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. Immunophenotyping of freshly isolated and recombinant chicken interleukin-2-stimulated d14E CD8αα(+) CD3(-) splenocytes provided further evidence for population heterogeneity. Complex patterns of expression were found for CD8α, chB6 (Bu-1), CD1-1, CD56 (NCAM), KUL01, CD5, and CD44. Mass spectrometry-based proteomics revealed an array of NK cell proteins, including the NKR2B4 receptor. DAVID and KEGG analyses and additional immunophenotyping revealed NK cell activation pathways and evidence for monocytes within the splenocyte cultures. This study provides an underpinning for further investigation into the specificity and function of NK cells in birds.

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. This evaluation establishes a false protein identification rate below 15% for this dataset. Western blot analysis is performed to validate the differential expression of the MDF-identified protein VDAC1 on the original tissue samples. The limits of MDF are further assessed by a simulation study where key parameters such as database size, query size, and mass accuracy are varied. The results of this simulation study demonstrate that fingerprinting with three dimensions yields low FDR values even for large queries on the complete human proteome without the need for prior peptide sequencing by tandem mass spectrometry. Specifically, when mass accuracy is 10 ppm or lower, full human proteome searches can achieve FDR values of 10% or less.

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. Application of a Bayesian probability model of the data and partial knowledge of the composition of the sample suffice to estimate both the systematic and random errors in measured masses.

Determination of the systematic and random measurement error in an LC-FTICR mass spectrometry analysis of a partially characterized complex peptide mixture

In high-throughput proteomics, a promising approach presently being explored is the use of liquid chromatography coupled to Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS) to provide measurements of the masses of tryptic peptides in complex mixtures, which can then be used to identify the proteins which gave rise to those peptides. In order 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 in measured masses. In this investigation, a complex mixture of peptides derived from a partially characterized sample was analyzed by LC-FTICR-MS. Through the application of a Bayesian probability model of the data, partial knowledge of the composition of the sample is sufficient both to determine any systematic error and to estimate the random error in measured masses.

Surface analysis of peptide mass spectra to improve time and mass localization

Current peak detections algorithms for processing mass spectrometry (MS) spectra generally rely on two dimensional techniques for identifying the location and intensity of peaks from a single spectrum. However, when high performance liquid chromatography (HPLC) is coupled with mass spectrometry, a third dimension, retention time, is introduced. The ensemble of MS spectra may then be regarded as a 3D surface where spectral intensity is a function of m/z (mass-to-charge) and time. This suggests that peak localization can be improved by incorporating the time domain data and average data across both dimensions. This work describes a surface intensity analysis algorithm and the results of its use.

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. Results demonstrated that one could optimize for quality or quantity, with the number of peptides fragmented decreasing as quality increased. The predicted optimal operating curve indicated that significant improvements can be realized by selecting the appropriate data acquisition parameters. The simulation results were confirmed experimentally by testing 10 LC MS/MS data acquisition parameter sets on an LC-Q-TOF-MS. Database matching of the experimental fragmentation returned peptide scores consistent with the predictions of the model. The results of the simulations of mass spectrometer data acquisition routines reveal an inverse relationship between the quality and the quantity of peptide identifications and predict an optimal operating curve that can be used to select an optimal data acquisition parameter for a given (or any) sample.