Ratliff Galloway (firedwindow1)

Dermatan sulphate (DS), a glycosaminoglycan, is present in the extracellular matrix and on the cell surface. Previously, we showed that heparan sulphate plays a key role in the maintenance of the undifferentiated state in mouse embryonic stem cells (mESCs) and in the regulation of their differentiation. Chondroitin sulphate has also been to be important for pluripotency and differentiation of mESCs. Keratan sulphate is a marker of human pluripotent stem cells. To date, however, the function of DS in mESCs has not been clarified. Dermatan 4 sulfotransferase 1, which transfers sulphate to the C-4 hydroxyl group of N-acetylgalactosamine of DS, contributes to neuronal differentiation of mouse neural progenitor cells. Therefore, we anticipated that neuronal differentiation would be induced in mESCs in culture by the addition of DS. To test this expectation, we investigated neuronal differentiation in mESCs and human neural stem cells (hNSCs) cultures containing DS. In mESCs, DS promoted neuronal differentiation by activation of extracellular signal-regulated kinase 1/2 and also accelerated neurite outgrowth. In hNSCs, DS promoted neuronal differentiation and neuronal migration, but not neurite outgrowth. Thus, DS promotes neuronal differentiation in both mouse and human stem cells, suggesting that it offers a novel method for efficiently inducing neuronal differentiation. Recent technological advances produce a wealth of high dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single cell RNA-sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low dimensional space. We propose an unsupervised method to identify the subset of features that define a low dimensional subspace in which clustering can be conducted. read more This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low dimensional subspace. https//github.com/smelton/SMD. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. Accurate classification of patients into molecular subgroups is critical for the development of effective therapeutics and for deciphering what drives these subgroups to cancer. The availability of multi-omics data catalogs for large cohorts of cancer patients provides multiple views into the molecular biology of the tumors with unprecedented resolution. We develop PAMOGK (Pathway based Multi Omic Graph Kernel clustering) that integrates multi-omics patient data with existing biological knowledge on pathways. We develop a novel graph kernel that evaluates patient similarities based on a single molecular alteration type in the context of a pathway. To corroborate multiple views of patients evaluated by hundreds of pathways and molecular alteration combinations, we use multi-view kernel clustering. Applying PAMOGK to kidney renal clear cell carcinoma (KIRC) patients results in four clusters with significantly different survival times (p-value = 1.24e-11). When we co