DeepQuanTR: MALDI‐MS‐based label‐free quantification of proteins in complex biological samples

The quantification of changes in protein abundance in complex biological specimens is essential for proteomic studies in basic and applied research. Here we report on the development and validation of the DeepQuanTR software for identification and quantification of differentially expressed proteins using LC‐MALDI‐MS. Following enzymatic digestion, HPLC peptide separation and normalization of MALDI‐MS signal intensities to the ones of internal standards, the software extracts peptide features, adjusts differences in HPLC retention times and performs a relative quantification of features. The annotation of multiple peptides to the corresponding parent protein allows the definition of a Protein Quant Value, which is related to protein abundance and which allows inter‐sample comparisons. The performance of DeepQuanTR was evaluated by analyzing 24 samples deriving from human serum spiked with different amounts of four proteins and eight complex samples of vascular proteins, derived from surgically resected human kidneys with cancer following ex vivo perfusion with a reactive ester biotin derivative. The identification and experimental validation of proteins, which were differentially regulated in cancerous lesions as compared with normal kidney, was used to demonstrate the power of DeepQuanTR. This software, which can easily be used with established proteomic methodologies, facilitates the relative quantification of proteins derived from a wide variety of different samples.     

Profiling human brain proteome by multi-dimensional separations coupled with MS

In our initial attempt to analyze the human brain proteome, we applied multi-dimensional protein separation and identification techniques using a combination of sample fractionation, 1-D SDS-PAGE, and MS analysis. The complexity of human brain proteome requires multiple fractionation strategies to extend the range and total number of proteins identified. According to the method of Klose (Methods Mol. Biol. 1999, 112, 67), proteins of the temporal lobe of human brain were fractionated into (i) cytoplasmic and nucleoplasmic, (ii) membrane and other structural, and (iii) DNA-binding proteins. Each fraction was then separated by SDS-PAGE, and the resulting gel line was cut into approximately 50 bands. After trypsin digestion, the resulting peptides from each band were analyzed by RP-LC/ESI-MS/MS using an LTQ spectrometer. The SEQUEST search program, which searched against the IPI database, was used for peptide sequence identification, and peptide sequences were validated by reversed sequence database search and filtered by the Protein Hit Score. Ultimately, 1533 proteins could be detected from the human brain. We classified the identified proteins according to their distribution on cellular components. Among these proteins, 24% were membrane proteins. Our results show that the multiple separation strategy is effective for high-throughput characterization of proteins from complex proteomic mixtures.     

Proteomic Strategy for Identifying Mollusc Shell Proteins Using Mild Chemical Degradation and Trypsin Digestion of Insoluble Organic Shell Matrix: A Pilot Study on Haliotis tuberculata

A successful strategy for the identification of shell proteins is based on proteomic analyses where soluble and insoluble fractions isolated from organic shell matrix are digested with trypsin with the aim of generating peptides, which are used to identify novel shell proteins contained in databases. However, using trypsin as a sole degradative agent is limited by the enzyme's cleavage specificity and is dependent upon the occurrence of lysine and arginine in the shell protein sequence. To bypass this limitation, we investigated the ability of trifluoroacetic acid (TFA), a low-specificity chemical degradative agent, to generate clusters of analyzable peptides from organic shell matrix, suitable for database annotation. Acetic acid-insoluble fractions from Haliotis tuberculata shell were processed by trypsin followed by TFA digestion. The hydrolysates were used to annotate an expressed sequence tag library constructed from the mantle tissue of Haliotis asinina, a tropical abalone species. The characterization of sequences with repeat motifs featured in some of the shell matrix proteins benefited from TFA-induced serial cutting, which can result in peptide ladder series. Using the degradative specificities of TFA and trypsin, we were able to identify five novel shell proteins. This pilot study indicates that a mild chemical digestion of organic shell matrix combined with trypsin generates peptides suitable for proteomic analysis for better characterization of mollusc shell matrix proteins.     

Concentration and desalting of peptide and protein samples with a newly developed C18 membrane in a microspin column format.

Protein identification plays an important role in today's academic and industrial proteomic research. Commonly used methods for the separation of proteins from complex samples include liquid chromatography (e.g., ion exchange, reversed-phase, hydrophobic interaction), or types of gel electrophoresis (e.g., 1d and 2d PAGE). Relevant proteins separated in the latter way are often cut out, cleaved with trypsin "in gel," and the resulting peptide mixtures combined with matrix and spotted onto a target plate for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF-ms) analysis. Subsequently, proteins can be identified by comparison of the resulting peptide mass fingerprints against different databases.(1) since the success of protein identification can be enhanced by the desalting and concentration of the samples, an innovative C18-membrane was incorporated into a microspin column (Vivapure C18 micro spin column, Vivascience AG, Hannover, Germany) to analyze its performance for sample preparation prior to MALDI-ToF-ms. Rapid concentration of single or multiple 200-microl volumes through an available membrane only 2 mm in diameter allowed for analysis of very dilute samples. We observed the successful and rapid desalting of urea-containing protein samples at 100 fmol/mul up to a mass of approximately 70 KDA and the concentration of digest peptides from a solution of 1 fmol/microl using C18-membrane technology.     

Tissue proteomics using chemical immobilization and mass spectrometry

Proteomics analysis is important for characterizing tissues to gain biological and pathological insights, which could lead to the identification of disease-associated proteins for disease diagnostics or targeted therapy. However, tissues are commonly embedded in optimal cutting temperature medium (OCT) or are formalin-fixed and paraffin-embedded (FFPE) in order to maintain tissue morphology for histology evaluation. Although several tissue proteomic analyses have been performed on FFPE tissues using advanced mass spectrometry (MS) technologies, high-throughput proteomic analysis of OCT-embedded tissues has been difficult due to the interference of OCT in the MS analysis. In addition, molecules other than proteins present in tissues further complicate tissue proteomic analysis. Here, we report the development of a method using chemical immobilization of proteins for peptide extraction (CIPPE). In this method, proteins are chemically immobilized onto a solid support; interferences from tissues and OCT embedding are removed by extensive washing of proteins conjugated on the solid support. Peptides are then released from the solid phase by proteolysis, enabling MS analysis. This method was first validated by eliminating OCT interference from a standard protein, human serum albumin, where all of the unique peaks contributed by OCT contamination were eradicated. Finally, this method was applied for the proteomic analysis of frozen and OCT-embedded tissues using iTRAQ (isobaric tag for relative and absolute quantitation) labeling and two-dimensional liquid chromatography tandem mass spectrometry. The data showed reproducible extraction and quantitation of 10,284 proteins from 3996 protein groups and a minimal impact of OCT embedding on the analysis of the global proteome of the stored tissue samples.     

Automated methods for improved protein identification by peptide mass fingerprinting

In order to maximize protein identification by peptide mass fingerprinting noise peaks must be removed from spectra and recalibration is often required. The preprocessing of the spectra before database searching is essential but is time-consuming. Nevertheless, the optimal database search parameters often vary over a batch of samples. For high-throughput protein identification, these factors should be set automatically, with no or little human intervention. In the present work automated batch filtering and recalibration using a statistical filter is described. The filter is combined with multiple data searches that are performed automatically. We show that, using several hundred protein digests, protein identification rates could be more than doubled, compared to standard database searching. Furthermore, automated large-scale in-gel digestion of proteins with endoproteinase LysC, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) analysis, followed by subsequent trypsin digestion and MALDI-TOF analysis were performed. Several proteins could be identified only after digestion with one of the enzymes, and some less significant protein identifications were confirmed after digestion with the other enzyme. The results indicate that identification of especially small and low-abundance proteins could be significantly improved after sequential digestions with two enzymes.     

Proteomics of human umbilical vein endothelial cells applied to etoposide-induced apoptosis

We have undertaken to continue the proteomic study of human umbilical vein endothelial cells (HUVECs) using the combination of 2-DE, automated trypsin digestion, and PMF analysis after MALDI-TOF MS and peptide sequencing using nano LC-ESI-MS/MS. The overall functional characterization of the 162 identified proteins from primary cultures of HUVECs confirms the metabolic capabilities of endothelium and illustrates various cellular functions more related to cell motility and angiogenesis, protein folding, anti-oxidant defenses, signal transduction, proteasome pathway and resistance to apoptosis. In comparison with controls cells, the differential proteomic analysis of HUVECs treated by the pro-apoptotic topoisomerase inhibitor etoposide further revealed the variation of eight proteins, namely, GRP78, GRP94, valosin-containing protein, proteinase inhibitor 9, cofilin, 37-kDa laminin receptor protein, bovine apolipoprotein, and tropomyosin. These data suggest that etoposide-induced apoptosis of human vascular endothelial cells results from the intricate involvement of multiple apoptosis processes including at least the mitochondrial and the ER stress pathways. The presented 2-D pattern and protein database, as well as the data related to apoptosis of HUVECs, are available at http://www.huvec.com.     

Proteomic analysis of the fetal brain

We applied proteomic technologies to analyze the human fetal brain. Such an analysis could provide us with important information on the development of the early neuronal life in healthy and diseased states. The proteins from the cerebellum of control subjects were analyzed by two-dimensional electrophoresis and identified by matrix-assisted laser desorption/ionization-mass spectrometry on the basis of peptide mass fingerprinting, following in-gel digestion with trypsin. Approximately 3,000 spots, excised from three two-dimensional gels, were analyzed which resulted in the identification of about 1,700 proteins that were the products of 437 different genes. About half of them are enzyme subunits and are mainly localized in the cytosol and in mitochondria. The most frequently identified proteins in the various gels were heat shock proteins, house-keeping enzymes, such as ATP synthase chains, protein disulfide isomerase, and structural proteins, such as tubulin chains. Seven gene products were identified for the first time in the fetal brain. The other proteins had also been detected in other human samples which were analyzed in our laboratory. Most proteins were represented by multiple spots. In average, about 3-5 spots were detected per gene product. The fetal brain database includes proteins with important functions and also with unknown functions and represents today one of the largest two-dimensional databases for higher eukaryotic proteomes. It may be a useful tool in the investigation of protein changes in neurodegenerative diseases early in life.     

Identification of additional proteins in differential proteomics using protein interaction networks

In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest‐ranked proteins, beta‐arrestin 1, and beta‐arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost‐effective means to identify additional proteins that remain elusive for current 2D gel‐based proteomic profiling techniques.     

The proteomic reactor: a microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis

Gel-free proteomics has emerged as a complement to conventional gel-based proteomics. Gel-free approaches focus on peptide or protein fractionation, but they do not address the efficiency of protein processing. We report the development of a microfluidic proteomic reactor that greatly simplifies the processing of complex proteomic samples by combining multiple proteomic steps. Rapid extraction and enrichment of proteins from complex proteomic samples or directly from cells are readily performed on the reactor. Furthermore, chemical and enzymatic treatments of proteins are performed in 50 nL effective volume, which results in an increased number of generated peptides. The products are compatible with mass spectrometry. We demonstrated that the proteomic reactor is at least 10 times more sensitive than current gel-free methodologies with one protein identified per 440 pg of protein lysate injected on the reactor. Furthermore, as little as 300 cells can be directly introduced on the proteomic reactor and analyzed by mass spectrometry.     

A feasibility study to identify proteins in the residual Pap test fluid of women with normal cytology by mass spectrometry-based proteomics

Background

The proteomic analysis of body fluids is a growing technology for the identification of protein biomarkers of disease. Given that Papanicolaou tests (Pap tests) are routinely performed on over 30 million women annually in the U.S. to screen for cervical cancer, we examined the residual Pap test fluid as a source of protein for analysis by mass spectrometry (MS). In the liquid-based Pap test, cervical cells are collected from the ectocervix and placed into an alcohol-based fixative prior to staining and pathologic examination. We hypothesized that proteins shed by cells of the female genital tract can be detected in the Pap test fixative by MS-based proteomic techniques. We examined the feasibility of using residual fluid from discarded Pap tests with cytologically “normal” results to optimize sample preparation for MS analysis. The protein composition of the cell-free Pap test fluid was determined by silver staining of sodium dodecyl sulfate -polyacrylamide gels, and the abundance of serum proteins was examined by Western immunoblot using an antibody against human serum albumin. Both pooled and individual samples were trypsin digested and analyzed by two-dimensional MS/MS. Proteins were identified by searching against the Human Uniprot database, and characterized for localization, function and relative abundance.

Results

The average volume of the residual Pap test fluid was 1.5 ml and the average protein concentration was 0.14 mg/ml. By Western immunoblot we showed that the amount of albumin in each sample was significantly reduced compared to normal serum. By MS/MS, we identified 714 unique proteins in pooled Pap test samples and an average of 431 proteins in individual samples. About 40% of the proteins identified were extracellular or localized to the plasma membrane. Almost 20% of the proteins identified were involved in immunity and defense, characteristic of the healthy cervical-vaginal proteome. By merging the protein sets from the individual and pooled Pap test samples, we created a “Normal Pap test Core Proteome” consisting of 153 proteins.

Conclusions

Residual Pap test fluid contains a sufficient amount of protein for analysis by MS and represents a valuable biospecimen source for the identification of protein biomarkers for gynecological diseases.     

Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry

Archived formalin-fixed paraffin-embedded (FFPE) tissue collections represent a valuable informational resource for proteomic studies. Multiple FFPE core biopsies can be assembled in a single block to form tissue microarrays (TMAs). We describe a protocol for analyzing protein in FFPE-TMAs using matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). The workflow incorporates an antigen retrieval step following deparaffinization, in situ trypsin digestion, matrix application and then mass spectrometry signal acquisition. The direct analysis of FFPE-TMA tissue using IMS allows direct analysis of multiple tissue samples in a single experiment without extraction and purification of proteins. The advantages of high speed and throughput, easy sample handling and excellent reproducibility make this technology a favorable approach for the proteomic analysis of clinical research cohorts with large sample numbers. For example, TMA analysis of 300 FFPE cores would typically require 6 h of total time through data acquisition, not including data analysis.     

Gel-free analysis of the human brain proteome: application of liquid chromatography and mass spectrometry on biopsy and autopsy samples

This paper reports on the findings of the Biomedical Research Institute, as one of the participants in the pilot study of the HUPO Brain Proteome Project. A biopsy and autopsy study sample derived from human brain was distributed among the participants for proteomic analysis. In our laboratory, attention was focused on protein identification using the bottom-up shotgun approach. Protein extracts derived from both samples were trypsinized and analyzed separately by 2-D LC and MS. In a complementary approach, the tryptic digests were analyzed directly by LC-ESI-MS/MS and gas-phase fractionation in the mass spectrometer. Taken together, both proteomic approaches in combination with a stringent evaluation process, resulted in the confident identification of 209 proteins in the human brain samples under investigation.     

Development and applications of in-gel CNBr/tryptic digestion combined with mass spectrometry for the analysis of membrane proteins

Hydrophobic membrane proteins often have complex functions and are thus of great interest. However, their analysis presents a challenge because they are not readily soluble in polar solvents and often undergo aggregation. We present a sequential CNBr and trypsin in-gel digestion method combined with mass spectrometry for membrane protein analysis. CNBr selectively cleaves methionine residues. But due to the low number of methionines in proteins, CNBr cleavage produces a small number of large peptide fragments with MWs typically >2000, which are difficult to extract from gel pieces. To produce a larger number of smaller peptides than that obtained by using CNBr alone, we demonstrate that trypsin can be used to further digest the sample in gel. The use of n-octyl glucoside (n-OG) to enhance the digestion efficiency and peptide recovery was also studied. We demonstrate that the sensitivity of this membrane protein identification method is in the tens of picomole regime, which is compatible to the Coomassie staining gel-spot visualization method, and is more sensitive than other techniques reported in the literature. This CNBr/trypsin in-gel digestion method is also found to be very reproducible and has been successfully applied for the analysis of complex protein mixtures extracted from biological samples. The results are presented from a study of the analysis of bacteriorhodopsin, nitrate reductase 1 gamma chain, and a complex protein mixture extracted from the endoplasmic recticulum membrane of mouse liver.     

A novel approach and protocol for discovering extremely low-abundance proteins in serum

The proteomic analysis of serum (plasma) has been a major approach to determining biomarkers essential for early disease diagnoses and drug discoveries. The determination of these biomarkers, however, is analytically challenging since the dynamic concentration range of serum proteins/peptides is extremely wide (more than 10 orders of magnitude). Thus, the reduction in sample complexity prior to proteomic analyses is essential, particularly in analyzing low-abundance protein biomarkers. Here, we demonstrate a novel approach to the proteomic analyses of human serum that uses an originally developed serum protein separation device and a sequentially linked 3-D-LC-MS/MS system. Our hollow-fiber-membrane-based serum pretreatment device can efficiently deplete high-molecular weight proteins and concentrate low-molecular weight proteins/peptides automatically within 1 h. Four independent analyses of healthy human sera pretreated using this unique device, followed by the 3-D-LC-MS/MS successfully produced 12 000-13 000 MS/MS spectra and hit around 1800 proteins (>95% reliability) and 2300 proteins (>80% reliability). We believe that the unique serum pretreatment device and proteomic analysis protocol reported here could be a powerful tool for searching physiological biomarkers by its high throughput (3.7 days per one sample analysis) and high performance of finding low abundant proteins from serum or plasma samples.     

Membrane protein analysis using an improved peptic in‐solution digestion protocol

In the proteomic analysis of membrane proteins, less‐specific proteases have become a promising tool to overcome fundamental limitations of trypsin with its unique specificity for basic residues. Pepsin is well‐known to be utilized for specific applications that require acidic conditions, but in terms of membrane protein identification and characterization, it has been disregarded for the most part. This work presents an optimization of an existing peptic digest protocol for the analysis of membrane proteins using bacteriorhodopsin from purple membranes as reference.     

Extraction and proteomic analysis of proteins from normal and multiple sclerosis postmortem brain

In this study, a reproducible fractionation procedure was developed to reduce levels of the abundant cytoskeletal proteins that are present in normal and pathological central nervous system (CNS) tissues. The fractionation and proteomic analysis techniques employed greatly facilitated comparison of the spectrum of proteins in normal postmortem brain with proteins in samples from patients with multiple sclerosis, an inflammatory demyelinating disease in which complex changes in protein expression occur as lesions develop. This approach may be of value for the proteomic identification and quantitation of proteins which undergo disease-related changes in CNS disorders, and also for protein expression studies on normal adult and developing CNS tissues.     

基于头发蛋白质组学区分个人特质的方法学探究

目的 头发是一类重要的皮肤附属物,主要由角蛋白和角蛋白相关蛋白等组成。不同种族及性别样本的头发蛋白质组成和占比存在差异,且目前缺乏高效率提取头发蛋白的方法。本文探究基于定量头发蛋白质组学方法,旨在探索该方法区分不同个体的可能性。方法 以3例头发样本,对样品处理方法和裂解缓冲液进行探究,发展一种名为PLEE (PTM lab for protein extraction from hair with high efficiency)的稳定、高效的头发蛋白质提取方法,对7例人发样本,以PLEE法进行提取,结合胶内消化方法进行蛋白质组学实验,产出蛋白质组学数据,分析个体间的头发蛋白质组成及占比。结果 共鉴定274种蛋白质,共有的蛋白质107种,非共有蛋白质种类在57～119,部分样本存在独特鉴定蛋白。使用共鉴定107种蛋白质进行定量蛋白质组分析,通过聚类和主成分分析,可将各样本进行区分,且技术重复样本可聚在一起,表明流程的稳定性。另外,筛选出10个关键蛋白（KRT33A、KRTAP9-6、KRT83、KRTAP7-1、KRT32、BLMH、KRT38、KRTAP11-1、NPAS1、KRTA...     

Proteomic parsimony through bipartite graph analysis improves accuracy and transparency

Assembling peptides identified from LC-MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, na?ve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide-protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/.