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Protein Expression of AEBP1, MCM4, and FABP4 Differentiate Osteogenic,Adipogenic, and Mesenchymal Stromal Stem Cells
Authors:Thorben Sauer  Giulia Facchinetti  Michael Kohl  Justyna M Kowal  Svitlana Rozanova  Julia Horn  Hagen Schmal  Ivo Kwee  Arndt-Peter Schulz  Sonja Hartwig  Moustapha Kassem  Jens K Habermann  Timo Gemoll
Abstract:Mesenchymal stem cells (MSCs) gain an increasing focus in the field of regenerative medicine due to their differentiation abilities into chondrocytes, adipocytes, and osteoblastic cells. However, it is apparent that the transformation processes are extremely complex and cause cellular heterogeneity. The study aimed to characterize differences between MSCs and cells after adipogenic (AD) or osteoblastic (OB) differentiation at the proteome level. Comparative proteomic profiling was performed using tandem mass spectrometry in data-independent acquisition mode. Proteins were quantified by deep neural networks in library-free mode and correlated to the Molecular Signature Database (MSigDB) hallmark gene set collections for functional annotation. We analyzed 4108 proteins across all samples, which revealed a distinct clustering between MSCs and cell differentiation states. Protein expression profiling identified activation of the Peroxisome proliferator-activated receptors (PPARs) signaling pathway after AD. In addition, two distinct protein marker panels could be defined for osteoblastic and adipocytic cell lineages. Hereby, overexpression of AEBP1 and MCM4 for OB as well as of FABP4 for AD was detected as the most promising molecular markers. Combination of deep neural network and machine-learning algorithms with data-independent mass spectrometry distinguish MSCs and cell lineages after adipogenic or osteoblastic differentiation. We identified specific proteins as the molecular basis for bone formation, which could be used for regenerative medicine in the future.
Keywords:protein profiling  data-independent acquisition mass spectrometry  SWATH  human stromal/mesenchymal stem cells  differentiation markers  machine learning
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