Protein Abundance Ratios Research Articles

Quantitative proteome analysis of breast cancer cell lines using18O-labeling and an accurate mass and time tag strategy

Proteome comparison of cell lines derived from cancer and normal breast epithelium provide opportunities to identify differentially expressed proteins and pathways associated with specific phenotypes. We employed 16O/18O peptide labeling, FT-ICR MS, and an accurate mass and time (AMT) tag strategy to simultaneously compare the relative abundance of hundreds of proteins in non-cancer and cancer cell lines derived from breast tissue. A cell line reference panel allowed relative protein abundance comparisons among multiple cell lines and across multiple experiments. A peptide database generated from multidimensional LC separations and MS/MS analysis was used for subsequent AMT tag-based peptide identifications. This peptide database represented a total of 2299 proteins, including 514 that were quantified in five cell lines using the AMT tag and 16O/18O strategies. Eighty-six proteins showed at least a threefold protein abundance change between cancer and non-cancer cell lines. Hierarchical clustering of protein abundance ratios revealed that several groups of proteins were differentially expressed between the cancer cell lines.

Proteomic Analysis of Gray Platelet Syndrome by iTRAQ Labelling and Mass Spectroscopy: A Potential New Diagnostic Strategy for Platelet Disorders.

The recent introduction of novel proteomic techniques to evaluate normal human platelet proteins and their modifications following activation may also help to elucidate the causes and improve diagnoses of platelet-related bleeding disorders. Previous studies from this and other laboratories have demonstrated the sensitivity and reliability of this new technique to detect minor changes (+/−20%) in the protein content of platelets. More than 600 proteins shared by normal platelets were identified, and >200 in platelet-derived microparticles (MPs) generated by ionophore or Thrombin Receptor Activating Peptide (TRAP) activation. As a prelude to our ultimate goal of establishing this new technology, we applied the iTRAQ method (multiplexed relative protein quantitation by mass spectrometry) in the study of two unrelated patients with the Gray Platelet Syndrome (GPS).Megakaryocytes of patients with GPS can synthesize alpha-granule proteins and enclose them within membranes. However, the membranes lack structure linked latency, and enclosed proteins leak out of the organelles before platelets are delivered to circulating blood. Thus, GPS platelets contain empty alpha-granule vacuoles instead of alpha-granules. Due to large changes in platelet properties, it was surprising to find that cytoskeletal proteins of GPS were present in similar abundance to normal platelets (i.e. in the range of 0.80–1.20). Receptor proteins showed a similar distribution. As expected, the content of most alpha-granule proteins in the platelets from the 2 GPS patients was markedly reduced, with individual protein abundance ratios of 0.10 to 0.52 compared to normal platelets. However, certain trans-membrane proteins of alpha-granules (such as vesicle-associated proteins and P-selectin) were better preserved in GPS platelets, with ratios in the range of 0.73 to 1.16 compared to normal platelets. These results showed that many structural components (such as cytoskeleton and receptor proteins) are unchanged in GPS platelets. The results support our previous data indicating that detection of a change of as little as 20% may indicate a pathological change in the platelet proteome that may impact normal function. Our results therefore demonstrate that highly sensitive methods, capable of detecting small protein changes, may be needed to fully appreciate pathological disorders. The iTRAQ technique is able to detect low level changes and may be a leading tool that can generate new insights into the molecular pathophysiology and genetic variations of platelet disorders.

Interpretation of Shotgun Proteomic Data

The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets.

High Throughput Quantitative Analysis of Serum Proteins Using Glycopeptide Capture and Liquid Chromatography Mass Spectrometry

It is expected that the composition of the serum proteome can provide valuable information about the state of the human body in health and disease and that this information can be extracted via quantitative proteomic measurements. Suitable proteomic techniques need to be sensitive, reproducible, and robust to detect potential biomarkers below the level of highly expressed proteins, generate data sets that are comparable between experiments and laboratories, and have high throughput to support statistical studies. Here we report a method for high throughput quantitative analysis of serum proteins. It consists of the selective isolation of peptides that are N-linked glycosylated in the intact protein, the analysis of these now deglycosylated peptides by liquid chromatography electrospray ionization mass spectrometry, and the comparative analysis of the resulting patterns. By focusing selectively on a few formerly N-linked glycopeptides per serum protein, the complexity of the analyte sample is significantly reduced and the sensitivity and throughput of serum proteome analysis are increased compared with the analysis of total tryptic peptides from unfractionated samples. We provide data that document the performance of the method and show that sera from untreated normal mice and genetically identical mice with carcinogen-induced skin cancer can be unambiguously discriminated using unsupervised clustering of the resulting peptide patterns. We further identify, by tandem mass spectrometry, some of the peptides that were consistently elevated in cancer mice compared with their control littermates.

ANALYSIS OF PROSTATE CANCER BY PROTEOMICS USING TISSUE SPECIMENS

Prostate cancer cells differ from their normal counterpart in gene expression. Therefore, cancer and normal tissue/cells would show differences in their proteomes. From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. We used ProteinChip Array (Ciphergen Biosystems, Fremont, California) surface enhanced laser desorption/ionization (SELDI) time of flight mass spectrometry to profile prostate tissue samples to generate phenomic fingerprints. We used quantitative proteomics based on glycopeptide capture followed by tandem mass spectrometry to identify differentially expressed proteins. Patient matched cancer and noncancer specimens were digested by collagenase to single cells. After digestion the cells were pelleted and the cell-free supernatant was used for analysis. A reversed phase hydrophobic ProteinChip Array was used to generate SELDI patterns from 43 primary prostate tumors, including 26 with matched noncancer specimens. Quantitative proteomics was applied to 1 specimen and the expression pattern was verified by Western blotting and immunohistochemistry. SELDI profiles showed that cancers of similar TNM stages were more likely to have similar profiles. On quantitative proteomics tissue metalloproteinase inhibitor-1 was identified to be down-regulated in cancer. Tissue metalloproteinase inhibitor-1 expression was localized to secretory cells. Protein profiling by SELDI is relatively easy to perform and it has great potential in prostate cancer diagnosis through pattern recognition. Quantitative proteomics can potentially determine the identity of many biomarkers specific for prostate cancer.

Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry.

We describe an algorithm for the automated statistical analysis of protein abundance ratios (ASAPRatio) of proteins contained in two samples. Proteins are labeled with distinct stable-isotope tags and fragmented, and the tagged peptide fragments are separated by liquid chromatography (LC) and analyzed by electrospray ionization (ESI) tandem mass spectrometry (MS/MS). The algorithm utilizes the signals recorded for the different isotopic forms of peptides of identical sequence and numerical and statistical methods, such as Savitzky-Golay smoothing filters, statistics for weighted samples, and Dixon's test for outliers, to evaluate protein abundance ratios and their associated errors. The algorithm also provides a statistical assessment to distinguish proteins of significant abundance changes from a population of proteins of unchanged abundance. To evaluate its performance, two sets of LC-ESI-MS/MS data were analyzed by the ASAPRatio algorithm without human intervention, and the data were related to the expected and manually validated values. The utility of the ASAPRatio program was clearly demonstrated by its speed and the accuracy of the generated protein abundance ratios and by its capability to identify specific core components of the RNA polymerase II transcription complex within a high background of copurifying proteins.

Approaches for the quantification of protein concentration ratios.

The function of a protein is modulated by its abundance and its degree of specific post-translational modifications such as phosphorylation, glycosylation or truncation. Consequently, changes of protein concentration and the extent of their post-translational modifications has a great influence on the activity of intracellular substrate degradation processes, on the activity of intracellular biosynthetic pathways, on the cell cycle or on the function of a single cell in a whole organism. Defects in this area lead to diseases like cancer or neurodegeneration. Therefore, it is a challenge to quantify changes within the proteome in the diseased state or between developmental stages and to use the results obtained for the maximization of product yields in biotechnology or for the development of new drug targets to fight against diseases. In order to determine the intracellular concentration of a protein it is necessary to spike the cell sample with the same protein in a pure form. If the concentration changes of many proteins have to be determined, it takes a long time to obtain all these proteins in a pure form. Therefore, most approaches in this field are restricted to the determination of protein abundance ratios between two states such as diseased or healthy tissues. In this case the proteins in the sample of state A function as an internal standard for the proteins in the sample of state B and vice versa. The most common techniques in this field are the comparison of two-dimensional gel spot intensities after staining or the integration of mass spectrometric peak intensities after stable isotope labelling with (13)C, (15)N, (18)O or deuterium. The results, advantages and drawbacks of these approaches are discussed. Stable isotope labelling in combination with mass spectrometry is more accurate than the comparison of spot intensities and has the potential for the investigation of highly complex tissue samples.

Complementary Profiling of Gene Expression at the Transcriptome and Proteome Levels in Saccharomyces cerevisiae

Using an integrated genomic and proteomic approach, we have investigated the effects of carbon source perturbation on steady-state gene expression in the yeast Saccharomyces cerevisiae growing on either galactose or ethanol. For many genes, significant differences between the abundance ratio of the messenger RNA transcript and the corresponding protein product were observed. Insights into the perturbative effects on genes involved in respiration, energy generation, and protein synthesis were obtained that would not have been apparent from measurements made at either the messenger RNA or protein level alone, illustrating the power of integrating different types of data obtained from the same sample for the comprehensive characterization of biological systems and processes.