Detecting differential and correlated protein expression in label-free shotgun proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.     

Enhanced detection of low abundance human plasma proteins using a tandem IgY12-SuperMix immunoaffinity separation strategy

The enormous dynamic range of human bodily fluid proteomes poses a significant challenge for current MS-based proteomics technologies as it makes it especially difficult to detect low abundance proteins in human biofluids such as blood plasma, which is an essential aspect for successful biomarker discovery efforts. Here we present a novel tandem IgY12-SuperMix immunoaffinity separation system for enhanced detection of low abundance proteins in human plasma. The tandem IgY12-SuperMix system separates approximately 60 abundant proteins from the low abundance proteins in plasma, allowing for significant enrichment of low abundance plasma proteins in the SuperMix flow-through fraction. High reproducibility of the tandem separations was observed in terms of both sample processing recovery and LC-MS/MS identification results based on spectral count data. The ability to quantitatively measure differential protein abundances following application of the tandem separations was demonstrated by spiking six non-human standard proteins at three different levels into plasma. A side-by-side comparison between the SuperMix flow-through and IgY12 flow-through samples analyzed by both one- and two-dimensional LC-MS/MS revealed a 60-80% increase in proteome coverage as a result of the SuperMix separations, suggesting significantly enhanced detection of low abundance proteins. A total of 695 plasma proteins were confidently identified in a single analysis (with a minimum of two peptides per protein) by coupling the tandem separation strategy with two-dimensional LC-MS/MS, including 42 proteins with reported normal concentrations of approximately 100 pg/ml to 100 ng/ml. The concentrations of two selected proteins, macrophage colony-stimulating factor 1 and matrix metalloproteinase-8, were independently validated by ELISA as 202 pg/ml and 12.4 ng/ml, respectively. Evaluation of binding efficiency revealed that 45 medium abundance proteins were efficiently captured by the SuperMix column with >90% retention. Taken together, these results illustrate the potential broad utilities of this tandem IgY12-SuperMix strategy for proteomics applications involving human biofluids where effectively addressing the dynamic range challenge of the specimen is imperative.     

Increased Power for the Analysis of Label-free LC-MS/MS Proteomics Data by Combining Spectral Counts and Peptide Peak Attributes

Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be rolled up into protein-level information. We propose a principal component analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data that incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume, or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperformed existing quantitative methods for peptide-protein roll-up, including spectral counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA was validated using a data set derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by The Association of Biomolecular Resource Facilities Proteomics Standards Research Group in 2006). Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identified more differentially abundant proteins than competing methods.One of the fundamental goals of proteomics methods for the biological sciences is to identify and quantify all proteins present in a sample. LC-MS/MS-based proteomics methodologies offer a promising approach to this problem (1–3). These methodologies allow for the acquisition of a vast amount of information about the proteins present in a sample. However, extracting reliable protein abundance information from LC-MS/MS data remains challenging. In this work, we were primarily concerned with the analysis of data acquired using bottom-up label-free LC-MS/MS-based proteomics techniques where “bottom-up” refers to the fact that proteins are enzymatically digested into peptides prior to query by the LC-MS/MS instrument platform (4), and “label-free” indicates that analyses are performed without the aid of stable isotope labels. One challenge inherent in the bottom-up approach to proteomics is that information directly available from the LC-MS/MS data is at the peptide level. When a protein-level analysis is desired, as is often the case with discovery-driven LC-MS research, peptide-level information must be rolled up into protein-level information.Spectral counting (5–10) is a straightforward and widely used example of peptide-protein roll-up for LC-MS/MS data. Information experimentally acquired in single stage (MS) and tandem (MS/MS) spectra may lead to the assignment of MS/MS spectra to peptide sequences in a database-driven or database-free manner using various peptide identification software platforms (SEQUEST (11) and Mascot (12), for instance); the identified peptide sequences correspond, in turn, to proteins. In principle, the number of tandem spectra matched to peptides corresponding to a certain protein, the spectral count (SC),1 is positively associated with the abundance of a protein (5). In spectral counting techniques, raw or normalized SCs are used as a surrogate for protein abundance. Spectral counting methods have been moderately successful in quantifying protein abundance and identifying significant proteins in various settings. However, SC-based methods do not make full use of information available from peaks in the LC-MS domain, and this surely leads to loss of efficiency.Peaks in the LC-MS domain corresponding to peptide ion species are highly sensitive to differences in protein abundance (13, 14). Identifying LC-MS peaks that correspond to detected peptides and measuring quantitative attributes of these peaks (such as height, area, or volume) offers a promising alternative to spectral counting methods. These methods have become especially popular in applications using stable isotope labeling (15). However, challenges remain, especially in the label-free analysis of complex proteomics samples where complications in peak detection, alignment, and integration are a significant obstacle. In practice, alignment, identification, and quantification of LC-MS peptide peak attributes (PPAs) may be accomplished using recently developed peak matching platforms (16–18). A highly sensitive indicator of protein abundance may be obtained by rolling up PPA measurements into protein-level information (16, 19, 20). Existing peptide-protein roll-up procedures based on PPAs typically involve taking the mean of (possibly normalized) PPA measurements over all peptides corresponding to a protein to obtain a protein-level estimate of abundance. Despite the promise of PPA-based procedures for protein quantification, the performance of PPA-based methods may vary widely depending on the particular roll-up procedure used; furthermore, PPA-based procedures are limited by difficulties in accurately identifying and measuring peptide peak attributes. These two issues are related as the latter issue affects the robustness of PPA-based roll-up methods. Indeed, existing peak matching and quantification platforms tend to result in PPA measurement data sets with substantial missingness (16, 19, 21), especially when working with very complex samples where substantial dynamic ranges and ion suppression are difficulties that must be overcome. Missingness may, in turn, lead to instability in protein-level abundance estimates. A good peptide-protein roll-up procedure that utilizes PPAs should account for this missingness and the resulting instability in a principled way. However, even in the absence of missingness, there is no consensus in the existing literature on peptide-protein roll-up for PPA measurements.In this work, we propose ProPCA, a peptide-protein roll-up method for efficiently extracting protein abundance information from bottom-up label-free LC-MS/MS data. ProPCA is an easily implemented, unsupervised method that is related to principle component analysis (PCA) (22). ProPCA optimally combines SC and PPA data to obtain estimates of relative protein abundance. ProPCA addresses missingness in PPA measurement data in a unified way while capitalizing on strengths of both SCs and PPA-based roll-up methods. In particular, ProPCA adapts to the quality of the available PPA measurement data. If the PPA measurement data are poor and, in the extreme case, no PPA measurements are available, then ProPCA is equivalent to spectral counting. On the other hand, if there is no missingness in the PPA measurement data set, then the ProPCA estimate is a weighted mean of PPA measurements and spectral counts where the weights are chosen to reflect the ability of spectral counts and each peptide to predict protein abundance.Below, we assess the performance of ProPCA using a data set obtained from the LC-MS/MS analysis of protein standards (UPS2 proteomic dynamic range standard set² manufactured by Sigma-Aldrich) and show that ProPCA outperformed other existing roll-up methods by multiple metrics. The applicability of ProPCA is not limited by the quantification platform used to obtain SCs and PPA measurements. To demonstrate this, we show that ProPCA continued to perform well when used with an alternative quantification platform. Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total human hepatocellular carcinoma (HepG2) cell lysates prepared for LC-MS/MS analysis by alternative lysis methods. We show that ProPCA identified more differentially abundant proteins than competing methods.     

Global proteome discovery using an online three-dimensional LC-MS/MS

We have developed a proteomics technology featuring on-line three-dimensional liquid chromatography coupled to tandem mass spectrometry (3D LC-MS/MS). Using 3D LC-MS/MS, the yeast-soluble, urea-solubilized peripheral membrane and SDS-solubilized membrane protein samples collectively yielded 3019 unique yeast protein identifications with an average of 5.5 peptides per protein from the 6300-gene Saccharomyces Genome Database searched with SEQUEST. A single run of the urea-solubilized sample yielded 2255 unique protein identifications, suggesting high peak capacity and resolving power of 3D LC-MS/MS. After precipitation of SDS from the digested membrane protein sample, 3D LC-MS/MS allowed the analysis of membrane proteins. Among 1221 proteins containing two or more predicted transmembrane domains, 495 such proteins were identified. The improved yeast proteome data allowed the mapping of many metabolic pathways and functional categories. The 3D LC-MS/MS technology provides a suitable tool for global proteome discovery.     

PTRF/cavin-1 and MIF proteins are identified as non-small cell lung cancer biomarkers by label-free proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the "omics" era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.     

The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments

Background  

Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.     

Evaluation of strong cation exchange versus isoelectric focusing of peptides for multidimensional liquid chromatography-tandem mass spectrometry

Shotgun proteome analysis platforms based on multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) provide a powerful means to discover biomarker candidates in tissue specimens. Analysis platforms must balance sensitivity for peptide detection, reproducibility of detected peptide inventories and analytical throughput for protein amounts commonly present in tissue biospecimens (< 100 microg), such that platform stability is sufficient to detect modest changes in complex proteomes. We compared shotgun proteomics platforms by analyzing tryptic digests of whole cell and tissue proteomes using strong cation exchange (SCX) and isoelectric focusing (IEF) separations of peptides prior to LC-MS/MS analysis on a LTQ-Orbitrap hybrid instrument. IEF separations provided superior reproducibility and resolution for peptide fractionation from samples corresponding to both large (100 microg) and small (10 microg) protein inputs. SCX generated more peptide and protein identifications than did IEF with small (10 microg) samples, whereas the two platforms yielded similar numbers of identifications with large (100 microg) samples. In nine replicate analyses of tryptic peptides from 50 microg colon adenocarcinoma protein, overlap in protein detection by the two platforms was 77% of all proteins detected by both methods combined. IEF more quickly approached maximal detection, with 90% of IEF-detectable medium abundance proteins (those detected with a total of 3-4 peptides) detected within three replicate analyses. In contrast, the SCX platform required six replicates to detect 90% of SCX-detectable medium abundance proteins. High reproducibility and efficient resolution of IEF peptide separations make the IEF platform superior to the SCX platform for biomarker discovery via shotgun proteomic analyses of tissue specimens.     

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR = 1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.     

草菇子实体不同成熟阶段的比较蛋白质组学分析

采用iTRAQ标记结合二维液相色谱串联质谱技术对草菇不同成熟阶段的差异蛋白质组进行研究。首先将提取的草菇不同成熟阶段蛋白样品进行SDS-PAGE分析,其次将经二维液相色谱串联质谱技术获取的串联质谱数据通过MASCOT软件搜库,之后对鉴定蛋白质数据进行了KEGG代谢通路分析。试验共计获得2 335个不同肽段, 鉴定到1 039个蛋白质,其中1 030个蛋白质具有定量信息。与蛋形期相比,在伸长期和成熟期阶段显著上调蛋白质85个,下调蛋白质103个。KEGG代谢通路分析结果显示,草菇不同成熟阶段中的68个差异蛋白质可定位于4种伞菌目模式真菌（灰盖鬼伞、双色蜡蘑、可可丛枝病菌和裂褶菌）的45个不同生物代谢途径,全景展示出草菇成熟阶段差异表达蛋白质定位的代谢网络。结果表明,iTRAQ标记技术结合二维液相色谱串联质谱可对不同生长发育时期的草菇蛋白样品进行有效地分离和鉴定。     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

Analysis of membrane proteins from human chronic myelogenous leukemia cells: comparison of extraction methods for multidimensional LC-MS/MS

An important strategy for "shotgun proteomics" profiling involves solution proteolysis of proteins, followed by peptide separation using multidimensional liquid chromatography and automated sequencing by mass spectrometry (LC-MS/MS). Several protocols for extracting and handling membrane proteins for shotgun proteomics experiments have been reported, but few direct comparisons of different protocols have been reported. We compare four methods for preparing membrane proteins from human cells, using acid labile surfactants (ALS), urea, and mixed organic-aqueous solvents. These methods were compared with respect to their efficiency of protein solubilization and proteolysis, peptide and protein recovery, membrane protein enrichment, and peptide coverage of transmembrane proteins. Overall, approximately 50-60% of proteins recovered were membrane-associated, identified from Gene Ontology annotations and transmembrane prediction software. Samples extracted with ALS, extracted with urea followed by dilution, or extracted with urea followed by desalting yielded comparable peptide recoveries and sequence coverage of transmembrane proteins. In contrast, suboptimal proteolysis was observed with organic solvent. Urea extraction followed by desalting may be a particularly useful approach, as it is less costly than ALS and yields satisfactory protein denaturation and proteolysis under conditions that minimize reactivity with urea-derived cyanate. Spectral counting was used to compare datasets of proteins from membrane samples with those of soluble proteins from K562 cells, and to estimate fold differences in protein abundances. Proteins most highly abundant in the membrane samples showed enrichment of integral membrane protein identifications, consistent with their isolation by differential centrifugation.     

Identification of highly expressed, soluble proteins using an improved, high-throughput pooled ORF expression technology

This article describes an improved pooled open reading frame (ORF) expression technology (POET) that uses recombinational cloning and solution-based tandem mass spectrometry (MS/MS) to identify ORFs that yield high levels of soluble, purified protein when expressed in Escherichia coli. Using this method, three identical pools of 512 human ORFs were subcloned, purified, and transfected into three separate E. coli cultures. After bulk expression and purification, the proteins from the three separate pools were digested into tryptic peptides. Each of these samples was subsequently analyzed in triplicate using reversed-phase high-performance liquid chromatography (LC) coupled directly online with MS/MS. The abundance of each protein was determined by calculating the average exponentially modified protein abundance index (emPAI) of each protein across the three protein pools. Human proteins that consistently gave high emPAI values were subjected to small-scale expression and purification. These clones showed high levels of expression of soluble protein. Conversely, proteins that were not observed by LC-MS/MS did not show any detectable soluble expression in small-scale validation studies. Using this improved POET method allows the expression characteristics of hundreds of proteins to be quickly determined in a single experiment.     

Identification of protein fragments as pattern features in MALDI-MS analyses of serum

The use of matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) to acquire spectral profiles has become a common approach to detect proteomic biomarkers of disease. MALDI-MS signals may represent both intact proteins as well as proteolysis products. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis can tentatively identify the corresponding proteins Here, we describe the application of a data analysis utility called FragMint, which combines MALDI-MS spectral data with LC-MS/MS based protein identifications to generate candidate protein fragments consistent with both types of data. This approach was used to identify protein fragments corresponding to spectral signals in MALDI-MS analyses of unfractionated human serum. The serum also was analyzed by one-dimensional SDS-PAGE and bands corresponding to the MALDI-MS signal masses were excised and subjected to in-gel digestion and LC-MS/MS analysis. Database searches mapped all of the identified peptides to abundant blood proteins larger than the observed MALDI-MS signals. FragMint identified fragments of these proteins that contained the MS/MS identified sequences and were consistent with the observed MALDI-MS signals. This approach should be generally applicable to identify protein species corresponding to MALDI-MS signals.     

Proteomic analysis of high-grade dysplastic cervical cells obtained from ThinPrep slides using laser capture microdissection and mass spectrometry

The purpose of this discovery phase study was to identify candidate protein biomarkers for high-grade dysplastic cervical cells using mass spectrometry. Laser Capture Microdissection (LCM) was utilized to isolate high-grade dysplastic and normal cells from ThinPrep slides prepared from cervical cytological specimens. Following cell capture, samples were solubilized and proteins separated by gel electrophoresis in preparation for enzymatic digestion and liquid chromatography mass spectrometry analysis (LC-MS). Processed samples were subsequently analyzed using a linear ion trap coupled with a Fourier transform mass spectrometer (LTQ-FT MS). It was determined that both PreservCyt Solution and ThinPrep Pap Stain (Cytyc Corporation) were compatible with the sample processing and LC-MS analysis. In total, from 9 normal and 9 abnormal cervical cytological specimens, more than 1000 unique proteins were identified with high confidence, based on approximately 12,000 captured cells per specimen. Quantitative protein differences between HSIL (High-Grade Squamous Intraepithelial Lesion) and NILM (Negative for Intraepithelial Lesions or Malignancy) samples were determined by comparing the intensities of the representative (label-free) peptide ions. More than 200 proteins were found to exhibit a 3-fold difference in protein level. Interestingly, significant up-regulation of nuclear and mitochondrial proteins in HSIL specimens was noted. In several cases, the increased protein abundance observed in high-grade cells, as determined by quantitative LC-MS, was validated by immunocytochemical methods using ThinPrep cervical specimens. With the study of additional clinical specimens, the differential abundance of proteins in high-grade dysplastic cells versus morphologically normal cervical cells may lead to validated novel biomarkers for cervical disease.     

基于谱图库的蛋白质鉴定策略研究进展

基于质谱的蛋白质组学快速发展,蛋白质质谱数据也呈指数式增长。寻找速度快、准确度高以及重复性好的鉴定方法是该领域的一项重要任务。谱图库搜索策略直接比较实验谱图与谱图库中的真实谱图,充分利用了谱图中的丰度、非常规碎裂模式和其他的一些特征,使得搜索更加快速和准确,成为蛋白质组学的主流鉴定方法之一。文中介绍基于谱图库的蛋白质组质谱数据鉴定策略,并针对其中两个关键步骤——谱图库构建方法和谱图库搜索方法进行深入介绍,探讨了谱图库策略的进展和挑战。