Insulin stimulated MCF7 breast cancer cells: Proteome dataset.

The proteome data provided in this article were acquired from MCF7 breast cancer cells stimulated with insulin, and were generated by using a 2D-SCX (strong cation exchange)/RPLC (reversed phase liquid chromatography) separation protocol followed by tandem mass spectrometry (MS) detection. To facilitate data re-processing by more advanced search engines and the extraction of additional information from already existing files, both raw and processed data are provided. The sample preparation, data acquisition and processing protocols are described in detail. The raw data relate to work published in "Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation" (Sarvaiya et al., 2006) [1] and are made available through the PRIDE (PRoteomics IDEntifications)/ProteomeXchange public repository with identifier PRIDE: PXD004051 ("2016 update of the PRIDE database and tools" (Vizcaino et al., 2016) [2]).

Microfluidic platform with mass spectrometry detection for the analysis of phosphoproteins.

The development of novel and reliable technologies for the analysis of proteins and their post-translational modifications, in particular, has recently received much attention and interest. The implementation of a fully integrated microfluidic device interfaced with MS detection for the analysis of phosphoproteins is presented in this paper. The microfluidic platform (3''x1.5'') comprises two individual sample processing systems: one for performing direct sample infusion and one for performing microfluidic LC separations. Various MS detection strategies, specific for the study of post-translational modifications, were conducted using alpha-casein as a model protein. Neutral loss ion mapping, data-dependent triple-play and neutral loss analysis, and in situ dephosphorylation followed by LC separation and MS detection were performed. Consistent results in identifying phosphopeptides with conventional and microfluidic instrumentation have been obtained. Unlike with conventional instrumentation, however, the microfluidic device enabled the completion of each analysis from only a few microliters of sample, in approximately 10-15 min, and on a bioanalytical platform that facilitates multiplexing and disposability, and thus high-throughput, contamination-free analysis.

Proteome profile of the MCF7 cancer cell line: a mass spectrometric evaluation.

The development of novel proteomic technologies that will enable the discovery of disease specific biomarkers is essential in the clinical setting to facilitate early diagnosis and increase survivability rates. We are reporting a shotgun two-dimensional (2D) strong cationic exchange/reversed-phase liquid chromatography/electrospray ionization tandem mass spectrometry (SCX/RPLC/ESI-MS/MS) protocol for the analysis of proteomic constituents in cancerous cells. The MCF7 breast cancer cell line was chosen as a model system. A series of optimization steps were performed to improve the LC/MS experimental setup, sample preparation, data acquisition and database search protocols, and a data filtering strategy was developed to enable confident identification of a large number of proteins and potential biomarkers. This research has resulted in the identification of >2000 proteins using multiple filtering and p-value sorting. Approximately 1600-1900 proteins had p < 0.001, and, of these, approximately 60% were matched by >or=2 unique peptides. Alternatively, >99% of the proteins identified by >or=2 unique peptides had p < 0.001. When searching the data against a reversed database of proteins, the rate of false positive identifications was 0.1% at the peptide level and 0.4% at the protein level. The typical reproducibility in detecting overlapping proteins across replicate runs exceeded 90% for proteins matched by >or=2 unique peptides. According to their biological function, approximately 200 proteins were involved in cancer-relevant cellular processes, and over 25 proteins were previously described in the literature as putative cancer biomarkers, as they were found to be differentially expressed between normal and cancerous cell states. Among these, biomarkers such PCNA, cathepsin D, E-cadherin, 14-3-3-sigma, antigen Ki-67, TP53RK, and calreticulin were identified. These data were generated by subjecting to MS analysis approximately 42 microg of sample, analyzing 16 SCX peptide fractions, and interpreting approximately 55,000 MS2 spectra. Total MS time required for analysis was 40 h.

Microfluidic liquid chromatography system for proteomic applications and biomarker screening.

A microfluidic liquid chromatography (LC) system for proteomic investigations that integrates all the necessary components for stand-alone operation, i.e., pump, valve, separation column, and electrospray interface, is described in this paper. The overall size of the LC device is small enough to enable the integration of two fully functional separation systems on a 3 in. x 1 in. glass microchip. A multichannel architecture that uses electroosmotic pumping principles provides the necessary functionality for eluent propulsion and sample valving. The flow rates generated within these chips are fully consistent with the requirements of nano-LC platforms that are routinely used in proteomic applications. The microfluidic device was evaluated for the analysis of a protein digest obtained from the MCF7 breast cancer cell line. The cytosolic protein extract was processed according to a shotgun protocol, and after tryptic digestion and prefractionation using strong cation exchange chromatography (SCX), selected sample subfractions were analyzed with conventional and microfluidic LC platforms. Using similar experimental conditions, the performance of the microchip LC was comparable to that obtained with benchtop instrumentation, providing an overlap of 75% in proteins that were identified by more than two unique peptides. The microfluidic LC analysis of a protein-rich SCX fraction enabled the confident identification of 77 proteins by using conventional data filtering parameters, of 39 proteins with p < 0.001, and of 5 proteins that are known to be cancer-specific biomarkers, demonstrating thus the potential applicability of these chips for future high-throughput biomarker screening applications.