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Enhanced analysis of human breast cancer proteomes using micro-scale solution isoelectrofocusing combined with high resolution 1-D and 2-D gels.

Current methods for quantitatively comparing proteomes (protein profiling) have inadequate resolution and dynamic range for complex proteomes such as those from mammalian cells or tissues. More extensive profiling of complex proteomes would be obtained if the proteomes could be reproducibly divided into a moderate number of well-separated pools. But the utility of any prefractionation is dependent upon the resolution obtained because extensive cross contamination of many proteins among different pools would make quantitative comparisons impractical. The current study used a recently developed microscale solution isoelectrofocusing (musol-IEF) method to separate human breast cancer cell extracts into seven well-resolved pools. High resolution fractionation could be achieved in a series of small volume tandem chambers separated by thin acrylamide partitions containing covalently bound immobilines that establish discrete pH zones to separate proteins based upon their pIs. In contrast to analytical 2-D gels, this prefractionation method was capable of separating very large proteins (up to about 500 kDa) that could be subsequently profiled and quantitated using large-pore 1-D SDS gels. The pH 4.5-6.5 region was divided into four 0.5 pH unit ranges because this region had the greatest number of proteins. By using very narrow pH range fractions, sample amounts applied to narrow pH range 2-D gels could be increased to detect lower abundance proteins. Although 1.0 pH range 2-D gels were used in these experiments, further protein resolution should be feasible by using 2-D gels with pH ranges that are only slightly wider than the pH ranges of the musol-IEF fractions. By combining musol-IEF prefractionation with subsequent large pore 1-D SDS-PAGE (>100 kDa) and narrow range 2-D gels (<100 kDa), large proteins can be reliably quantitated, many more proteins can be resolved, and lower abundance proteins can be detected.

Electrophoresis, Polyacrylamide Gel↗

Application of proteomics in the study of tumor metastasis.

Tumor metastasis is the dominant cause of death in cancer patients. However, the molecular and cellular mechanisms underlying tumor metastasis are still elusive. The identification of protein molecules with their expressions correlated to the metastatic process would help to understand the metastatic mechanisms and thus facilitate the development of strategies for the therapeutic interventions and clinical management of cancer. Proteomics is a systematic research approach aiming to provide the global characterization of protein expression and function under given conditions. Proteomic technology has been widely used in biomarker discovery and pathogenetic studies including tumor metastasis. This article provides a brief review of the application of proteomics in identifying molecular factors in tumor metastasis process. The combination of proteomics with other experimental approaches in biochemistry, cell biology, molecular genetics and chemistry, together with the development of new technologies and improvements in existing methodologies will continue to extend its application in studying cancer metastasis.

Gene Expression Profiling↗

Identification of bacteria using tandem mass spectrometry combined with a proteome database and statistical scoring.

Detection and identification of pathogenic bacteria and their protein toxins play a crucial role in a proper response to natural or terrorist-caused outbreaks of infectious diseases. The recent availability of whole genome sequences of priority bacterial pathogens opens new diagnostic possibilities for identification of bacteria by retrieving their genomic or proteomic information. We describe a method for identification of bacteria based on tandem mass spectrometric (MS/MS) analysis of peptides derived from bacterial proteins. This method involves bacterial cell protein extraction, trypsin digestion, liquid chromatography MS/MS analysis of the resulting peptides, and a statistical scoring algorithm to rank MS/MS spectral matching results for bacterial identification. To facilitate spectral data searching, a proteome database was constructed by translating genomes of bacteria of interest with fully or partially determined sequences. In this work, a prototype database was constructed by the automated analysis of 87 publicly available, fully sequenced bacterial genomes with the GLIMMER gene finding software. MS/MS peptide spectral matching for peptide sequence assignment against this proteome database was done by SEQUEST. To gauge the relative significance of the SEQUEST-generated matching parameters for correct peptide assignment, discriminant function (DF) analysis of these parameters was applied and DF scores were used to calculate probabilities of correct MS/MS spectra assignment to peptide sequences in the database. The peptides with DF scores exceeding a threshold value determined by the probability of correct peptide assignment were accepted and matched to the bacterial proteomes represented in the database. Sequence filtering or removal of degenerate peptides matched with multiple bacteria was then performed to further improve identification. It is demonstrated that using a preset criterion with known distributions of discriminant function scores and probabilities of correct peptide sequence assignments, a test bacterium within the 87 database microorganisms can be unambiguously identified.

Algorithms↗

Automated 20 kpsi RPLC-MS and MS/MS with chromatographic peak capacities of 1000-1500 and capabilities in proteomics and metabolomics.

Proteomics analysis based-on reversed-phase liquid chromatography (RPLC) is widely practiced; however, variations providing cutting-edge RPLC performance have generally not been adopted even though their benefits are well established. Here, we describe an automated format 20 kpsi RPLC system for proteomics and metabolomics that includes on-line coupling of micro-solid phase extraction for sample loading and allows electrospray ionization emitters to be readily replaced. The system uses 50 microm i.d. x 40-200 cm fused-silica capillaries packed with 1.4-3-microm porous C18-bonded silica particles to obtain chromatographic peak capacities of 1000-1500 for complex peptide and metabolite mixtures. This separation quality provided high-confidence identifications of >12 000 different tryptic peptides from >2000 distinct Shewanella oneidensis proteins (approximately 40% of the proteins predicted for the S. oneidensis proteome) in a single 12-h ion trap tandem mass spectrometry (MS/MS) analysis. The protein identification reproducibility approached 90% between replicate experiments. The average protein MS/MS identification rate exceeded 10 proteins/min, and 1207 proteins were identified in 120 min through assignment of 5944 different peptides. The proteomic analysis dynamic range of the 20 kpsi RPLC-ion trap MS/MS was approximately 10(6) based on analyses of a human blood plasma sample, for which 835 distinct proteins were identified with high confidence in a single 12-h run. A single run of the 20 kpsi RPLC-accurate mass MS detected >5000 different compounds from a metabolomics sample.

Automation↗

Evaluation and Optimization of Different Digestion Strategies for In-Depth Proteomic Characterization of Residual Host Cell Proteins in rAAV-Based Gene Therapy Products.

Recombinant adeno-associated virus vectors (rAAVs) are the most important vectors for in vivo gene therapies, yet their safety relies on low levels of residual host cell proteins (HCPs). While mass spectrometry-based proteomics enables sensitive and untargeted HCP profiling, it faces challenges with matrix interferences from purification buffers and the high dynamic range between abundant viral capsids and trace HCPs. Although sample preparation methods that address these challenges are well-established for antibody products, their adaptation to rAAV purification stages and products remains largely unexplored. In this study, we systematically evaluated three widely used proteomic sample preparation workflows&#x2500;In-Solution, FASP, and SP3&#x2500;across different stages of rAAV purification. In addition, each workflow was tested under both standard denatured digestion conditions and a "native" digestion strategy designed to reduce dynamic range by preserving capsid integrity while selectively digesting HCPs. This comparison identified the native FASP protocol as the most effective sample preparation method for overcoming matrix interference and dynamic range challenges, consistently outperforming other workflows in HCP identification across purification stages. Further optimization of the native FASP workflow enhanced its performance, achieving the highest host cell (HC) proteome depth, particularly in highly purified drug substance samples. This optimized sample preparation strategy provides a robust and easy-to-use framework for deep characterization of the host cell proteome in rAAV samples. By enabling deeper insights into the HCP profile, this approach supports improved understanding of the rAAV purification process and facilitates the development of targeted strategies to enhance product quality and safety.

Dependovirus↗

Spectral-Proteomic Integration Analysis (SPIA) Deciphers Molecular Trajectories of Breast Cancer and Enables Multitarget Therapeutic Assessment.

Raman spectroscopy and mass spectrometry-based proteomics offer deeply complementary yet largely disconnected views of cancer biology: the former provides a label-free, real-time biochemical phenotype, while the latter delivers a quantitative inventory of specific protein effectors. Bridging this gap remains a fundamental challenge in analytical biomedicine. Here, we introduce Spectral-Proteomic Integration Analysis (SPIA)&#x2500;a novel, data-driven integrative framework that systematically links Raman spectroscopic phenotypes with quantitative proteomic profiles through machine learning and statistical correlation. Using a DMBA-induced rat breast cancer model with and without Toremifene (TOR) intervention, SPIA dynamically maps tumor microenvironment remodeling, capturing progressive collagen deposition and lipid metabolic reprogramming. An SVM classifier trained on Raman spectra achieves exceptional diagnostic accuracy (AUC &#x2265; 99.0%) and successfully predicts TOR therapeutic response. Proteomic analysis identifies 1,350 differentially expressed proteins, with convergent machine learning feature selection (LASSO, Random Forest, XGBoost) pinpointing core regulators including Luc7l2, Nucb1, Cbx3, and Csnk2a1. Crucially, Spearman correlation analysis between key Raman bands and core DEPs reveals strong, statistically robust associations (median &#x3c1; &#x223c; 0.75 in the 1533-1669 cm-1 region), empirically validating SPIA's core integrative logic. Leveraging this multimodal map, we elucidate a multitarget mechanism for TOR involving concurrent suppression of collagen deposition and correction of aberrant lipid metabolism. SPIA establishes a powerful, generalizable paradigm for integrating phenotypic and molecular data, with broad implications for biomarker discovery, drug mechanism elucidation, and precision oncology.

Animals↗

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.

Animals↗

On characterization of dose variations of 2-D proteomics maps by matrix invariants.

We explore the characterization of 2-D electrophoresis proteomics maps by certain structural invariants derived from matrixes constructed by considering for all pairs of spots in a proteomics maps the shortest (Euclidean) distances and distances measured along zigzag lines connecting protein spots of the neighboring abundance. This paper is a sequel to previous papers in which we outlined the idea of characterizing 2-D proteomics maps by graph-theoretical descriptors. To illustrate the approach, we selected data of Anderson et al. (Anderson, N. L.; Esquer-Blasco, R.; Richardson, F.; Foxworthy, P.; Eacho, P. The effects of peroxisome proliferators on protein abundances in mouse liver. Toxicol. Appl. Pharmacol. 1996, 137, 75-89) on protein abundance in mouse liver under a series of dose of peroxisome proliferator LY1711883. We found strong linear correlation between the experimentally applied doses and the leading eigenvalue of a D/D-type matrix (Randić, M.; Kleiner, A. F.; DeAlba, L. M. Distance/ distance matrices. J. Chem. lnf. Comput. Sci. 1994, 34, 277-286) constructed for the experimental proteomics maps.

Acetophenones↗

Prefractionation of proteome by liquid isoelectric focusing prior to two-dimensional liquid chromatography mass spectrometric identification.

Due to the complexity of proteomes, developing methods of sample fractionation, separation, concentration, and detection have become urgent to the identification of large numbers of proteins, as well as the acquisition of those proteins in low abundance. In this work, liquid isoelectric focusing (LIEF) combined with 2D-LC-MS/MS was applied to the proteome of Saccharomyces cerevisiae. This yielded a total of 1795 proteins that were detected and identified by 30 fractions of protein prefractionation. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis without protein fractionation. LIEF-2D-LC-MS/MS also produced improved resolution of low-abundance proteins. Furthermore, we analyzed the characteristics of proteins obtained by LIEF-2D-LC-MS/MS. 1103 proteins with CAI under 0.2 were identified, allowing us to specifically obtain detailed biochemical information on these kind proteins. It was observed that LIEF-2D-LC-MS/MS is useful for large-scale proteome analysis and may be specifically applied to systems with wide dynamic ranges.

Chromatography, Liquid↗

Proteome analysis of primary neurons and astrocytes from rat cerebellum.

Neurons and astrocytes are predominant cell types in brain and have distinguished morphological and functional features. Although several proteomics studies were carried out on the brain, work on individual brain cells is limited. Generating individual proteomes of neurons and astrocytes, however, is mandatory to assign protein expression to cell types rather than to tissues. We aimed to provide maps of rat primary neurons and astrocytes using two-dimensional gel electrophoresis with subsequent in-gel digestion, followed by MALDI-TOF/TOF. 428 protein spots corresponding to 226 individual proteins in neurons and 406 protein spots representing 228 proteins in astrocytes were unambiguously identified. Proteome data include proteins from several cascades differentially expressed in neurons and astrocytes, and specific expressional patterns of antioxidant, signaling, chaperone, cytoskeleton, nucleic acid binding, proteasomal, and metabolic proteins are demonstrated. We herein present a reference database of primary rat primary neuron and astrocyte proteomes and provide an analytical tool for these structures. The concomitant expressional patterns of several protein classes are given and potential neuronal and astrocytic marker candidates are presented.

Animals↗

Differential detergent fractionation for non-electrophoretic eukaryote cell proteomics.

Differential detergent fractionation (DDF), which relies on detergents to sequentially extract proteins from eukaryotic cells, has been used to increase proteome coverage of 2D-PAGE. Here, we used DDF extraction in conjunction with the nonelectrophoretic proteomics method of liquid chromatography and electrospray ionization tandem mass spectrometry. We demonstrate that DDF can be used with 2D-LC ESI MS2 for comprehensive cellular proteomics, including a large proportion of membrane proteins. Compared to some published methods designed to isolate membrane proteins specifically, DDF extraction yields comprehensive proteomes which include twice as many membrane proteins. Two-thirds of these membrane proteins have more than one trans-membrane domain. Since DDF separates proteins based upon their physicochemistry and subcellular localization, this method also provides data useful for functional genome annotation. As more genome sequences are completed, methods which can aid in functional annotation will become increasingly important.

Animals↗

Analysis of the dorsal spinal cord synaptic architecture by combined proteome analysis and in situ hybridization.

The proteomic analysis of tissue samples is an analytical challenge, because identified gene products not only have to be assigned to subcellular structures, but also to cell subpopulations. We here report a strategy of combined subcellular proteomic profiling and in situ hybridization to assign proteins to subcellular sites in subsets of cells within the dorsal region of rat spinal cord. With a focus on synaptic membranes, which represent a complex membrane protein structure composed of multiple integral membrane proteins and networks of accessory structural proteins, we also compared different two-dimensional gel electrophoresis systems for the separation of the proteins. Using MALDI mass spectrometric protein identification based on peptide mass fingerprints, we identified in total 122 different gene products within the different synaptic membrane subfractions. The tissue structure of the dorsal region of the spinal cord is complex, and different layers of neurons can be distinguished neuroanatomically. Proteomic data combined with an in situ hybridization analysis for the detection of mRNA was used to assign selected gene products, namely the optical atrophy protein OPA-1, the presynaptic cytomatrix protein KIAA0378/CAST1, and the uncharacterized coiled-coil-helix-coiled-coil-helix domain containing protein 3 (hypothetical protein FLJ20420), to cell subsets of the dorsal area of the spinal cord. Most striking, KIAA0378/CAST1 mRNA was found only sparsely within the dorsal horn of the spinal cord, but highly abundant within the dorsal root ganglion. This finding, combined with the identification of KIAA0378/CAST1 within the synaptic membrane fraction of the spinal cord at the protein level, are consistent with the reported presynaptic localization of CAST, predominantly within the tissue we investigated primarily attributable to primary afferent sensory neurons. Our approach may be of use in broader studies to characterize the proteomes of neural tissue.

Animals↗

Proteome analysis of tobacco bright yellow-2 (BY-2) cell culture plastids as a model for undifferentiated heterotrophic plastids.

We analyzed the proteome of undifferentiated plastids from a tobacco BY-2 cell culture by shotgun proteomics following multidimensional protein fractionation. The fractionation strategy initiated with the serial extraction of proteins from membranes which allowed us to distinguish soluble, peripheral, and integral membrane proteins. The majority of the identified proteins have a function in the cellular metabolism and most of them are active in amino acid synthesis pathways. A significant number of the identified proteins was not identified in chloroplast proteome analyses before. This suggests BY-2 plastid specific functions that differ from the major activities of chloroplasts. We have used the BY-2 plastid proteins reported here to assess the metabolic activities of undifferentiated heterotrophic plastids and compared the functional profile with that of differentiated heterotrophic amyloplasts. Comparative shotgun proteome analyses as reported here provide information about prevalent metabolic activities of different plastid types.

Cell Differentiation↗

Novel prefractionation method can be used in proteomic analysis.

For the first time, a novel prefractionation method used in proteomic analysis was developed, which is performed by a novel aqueous two-phase system (NATPS) composed of n-butanol, (NH(4))(2)SO(4), and water. It can separate proteomic proteins into multigroups by one-step extraction. The phase-separation conditions of n-butanol solutions were studied in the presence of commonly used inorganic salts. The NATPS was subsequently developed. Using human serum albumin, zein, and gamma-globulin as model proteins, the separation effectiveness of the NATPS for protein was studied under affection factors, i.e., pH, n-butanol volume, protein, or salt concentration. The model and actual protein samples were separated by the NATPS and then directly used for gel electrophoresis without separating the target proteins from phase-forming reagents. It revealed that the NATPS could separate proteomic proteins into multigroups by one-step extraction. The NATPS has the advantages of rapidity, simplicity, low cost, biocompability, and high efficiency. It need not separate target proteins from the phase-forming reagents. The NATPS has great significance in separation and extraction of proteomic proteins, as well as in methodology.

1-Butanol↗

InSilicoSpectro: an open-source proteomics library.

We present a new proteomics open-source project, InSilicoSpectro, aimed at implementing recurrent computations that are necessary for proteomics data analysis. Illustrative examples are mass list file format conversions, protein sequence digestion, theoretical peptide and fragment mass computations, graphical display, matching with experimental data, isoelectric point estimation, and peptide retention time prediction. The project library is written in Perl, a widely used scripting language in bioinformatics, and it offers a unique framework of integrated objects to implement complex proteomics data analyses. For instance, only a few lines of code are required to digest a protein with fixed and variable modifications, label peptides with 18O, compute the fragmentation spectra and display their match with experimental spectra. We believe that InSilicoSpectro will be of great help to bioinformaticians, without detailed knowledge of proteomics specifics, and to mass spectrometrists with computer programming interest as well.

Amino Acid Sequence↗

The nuclear proteome of chickpea (Cicer arietinum L.) reveals predicted and unexpected proteins.

Nuclear proteins constitute a highly organized, complex network that plays diverse roles during cellular development and other physiological processes. The yeast nuclear proteome corresponds to about one-fourth of the total cellular proteins, suggesting the involvement of the nucleus in a number of diverse functions. In an attempt to understand the complexity of plant nuclear proteins, we have developed a proteome reference map of a legume, chickpea, using two-dimensional gel electrophoresis (2-DE). Approximately, 600 protein spots were detected, and LC-ESI-MS/MS analyses led to the identification of 150 proteins that have been implicated in a variety of cellular functions. The largest percentage of the identified proteins was involved in signaling and gene regulation (36%), while 17% were involved in DNA replication and transcription. The chickpea nuclear proteome indicates the presence of few new nuclear proteins of unknown functions vis-à-vis many known resident proteins. To the best of our knowledge, this is the first report of a nuclear proteome of an unsequenced genome.

Chromatography, Liquid↗

Two-dimensional microcolumn separation platform for proteomics consisting of on-line coupled capillary isoelectric focusing and capillary electrochromatography. 1. Evaluation of the capillary-based two-dimensional platform with proteins, peptides, and human serum.

In this report, an on-line coupling of capillary isoelectric focusing (CIEF) to capillary electrochromatography (CEC) is developed via a nanoinjector valve for performing two-dimensional (2D) proteomics separation. CIEF constitutes the first separation dimension, while CEC operates as the second separation dimension. Besides the orthogonal migration mechanisms of the two capillary-based separation modes, which lead to a 2D system whose overall peak capacity is the product of the peak capacity of the individual modes, the solvent of the CIEF mode is a weak eluent for the reversed-phase CEC (RP-CEC) mode, thus, allowing the transferring of focused fractions from CIEF to CEC without inducing band broadening, and instead zone sharpening would result. In fact, the transferred focused protein fraction from the CIEF column to the CEC column will stay tightly adsorbed to the inlet top of the CEC column until it will be eluted and separated into its protein components with a hydro-organic mobile phase. The theoretical peak capacity of the CIEF-CEC 2D platform is estimated at n(CIEF) (= 560) x n(CEC) (= 97) = 54 320. This peak capacity is more than needed for proteomics profiling. Also, only a fraction of this peak capacity is needed when looking at heart cuts for performing subproteomics. The 2D platform described here offers the convenience to generate the needed peak capacity to solve a given proteomic separation problem. This is facilitated by the RP-CEC dimension, which ensures rapid isocratic separation of proteins and peptides and rapid solvent change and column equilibration and avoids lengthy gradient elution. The RP-CEC column is based on neutral C17 monolith, which offers high separation efficiency and relatively high column permeability. To the best of our knowledge, the proposed 2D platform combining CIEF and CEC is reported for the first time for proteins and proteomics.

Amino Acid Sequence↗

Investigating the dynamic nature of the interactions between nuclear proteins and histones upon DNA damage using an immobilized peptide chemical proteomics approach.

As a result of the complexity and dynamic range of the cellular proteome, including mutual interactions and interactions with other molecules, focused proteomic approaches are important to study subsets of physiologically important proteins. In one such approach, a small molecule or part of a protein is immobilized on a solid phase and used as bait to fish out interacting proteins from complex mixtures such as cellular lysates. Here, such a chemical proteomics experiment is presented to explore the range of proteins that interact with the N-terminal tail of core histones. Therefore, a core histone consensus N-terminal tail (NTT) peptide was synthesized and immobilized on agarose. Interactions between histone NTTs and proteins are extremely important as they regulate chromatin structure, which is important in many DNA-related processes, like transcription and DNA repair. Induction of DNA damage, like DNA double strand breaks, is known to trigger chromatin remodeling events through interactions between histone NTTs and so-called histone chaperones. Therefore, we set out to investigate specific changes in interactions of nuclear proteins before and shortly after DNA double strand break induction. Over 700 proteins were found to bind specifically to the NTT peptide, which makes our study the most comprehensive proteomic survey of the broad spectrum of nuclear proteins interacting with the NTT of core histones in nucleosomes. Apart from a few exceptions, the abundance of the majority of NTT binding proteins was found to be unchanged following DNA damage. However, an in-depth analysis of protein phosphorylation (we detected more than 90 unique sites in about 60 proteins) revealed that the phosphorylation status of several proteins involved in chromatin remodeling changes upon DNA damage. We observed that in these differentially phosphorylated chaperones are part of closely interacting protein complexes involved in regulatory mechanisms at the crossroads of nucleosome assembly, DNA replication, transcription, and the early onset of DNA damage repair.

Amino Acid Sequence↗