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Glycomic analysis is an increasingly important field in biological and biomedical research as glyeosylation is one of the most important protein post-translational modifications. We have developed a new technique to detect carbohydrates using surface enhanced Raman spectroscopy (SERS) by designing and applying a Rhodamine B derivative as the SERS tag. Using a reductive amination reaction, the Rhodamine-based tag (RT) was successfully conjugated to three model carbohydrates (glucose, lactose, and glucuronic acid). SERS detection limits obtained with a 633 nm HeNe laser were ~1 nM in concentration for all the RT-carbohydrate conjugates and ~10 fmol in total sample consumption. The dynamic range of the SERS method is about 4 orders of magnitude, spanning from 1 nM to 5 [micro]M. Ratiometric SERS quantification using isotope-substituted SERS internal references allows Comparative quantifications of carbohydrates labeled with RT and deuterium/hydrogen substituted RT tags, respectively. In addition to enhancing the SERS detection of the tagged carbohydrates, the Rhodamine tagging facilitates fluorescence and mass spectrometric detection of carbohydrates. Current fluorescence sensitivity of RT-carbohydrates is ~3 nM in concentration while the mass spectrometry (MS) sensitivity is about 1 fmol, achieved with a linear ion trap electrospray ionization (ESI)-MS instrument. Potential applications that take advantage of the high SERS, fluorescence, and MS sensitivity of this SERS tagging strategy are discussed for practical glycomic analysis where carbohydrates may be quantified with a fluorescence and SERS technique and then identified with ESI-MS techniques. 10.1021/ac102284x

The potential of getting a significant number of false positives (FPs) in peptide-spectrum matches (PSMs) obtained by proteomic database search has been well-recognized. Among the attempts to assess FPs, the concomitant use of target and decoy databases is widely practiced. By adjusting filtering criteria, FPs and false discovery rate (FDR) can be controlled at a desired level. Although the target-decoy approach is gaining in popularity, subtle differences in decoy construction (e.g., reversing vs stochastic methods), rate calculation (e.g., total vs unique PSMs), or searching (separate vs composite) do exist among various implementations. In the present study, we evaluated the effects of these differences on FP and FDR estimations using a rat kidney protein sample and the SEQUEST search engine as an example. On the effects of decoy construction, we found that, when a single scoring filter (XCorr) was used, stochastic methods generated a higher estimation of FPs and FDR than sequence reversing methods, likely due to an increase in unique peptides. This higher estimation could largely be attenuated by creating decoy databases similar in effective size but not by a simple normalization with a unique-peptide coefficient. When multiple filters were applied, the differences seen between reversing and stochastic methods significantly diminished, suggesting multiple filterings reduce the dependency on how a decoy is constructed. For a fixed set of filtering criteria, FDR and FPs estimated by using unique PSMs were almost twice those using total PSMs. The higher estimation seemed to be dependent on data acquisition setup. As to the differences between performing separate or composite searches, in general, FDR estimated from the separate search was about three times that from the composite search. The degree of difference gradually decreased as the filtering criteria became more stringent. Paradoxically, the estimated true positives in separate search were higher when multiple filters were used. By analyzing a standard protein mixture, we demonstrated that the higher estimation of FDR and FPs in the separate search likely reflected an overestimation, which could be corrected with a simple merging procedure. Our study illustrates the relative merits of different implementations of the target-decoy strategy, which should be worth contemplating when large-scale proteomic biomarker discovery is to be attempted.

LC--M[S.sup.n] has become a popular option for high-throughput quantitative proteomics, thanks to the availability of stable-isotope labeling reagents. However, the vast quantity of data generated from LC--M[S.sup.n] continues to make the postacquisition quantification analyses challenging, especially in experiments involving multiple samples per experimental condition. To facilitate data analysis, we developed a computer program, QUIL, for automated protein quantification. QUIL accounts for the dynamic nature of spectral background and subtracts this background accordingly during ion chromatogram reconstruction. For elution profile identification, QUIL minimizes the inclusion of coeluted neighbor peaks, yet tolerates imperfect peak shapes. Outlier-resistant methods have been implemented for better protein ratio estimation. The utility of QUIL was validated by quantitative analyses of a standard protein as well as complex protein mixtures, which were labeled with cICAT or [sup.18]O and analyzed using LCQ, LTQ, or FT-ICR instruments. For samples that no prior knowledge of relative protein quantities was available, Western blotting was performed for confirmation. For the standard protein, the coefficient of variation (CV) of peptide ratio estimation was 6%. For complex mixtures, the median CV for protein ratio calculations was less than 10%. Computed protein abundance ratios exhibited a relatively high degree of correlation with those obtained from Western blot analyses. Compared with a widely used commercial software tool, QUIL showed improvement in ion chromatogram construction and peak integration and significantly reduced relative errors in abundance ratio assessment.

A simple and reliable method is described here for the identification and relative quantification of proteins in complex mixtures using two-dimensional liquid chromatography/tandem mass spectrometry. The method is based on the classical proteomic analysis where proteins are digested with trypsin and the resulting peptides are separated by multidimensional liquid chromatography. The separated peptides are analyzed by tandem mass spectrometry and identified via a database search algorithm such as SEQUEST. The peak areas (integrated ion counts over the peptide elution time) of all identified peptides are calculated, and the relative concentration of each protein is determined by comparing the peak areas of all peptides from that protein in one sample versus those from the other. Using this strategy, we compared the relative level of protein expression of A431 cells (an epidermal cell line) grown in the presence or absence of epidermal growth factor (EGF). Our results are consistent with the published observations of the transient effects of EGF. In addition, the difference in the concentrations of several phosphopeptides determined in our studies suggests the possibility of several new targets involved in the EGF cell-signaling pathway. This global protein identification and quantification technology should prove to be a valuable means for comparing proteomes in biological samples subjected to differential treatments.