Mesenchymal stem cells (MSCs) have emerged as a promising means for treating degenerative or incurable diseases. Recent studies have shown that microvesicles (MVs) from MSCs (MSC-MVs) contribute to recovery of damaged tissues in animal disease models. Here, we profiled the MSC-MV proteome to investigate their therapeutic effects. LC-MS/MS analysis of MSC-MVs identified 730 MV proteins. The MSC-MV proteome included five positive and two variable known markers of MSCs, but no negative marker, as well as 43 surface receptors and signaling molecules controlling self-renewal and differentiation of MSCs. Functional enrichment analysis showed that cellular processes represented by the MSC-MV proteins include cell proliferation, adhesion, migration, and morphogenesis. Integration of MSC's self-renewal and differentiation-related genes and the proteome of MSC-conditioned media (MSC-CM) with the MSC-MV proteome revealed potential MV protein candidates that can be associated with the therapeutic effects of MSC-MVs: (1) surface receptors (PDGFRB, EGFR, and PLAUR); (2) signaling molecules (RRAS/NRAS, MAPK1, GNA13/GNG12, CDC42, and VAV2); (3) cell adhesion (FN1, EZR, IQGAP1, CD47, integrins, and LGALS1/LGALS3); and (4) MSC-associated antigens (CD9, CD63, CD81, CD109, CD151, CD248, and CD276). Therefore, the MSC-MV proteome provides a comprehensive basis for understanding the potential of MSC-MVs to affect tissue repair and regeneration.

Disrupted cortical cytoarchitecture in cerebellum is a typical pathology in reeler. Particularly interesting are structural problems at the cellular level: dendritic morphology has important functional implication in signal processing. Here we describe a combinatorial imaging method of synchrotron X-ray microtomography with Golgi staining, which can deliver 3-dimensional(3-D) micro-architectures of Purkinje cell(PC) dendrites, and give access to quantitative information in 3-D geometry. In reeler, we visualized in 3-D geometry the shape alterations of planar PC dendrites (i.e., abnormal 3-D arborization). Despite these alterations, the 3-D quantitative analysis of the branching patterns showed no significant changes of the 77 ± 8° branch angle, whereas the branch segment length strongly increased with large fluctuations, comparing to control. The 3-D fractal dimension of the PCs decreased from 1.723 to 1.254, indicating a significant reduction of dendritic complexity. This study provides insights into etiologies and further potential treatment options for lissencephaly and various neurodevelopmental disorders.

Neural stem cells (NSCs) are self-renewing, multipotent cells that can generate neurons, astrocytes, and oligodendrocytes of the nervous system. NSCs have been extensively studied because they can be used to treat impaired cells and tissues or improve regenerative power of degenerating cells in neurodegenerative diseases or spinal cord injuries. For successful clinical applications of NSCs, it is essential to understand the mechanisms underlying self-renewal and differentiation of NSCs, which involve complex interplays among key factors including transcription factors, epigenetic control, microRNAs, and signaling pathways. Despite numerous studies on such factors, a holistic view of their interplays during neural development still remains elusive. In this review, we present recently identified potential regulatory factors and their targets by genomics and proteomics technologies and then integrate them into regulatory networks that describe their complex interplays to achieve self-renewal and differentiation of NSCs.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) has been a useful tool to profile secondary ions from the near surface region of specimens with its high molecular specificity and submicrometer spatial resolution. However, the TOF-SIMS analysis of even a moderately large size of samples has been hampered due to the lack of tools for automatically analyzing the huge amount of TOF-SIMS data. Here, we present a computational platform to automatically identify and align peaks, find discriminatory ions, build a classifier, and construct networks describing differential metabolic pathways. To demonstrate the utility of the platform, we analyzed 43 data sets generated from seven gastric cancer and eight normal tissues using TOF-SIMS. A total of 87 138 ions were detected from the 43 data sets by TOF-SIMS. We selected and then aligned 1286 ions. Among them, we found the 66 ions discriminating gastric cancer tissues from normal ones. Using these 66 ions, we then built a partial least square-discriminant analysis (PLS-DA) model resulting in a misclassification error rate of 0.024. Finally, network analysis of the 66 ions showed disregulation of amino acid metabolism in the gastric cancer tissues. The results show that the proposed framework was effective in analyzing TOF-SIMS data from a moderately large size of samples, resulting in discrimination of gastric cancer tissues from normal tissues and identification of biomarker candidates associated with the amino acid metabolism.

BackgroundCellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process.ResultsWe suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator.ConclusionsHeretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0417-1) contains supplementary material, which is available to authorized users.