MHC class I target recognition, immunophenotypes and proteomic profiles of natural killer cells within the spleens of day-14 chick embryos.

Chicken natural killer (NK) cells are not well defined, so little is known about the molecular interactions controlling their activity. At day 14 of embryonic development, chick spleens are a rich source of T-cell-free CD8αα(+), CD3(-) cells with natural killing activity. Cell-mediated cytotoxicity assays revealed complex NK cell discrimination of MHC class I, suggesting the presence of multiple NK cell receptors.

Proteomic analysis of surface and endosomal membrane proteins from the avian LMH epithelial cell line.

Proteins at the cell surface and within the endocytic pathway are increasingly being recognized for their roles in a wide variety of intercellular interactions. Here we used the inherent hydrophobicity and N-glycosylation of membrane proteins to enrich these proteins from the surface and endosome of avian LMH epithelial cells for mass spectrometric analysis. The cycling of many different types of proteins from the cell surface into the endosome and sometimes back to the surface again makes it appropriate to analyze these two membranous cellular components together.

An evaluation of multidimensional fingerprinting in the context of clinical proteomics.

Multidimensional fingerprinting (MDF) utilizes measurable peptide characteristics to identify proteins. In this study, 3-D fingerprinting, namely, parent protein molecular weight, peptide mass, and peptide retention time on RPLC, is used to identify 331 differentially expressed proteins between normal and human colon cancer plasma membrane samples. A false discovery rate (FDR) procedure is introduced to evaluate the performance of MDF on the colon cancer dataset.

Multicomponent internal recalibration of an LC-FTICR-MS analysis employing a partially characterized complex peptide mixture: systematic and random errors.

In high-throughput proteomics, a promising current approach is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) of tryptic peptides from complex mixtures of proteins. To apply this method, it is necessary to account for any systematic measurement error, and it is useful to have an estimate of the random error expected in the measured masses. Here, we analyze by LC-FTICR-MS a complex mixture of peptides derived from a sample previously characterized by LC-QTOF-MS.

Increasing peptide identification in tandem mass spectrometry through automatic function switching optimization.

Comprehensive proteomic studies that employ MS directed peptide sequencing are limited by optimal peptide separation and MS and tandem MS data acquisition routines. To identify the optimal parameters for data acquisition, we developed a system that models the automatic function switching behavior of a mass spectrometer using an MS-only dataset. Simulations were conducted to characterize the number and the quality of simulated fragmentation as a function of the data acquisition routines and used to construct operating curves defining tandem mass spectra quality and the number of peptides fragmented.