Differential Proteome Analysis of Extracellular Vesicles from Breast Cancer Cell Lines by Chaperone Affinity Enrichment.
Steven G GriffithsMichelle T CormierAled ClaytonAlan A DoucettePublished in: Proteomes (2017)
The complexity of human tissue fluid precludes timely identification of cancer biomarkers by immunoassay or mass spectrometry. An increasingly attractive strategy is to primarily enrich extracellular vesicles (EVs) released from cancer cells in an accelerated manner compared to normal cells. The Vn96 peptide was herein employed to recover a subset of EVs released into the media from cellular models of breast cancer. Vn96 has affinity for heat shock proteins (HSPs) decorating the surface of EVs. Reflecting their cells of origin, cancer EVs displayed discrete differences from those of normal phenotype. GELFrEE LC/MS identified an extensive proteome from all three sources of EVs, the vast majority having been previously reported in the ExoCarta database. Pathway analysis of the Vn96-affinity proteome unequivocally distinguished EVs from tumorigenic cell lines (SKBR3 and MCF-7) relative to a non-tumorigenic source (MCF-10a), particularly with regard to altered metabolic enzymes, signaling, and chaperone proteins. The protein data sets provide valuable information from material shed by cultured cells. It is probable that a vast amount of biomarker identities may be collected from established and primary cell cultures using the approaches described here.
Keyphrases
- heat shock
- induced apoptosis
- cell cycle arrest
- mass spectrometry
- endothelial cells
- heat shock protein
- cell death
- oxidative stress
- capillary electrophoresis
- squamous cell carcinoma
- endoplasmic reticulum stress
- signaling pathway
- stem cells
- healthcare
- machine learning
- emergency department
- heat stress
- social media
- cell proliferation
- single cell
- high performance liquid chromatography
- gas chromatography
- drinking water
- liquid chromatography
- adverse drug
- small molecule
- sensitive detection
- drug induced
- data analysis