of the cell pathophysiological state and rely on (1) image-processing algorithms for the extraction of morphological features and (2) machine-learning strategies to mine the cell morphology data (Crane et al., 2012; Jones et al., 2009; Treiser et al., 2010).