Quantitative Proteomic Profiling of Small Molecule Treated Mesenchymal Stem Cells Using Chemical Probes

Author:

Santos JerranORCID,Dolai Sibasish,O’Rourke Matthew B.ORCID,Liu FeiORCID,Padula Matthew P.ORCID,Molloy Mark P.,Milthorpe Bruce K.ORCID

Abstract

The differentiation of human adipose derived stem cells toward a neural phenotype by small molecules has been a vogue topic in the last decade. The characterization of the produced cells has been explored on a broad scale, examining morphological and specific surface protein markers; however, the lack of insight into the expression of functional proteins and their interactive partners is required to further understand the extent of the process. The phenotypic characterization by proteomic profiling allows for a substantial in-depth analysis of the molecular machinery induced and directing the cellular changes through the process. Herein we describe the temporal analysis and quantitative profiling of neural differentiating human adipose-derived stem cells after sub-proteome enrichment using a bisindolylmaleimide chemical probe. The results show that proteins enriched by the Bis-probe were identified reproducibly with 133, 118, 126 and 89 proteins identified at timepoints 0, 1, 6 and 12, respectively. Each temporal timepoint presented several shared and unique proteins relative to neural differentiation and their interactivity. The major protein classes enriched and quantified were enzymes, structural and ribosomal proteins that are integral to differentiation pathways. There were 42 uniquely identified enzymes identified in the cells, many acting as hubs in the networks with several interactions across the network modulating key biological pathways. From the cohort, it was found by gene ontology analysis that 18 enzymes had direct involvement with neurogenic differentiation.

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

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