Qualitative and Quantitative Proteomic Profiling of Cripto−/− Embryonic Stem Cells by Means of Accurate Mass LC−MS Analysis

Whey milk proteomics from Schistosoma mansoni-infected mice reveals proteins involved in immunomodulation of the offspring

Milk from schistosomotic mothers can modulate the immune response of their offspring. However, its characterization and potential of modulating immunity has not yet been fully elucidated. Thus, the aim of this study was to evaluate whey proteins from the milk of Schistosoma mansoni-infected mice in order to identify the fractions which can act as potential immunomodulatory tools. For this, we did a mass spectrometry (nanoUPLC-MSE) analysis to characterize the proteomic profile of milk from infected (MIM) and non-infected mice (MNIM). It was possible to identify 29 differentially expressed proteins: 15 were only found in MIM, 10 only found in MNIM, and 4 were downregulated in MIM group. Gene Ontology (GO), pathway enrichment analysis, and protein-protein interaction (PPI) analyses indicated differentially expressed proteins linked to biological processes and pathways in MIM group such as the following: fructose 1,6-biphosphate metabolic and glycolytic processes, glucose metabolism, and neutrophil degranulation pathways. The downregulated and unique proteins identified in MNIM group were involved in the positive regulation of B cell activation and receptor signaling pathway, in the innate immune response, complement activation, and phagocytosis. The present findings revealed a protein profile that may be involved in the activation and deactivation of the offspring's immune system in the long term, conferring a protective character due to the previous contact with milk from infected mothers.

Zeb2 Regulates Cell Fate at the Exit from Epiblast State in Mouse Embryonic Stem Cells

In human embryonic stem cells (ESCs) the transcription factor Zeb2 regulates neuroectoderm versus mesendoderm formation, but it is unclear how Zeb2 affects the global transcriptional regulatory network in these cell‐fate decisions. We generated Zeb2 knockout (KO) mouse ESCs, subjected them as embryoid bodies (EBs) to neural and general differentiation and carried out temporal RNA‐sequencing (RNA‐seq) and reduced representation bisulfite sequencing (RRBS) analysis in neural differentiation. This shows that Zeb2 acts preferentially as a transcriptional repressor associated with developmental progression and that Zeb2 KO ESCs can exit from their naïve state. However, most cells in these EBs stall in an early epiblast‐like state and are impaired in both neural and mesendodermal differentiation. Genes involved in pluripotency, epithelial‐to‐mesenchymal transition (EMT), and DNA‐(de)methylation, including Tet1, are deregulated in the absence of Zeb2. The observed elevated Tet1 levels in the mutant cells and the knowledge of previously mapped Tet1‐binding sites correlate with loss‐of‐methylation in neural‐stimulating conditions, however, after the cells initially acquired the correct DNA‐methyl marks. Interestingly, cells from such Zeb2 KO EBs maintain the ability to re‐adapt to 2i + LIF conditions even after prolonged differentiation, while knockdown of Tet1 partially rescues their impaired differentiation. Hence, in addition to its role in EMT, Zeb2 is critical in ESCs for exit from the epiblast state, and links the pluripotency network and DNA‐methylation with irreversible commitment to differentiation. Stem Cells2017;35:611–625

To the novel paradigm of proteome-based cell therapy of tumors: through comparative proteome mapping of tumor stem cells and tissue-specific stem cells of humans

We performed proteome mapping (PM), cataloging, and bioinformation analysis of protein lysates of human neural (CD133(+)) progenitor and stem cells (NPSCs) isolated from the olfactory sheath of a nose, multipotent mesenchymal (CD29(+), CD44(+), CD73(+), CD90(+), CD34(-)) stromal cells (MMSCs) isolated from human bone marrow, and tumor (CD133(+)) stem cells (TSCs) isolated from the human U87 glioblastoma (GB) cell line. We identified 1,664 proteins in the examined lysates of stem cells (SCs), 1,052 (63.2%) of which are identical in NPSCs and TSCs and 607 proteins (36.47%) of which are identical in MMSCs and TSCs. Other proteins in U87 GB TSCs are oncospecific or carcinogenesis associated. The biological processes, molecular functions, cell localization, and protein signal pathways of the proteins available in all three proteomes were annotated by PubMed (http://www.ncbi.nlm.nih.gov/pubmed/), PANTHER (http://www.pantherdb.org/), GeneOntology (http://www.geneontology.org/), and KEGG (http://www.genome.jp/kegg/) databases. It was shown that gliomaspheres of U87 GB had only 10 intracellular signal transduction pathways (ISTP) that were not modified by the neoplastic process, but only two of them (integrin and focal adhesion pathways) were accessible for regulatory action on gene candidates in the TSC nucleus. Carcinogenesis-free membrane proteins, IPST, and genes expressing proteins of these pathways in U87 GB TSCs can be viewed as main targets for regulatory effects on TSCs. We offer a novel concept of proteome-based complex therapy of tumors. This manuscript is published as part of the International Association of Neurorestoratology (IANR) special issue of Cell Transplantation.

Application of label-free shotgun nUPLC-MS(E) and 2-DE approaches in the study of Botrytis cinerea mycelium

The phytopathogenic fungus Botrytis cinerea infects more than different 200 plant species and causes substantial losses in numerous crops. The B05.10 and T4 wild-type strain genomes have been recently sequenced, becoming a model system for necrotrophic pathogens, as well as opening up new alternatives in functional genomics, such as proteomics. We analyzed B. cinerea mycelium from these two wild-type strains, introducing label-free shotgun nUPLC-MS(E) methodology to complement the 2-DE-MS-based approach. We assessed the label-free nUPLC-MS(E) methodology for protein identification and quantification using five mycelium protein dilutions. A total of 225 and 170 protein species were identified by nUPLC-MS(E) in the B05.10 and T4 strains, respectively. Moreover, 129 protein species were quantified in both strains. Significant differences in protein abundance were found in 15 more abundant and 16 less abundant protein species in the B05.10 strain compared to the T4 strain. Twenty-nine qualitative and 15 significant quantitative differences were found using 2-DE. The label-free nUPLC-MS(E) was a reliable, reproducible and sensitive method for protein identification and quantification to study the B. cinerea mycelial proteome. Results obtained by gel-based and gel-free complementary approaches allow a deeper characterization of this fungus, as well as the identification of potential virulence factors.

A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

Pluripotent stem cell heterogeneity and the evolving role of proteomic technologies in stem cell biology

Stem cells represent obvious choices for regenerative medicine and are invaluable for studies of human development and drug testing. The proteomic landscape of pluripotent stem cells (PSCs), in particular, is not yet clearly defined; consequently, this field of research would greatly benefit from concerted efforts designed to better characterize these cells. In this concise review, we provide an overview of stem cell potency, highlight the types and practical implications of heterogeneity in PSCs and provide a detailed analysis of the current view of the pluripotent proteome in a unique resource for this rapidly evolving field. Our goal in this review is to provide specific insights into the current status of the known proteome of both mouse and human PSCs. This has been accomplished by integrating published data into a unified PSC proteome to facilitate the identification of proteins, which may be informative for the stem cell state as well as to reveal areas where our current view is limited. These analyses provide insight into the challenges faced in the proteomic analysis of PSCs and reveal one area--the cell surface subproteome--that would especially benefit from enhanced research efforts.

Label-free protein profiling of adipose-derived human stem cells under hyperosmotic treatment

Our previous work suggested that treatment of cells with hyperosmotic media during 2D passaging primes cells for cartilage tissue engineering applications. Here, we used label-free proteomic profiling to evaluate the effects of control and hyperosmotic treatment environments on the phenotype of multipotent adipose-derived stem cells (ASCs) cultivated with a chondrogenic growth factor cocktail. Spectra were recorded in a data-independent fashion at alternate low (precursor) and high (product) fragmentation voltages (MS(E)). This method was supplemented with data mining of accurate mass and retention time matches in precursor ion spectra across the experiment. The results indicated a complex cellular response to osmotic treatment, with a number of proteins differentially expressed between control and treated cell groups. The roles of some of these proteins have been documented in the literature as characteristic of the physiological states studied, especially aldose reductase (osmotic stress). This protein acted as a positive control in this work, providing independent corroborative validation. Other proteins, including 5'-nucleotidase and transgelin, have been previously linked to cell differentiation state. This study demonstrates that label-free profiling can serve as a useful tool in characterizing cellular responses to chondrogenic treatment regimes, recommending its use in optimization of cell priming protocols for cartilage tissue engineering.

Quantitative neuroproteomics: classical and novel tools for studying neural differentiation and function

Mechanisms underlying neural stem cell proliferation, differentiation and maturation play a critical role in the formation and wiring of neuronal connections. This process involves the activation of multiple serial events, which guide the undifferentiated cells to different lineages via distinctive developmental programs, forming neuronal circuits and thus shaping the adult nervous system. Furthermore, alterations within these strictly regulated pathways can lead to severe neurological and psychiatric diseases. In this framework, the investigation of the high dynamic protein expression changes and other factors affecting protein functions, for example post-translational modifications, the alterations of protein interaction networks, is of pivotal importance for the understanding of the molecular mechanisms responsible for cell differentiation. More recently, proteomic studies in neuroscience ("neuroproteomics") are receiving increased interest for the primary understanding of the regulatory networks underlying neuronal differentiation processes. Besides the classical two-dimensional-based proteomic strategies, the emerging platforms for LC-MS shotgun proteomic analysis hold great promise in unraveling the molecular basis of neural stem cell differentiation. In this review, recent advancements in label-free LC-MS quantitative neuroproteomics are highlighted as a new tool for the study of neural differentiation and functions, in comparison to mass spectrometry-based labeling approaches. The more commonly used protein profiling strategies and model systems for the analysis of neural differentiation are also discussed, along with the challenging proteomic approaches aimed to analyze the nervous system-specific organelles, the neural cells secretome and the specific protein interaction networks.

Towards a proteome signature for invasive ductal breast carcinoma derived from label-free nanoscale LC-MS protein expression profiling of tumorous and glandular tissue

As more and more alternative treatments become available for breast carcinoma, there is a need to stratify patients and individual molecular information seems to be suitable for this purpose. In this study, we applied label-free protein quantitation by nanoscale LC-MS and investigated whether this approach could be used for defining a proteome signature for invasive ductal breast carcinoma. Tissue samples from healthy breast and tumor were collected from three patients. Protein identifications were based on LC-MS peptide fragmentation data which were obtained simultaneously to the quantitative information. Hereby, an invasive ductal breast carcinoma proteome signature was generated which contains 60 protein entries. The on-column concentrations for osteoinductive factor, vimentin, GAP-DH, and NDKA are provided as examples. These proteins represent distinctive gene ontology groups of differentially expressed proteins and are discussed as risk markers for primary tumor pathogenesis. The developed methodology has been found well applicable in a clinical environment in which standard operating procedures can be kept; a prerequisite for the definition of molecular parameter sets that shall be capable for stratification of patients.