Stable isotopic labeling and mass spectrometry as a means to determine differences in protein expression

Neutron-encoded signatures enable product ion annotation from tandem mass spectra

We report the use of neutron-encoded (NeuCode) stable isotope labeling of amino acids in cell culture for the purpose of C-terminal product ion annotation. Two NeuCode labeling isotopologues of lysine, (13)C6(15)N2 and (2)H8, which differ by 36 mDa, were metabolically embedded in a sample proteome, and the resultant labeled proteins were combined, digested, and analyzed via liquid chromatography and mass spectrometry. With MS/MS scan resolving powers of ~50,000 or higher, product ions containing the C terminus (i.e. lysine) appear as a doublet spaced by exactly 36 mDa, whereas N-terminal fragments exist as a single m/z peak. Through theory and experiment, we demonstrate that over 90% of all y-type product ions have detectable doublets. We report on an algorithm that can extract these neutron signatures with high sensitivity and specificity. In other words, of 15,503 y-type product ion peaks, the y-type ion identification algorithm correctly identified 14,552 (93.2%) based on detection of the NeuCode doublet; 6.8% were misclassified (i.e. other ion types that were assigned as y-type products). Searching NeuCode labeled yeast with PepNovo(+) resulted in a 34% increase in correct de novo identifications relative to searching through MS/MS only. We use this tool to simplify spectra prior to database searching, to sort unmatched tandem mass spectra for spectral richness, for correlation of co-fragmented ions to their parent precursor, and for de novo sequence identification.

Using a spike-in experiment to evaluate analysis of LC-MS data

BACKGROUND

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards.

RESULTS

The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose.

CONCLUSIONS

The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.

LC-MS data analysis for differential protein expression detection

In proteomic studies, liquid chromatography coupled with mass spectrometry (LC-MS) is a common platform to compare the abundance of various peptides that characterize particular proteins in biological samples. Each LC-MS run generates data consisting of thousands of peak intensities for peptides represented by retention time (RT) and mass-to-charge ratio (m/z) values. In label-free differential protein expression studies, multiple LC-MS runs are compared to identify differentially abundant peptides between distinct biological groups. This approach presents a computational challenge because of the following reasons (i) substantial variation in RT across multiple runs due to the LC instrument conditions and the variable complexity of peptide mixtures, (ii) variation in m/z values due to occasional drift in the calibration of the mass spectrometry instrument, and (iii) variation in peak intensities caused by various factors including noise and variability in sample handling and processing. In this chapter, we present computational methods for quantification and comparison of peptides by label-free LC-MS analysis. We discuss data preprocessing methods for alignment and normalization of LC-MS data. Also, we present multivariate statistical methods and pattern recognition methods for detection of differential protein expression from preprocessed LC-MS data.

Rapid validation of Mascot search results via stable isotope labeling, pair picking, and deconvolution of fragmentation patterns

Conventional LC-MS/MS data analysis matches each precursor ion and fragmentation pattern to their best fit within databases of theoretical spectra, yielding a peptide identification. Confidence is estimated by a score but can be validated by statistics, false discovery rates, and/or manual validation. A weakness is that each ion is evaluated independently, discarding potentially useful cross-correlations. In a classical approach to de novo sequence analysis, mixtures of peptides differing only in a carboxyl-terminal isotopic label yield fragmentation spectra with single, unlabeled b-type ions but pairs of isotope-labeled y-type ions, facilitating confident assignments. To apply this principle to identification by fragmentation pattern matching, we developed Validator, software that recognizes isotopic peptide pairs and compares their identifications and fragmentation patterns. Testing Validator 1 on a Mascot results file from FT-ICR LC-MS/MS of (16)O/(18)O-labeled yeast cell lysate peptides yielded 2,775 peptide pairs sharing a common identification but differing in carboxyl-terminal label. Comparing observed b- and y-ions with the predicted fragmentation pattern improved the threshold Mascot score for 5% false discovery from 36 to 22, significantly increasing both sensitivity and specificity. Validator 2, which identifies pairs by precursor mass difference alone before comparing observed fragmentation with that predicted by Mascot, found 2,021 isotopic pairs, similarly achieving improved sensitivity and specificity. Finally Validator 3, which finds pairs based on mass difference alone and then deconvolutes fragmentation patterns independently of Mascot, found 964 predicted peptides. Validator 3 allowed raw mass spectrometry data to be mined not only to validate Mascot results but also to discover peptides missed by Mascot. Using standard desktop hardware, the Validator 1-3 software processed the 11,536 spectra in the 93-MB Mascot .DAT file in less than 6 min (32 spectra/s), revealing high confidence peptide identifications without regard to Mascot score, far faster than manual or other independent validation methods.

Metaproteomics: a new approach for studying functional microbial ecology

In the postgenomic era, there is a clear recognition of the limitations of nucleic acid-based methods for getting information on functions expressed by microbial communities in situ. In this context, the large-scale study of proteins expressed by indigenous microbial communities (metaproteome) should provide information to gain insights into the functioning of the microbial component in ecosystems. Characterization of the metaproteome is expected to provide data linking genetic and functional diversity of microbial communities. Studies on the metaproteome together with those on the metagenome and the metatranscriptome will contribute to progress in our knowledge of microbial communities and their contribution in ecosystem functioning. Effectiveness of the metaproteomic approach will be improved as increasing metagenomic information is made available thanks to the environmental sequencing projects currently running. More specifically, analysis of metaproteome in contrasted environmental situations should allow (1) tracking new functional genes and metabolic pathways and (2) identifying proteins preferentially associated with specific stresses. These proteins considered as functional bioindicators should contribute, in the future, to help policy makers in defining strategies for sustainable management of our environment.

Minimizing back exchange in 18O/16O quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step

Differential labeling of peptides via the use of the 18O-water proteolytic labeling method has been widely adopted for quantitative shotgun proteomics studies due to its simplicity and low reagent costs. In this report, the use of immobilized trypsin in the initial digestion step, in addition to the initial digestion step, is explored as a means to minimize postlabeling back exchange of 18O-labeled peptides into the 16O form when multidimensional peptide separation methods (here, isoelectric focusing of peptides) are incorporated into the sample workflow. Examples are shown with a mixture of standard proteins and a sample from an ongoing clinical proteomics study.

Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.

Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing

Protein identification has been greatly facilitated by database searches against protein sequences derived from product ion spectra of peptides. This approach is primarily based on the use of fragment ion mass information contained in a MS/MS spectrum. Unambiguous protein identification from a spectrum with low sequence coverage or poor spectral quality can be a major challenge. We present a two-dimensional (2D) mass spectrometric method in which the numbers of nitrogen atoms in the molecular ion and the fragment ions are used to provide additional discriminating power for much improved protein identification and de novo peptide sequencing. The nitrogen number is determined by analyzing the mass difference of corresponding peak pairs in overlaid spectra of (15)N-labeled and unlabeled peptides. These peptides are produced by enzymatic or chemical cleavage of proteins from cells grown in (15)N-enriched and normal media, respectively. It is demonstrated that, using 2D information, i.e., m/z and its associated nitrogen number, this method can, not only confirm protein identification results generated by MS/MS database searching, but also identify peptides that are not possible to identify by database searching alone. Examples are presented of analyzing Escherichia coli K12 extracts that yielded relatively poor MS/MS spectra, presumably from the digests of low abundance proteins, which can still give positive protein identification using this method. Additionally, this 2D MS method can facilitate spectral interpretation for de novo peptide sequencing and identification of posttranslational or other chemical modifications. We envision that this method should be particularly useful for proteome expression profiling of organelles or cells that can be grown in (15)N-enriched media.

Synthesis/degradation ratio mass spectrometry for measuring relative dynamic protein turnover

One of the major unanswered questions in quantitative proteomics is that of dynamic protein turnover in the cell. Here we present a new approach to quantitative proteomics that measures the relative dynamic turnover of proteins in cellular systems. In this approach, termed synthesis/degradation ratio mass spectrometry, stable isotope labeling is employed to calculate a relative synthesis/degradation ratio that reflects the relative rate at which 13C is incorporated into individual proteins in the cell. This synthesis/degradation ratio calculation is based on a Poisson distribution model that is designed to support high-throughput analysis. Protein separation and analysis is accomplished by utilizing one-dimensional SDS-PAGE gel electrophoresis followed by cutting the gel into a series of bands for in-gel digestion. The resulting peptide mixtures are analyzed via solid-phase MALDI LC-MS and LC-MS/MS using a tandem time-of-flight mass spectrometer. A portion of the soluble protein fraction from an E. coli K-12 strain was analyzed with synthesis/degradation ratios varying from approximately 0.1 to 4.4 for a variety of different proteins. Unlike other quantitative techniques, synthesis/degradation ratio mass spectrometry requires only a single cell culture to obtain useful biological information about the processes occurring inside a cell. This technique is highly amenable to shotgun proteomics-based approaches and thus should allow relative turnover measurements for whole proteomes in the future.