Quantitative Proteomics Evaluation of Human Multipotent Stromal Cell for β Cell Regeneration.

Cell Rep

Don Rix Protein Identification Facility, Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada. Electronic address:

Published: November 2018

AI Article Synopsis

  • Human multipotent stromal cells (hMSCs) are important in regenerative medicine, particularly for their ability to aid in recovery from diabetes.
  • Traditional methods of testing hMSC effectiveness are time-consuming and expensive, prompting the development of a quicker proteomics assay to assess their β cell regenerative capacity.
  • The study identified 16 proteins in hMSC conditioned media that can successfully distinguish effective regenerative cells, validating the results through various assays and animal models.

Article Abstract

Human multipotent stromal cells (hMSCs) are one of the most versatile cell types used in regenerative medicine due to their ability to respond to injury. In the context of diabetes, it has been previously shown that the regenerative capacity of hMSCs is donor specific after transplantation into streptozotocin (STZ)-treated immunodeficient mice. However, in vivo transplantation models to determine regenerative potency of hMSCs are lengthy, costly, and low throughput. Therefore, a high-throughput quantitative proteomics assay was developed to screen β cell regenerative potency of donor-derived hMSC lines. Using proteomics, we identified 16 proteins within hMSC conditioned media that effectively identify β cell regenerative hMSCs. This protein signature was validated using human islet culture assay, ELISA, and the potency was confirmed by recovery of hyperglycemia in STZ-treated mice. Herein, we demonstrated that quantitative proteomics can determine sample-specific protein signatures that can be used to classify previously uncharacterized hMSC lines for β cell regenerative clinical applications.

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Source
http://dx.doi.org/10.1016/j.celrep.2018.10.107DOI Listing

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