Use of stable isotope labeling by amino acids in cell culture as a spike-in standard in quantitative proteomics

Mass spectrometry (MS)-based proteomics is increasingly applied in a quantitative format, often based on labeling of samples with stable isotopes that are introduced chemically or metabolically. In the stable isotope labeling by amino acids in cell culture (SILAC) method, two cell populations are cultured in the presence of heavy or light amino acids (typically lysine and/or arginine), one of them is subjected to a perturbation, and then both are combined and processed together. In this study, we describe a different approach--the use of SILAC as an internal or 'spike-in' standard--wherein SILAC is only used to produce heavy labeled reference proteins or proteomes. These are added to the proteomes under investigation after cell lysis and before protein digestion. The actual experiment is therefore completely decoupled from the labeling procedure. Spike-in SILAC is very economical, robust and in principle applicable to all cell- or tissue-based proteomic analyses. Applications range from absolute quantification of single proteins to the quantification of whole proteomes. Spike-in SILAC is especially advantageous when analyzing the proteomes of whole tissues or organisms. The protocol describes the quantitative analysis of a tissue sample relative to super-SILAC spike-in, a mixture of five SILAC-labeled cell lines that accurately represents the tissue. It includes the selection and preparation of the spike-in SILAC standard, the sample preparation procedure, and analysis and evaluation of the results.     

同位素稀释法在绝对定量蛋白质组中的研究进展

绝对定量蛋白质组是指基于蛋白质组学方法对细胞、组织或体液中的蛋白质进行绝对量或浓度测定．目前,常用的绝对定量方法主要有基于同位素稀释法的蛋白质组学绝对定量方法和基于质谱数据统计分析的非标记方法．基于同位素稀释法的绝对定量方法是用已知量的同位素标记物对与其混合的样本蛋白质浓度进行测定．常见的同位素标记物包括：由AQUA法、QconCAT法产生的特异性水解肽段,由PSAQ法、Absolute SILAC法产生的标记蛋白和由PrESTs-SILAC法产生的蛋白抗原表位标签．由于同位素稀释法可以对蛋白质进行准确和精确定量,对于临床疾病的诊断和治疗具有明显的现实意义．本文对同位素稀释法在绝对定量蛋白质组中的研究进展及其优缺点和最新应用进行了评述．     

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants. Low affinity interactions can be preserved through the use of less-stringent buffer conditions and remain readily identifiable. This protocol discusses the labeling of tissue culture cells with stable isotope labeled amino acids, transfection and immunoprecipitation of an affinity tagged protein of interest, followed by the preparation for submission to a mass spectrometry facility. This protocol then discusses how to analyze and interpret the data returned from the mass spectrometer in order to identify cellular partners interacting with a protein of interest. As an example this technique is applied to identify proteins binding to the eukaryotic translation initiation factors: eIF4AI and eIF4AII.     

Quantitative analysis of SILAC data sets using spectral counting

We report a new quantitative proteomics approach that combines the best aspects of stable isotope labeling of amino acids in cell culture (SILAC) labeling and spectral counting. The SILAC peptide count ratio analysis (SPeCtRA, http://proteomics.mcw.edu/visualize ) method relies on MS² spectra rather than ion chromatograms for quantitation and therefore does not require the use of high mass accuracy mass spectrometers. The inclusion of a stable isotope label allows the samples to be combined before sample preparation and analysis, thus avoiding many of the sources of variability that can plague spectral counting. To validate the SPeCtRA method, we have analyzed samples constructed with known ratios of protein abundance. Finally, we used SPeCtRA to compare endothelial cell protein abundances between high (20 mM) and low (11 mM) glucose culture conditions. Our results demonstrate that SPeCtRA is a protein quantification technique that is accurate and sensitive as well as easy to automate and apply to high‐throughput analysis of complex biological samples.     

Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide‐based quantitative proteomics

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide‐based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an “empirical rule for linearly correlated peptide selection (ERLPS)” in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label‐free to O18/O16‐labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide‐based quantitative proteomics over a large dynamic range.     

Relative protein quantification by isobaric SILAC with immonium ion splitting (ISIS)

Metabolic labeling techniques have recently become popular tools for the quantitative profiling of proteomes. Classical stable isotope labeling with amino acids in cell cultures (SILAC) uses pairs of heavy/light isotopic forms of amino acids to introduce predictable mass differences in protein samples to be compared. After proteolysis, pairs of cognate precursor peptides can be correlated, and their intensities can be used for mass spectrometry-based relative protein quantification. We present an alternative SILAC approach by which two cell cultures are grown in media containing isobaric forms of amino acids, labeled either with 13C on the carbonyl (C-1) carbon or 15N on backbone nitrogen. Labeled peptides from both samples have the same nominal mass and nearly identical MS/MS spectra but generate upon fragmentation distinct immonium ions separated by 1 amu. When labeled protein samples are mixed, the intensities of these immonium ions can be used for the relative quantification of the parent proteins. We validated the labeling of cellular proteins with valine, isoleucine, and leucine with coverage of 97% of all tryptic peptides. We improved the sensitivity for the detection of the quantification ions on a pulsing instrument by using a specific fast scan event. The analysis of a protein mixture with a known heavy/light ratio showed reliable quantification. Finally the application of the technique to the analysis of two melanoma cell lines yielded quantitative data consistent with those obtained by a classical two-dimensional DIGE analysis of the same samples. Our method combines the features of the SILAC technique with the advantages of isobaric labeling schemes like iTRAQ. We discuss advantages and disadvantages of isobaric SILAC with immonium ion splitting as well as possible ways to improve it.     

Monitoring of changes in the membrane proteome during stationary phase adaptation of Bacillus subtilis using in vivo labeling techniques

Bacillus subtilis has been developed as a model system for physiological proteomics. However, thus far these studies have mainly been limited to cytoplasmic, extracellular, and cell-wall attached proteins. Although being certainly important for cell physiology, the membrane protein fraction has not been studied in comparable depth due to inaccessibility by traditional 2-DE-based workflows and limitations in reliable quantification. In this study, we now compare the potential of stable isotope labeling with amino acids (SILAC) and (14)N/(15)N-labeling for the analysis of bacterial membrane fractions in physiology-driven proteomic studies. Using adaptation of B. subtilis to amino acid (lysine) and glucose starvation as proof of principle scenarios, we show that both approaches provide similarly valuable data for the quantification of bacterial membrane proteins. Even if labeling with stable amino acids allows a more straightforward analysis of data, the (14)N/(15)N-labeling has some advantages in general such as labeling of all amino acids and thereby increasing the number of peptides for quantification. Both, SILAC as well as (14)N/(15)N-labeling are compatible with 2-DE, 2-D LC-MS/MS, and GeLC-MS/MS and thus will allow comprehensive simultaneous interrogation of cytoplasmic and enriched membrane proteomes.     

A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC)

Stable isotope labeling by amino acids in cell culture (SILAC) is a simple, robust, yet powerful approach in mass spectrometry (MS)-based quantitative proteomics. SILAC labels cellular proteomes through normal metabolic processes, incorporating non-radioactive, stable isotope-containing amino acids in newly synthesized proteins. Growth medium is prepared where natural ("light") amino acids are replaced by "heavy" SILAC amino acids. Cells grown in this medium incorporate the heavy amino acids after five cell doublings and SILAC amino acids have no effect on cell morphology or growth rates. When light and heavy cell populations are mixed, they remain distinguishable by MS, and protein abundances are determined from the relative MS signal intensities. SILAC provides accurate relative quantification without any chemical derivatization or manipulation and enables development of elegant functional assays in proteomics. In this protocol, we describe how to apply SILAC and the use of nano-scale liquid chromatography coupled to electrospray ionization mass spectrometry for protein identification and quantification. This procedure can be completed in 8 days.     

Preventing arginine-to-proline conversion in a cell-line-independent manner during cell cultivation under stable isotope labeling by amino acids in cell culture (SILAC) conditions

Quantitative proteomics has increasingly gained impact in life science research as a tool to describe changes in protein expression between different cellular states. Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful technique for relative quantification of proteins. However, the accuracy of quantification is impaired by the metabolic conversion of arginine to proline resulting in additional heavy labeled proline peptide satellites. Here we reinvestigated the addition of unlabeled proline during cell cultivation under SILAC conditions considering several thousand peptides and demonstrated that the arginine-to-proline conversion is prevented independent of the cell line used.     

Quantitative analysis of the detergent-insoluble brain proteome in frontotemporal lobar degeneration using SILAC internal standards

A hallmark of neurodegeneration is the aggregation of disease related proteins that are resistant to detergent extraction. In the major pathological subtype of frontotemporal lobar degeneration (FTLD), modified TAR-DNA binding protein 43 (TDP-43), including phosphorylated, ubiquitinated, and proteolytically cleaved forms, is enriched in detergent-insoluble fractions from post-mortem brain tissue. Additional proteins that accumulate in the detergent-insoluble FTLD brain proteome remain largely unknown. In this study, we used proteins from stable isotope-labeled (SILAC) human embryonic kidney 293 cells (HEK293) as internal standards for peptide quantitation across control and FTLD insoluble brain proteomes. Proteins were identified and quantified by liquid-chromatography coupled with tandem mass spectrometry (LC-MS/MS) and 21 proteins were determined to be enriched in FTLD using SILAC internal standards. In parallel, label-free quantification of only the unlabeled brain derived peptides by spectral counts (SC) and G-test analysis identified additional brain-specific proteins significantly enriched in disease. Several proteins determined to be enriched in FTLD using SILAC internal standards were not considered significant by G-test due to their low total number of SC. However, immunoblotting of FTLD and control samples confirmed enrichment of these proteins, highlighting the utility of SILAC internal standard to quantify low-abundance proteins in brain. Of these, the RNA binding protein PTB-associated splicing factor (PSF) was further characterized because of structural and functional similarities to TDP-43. Full-length PSF and shorter molecular weight fragments, likely resulting from proteolytic cleavage, were enriched in FTLD cases. Immunohistochemical analysis of PSF revealed predominately nuclear localization in control and FTLD brain tissue and was not associated with phosphorylated pathologic TDP-43 neuronal inclusions. However, in a subset of FTLD cases, PSF was aberrantly localized to the cytoplasm of oligodendrocytes. These data raise the possibility that PSF directed RNA processes in oligodendrocytes are altered in neurodegenerative disease.     

Identification of protein complexes that bind to histone H3 combinatorial modifications using super-SILAC and weighted correlation network analysis

The large number of chemical modifications that are found on the histone proteins of eukaryotic cells form multiple complex combinations, which can act as recognition signals for reader proteins. We have used peptide capture in conjunction with super-SILAC quantification to carry out an unbiased high-throughput analysis of the composition of protein complexes that bind to histone H3K9/S10 and H3K27/S28 methyl-phospho modifications. The accurate quantification allowed us to perform Weighted correlation network analysis (WGCNA) to obtain a systems-level view of the histone H3 histone tail interactome. The analysis reveals the underlying modularity of the histone reader network with members of nuclear complexes exhibiting very similar binding signatures, which suggests that many proteins bind to histones as part of pre-organized complexes. Our results identify a novel complex that binds to the double H3K9me3/S10ph modification, which includes Atrx, Daxx and members of the FACT complex. The super-SILAC approach allows comparison of binding to multiple peptides with different combinations of modifications and the resolution of the WGCNA analysis is enhanced by maximizing the number of combinations that are compared. This makes it a useful approach for assessing the effects of changes in histone modification combinations on the composition and function of bound complexes.     

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC–MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC–MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC–MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.     

Quantitative cancer proteomics: stable isotope labeling with amino acids in cell culture (SILAC) as a tool for prostate cancer research

Microarrays have been the primary means for large-scale analyses of genes implicated in cancer progression. However, more recently a need has been recognized for investigating cancer development directly at the protein level. In this report, we have applied a comparative proteomic technique to the study of metastatic prostate cancer. This technology, termed stable isotope labeling with amino acids in cell culture (SILAC), has recently gained popularity for its ability to compare the expression levels of hundreds of proteins in a single experiment. SILAC makes use of (12)C- and (13)C-labeled amino acids added to the growth media of separately cultured cell lines, giving rise to cells containing either "light" or "heavy" proteins, respectively. Upon mixing lysates collected from these cells, proteins can be identified by tandem mass spectrometry. The incorporation of stable isotopes also allows for a quantitative comparison between the two samples. Using this method, we compared the expression levels for more than 440 proteins in the microsomal fractions of prostate cancer cells with varying metastatic potential. Of these, 60 were found elevated greater than 3-fold in the highly metastatic cells, whereas 22 were reduced by equivalent amounts. Western blotting provided further confirmation of the mass spectrometry-based quantification. Our results demonstrate the applicability of this novel approach toward the study of cancer progression using defined cell lines.     

Label-free quantification in clinical proteomics

Nowadays, proteomic studies no longer focus only on identifying as many proteins as possible in a given sample, but aiming for an accurate quantification of them. Especially in clinical proteomics, the investigation of variable protein expression profiles can yield useful information on pathological pathways or biomarkers and drug targets related to a particular disease. Over the time, many quantitative proteomic approaches have been established allowing researchers in the field of proteomics to refer to a comprehensive toolbox of different methodologies. In this review we will give an overview of different methods of quantitative proteomics with focus on label-free proteomics and its use in clinical proteomics.     

Mass spectrometry–based relative quantification of proteins in precatalytic and catalytically active spliceosomes by metabolic labeling (SILAC), chemical labeling (iTRAQ), and label-free spectral count

The spliceosome undergoes major changes in protein and RNA composition during pre-mRNA splicing. Knowing the proteins—and their respective quantities—at each spliceosomal assembly stage is critical for understanding the molecular mechanisms and regulation of splicing. Here, we applied three independent mass spectrometry (MS)–based approaches for quantification of these proteins: (1) metabolic labeling by SILAC, (2) chemical labeling by iTRAQ, and (3) label-free spectral count for quantification of the protein composition of the human spliceosomal precatalytic B and catalytic C complexes. In total we were able to quantify 157 proteins by at least two of the three approaches. Our quantification shows that only a very small subset of spliceosomal proteins (the U5 and U2 Sm proteins, a subset of U5 snRNP-specific proteins, and the U2 snRNP-specific proteins U2A′ and U2B′′) remains unaltered upon transition from the B to the C complex. The MS-based quantification approaches classify the majority of proteins as dynamically associated specifically with the B or the C complex. In terms of experimental procedure and the methodical aspect of this work, we show that metabolically labeled spliceosomes are functionally active in terms of their assembly and splicing kinetics and can be utilized for quantitative studies. Moreover, we obtain consistent quantification results from all three methods, including the relatively straightforward and inexpensive label-free spectral count technique.     

Comparative Proteomics of Ovarian Cancer Aggregate Formation Reveals an Increased Expression of Calcium-activated Chloride Channel Regulator 1 (CLCA1)

Ovarian cancer is a lethal gynecological disease that is characterized by peritoneal metastasis and increased resistance to conventional chemotherapies. This increased resistance and the ability to spread is often attributed to the formation of multicellular aggregates or spheroids in the peritoneal cavity, which seed abdominal surfaces and organs. Given that the presence of metastatic implants is a predictor of poor survival, a better understanding of how spheroids form is critical to improving patient outcome, and may result in the identification of novel therapeutic targets. Thus, we attempted to gain insight into the proteomic changes that occur during anchorage-independent cancer cell aggregation. As such, an ovarian cancer cell line, OV-90, was cultured in adherent and non-adherent conditions using stable isotope labeling with amino acids in cell culture (SILAC). Anchorage-dependent cells (OV-90AD) were grown in tissue culture flasks, whereas anchorage-independent cells (OV-90AI) were grown in suspension using the hanging-drop method. Cellular proteins from both conditions were then identified using LC-MS/MS, which resulted in the quantification of 1533 proteins. Of these, 13 and 6 proteins were up-regulated and down-regulated, respectively, in aggregate-forming cells compared with cells grown as monolayers. Relative gene expression and protein expression of candidates were examined in other cell line models of aggregate formation (TOV-112D and ES-2), which revealed an increased expression of calcium-activated chloride channel regulator 1 (CLCA1). Moreover, inhibitor and siRNA transfection studies demonstrated an apparent effect of CLCA1 on cancer cell aggregation. Further elucidation of the role of CLCA1 in the pathogenesis of ovarian cancer is warranted.     

基于数据非依赖采集的以肽为中心分析算法和软件的研究进展

数据非依赖采集(data-independent acquisition,DIA)是一种高通量、无偏性的质谱数据采集方法,具有定量结果重现性好,对低丰度蛋白质友好的特点,是近年来进行大队列蛋白质组研究的首选方法之一。由于DIA产生的二级谱是混合谱,包含了多个肽段的碎片离子信息,使得蛋白质鉴定和定量更加困难。目前,DIA数据分析方法分为两大类,即以肽为中心和以谱图为中心。其中,以肽为中心的分析方法鉴定更灵敏,定量更准确,已成为DIA数据解析的主流方法。其分析流程包括构建谱图库、提取色谱峰群、特征打分和结果质控4个关键步骤。本文综述了以肽为中心的DIA数据分析流程,介绍了基于此流程的数据分析软件及相关比较评估工作,进一步总结了已有的算法改进工作,最后对未来发展方向进行了展望。     

Analysis of protein expression in cell microarrays: a tool for antibody-based proteomics.

Tissue microarray (TMA) technology provides a possibility to explore protein expression patterns in a multitude of normal and disease tissues in a high-throughput setting. Although TMAs have been used for analysis of tissue samples, robust methods for studying in vitro cultured cell lines and cell aspirates in a TMA format have been lacking. We have adopted a technique to homogeneously distribute cells in an agarose gel matrix, creating an artificial tissue. This enables simultaneous profiling of protein expression in suspension- and adherent-grown cell samples assembled in a microarray. In addition, the present study provides an optimized strategy for the basic laboratory steps to efficiently produce TMAs. Presented modifications resulted in an improved quality of specimens and a higher section yield compared with standard TMA production protocols. Sections from the generated cell TMAs were tested for immunohistochemical staining properties using 20 well-characterized antibodies. Comparison of immunoreactivity in cultured dispersed cells and corresponding cells in tissue samples showed congruent results for all tested antibodies. We conclude that a modified TMA technique, including cell samples, provides a valuable tool for high-throughput analysis of protein expression, and that this technique can be used for global approaches to explore the human proteome.