Human Wharton's Jelly-Derived Stem Cells Display a Distinct Immunomodulatory and Proregenerative Transcriptional Signature Compared to Bone Marrow-Derived Stem Cells.

Mesenchymal stromal cells (MSCs) are multipotent stem cells with immunosuppressive and trophic support functions. While MSCs from different sources frequently display a similar appearance in culture, they often show differences in their surface marker and gene expression profiles. Although bone marrow is considered the "gold standard" tissue to isolate classical MSCs (BM-MSC), MSC-like cells are currently also derived from more easily accessible extra-embryonic tissues such as the umbilical cord. In this study, we defined the best way to isolate MSCs from the Wharton's jelly of the human umbilical cord (WJ-MSC) and assessed the mesenchymal and immunological phenotype of BM-MSC and WJ-MSC. Moreover, the gene expression profile of established WJ-MSC cultures was compared to two different bone marrow-derived stem cell populations (BM-MSC and multipotent adult progenitor cells or MAPC®). We observed that explant culturing of Wharton's jelly matrix is superior to collagenase tissue digestion for obtaining mesenchymal-like cells, with explant isolated cells displaying increased expansion potential. While being phenotypically similar to adult MSCs, WJ-MSC show a different gene expression profile. Gene ontology analysis revealed that genes associated with cell adhesion, proliferation, and immune system functioning are enriched in WJ-MSC. In vivo transplantation confirms their immune modulatory effect on T cells, similar to BM-MSC and MAPC. Furthermore, WJ-MSC intrinsically overexpress genes involved in neurotrophic support and their secretome induces neuronal maturation of SH-SY5Y neuroblastoma cells to a greater extent than BM-MSC. This signature makes WJ-MSC an attractive candidate for cell-based therapy in neurodegenerative and immune-mediated central nervous system disorders such as multiple sclerosis, Parkinson's disease, or amyotrophic lateral sclerosis.

Classifying pairs with trees for supervised biological network inference.

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.

On protocols and measures for the validation of supervised methods for the inference of biological networks.

Networks provide a natural representation of molecular biology knowledge, in particular to model relationships between biological entities such as genes, proteins, drugs, or diseases. Because of the effort, the cost, or the lack of the experiments necessary for the elucidation of these networks, computational approaches for network inference have been frequently investigated in the literature. In this paper, we examine the assessment of supervised network inference. Supervised inference is based on machine learning techniques that infer the network from a training sample of known interacting and possibly non-interacting entities and additional measurement data. While these methods are very effective, their reliable validation in silico poses a challenge, since both prediction and validation need to be performed on the basis of the same partially known network. Cross-validation techniques need to be specifically adapted to classification problems on pairs of objects. We perform a critical review and assessment of protocols and measures proposed in the literature and derive specific guidelines how to best exploit and evaluate machine learning techniques for network inference. Through theoretical considerations and in silico experiments, we analyze in depth how important factors influence the outcome of performance estimation. These factors include the amount of information available for the interacting entities, the sparsity and topology of biological networks, and the lack of experimentally verified non-interacting pairs.