On graphical and numerical characterization of proteomics maps

We outlined a mathematical approach suitable for characterization of experimental data given by 2-D densitograms. In particular we consider numerical characterization of proteomics maps. The basis of our approach is to order "spots" of a 2-D map and assign them unique labels (that in general will depend on the criteria used for ordering). In this way a map is "translated" into a sequence. In the next step one associates with the generated sequence a geometrical path and views such a path as a mathematical object that needs characterization. We have ordered spots representing proteins in 2-D gel plates according to their relative intensities which results in a zigzag path that produces a complicated "fingerprint" pattern. Mathematical characterization of zigzag pattern follows similar mathematical characterizations of embedded patterns based on matrices, the elements of which are given as quotients of Euclidean distance between spots and the distance along the zigzag path. The leading eigenvalue of constructed matrices is taken to represent characterization of the original 2-D map. Comparison of different 2-D maps (simulated by using random generator) allows one to construct partial order, which although qualitative in nature gives some insight into perturbation induced by foreign agents to the proteome of the control cell.     

Novel matrix invariants for characterization of changes of proteomics maps

Previous studies on mathematical characterization of proteomics maps by sets of map invariants were based on the construction of a set of distance-related matrices obtained by matrix multiplication of a single matrix by itself. Here we consider an alternative characterization of proteomics maps based on a set of matrices characterizing local features of an embedded zigzag curve over the map. It is shown that novel invariants can well characterize proteomics maps. Advantages of the novel approach are discussed.     

On the dependence of a characterization of proteomics maps on the number of protein spots considered

We have reexamined the numerical characterization of proteomics maps based on the construction of novel distance matrices associated with the nearest neighbor graph for the protein spots. In particular we consider dependence of a characterization of proteomics map on the number of proteins considered in the analysis. We examined a collection of proteomics maps in which we approximately doubled the number of spots to be used for quantitative analysis, considering cases of maps having 30, 50, 100, 250, 500, and 1054 protein spots. For each case we have compared the similarity-dissimilarity results for five proteomics maps of rat liver cells associated with the control group and four proliferators administrated by intraperitoneal injection. We found that proteins maps based on a set of about the 250 most abundant proteins spots suffice for a satisfactory numerical characterization of such maps.     

Novel matrix invariants for characterization of changes of proteomics maps

Previous studies on mathematical characterization of proteomics maps by sets of map invariants were based on the construction of a set of distance-related matrices obtained by matrix multiplication of a single matrix by itself. Here we consider an alternative characterization of proteomics maps based on a set of matrices characterizing local features of an embedded zigzag curve over the map. It is shown that novel invariants can well characterize proteomics maps. Advantages of the novel approach are discussed.     

Novel characterization of proteomics maps by sequential neighborhoods of protein spots

We consider a characterization of proteomics maps based on an alternative kind of neighborhood graphs for the protein spots on 2-D gel. The novel approach considers for every protein spot only the nearest neighborhood consisting of protein spots of higher abundance. The approach has the simplicity and advantages of the recently introduced characterization of proteome maps based on considering the nearest neighborhoods of protein spots, but it also has important additional desirable computational features. The characterization of the nearest neighborhood graphs of 2-D gel proteomics maps is sensitive to the number of spots considered and may lead to changes in the degree of similarity of different maps when the number of points has been changed, thus imposing restrictions on the protocol used for comparison of maps. The novel approach presented in this work is less sensitive to the number of points used in the analysis because graphs are constructed in a stepwise process in which the role of more distant neighbors has been diminished by linking a new spot to the nearest spot that has been already part of the neighborhood graph. In this way a graph with N + 1 spots is obtained from the graph on N spots by adding a single new link, while in the case of the nearest neighborhood graphs adding a new spot introduces novel neighborhoods and generally results in a graph that may differ significantly from the neighborhood graph on N points.     

On 3-D graphical representation of proteomics maps and their numerical characterization

We consider numerical characterization of proteomics maps by representing a map as a three-dimensional graphical object based on x, y coordinates of the spots and using their relative abundance as the z coordinate. In our representation the protein spots are first ordered based on their relative abundance and labeled accordingly. In the next step a 3-D path is constructed connecting spots having adjacent labels. Finally a matrix is constructed by assigning to each pairs of labels (i, j) matrix element, the numerical value of which is based on the quotients of the Euclidean distance and the distance along the 3-D zigzag between the two points. The approach has been illustrated on a fragment of a proteomics map and compared with 2-D graphical representation of proteomics maps.     

Mass spectrometry of the human pituitary proteome: identification of selected proteins

The field of proteomics involves the combined application of advanced separation techniques, mass spectrometry, and bioinformatics tools to characterize proteins in complex biological mixtures. Here we report the identification of nine proteins from the human pituitary proteome, using the proteomics approach. The pituitary proteins were separated by two-dimensional electrophoresis, and were visualized by silver staining. The proteins of interest were subjected to in-gel digestion with trypsin, and the masses of the resulting peptides were determined by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. This tryptic mass map was used to identify the proteins through a search of a protein-sequence database. The identified proteins include important hormones, and enzymes with various catalytic activities. These proteins will be used to construct a two-dimensional reference database of the human pituitary. This database will be employed to study changes in the pituitary proteome that are associated with the formation of pituitary tumors.     

Two-dimensional electrophoresis map of the human hepatocellular carcinoma cell line, HCC-M, and identification of the separated proteins by mass spectrometry

Currently, one of the most popular applications of proteomics is in the area of cancer research. In Africa, Southeast Asia, and China, hepatocellular carcinoma is one of the most common cancers, occurring as one of the top five cancers in frequency. This project was initiated with the purpose of separating and identifying the proteins of a human hepatocellular carcinoma cell line, HCC-M. After two-dimensional gel electrophoresis separation, silver staining, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analyses, tryptic peptide masses were searched for matches in the SWISS-PROT and NCBI nonredundant databases. Approximately 400 spots were analyzed using this approach. Among the proteins identified were housekeeping proteins such as alcohol dehydrogenase, alpha-enolase, asparagine synthetase, isocitrate dehydrogenase, and glucose-6-phosphate 1-dehydrogenase. In addition, we also identified proteins with expression patterns that have been postulated to be related to the process of carcinogenesis. These include 14-3-3 protein, annexin, prohibitin, and thioredoxin peroxidase. This study of the HCC-M proteome, coupled with similar proteome analyses of normal liver tissues, tumors, and other hepatocellular carcinoma cell lines, represents the first step towards the establishment of protein databases, which are valuable resources in studies on the differential protein expressions of human hepatocellular carcinoma.     

Towards a two-dimensional proteome map of Mycoplasma pneumoniae

A Proteome map of the bacterium Mycoplasma pneumoniae was constructed using two-dimensional (2-D) gel electrophoresis in combination with mass spectrometry (MS). M. pneumoniae is a human pathogen with a known genome sequence of 816 kbp coding for only 688 open reading frames, and is therefore an ideal model system to explore the scope and limits of the current technology. The soluble protein content of this bacterium grown under standard laboratory conditions was separated by 1-D or 2-D gel electrophoresis applying various pH gradients, different acrylamide concentrations and buffer systems. Proteins were identified using liquid chromatography-electrospray ionization ion trap and matrix-assisted laser desorption/ionization-MS. Mass spectrometric protein identification was supported and controlled using N-terminal sequencing and immunological methods. So far, proteins from about 350 spots were characterized with MS by determining the molecular weights and partial sequences of their tryptic peptides. Comparing these experimental data with the DNA sequence-derived predictions it was possible to assign these 350 proteins to 224 genes. The importance of proteomics for genome analysis was shown by the identification of four proteins, not annotated in the original publication. Although the proteome map is still incomplete, it is already a useful reference for comparative analyses of M. pneumoniae cells grown under modified conditions.     

Proteome analysis of human stomach tissue: separation of soluble proteins by two-dimensional polyacrylamide gel electrophoresis and identification by mass spectrometry

Two-dimensional gel electrophoresis (2-DE) maps for human stomach tissue proteins have been prepared by displaying the protein components of the tissue by 2-DE and identifying them using mass spectrometry. This will enable us to present an overview of the proteins expressed in human stomach tissues and lays the basis for subsequent comparative proteome analysis studies with gastric diseases such as gastric cancer. In this study, 2-DE maps of soluble fraction proteins were prepared on two gel images with partially overlapping pH ranges of 4-7 and 6-9. On the gels covering pH 4-7 and pH 6-9, about 900 and 600 protein spots were detected by silver staining, respectively. For protein identification, proteins spots on micropreparative gels stained with colloidal Coomassie Brilliant Blue G-250 were excised, digested in-gel with trypsin, and analyzed by peptide mass fingerprinting with delayed extraction-matrix assisted laser desorption/ionization-mass spectrometry (DE-MALDI-MS). In all, 243 protein spots (168 spots in acidic map and 75 spots in basic map) corresponding to 136 different proteins were identified. Besides these principal maps, overview maps (displayed on pH 3-10 gels) for total homogenate and soluble fraction, are also presented with some identifications mapped on them. Based on the 2-DE maps presented in this study, a 2-DE database for human stomach tissue proteome has been constructed and is available at http://proteome.gsnu.ac.kr/DB/2DPAGE/Stomach/. The 2-DE maps and the database resulting from this study will serve important resources for subsequent proteomic studies for analyzing the normal protein variability in healthy tissues and specific protein variations in diseased tissues.     

A new approach for devising local graph invariants: Derived topological indices with low degeneracy and good correlation ability

A new approach is presented for obtaining graph invariants which have very high discriminating ability for different vertices within a graph. These invariants are obtained as the solution set (local invariant set, LOIS) of a system of linear equationsQ · X = R, whereQ is a topological matrix derived from the adjacency matrix of the graph, andR is a column vector which encodes either a topological property (vertex degree, number of vertices in the graph, distance sum) or a chemical property (atomic number). Twenty examples of LOOIs are given and their degeneracy and ordering ability of vertices is discussed. Interestingly, in some cases the ordering of vertices obtained by means of these invariants parallels closely the ordering from an entirely different procedure based on Hierarchically Ordered Extended Connectivities which was recently reported. New topological indices are easily constructed from LOISs. Excellent correlations are obtained for the boiling points and vaporization enthalpies of alkanesversus the topological index representing the sum of local vertex invariants. Les spectacular correlations with NMR chemical shifts, liquid phase density, partial molal volumes, motor octane numbers of alkanes or cavity surface areas of alcohols emphasize, however, the potential of this approach, which remains to be developed in the near future.     

An insight into the abundant proteome of 46BR.1G1 fibroblasts deficient of DNA ligase I

This work presents the proteome profile of cultured human skin fibroblasts established from a patient affected by DNA ligase I (Lig I) deficiency syndrome, a rare disorder characterized by immunodeficiency, growth retardation and sun sensitivity. 2-DE (in the 3-10 and 4-7 pH ranges) was the separation technique used for the production of maps. MALDI-TOF/MS and LC-MS/MS were the mass spectrometry platforms applied for the identification of proteins in gel spots. A total of 154 proteins, including 41 never detected before in skin fibroblasts with this approach, were identified in gel spots analyzed. This newly generated extensive database provides for the first time a global picture of abundant proteins in 46BR.1G1 skin fibroblasts. While being relevant to the particular disorder considered, these results may be regarded as an intriguing starting point on the way to achieve a reference map of the proteins highly expressed in an inherited syndrome with defect in DNA replication and repair pathways.     

How many proteins can be identified in a 2DE gel spot within an analysis of a complex human cancer tissue proteome?

Two‐dimensional gel electrophoresis (2DE) in proteomics is traditionally assumed to contain only one or two proteins in each 2DE spot. However, 2DE resolution is being complemented by the rapid development of high sensitivity mass spectrometers. Here we compared MALDI‐MS, LC‐Q‐TOF MS and LC‐Orbitrap Velos MS for the identification of proteins within one spot. With LC‐Orbitrap Velos MS each Coomassie Blue‐stained 2DE spot contained an average of at least 42 and 63 proteins/spot in an analysis of a human glioblastoma proteome and a human pituitary adenoma proteome, respectively, if a single gel spot was analyzed. If a pool of three matched gel spots was analyzed this number further increased up to an average of 230 and 118 proteins/spot for glioblastoma and pituitary adenoma proteome, respectively. Multiple proteins per spot confirm the necessity of isotopic labeling in large‐scale quantification of different protein species in a proteome. Furthermore, a protein abundance analysis revealed that most of the identified proteins in each analyzed 2DE spot were low‐abundance proteins. Many proteins were present in several of the analyzed spots showing the ability of 2DE‐MS to separate at the protein species level. Therefore, 2DE coupled with high‐sensitivity LC‐MS has a clearly higher sensitivity as expected until now to detect, identify and quantify low abundance proteins in a complex human proteome with an estimated resolution of about 500 000 protein species. This clearly exceeds the resolution power of bottom‐up LC‐MS investigations.     

Protein identification with Teflon as matrix-assisted laser desorption/ionization sample support

Protein identification is a critical step in proteomics, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) plays an important role in that identification. Polytetrafluoroethylene (Teflon) was tested as a new MALDI sample support to improve protein identification. The tryptic peptides obtained from a model protein were bound to the surface of a modified MALDI sample holder via the hydrophobic interactions that occur between the Teflon surface and the peptide ion-pairs, and the affinity of alpha-cyano-4-hydroxycinnamic acid for the peptides. During that surface-binding step, the peptide mixture was also desalted and concentrated. A greater number of matched peptides and a larger sequence coverage were obtained for the proteins when Teflon was used as the sample support compared with conventional sample preparation methods and a stainless-steel surface. In addition, the characterization of a small amount of protein was improved with Teflon. Nine silver-stained protein spots obtained from 2-D gel of a human cerebrospinal fluid (CSF) proteome were identified by this method. Among the nine protein spots, peptide 6:c3c fragment and procollagen c-proteinase enhancer were not annotated in any published 2-D map of human CSF. A Teflon MALDI sample support is a low-cost, simple, and effective method that can be used to improve the quality of the MALDI mass spectrum of a complex tryptic peptide mixture, and to achieve a higher level of reliability and success in protein identification.     

Proteomic Evaluation of Biomarkers to Determine the Postmortem Interval

Proteomics separates and analyzes proteins for investigation at the cellular level in regard to disease processing by analyzing the proteins’ expression, function, structure, post-translational modification, and protein–protein interaction. In general forensic investigations, the postmortem interval was evaluated by measuring changes in body temperature after death, along with forensic entomological knowledge. These investigations may be restrictive and subjective because of external factors. The objectives of this study are to sort biomarker candidates and develop a direct postmortem-interval characterization method using proteomics through analyzing and tracking down proteins in the deceased that change in accordance with the postmortem interval. The liver and heart tissues of rats were collected for protein extraction in the 24-h interval following death, and two-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis was conducted based on the isoelectric point and the molecular weight for separation. To validate protein spot changes on the gel, the stained electrophoresis gels were scanned and converted to digital images. Through image analysis, 14 liver proteins and 12 heart proteins were sorted and classified into four groups based on pattern changes. These proteins containing spots were extracted from the gel and analyzed by high-performance liquid chromatography with tandem mass spectrometry. Finally, 26 protein postmortem-interval relevant biomarker candidates were identified using software. Some of the proteins were muscle proteins while others were oxidation-related proteins. This study presents a new approach to the postmortem-interval research using proteomics and could be substituted for postmortem-interval evaluation.     

Protein profile of the HeLa cell line

HeLa cells are widely used for all kinds of in vitro studies in biochemistry, biology and medicine. Knowledge on protein expression is limited and no comprehensive study on the proteome of this cell type has been reported so far. We applied proteomics technologies to analyze the proteins of the HeLa cell line. The proteins were analyzed by two-dimensional (2D) gel electrophoresis and identified by matrix-assisted laser desorption ionization mass spectrometry (MS) on the basis of peptide mass fingerprinting, following in-gel digestion with trypsin. Approximately 3000 spots, excised from six two-dimensional gels, were analyzed. The analysis resulted in the identification of about 1200 proteins that were the products of 297 different genes. The HeLa cell database includes proteins with important functions and unknown functions, representing today one of the largest two-dimensional databases for eukaryotic proteomes and forming the basis for future expressional studies at the protein level.     

Two-dimensional gel protein database of Saccharomyces cerevisiae (update 1999).

By proving the opportunity to visualize several hundred proteins at a time, two-dimensional (2-D) gel electrophoresis is an important tool for proteome research. In order to take advantage of the full potential of this technique for yeast studies, we have undertaken a systematic identification of yeast proteins resolved by this technique. We report here the identification of 92 novel protein spots on the yeast 2-D protein map. These identifications extend the number of protein spots identified on our yeast reference map to 401. These spots correspond to the products of 279 different genes. They have been essentially identified by three methods: gene overexpression, amino acid composition and mass spectrometry. These data can be accessed on the Yeast Protein Map server (htpp://www.ibgc.u-bordeaux2.fr/YPM).     

Application of an improved proteomics method, fluorogenic derivatization-liquid chromatography-tandem mass spectrometry, to differential analysis of proteins in small regions of mouse brain

To identify age-related proteins in small regions of mouse brain, we improved a proteomics approach, fluorogenic derivatization-liquid chromatography-tandem mass spectrometry (FD-LC-MS/MS), and applied the method to the differential proteome analysis of aging in cerebral cortex, hippocampus and brainstem. The method showed good accuracy with RSDs <10% for between-day protein peak heights, and much better sensitivity for the detection of proteins compared to other proteomics approaches. The existence of 28 regionally specific age-related proteins in mouse brain was demonstrated. These results verified that the small brain regions could be the targets for proteome analysis by the FD-LC-MS/MS method.     

Proteome-on-a-chip: mirage, or on the horizon?

Proteomics has emerged as the next great scientific challenge in the post-genome era. But even the most basic form of proteomics, proteome profiling, i.e., identifying all of the proteins expressed in a given sample, has proven to be a demanding task. The proteome presents unique analytical challenges, including significant molecular diversity, an extremely wide concentration range, and a tendency to adsorb to solid surfaces. Microfluidics has been touted as being a useful tool for developing new methods to solve complex analytical challenges, and, as such, seems a natural fit for application to proteome profiling. In this review, we summarize the recent progress in the field of microfluidics in four key areas related to this application: chemical processing, sample preconcentration and cleanup, chemical separations, and interfaces with mass spectrometry. We identify the bright spots and challenges for the marriage of microfluidics and proteomics, and speculate on the outlook for progress.