Extracellular vesicles (EV) are involved in important processes of glioblastoma multiforme (GBM), including malignancy and invasion. EV secreted by glioblastoma cells may cross the hematoencephalic barrier and carry molecular cargo derived from the tumor into the peripheral circulation. Therefore, the determination of the molecular composition of exosomes released by glioblastoma cells seems to be a promising approach for the development of non-invasive methods of the detection of the specific exosomal protein markers in the peripheral blood. The present study aimed to determine the common exosomal proteins presented in preparations from different cell lines and search potential glioblastoma biomarkers in exosomes. We have performed proteomics analysis of exosomes obtained from the conditioned culture medium of five glioblastoma cell lines. A list of 133 proteins common for all these samples was generated. Based on the data obtained, virtual two-dimensional electrophoresis (2DE) maps of proteins presented in exosomes of glioblastoma cells were constructed and the gene ontology (GO) analysis of exosome proteins was performed. A correlation between overexpressed in glial cell proteins and their presence in exosomes have been found. Thus, the existence of many potential glioblastoma biomarkers in exosomes was confirmed.

Haptoglobin (Hp) is a blood plasma glycoprotein that plays a critical role in tissue protection and the prevention of oxidative damage. Haptoglobin is an acute-phase protein, its concentration in plasma changes in pathology, and the test for its concentration is part of normal clinical practice. Haptoglobin is a conservative protein and is the subject of research as a potential biomarker of many diseases, including malignant neoplasms. The Human Hp gene is polymorphic and controls the synthesis of three major phenotypes—homozygous Hp1-1 and Hp2-2, and heterozygous Hp2-1, determined by a combination of allelic variants that are inherited. Numerous studies indicate that the phenotype of haptoglobin can be used to judge the individual’s predisposition to various diseases. In addition, Hp undergoes various post-translational modifications (PTMs). Glioblastoma multiform (GBM) is the most malignant primary brain tumor. In our study, we have analyzed the state of Hp proteoforms in plasma and cells using 1D (SDS-PAGE) and 2D electrophoresis (2DE) with the following mass spectrometry (LC ES-MS/MS) or Western blotting. We found that the levels of α2- and β-chain proteoforms are up-regulated in the plasma of GBM patients. An unprocessed form of Hp2-2 (PreHp2-2, zonulin) with unusual biophysical parameters (pI/Mw) was also detected in the plasma of GBM patients and glioblastoma cells. Altogether, this data shows the possibility to use proteoforms of haptoglobin as a potential GBM-specific plasma biomarker.

The human proteome is composed of a diverse and heterogeneous range of gene products/proteoforms/protein species. Because of the growing amount of information about proteoforms generated by different methods, we need a convenient approach to make an inventory of the data. Here, we present a database of proteoforms that is based on information obtained by separation of proteoforms using 2DE followed by shotgun ESI–LC–MS/MS. The database's principles and structure are described. The database is called “2DE‐pattern” as it contains multiple isoform‐centric patterns of proteoforms separated according to 2DE principles. The database can be freely used at http://2de-pattern.pnpi.nrcki.ru.

The use of tumor markers aids in the early detection of cancer recurrence and prognosis. There is a hope that they might also be useful in screening tests for the early detection of cancer. Here, the question of finding ideal tumor markers, which should be sensitive, specific, and reliable, is an acute issue. Human plasma is one of the most popular samples as it is commonly collected in the clinic and provides noninvasive, rapid analysis for any type of disease including cancer. Many efforts have been applied in searching for “ideal” tumor markers, digging very deep into plasma proteomes. The situation in this area can be improved in two ways—by attempting to find an ideal single tumor marker or by generating panels of different markers. In both cases, proteomics certainly plays a major role. There is a line of evidence that the most abundant, so-called “classical plasma proteins”, may be used to generate a tumor biomarker profile. To be comprehensive these profiles should have information not only about protein levels but also proteoform distribution for each protein. Initially, the profile of these proteins in norm should be generated. In our work, we collected bibliographic information about the connection of cancers with levels of “classical plasma proteins”. Additionally, we presented the proteoform profiles (2DE patterns) of these proteins in norm generated by two-dimensional electrophoresis with mass spectrometry and immunodetection. As a next step, similar profiles representing protein perturbations in plasma produced in the case of different cancers will be generated. Additionally, based on this information, different test systems can be developed.

In the boundaries of the chromosome-centric Human Proteome Project (c-HPP) to obtain information about proteoforms coded by chromosome 18, several cell lines (HepG2, glioblastoma, LEH), normal liver, and plasma were analyzed. In our study, we have been using proteoform separation by two-dimensional electrophoresis (2DE) (a sectional analysis) and a semivirtual 2DE with following shotgun mass spectrometry using LC-ESI-MS/MS. Previously, we published a first draft of this research, where only HepG2 cells were tested. Here, we present the next step using more detailed analysis and more samples. Altogether, confident (2 significant sequences minimum) information about proteoforms of 117 isoforms coded by 104 genes of chromosome 18 was obtained. The 3D-graphs showing distribution of different proteoforms from the same gene in the 2D map were generated. Additionally, a semivirtual 2DE approach has allowed for detecting more proteoforms and estimating their pI more precisely. Data are available via ProteomeXchange with identifier PXD010142.