Exosomes Derived from Human Primary and Metastatic Colorectal Cancer Cells Contribute to Functional Heterogeneity of Activated Fibroblasts by Reprogramming Their Proteome

We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.

Exosomes are 40-100nm extracellular vesicles that are released from a multitude of cell types, and perform diverse cellular functions including intercellular communication, antigen presentation, and transfer of oncogenic proteins as well as mRNA and miRNA. Exosomes have been purified from biological fluids and in vitro cell cultures using a variety of strategies and techniques. However, all preparations invariably contain varying proportions of other membranous vesicles that co-purify with exosomes such as shed microvesicles and apoptotic blebs. Using the colorectal cancer cell line LIM1863 as a cell model, in this study we performed a comprehensive evaluation of current methods used for exosome isolation including ultracentrifugation (UC-Exos), OptiPrep™ density-based separation (DG-Exos), and immunoaffinity capture using anti-EpCAM coated magnetic beads (IAC-Exos). Notably, all isolations contained 40-100nm vesicles, and were positive for exosome markers (Alix, TSG101, HSP70) based on electron microscopy and Western blotting. We employed a proteomic approach to profile the protein composition of exosomes, and label-free spectral counting to evaluate the effectiveness of each method. Based on the number of MS/MS spectra identified for exosome markers and proteins associated with their biogenesis, trafficking, and release, we found IAC-Exos to be the most effective method to isolate exosomes. For example, Alix, TSG101, CD9 and CD81 were significantly higher (at least 2-fold) in IAC-Exos, compared to UG-Exos and DG-Exos. Application of immunoaffinity capture has enabled the identification of proteins including the ESCRT-III component VPS32C/CHMP4C, and the SNARE synaptobrevin 2 (VAMP2) in exosomes for the first time. Additionally, several cancer-related proteins were identified in IAC-Exos including various ephrins (EFNB1, EFNB2) and Eph receptors (EPHA2-8, EPHB1-4), and components involved in Wnt (CTNNB1, TNIK) and Ras (CRK, GRB2) signalling. Copyright © 2012 Elsevier Inc. All rights reserved.

Exosomes are small vesicles (50-150 nm) of endocytic origin that are released by many different cell types. Exosomes in the tumor microenvironment may play a key role in facilitating cell-cell communication. Exosomes are reported to predominantly contain RNA and proteins. In this study, we investigated whether exosomes from pancreatic cancer cells and serum from patients with pancreatic ductal adenocarcinoma contain genomic DNA. Our results provide evidence that exosomes contain >10-kb fragments of double-stranded genomic DNA. Mutations in KRAS and p53 can be detected using genomic DNA from exosomes derived from pancreatic cancer cell lines and serum from patients with pancreatic cancer. In addition, using whole genome sequencing, we demonstrate that serum exosomes from patients with pancreatic cancer contain genomic DNA spanning all chromosomes. These results indicate that serum-derived exosomes can be used to determine genomic DNA mutations for cancer prediction, treatment, and therapy resistance.