Cell Therapy for Type 1 Diabetes

Acknowledgements The work described in this review was supported by a grant from the MRC. K.R.M. is supported by a fellowship from the Scottish Translational Medicines and Therapeutics Initiative through the Wellcome Trust.Peer reviewedPublisher PD

Digit-Type Mechanisms in Cell Differentiation Process: a Theoretical Study

According to inductive conception, the interference of neighboring cells causes a production of broken spatial symmetry in an initially homogenous system (cell differentiation process) during embryo development. A concentration gradient of a specific substance (morphogen), which serves as an analog-type control signal, is proposed as an agent to provide this process. It is known fact, that genes’ activity are of a discreet-type (digit) and, therefore, cell differentiation mechanism based on the control digit-type signals in comparison with the analog-type signals is more or less probable. A model presented here simulates the cell differentiation process. The model is based on the assumption that only the digit-type interactions take place between adjacent cells (not analog-type interactions that are typical for the concentration gradient field). Within model assumptions, the genes’ interaction algorithms and boundary condition are postulated. Under the model assumptions, the cell differentiation process starts with the homogeneous blastula formation and comprises three consecutive stages. 1) The loop-like inhomogeneous cell formation development and corresponding set of the self-blocking genes activation - the set of the asymmetric pattern genes governs the process. 2) The line-type inhomogeneous cell formations, with their origins at different cells of the loop-type formation, development and corresponding self-blocking genes activation - the sets of the symmetric pattern genes govern the process. 3) The variety of the function genes activation in the complex inhomogeneous cell formation - the sets of the self-blocking genes govern the process. Under model assumptions the multi level tree-type inhomogeneous cell structures creation is possible. The number of the pattern genes limits the complexity of the inhomogeneous structure. According to the model, in order to provide the further blastula development process, the concentration gradient fields may appear after the initial stages of the cell differentiation process. As simulated by the model, results qualitatively coincide with some of the experimental facts

Kinetics and cellular site of glycolipid loading control

CD1d-restricted natural killer T cells (NKT cells) possess a wide range of effector and regulatory activities that are related to their ability to secrete both T helper 1 (Th1) cell- and Th2 cell-type cytokines. We analyzed presentation of NKT cell activating α galactosylceramide (αGalCer) analogs that give predominantly Th2 cell-type cytokine responses to determine how ligand structure controls the outcome of NKT cell activation. Using a monoclonal antibody specific for αGalCer-CD1d complexes to visualize and quantitate glycolipid presentation, we found that Th2 cell-type cytokinebiasing ligands were characterized by rapid and direct loading of cell-surface CD1d proteins. Complexes formed by association of these Th2 cell-type cytokine-biasing αGalCer analogs with CD1d showed a distinctive exclusion from ganglioside-enriched, detergent-resistant plasma membrane microdomains of antigen-presenting cells. These findings help to explain how subtle alterations in glycolipid ligand structure can control the balance of proinflammatory and antiinflammatory activities of NKT cells

Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology.

High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types

Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model

Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles. While the single-cell techniques are extremely powerful and hold great promise, they are currently still labor intensive, have a high cost per cell, and, most importantly, do not provide information on spatial distribution of cell types in specific regions of the brain. We propose a complementary approach that uses computational methods to infer the cell types and their gene expression profiles through analysis of brain-wide single-cell resolution in situ hybridization (ISH) imagery contained in the Allen Brain Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH image for each gene and model it as a spatial point process mixture, whose mixture weights are given by the cell types which express that gene. By fitting a point process mixture model jointly to the ISH images, we infer both the spatial point process distribution for each cell type and their gene expression profile. We validate our predictions of cell type-specific gene expression profiles using single cell RNA sequencing data, recently published for the mouse somatosensory cortex. Jointly with the gene expression profiles, cell features such as cell size, orientation, intensity and local density level are inferred per cell type