EuPA Open Proteomics最新文献

Laser capture microdissection (LCM) allows microscopic procurement of specific cell types from tissue sections. Here, we present an optimized workflow for coupling LCM to LC⿿MS/MS including: sectioning of tissue, a standard LCM workflow, protein digestion and advanced LC⿿MS/MS. Soluble proteins extracted from benign epithelial cells, their associated stroma, tumor epithelial cells and their associated stromal cells from a single patient tissue sample were digested and profiled using advanced LC⿿MS/MS. The correlation between technical replicates was R² = 0.99 with a mean % CV of 9.55% ± 8.73. The correlation between sample replicates was R² = 0.97 with a mean % CV of 13.83% ± 10.17. This represents a robust, systematic approach for profiling of the tumor microenvironment using LCM coupled to label-free LC⿿MS/MS.

The mitochondrial human proteome project (mt-HPP) was initiated by the Italian HPP group as a part of both the chromosome-centric initiative (C-HPP) and the ⿿biology and disease driven⿿ initiative (B/D-HPP). In recent years several reports highlighted how mitochondrial biology and disease are regulated by specific interactions with non-mitochondrial proteins. Thus, it is of great relevance to extend our present view of the mitochondrial proteome not only to those proteins that are encoded by or transported to mitochondria, but also to their interactors that take part in mitochondria functionality. Here, we propose a graphical representation of the functional mitochondrial proteome by retrieving mitochondrial proteins from the NeXtProt database and adding to the network their interactors as annotated in the IntAct database. Notably, the network may represent a reference to map all the proteins that are currently being identified in mitochondrial proteomics studies.

Malignant pleural mesothelioma (MPM) is a rare cancer originated from pleural mesothelial cells. MPM has been associated with long-term exposure to asbestos. In this work we performed a comparative proteomic analysis of biphasic pleural mesothelioma (B-PM).

Tissue biopsies were obtained from 61 patients who were subjected to a diagnostic thoracoscopy. 2D/MS based approach was used for proteomic analysis. The 22 proteins found differentially expressed in B-PM, with respect to benign, were analyzed by Ingenuity Pathways Analysis and compared with those obtained for epitheliod pleural mesothelioma (E-PM). A different activation of transcription factors, proteins and cytokines were observed between two subtypes.

The investigation on obesity and associated disorders have changed from an scenario in which genome drove the phenotype to a dynamic setup in which prenatal and early-postnatal conditions are determinant. However, research in human beings is difficult due to confounding factors (lifestyle and socioeconomic heterogeneity) plus ethical issues. Hence, there is currently an intensive effort for developing adequate preclinical models, aiming for an adequate combination of basic studies in rodent models and specific preclinical studies in large animals. The results of these research strategies may increase the identification and development of contrasted biomarkers and therapeutic targets.

The newly launched Affinity Binder Knockdown Initiative encourages antibody suppliers and users to join this public⿿private partnership, which uses crowdsourcing to collect characterization data on antibodies. Researchers are asked to share validation data from experiments where gene-editing techniques (such as siRNA or CRISPR) have been used to verify antibody binding. The initiative is launched under the aegis of Antibodypedia, a database designed to allow comparisons and scoring of publicly available antibodies towards human protein targets. What is known about an antibody is the foundation of the scoring and ranking system in Antibodypedia.

The analysis of mesenchymal stromal cells secretome is fundamental to identify key players of processes involving these cells. Truly secreted proteins may be difficult to detect in MS based analysis of conditioned media (CM) due to proteins supplemented with fetal bovine serum (FBS). We compared different growth conditions to determine the effect of varying FBS concentration on the number and quantity of truly secreted human proteins vs contaminating bovine proteins. The results suggest that to minimize interference cells should be grown in presence of FBS until confluence and transferred into a serum-free medium prior to secretome collection.

Large biobanks exist worldwide containing formalin-fixed, paraffin-embedded samples and samples stored in RNAlater. However, the impact of tissue preservation on the result of a quantative proteome analysis remains poorly described.

Human colon mucosal biopsies were extracted from the sigmoideum and either immediately frozen, stabilized in RNAlater, or stabilized by formalin-fixation. In one set of biopsies, formalin stabilization was delayed for 30 min. The protein content of the samples was characterized by high throughput quantitative proteomics.

We were able to identify a similar high number of proteins in the samples regardless of preservation method, with only minor differences in protein quantitation.

The advent of liquid chromatography mass spectrometry has seen a dramatic increase in the amount of data derived from proteomic biomarker discovery. These experiments have seemingly identified many potential candidate biomarkers. Frustratingly, very few of these candidates have been evaluated and validated sufficiently such that that they have progressed to the stage of routine clinical use. It is becoming apparent that the statistical methods used to evaluate the performance of new candidate biomarkers are a major limitation in their development. Bayesian methods offer some advantages over traditional statistical and machine learning methods. In particular they can incorporate external information into current experiments so as to guide biomarker selection. Further, they can be more robust to over-fitting than other approaches, especially when the number of samples used for discovery is relatively small.

In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.