Protein Expression of AEBP1, MCM4, and FABP4 Differentiate Osteogenic, Adipogenic, and Mesenchymal Stromal Stem Cells.
Thorben SauerGiulia FacchinettiMichael KohlJustyna M KowalSvitlana RozanovaJulia HornHagen SchmalIvo KweeArndt Peter SchulzSonja HartwigMoustapha KassemJens K HabermannTimo GemollPublished in: International journal of molecular sciences (2022)
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.
Keyphrases
- mesenchymal stem cells
- neural network
- single cell
- umbilical cord
- cell therapy
- bone marrow
- induced apoptosis
- machine learning
- stem cells
- rna seq
- tandem mass spectrometry
- liquid chromatography
- signaling pathway
- mass spectrometry
- vascular smooth muscle cells
- binding protein
- cell cycle arrest
- high performance liquid chromatography
- gas chromatography
- big data
- ultra high performance liquid chromatography
- adipose tissue
- pi k akt
- genome wide
- electronic health record
- simultaneous determination
- amino acid
- computed tomography
- solid phase extraction
- cell death
- protein protein
- copy number
- deep learning
- magnetic resonance imaging
- insulin resistance
- magnetic resonance
- ms ms
- genome wide identification
- small molecule
- data analysis