Comparison of stable-isotope labeling with amino acids in cell culture and spectral counting for relative quantification of protein expression

Mass Spectrometry-Based Analysis of Time-Resolved Proteome Quantification

The aspect of time is essential in biological processes and thus it is important to be able to monitor signaling molecules through time. Proteins are key players in cellular signaling and they respond to many stimuli and change their expression in many time-dependent processes. Mass spectrometry (MS) is an important tool for studying proteins, including their posttranslational modifications and their interaction partners-both in qualitative and quantitative ways. In order to distinguish the different trends over time, proteins, modification sites, and interacting proteins must be compared between different time points, and therefore relative quantification is preferred. In this review, the progress and challenges for MS-based analysis of time-resolved proteome dynamics are discussed. Further, aspects on model systems, technologies, sampling frequencies, and presentation of the dynamic data are discussed.

A Metabolic Labeling Strategy for Relative Protein Quantification in Clostridioides difficile

Clostridioides difficile (formerly Clostridium difficile) is a Gram-positive, anaerobe, spore-forming pathogen, which causes drug-induced diseases in hospitals worldwide. A detailed analysis of the proteome may provide new targets for drug development or therapeutic strategies to combat this pathogen. The application of metabolic labeling (ML) would allow for accurate quantification of significant differences in protein abundance, even in the case of very small changes. Additionally, it would be possible to perform more accurate studies of the membrane or surface proteomes, which usually require elaborated sample preparation. Such studies are therefore prone to higher standard deviations during the quantification. The implementation of ML strategies for C. difficile is complicated due to the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism of this anaerobic pathogen. Hence, quantitative proteome analyses could only be carried out by label free or chemical labeling methods so far. In this paper, a ML approach for C. difficile is described. A cultivation procedure with 15N-labeled media for strain 630Δerm was established achieving an incorporation rate higher than 97%. In a proof-of-principle experiment, the performance of the ML approach in C. difficile was tested. The proteome data of the cytosolic subproteome of C. difficile cells grown in complex medium as well as two minimal media in the late exponential and early stationary growth phase obtained via ML were compared with two label free relative quantification approaches (NSAF and LFQ). The numbers of identified proteins were comparable within the three approaches, whereas the number of quantified proteins were between 1,110 (ML) and 1,861 (LFQ) proteins. A hierarchical clustering showed clearly separated clusters for the different conditions and a small tree height with ML approach. Furthermore, it was shown that the quantification based on ML revealed significant altered proteins with small fold changes compared to the label free approaches. The quantification based on ML was accurate, reproducible, and even more sensitive compared to label free quantification strategies.

Identification of Epigenetic Factor Proteins Expressed in Human Embryonic Stem Cell-Derived Trophoblasts and in Human Placental Trophoblasts

Human embryonic stem cells (hESCs) have been used to derive trophoblasts through differentiation in vitro. Intriguingly, mouse ESCs are prevented from differentiation to trophoblasts by certain epigenetic factor proteins such as Dnmt1, thus necessitating the study of epigenetic factor proteins during hESC differentiation to trophoblasts. We used stable isotope labeling by amino acids in cell culture and quantitative proteomics to study changes in the nuclear proteome during hESC differentiation to trophoblasts and identified changes in the expression of 30 epigenetic factor proteins. Importantly, the DNA methyltransferases DNMT1, DNMT3A, and DNMT3B were downregulated. Additionally, we hypothesized that nuclear proteomics of hESC-derived trophoblasts may be used for screening epigenetic factor proteins expressed by primary trophoblasts in human placental tissue. Accordingly, we conducted immunohistochemistry analysis of six epigenetic factor proteins identified from hESC-derived trophoblasts-DNMT1, DNMT3B, BAF155, BAF60A, BAF57, and ING5-in 6-9 week human placentas. Indeed, expression of these proteins was largely, though not fully, consistent with that observed in 6-9 week placental trophoblasts. Our results support the use of hESC-derived trophoblasts as a model for placental trophoblasts, which will enable further investigation of epigenetic factors involved in human trophoblast development.

Functional Consequences of Mannose and Asialoglycoprotein Receptor Ablation

The mannose receptor (ManR, Mrc1) and asialoglycoprotein receptor (ASGR, Asgr1 and Asgr2) are highly abundant endocytic receptors expressed by sinusoidal endothelial cells and parenchymal cells in the liver, respectively. We genetically manipulated either receptor individually or in combination, revealing phenotypic changes in female and male mice associated with changes in circulating levels of many glycoproteins. Both receptors rise and fall in response to progesterone during pregnancy. Thirty percent of Asgr2(-/-) and 65% of Mrc1(-/-)Asgr2(-/-) mice are unable to initiate parturition at the end of pregnancy, whereas Mrc1(-/-) mice initiate normally. Twenty five percent of Mrc1(-/-)Asgr2(-/-) male mice develop priapism when mating due to thrombosis of the penile vein, but neither Mrc1(-/-) nor Asgr2(-/-) mice do so. The half-life for luteinizing hormone (LH) clearance increases in Mrc1(-/-) and Mrc1(-/-)Asgr2(-/-) mice but not in Asgr2(-/-) mice; however, LH and testosterone are elevated in all three knockouts. The ManR clears LH thus regulating testosterone production, whereas the ASGR appears to mediate clearance of an unidentified glycoprotein that increases LH levels. More than 40 circulating glycoproteins are elevated >3.0-fold in pregnant Mrc1(-/-)Asgr2(-/-) mice. Pregnancy-specific glycoprotein 23, undetectable in WT mice (<50 ng/ml plasma), reaches levels of 1-10 mg/ml in the plasma of Mrc1(-/-)Asgr2(-/-) and Asgr2(-/-) mice, indicating it is cleared by the ASGR. Elevation of multiple coagulation factors in Mrc1(-/-)Asgr2(-/-) mice may account for priapism seen in males. These male and female phenotypic changes underscore the key roles of the ManR and ASGR in controlling circulating levels of numerous glycoproteins critical for regulating reproductive hormones and blood coagulation.

Protein Mobility Shifts Contribute to Gel Electrophoresis Liquid Chromatography Analysis

Profiling of cellular and subcellular proteomes by liquid chromatography with tandem mass spectrometry (MS) after fractionation by SDS-PAGE is referred to as GeLC (gel electrophoresis liquid chromatography)-MS. The GeLC approach decreases complexity within individual MS analyses by size fractionation with SDS-PAGE. SDS-PAGE is considered an excellent fractionation technique for intact proteins because of good resolution for proteins of all sizes, isoelectric points, and hydrophobicities. Additional information derived from the mobility of the intact proteins is available after an SDS-PAGE fractionation, but that information is usually not incorporated into the proteomic analysis. Any chemical or proteolytic modification of a protein that changes the mobility of that protein in the gel can be detected. The ability of SDS-PAGE to resolve proteins with chemical modifications has not been widely utilized within profiling experiments. In this work, we examined the ability of the GeLC-MS approach to help identify proteins that were modified after a small hairpin RNA-dependent knockdown in an experiment using stable isotope labeling by amino acids in cell culture-based quantitation.

A Quantitative Glycomics and Proteomics Combined Purification Strategy

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define pathways, improve treatment, understand disease, and aid biomarker discovery. N-linked glycosylation is one of the most important and robust post-translational modifications on proteins and regulates critical cell functions such as signaling, adhesion, and enzymatic function. Analytical techniques to purify and analyze N-glycans have remained relatively static over the last decade. While accurate and effective, they commonly require significant expertise and resources. Though some high-throughput purification schemes have been developed, they have yet to find widespread adoption and often rely on the enrichment of glycopeptides. One promising method, developed by Thomas-Oates et al., filter aided N-glycan separation (FANGS), was qualitatively demonstrated on tissues. Herein, we adapted FANGS to plasma and coupled it to the individuality normalization when labeling with glycan hydrazide tags strategy in order to achieve accurate relative quantification by liquid chromatography mass spectrometry and enhanced electrospray ionization. Furthermore, we designed new functionality to the protocol by achieving tandem, shotgun proteomics and glycosylation site analysis on hen plasma. We showed that N-glycans purified on filter and derivatized by hydrophobic hydrazide tags were comparable in terms of abundance and class to those by solid phase extraction (SPE); the latter is considered a gold standard in the field. Importantly, the variability in the two protocols was not statistically different. Proteomic data that was collected in-line with glycomic data had the same depth compared to a standard trypsin digest. Peptide deamidation is minimized in the protocol, limiting non-specific deamidation detected at glycosylation motifs. This allowed for direct glycosylation site analysis, though the protocol can accommodate (18)O site labeling as well. Overall, we demonstrated a new in-line high-throughput, unbiased, filter based protocol for quantitative glycomics and proteomics analysis.

A quantitative proteomics analysis of MCF7 breast cancer stem and progenitor cell populations

Accumulating evidence has demonstrated that breast cancers are initiated and develop from a small population of stem-like cells termed cancer stem cells (CSCs). These cells are hypothesized to mediate tumor metastasis and contribute to therapeutic resistance. However, the molecular regulatory networks responsible for maintaining CSCs in an undifferentiated state have yet to be elucidated. In this study, we used CSC markers to isolate pure breast CSCs fractions (ALDH+ and CD44+CD24- cell populations) and the mature luminal cells (CD49f-EpCAM+) from the MCF7 cell line. Proteomic analysis was performed on these samples and a total of 3304 proteins were identified. A label-free quantitative method was applied to analyze differentially expressed proteins. Using the criteria of greater than twofold changes and p value <0.05, 305, 322 and 98 proteins were identified as significantly different in three pairwise comparisons of ALDH+ versus CD44+CD24-, ALDH+ versus CD49f-EpCAM+ and CD44+CD24- versus CD49f-EpCAM+, respectively. Pathway analysis of differentially expressed proteins by Ingenuity Pathway Analysis (IPA) revealed potential molecular regulatory networks that may regulate CSCs. Selected differential proteins were validated by Western blot assay and immunohistochemical staining. The use of proteomics analysis may increase our understanding of the underlying molecular mechanisms of breast CSCs. This may be of importance in the future development of anti-CSC therapeutics.

Systematic evaluation of label-free and super-SILAC quantification for proteome expression analysis

RATIONALE

Advanced implementations of mass spectrometry (MS)-based proteomics allow for comprehensive proteome expression profiling across many biological samples. The outcome of such studies critically depends on accurate and precise quantification, which has to be ensured for high-coverage proteome analysis possible on fast and sensitive mass spectrometers such as quadrupole orbitrap instruments.

METHODS

We conducted ultra-high-performance liquid chromatography (UHPLC)/MS experiments on a Q Exactive to systematically compare label-free proteome quantification across six human cancer cell lines with quantification against a shared reference mix generated by stable isotope labeling with amino acids in cell culture (super-SILAC).

RESULTS

Single-shot experiments identified on average about 5000 proteins in the label-free compared to about 3500 in super-SILAC experiments. Label-free quantification was slightly less precise than super-SILAC in replicate measurements, verifying previous results obtained for lower proteome coverage. Due to the higher number of quantified proteins, more significant differences were detected in label-free cell line comparisons, whereas a higher percentage of quantified proteins was identified as differentially expressed in super-SILAC experiments. Additional label-free replicate analyses effectively compensated for lower precision of quantification. Finally, peptide fractionation by high pH reversed-phase chromatography prior to LC/MS analysis further increased the robustness and precision of label-free quantification in conjunction with higher proteome coverage.

CONCLUSIONS

Our results benchmark and highlight the utility of label-free proteome quantification for applications such as target and biomarker discovery on state-of-the-art UHPLC/MS workflows.

Activin/nodal signaling switches the terminal fate of human embryonic stem cell-derived trophoblasts

Human embryonic stem cells (hESCs) have been routinely treated with bone morphogenetic protein and/or inhibitors of activin/nodal signaling to obtain cells that express trophoblast markers. Trophoblasts can terminally differentiate to either extravillous trophoblasts or syncytiotrophoblasts. The signaling pathways that govern the terminal fate of these trophoblasts are not understood. We show that activin/nodal signaling switches the terminal fate of these hESC-derived trophoblasts. Inhibition of activin/nodal signaling leads to formation of extravillous trophoblast, whereas loss of activin/nodal inhibition leads to the formation of syncytiotrophoblasts. Also, the ability of hESCs to form bona fide trophoblasts has been intensely debated. We have examined hESC-derived trophoblasts in the light of stringent criteria that were proposed recently, such as hypomethylation of the ELF5-2b promoter region and down-regulation of HLA class I antigens. We report that trophoblasts that possess these properties can indeed be obtained from hESCs.

Comparing SILAC- and stable isotope dimethyl-labeling approaches for quantitative proteomics

Stable isotope labeling is widely used to encode and quantify proteins in mass-spectrometry-based proteomics. We compared metabolic labeling with stable isotope labeling by amino acids in cell culture (SILAC) and chemical labeling by stable isotope dimethyl labeling and find that they have comparable accuracy and quantitative dynamic range in unfractionated proteome analyses and affinity pull-down experiments. Analyzing SILAC- and dimethyl-labeled samples together in single liquid chromatography–mass spectrometric analyses minimizes differences under analytical conditions, allowing comparisons of quantitative errors introduced during sample processing. We find that SILAC is more reproducible than dimethyl labeling. Because proteins from metabolically labeled populations can be combined before proteolytic digestion, SILAC is particularly suited to studies with extensive sample processing, such as fractionation and enrichment of peptides with post-translational modifications. We compared both methods in pull-down experiments using a kinase inhibitor, dasatinib, and tagged GRB2-SH2 protein as affinity baits. We describe a StageTip dimethyl-labeling protocol that we applied to in-solution and in-gel protein digests. Comparing the impact of post-digest isotopic labeling on quantitative accuracy, we demonstrate how specific experimental designs can benefit most from metabolic labeling approaches like SILAC and situations where chemical labeling by stable isotope-dimethyl labeling can be a practical alternative.

Glycoprotein biomarker panel for pancreatic cancer discovered by quantitative proteomics analysis

Pancreatic cancer is a lethal disease where specific early detection biomarkers would be very valuable to improve outcomes in patients. Many previous studies have compared biosamples from pancreatic cancer patients with healthy controls to find potential biomarkers. However, a range of related disease conditions can influence the performance of these putative biomarkers, including pancreatitis and diabetes. In this study, quantitative proteomics methods were applied to discover potential serum glycoprotein biomarkers that distinguish pancreatic cancer from other pancreas related conditions (diabetes, cyst, chronic pancreatitis, obstructive jaundice) and healthy controls. Aleuria aurantia lectin (AAL) was used to extract fucosylated glycoproteins and then both TMT protein-level labeling and label-free quantitative analysis were performed to analyze glycoprotein differences from 179 serum samples across the six different conditions. A total of 243 and 354 serum proteins were identified and quantified by label-free and TMT protein-level quantitative strategies, respectively. Nineteen and 25 proteins were found to show significant differences in samples between the pancreatic cancer and other conditions using the label-free and TMT strategies, respectively, with 7 proteins considered significant in both methods. Significantly different glycoproteins were further validated by lectin-ELISA and ELISA assays. Four candidates were identified as potential markers with profiles found to be highly complementary with CA 19–9 (p < 0.001). Obstructive jaundice (OJ) was found to have a significant impact on the performance of every marker protein, including CA 19–9. The combination of α-1-antichymotrypsin (AACT), thrombospondin-1 (THBS1), and haptoglobin (HPT) outperformed CA 19–9 in distinguishing pancreatic cancer from normal controls (AUC = 0.95), diabetes (AUC = 0.89), cyst (AUC = 0.82), and chronic pancreatitis (AUC = 0.90). A marker panel of AACT, THBS1, HPT, and CA 19–9 showed a high diagnostic potential in distinguishing pancreatic cancer from other conditions with OJ (AUC = 0.92) or without OJ (AUC = 0.95).

Practical Considerations and Current Limitations in Quantitative Mass Spectrometry-based Proteomics

Quantitative mass spectrometry (MS)-based proteomics continues to evolve through advances in sample preparation, chemical and biochemical reagents, instrumentation, and software. The breadth of proteomes and biological applications combined with unique experimental goals makes optimizing MS-based proteomics workflows a daunting task. Several MS-based instrument platforms are commercially available with LC-MS/MS being the most common for quantitative proteomics studies. Although the direction of LC-MS/MS instrumentation development is toward more user-friendly interfaces, there remain fundamental aspects of the technology that can be optimized for improving data quality. The intent of this chapter is to provide an introductory framework for understanding some of the more significant LC-MS/MS experimental conditions that can influence quantitative MS-based proteomics measurements, including electrospray ionization (ESI) bias and ion transmission efficiency. Because each commercial LC-MS/MS system is unique with regard to ESI source, transmission optics, ion isolation and trapping, ion fragmentation, and mass analysis, the use of design of experiments (DoE) is discussed as a potential approach for efficiently optimizing multiple inter-related factors.

Proteomic approaches in biomarker discovery: new perspectives in cancer diagnostics

Despite remarkable progress in proteomic methods, including improved detection limits and sensitivity, these methods have not yet been established in routine clinical practice. The main limitations, which prevent their integration into clinics, are high cost of equipment, the need for highly trained personnel, and last, but not least, the establishment of reliable and accurate protein biomarkers or panels of protein biomarkers for detection of neoplasms. Furthermore, the complexity and heterogeneity of most solid tumours present obstacles in the discovery of specific protein signatures, which could be used for early detection of cancers, for prediction of disease outcome, and for determining the response to specific therapies. However, cancer proteome, as the end-point of pathological processes that underlie cancer development and progression, could represent an important source for the discovery of new biomarkers and molecular targets for tailored therapies.

Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT): a novel glycan-relative quantification strategy

The Individuality Normalization when Labeling with Isotopic Glycan Hydrazide Tags (INLIGHT) strategy for the sample preparation, data analysis, and relative quantification of N-linked glycans is presented. Glycans are derivatized with either natural (L) or stable-isotope labeled (H) hydrazide reagents and analyzed using reversed phase liquid chromatography coupled online to a Q Exactive mass spectrometer. A simple glycan ladder, maltodextrin, is first used to demonstrate the relative quantification strategy in samples with negligible analytical and biological variability. It is shown that after a molecular weight correction attributable to isotopic overlap and a post-acquisition normalization of the data to account for any systematic bias, a plot of the experimental H:L ratio versus the calculated H:L ratio exhibits a correlation of unity for maltodextrin samples mixed in different ratios. We also demonstrate that the INLIGHT approach can quantify species over four orders of magnitude in ion abundance. The INLIGHT strategy is further demonstrated in pooled human plasma, where it is shown that the post-acquisition normalization is more effective than using a single spiked-in internal standard. Finally, changes in glycosylation are able to be detected in complex biological matrices, when spiked with a glycoprotein. The ability to spike in a glycoprotein and detect change at the glycan level validates both the sample preparation and data analysis strategy, making INLIGHT an invaluable relative quantification strategy for the field of glycomics.

Quantitative analysis of global phosphorylation changes with high-resolution tandem mass spectrometry and stable isotopic labeling

Quantitative measurement of specific protein phosphorylation sites is a primary interest of biologists, as site-specific phosphorylation information provides insights into cell signaling networks and cellular dynamics at a system level. Over the last decade, selective phosphopeptide enrichment methods including IMAC and metal oxides (TiO₂ and ZrO₂) have been developed and greatly facilitate large scale phosphoproteome analysis of various cells, tissues and living organisms, in combination with modern mass spectrometers featuring high mass accuracy and high mass resolution. Various quantification strategies have been applied to detecting relative changes in expression of proteins, peptides, and specific modifications between samples. The combination of mass spectrometry-based phosphoproteome analysis with quantification strategies provides a straightforward and unbiased method to identify and quantify site-specific phosphorylation. We describe common strategies for mass spectrometric analysis of stable isotope labeled samples, as well as two widely applied phosphopeptide enrichment methods based on IMAC(NTA-Fe³⁺) and metal oxide (ZrO₂). Instrumental configurations for on-line LC-tandem mass spectrometric analysis and parameters of conventional bioinformatic analysis of large data sets are also considered for confident identification, localization, and reliable quantification of site-specific phosphorylation.

Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.

Proteomic analysis of an unculturable bacterial endosymbiont (Blochmannia) reveals high abundance of chaperonins and biosynthetic enzymes

Many insect groups have coevolved with bacterial endosymbionts that live within specialized host cells. As a salient example, ants in the tribe Camponotini rely on Blochmannia, an intracellular bacterial mutualist that synthesizes amino acids and recycles nitrogen for the host. We performed a shotgun, label-free, LC/MS/MS quantitative proteomic analysis to investigate the proteome of Blochmannia associated with Camponotus chromaiodes. We identified more than 330 Blochmannia proteins, or 54% coverage of the predicted proteome, as well as 244 Camponotus proteins. Using the average intensity of the top 3 "best flier" peptides along with spiking of a surrogate standard at a known concentration, we estimated the concentration (fmol/μg) of those proteins with confident identification. The estimated dynamic range of Blochmannia protein abundance spanned 3 orders of magnitude and covered diverse functional categories, with particularly high representation of metabolism, information transfer, and chaperones. GroEL, the most abundant protein, totaled 6% of Blochmannia protein abundance. Biosynthesis of essential amino acids, fatty acids, and nucleotides, and sulfate assimilation had disproportionately high coverage in the proteome, further supporting a nutritional role of the symbiosis. This first quantitative proteomic analysis of an ant endosymbiont illustrates a promising approach to study the functional basis of intimate symbioses.

Comparison of protein expression ratios observed by sixplex and duplex TMT labeling method

Stable isotope labeling via isobaric derivatization of peptides is a universally applicable approach that enables concurrent identification and quantification of proteins in different samples using tandem mass spectrometry. In this study, we evaluated the performance of amine-reactive isobaric tandem mass tag (TMT), available as duplex and sixplex sets, with regard to their ability to elucidate protein expression changes. Using rat brain tissue from two different developmental time points, postnatal day 1 (p1) and 45 (p45), as a model system, we compared the protein expression ratios (p45/p1) observed using duplex TMT tags in triplicate measurements versus sixplex tag in a single LC-MS/MS analysis. A correlation of 0.79 in relative protein abundance was observed in the proteins quantified by these two sets of reagents. However, more proteins passed the criteria for significant fold change (-1.0 ≤ log(2) ratio (p45/p1) ≥ +1.0 and p < 0.05) in the sixplex analysis. Nevertheless, in both methods most proteins showing significant fold change were identified by multiple spectra, increasing their quantification precision. Additionally, the fold change in p45 rats against p1, observed in TMT experiments, was corroborated by a metabolic labeling strategy where relative quantification of differentially expressed proteins was obtained using (15)N-labeled p45 rats as an internal standard.

Proteomic profiling of paraffin-embedded samples identifies metaplasia-specific and early-stage gastric cancer biomarkers

Early diagnosis and curative resection are the predominant factors associated with increased survival in patients with gastric cancer. However, most gastric cancer cases are still diagnosed at later stages. Since most pathologic specimens are archived as FFPE samples, the ability to use them to generate expression profiles can greatly improve cancer biomarker discovery. We sought to uncover new biomarkers for stomach preneoplastic metaplasias and neoplastic lesions by generating proteome profiles using FFPE samples. We combined peptide isoelectric focusing and liquid chromatography-tandem mass spectrometry analysis to generate proteomic profiles from FFPE samples of intestinal-type gastric cancer, metaplasia, and normal mucosa. The expression patterns of selected proteins were analyzed by immunostaining first in single tissue sections from normal stomach, metaplasia, and gastric cancer and later in larger tissue array cohorts. We detected 60 proteins up-regulated and 87 proteins down-regulated during the progression from normal mucosa to metaplasia to gastric cancer. Two of the up-regulated proteins, LTF and DMBT1, were validated as specific markers for spasmolytic polypeptide-expressing metaplasia and intestinal metaplasia, respectively. In cancers, significantly lower levels of DMBT1 or LTF correlated with more advanced disease and worse prognosis. Thus, proteomic profiling using FFPE samples has led to the identification of two novel markers for stomach metaplasias and gastric cancer prognosis.

Comparability of differential proteomics data generated from paired archival fresh-frozen and formalin-fixed samples by GeLC-MS/MS and spectral counting

In this study, a Veterinary Department repository composed by paired formalin-fixed paraffin-embedded (FFPE) and fresh-frozen (FrFr) sets of the same tissues, routinely archived in the typical conditions of a clinical setting, was exploited to perform a comparative evaluation of the results generated by GeLC-MS/MS (1-DE followed by in-gel digestion and LC-MS/MS) and spectral counting with the two types of archival samples. Therefore, two parallel differential proteomic studies were performed using 3 canine mammary carcinomas and 3 normal controls in a paired fashion (6 FrFr and 6 FFPE in total). As a result, the FrFr and FFPE differential proteomic datasets exhibited fair consistency in differential expression trends, according to protein molecular function, cellular localization, networks, and pathways. However, FFPE samples were globally slightly less informative, especially concerning the high-MW subproteome. As a further investigation, new insights into the molecular aspects of protein fixation and retrieval were obtained. In conclusion, archival FFPE samples can be reliably used for differential proteomics studies employing a spectral counting GeLC-MS/MS approach, although some typical biases need to be taken into account, and FrFr specimens (when available) should still be considered as the gold standard for clinical proteomics.

Precision, proteome coverage, and dynamic range of Arabidopsis proteome profiling using (15)N metabolic labeling and label-free approaches

This study reports the comprehensive comparison of (15)N metabolic labeling and label free proteomic strategies for quantitation, with particular focus on plant proteomics. Our investigation of proteome coverage, dynamic range and quantitative precision for a wide range of mixing ratios and protein loadings aim to aid the investigators in the decision making process during experimental design. One of the main characteristics of the label free strategy is the applicability to all starting material, which is a limitation to the metabolic labeling. However, particularly at mixing ratios up to 10-fold the (15)N metabolic labeling proved to be more precise. Contrary to usual practice based on the results from this study, we suggest that nonequal mixing ratios in metabolic labeling could further increase the proteome coverage for quantitation. On the other hand, the label free strategy, in combination with low protein loading allows the extension of the dynamic range for quantitation and it is more precise at very high ratios, which could be important for certain types of experiments.

Causes and consequences of aneuploidy in cancer

Genetic instability, which includes both numerical and structural chromosomal abnormalities, is a hallmark of cancer. Whereas the structural chromosome rearrangements have received substantial attention, the role of whole-chromosome aneuploidy in cancer is much less well-understood. Here we review recent progress in understanding the roles of whole-chromosome aneuploidy in cancer, including the mechanistic causes of aneuploidy, the cellular responses to chromosome gains or losses and how cells might adapt to tolerate these usually detrimental alterations. We also explore the role of aneuploidy in cellular transformation and discuss the possibility of developing aneuploidy-specific therapies.

Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows

Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-to-day variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS- PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs.