3,359 research outputs found

    Cytotoxic T Lymphocyte Antigen-4 Accumulation in the Immunological Synapse Is Regulated by TCR Signal Strength

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    AbstractCD28 and CTLA-4 engagement with B7 expressed by APCs generates critical regulatory signals for T cell activation. CD28 is expressed on the T cell surface and enhances T cell expansion, while CTLA-4 localizes primarily to an intracellular compartment and inhibits T cell proliferation. We demonstrate that CTLA-4 has several unique trafficking properties that may regulate its ability to attenuate a T cell response. Importantly, accumulation of CTLA-4 at the immunological synapse is proportional to the strength of the TCR signal, suggesting that cells receiving stronger stimuli are more susceptible to CTLA-4-mediated inhibition. This may represent a novel feedback control mechanism in which a stimulatory signal regulates the recruitment of an inhibitory receptor to a functionally relevant site on the cell surface

    Rapid prediction of lab-grown tissue properties using deep learning

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    The interactions between cells and the extracellular matrix are vital for the self-organisation of tissues. In this paper we present proof-of-concept to use machine learning tools to predict the role of this mechanobiology in the self-organisation of cell-laden hydrogels grown in tethered moulds. We develop a process for the automated generation of mould designs with and without key symmetries. We create a large training set with N=6500N=6500 cases by running detailed biophysical simulations of cell-matrix interactions using the contractile network dipole orientation (CONDOR) model for the self-organisation of cellular hydrogels within these moulds. These are used to train an implementation of the \texttt{pix2pix} deep learning model, reserving 740740 cases that were unseen in the training of the neural network for training and validation. Comparison between the predictions of the machine learning technique and the reserved predictions from the biophysical algorithm show that the machine learning algorithm makes excellent predictions. The machine learning algorithm is significantly faster than the biophysical method, opening the possibility of very high throughput rational design of moulds for pharmaceutical testing, regenerative medicine and fundamental studies of biology. Future extensions for scaffolds and 3D bioprinting will open additional applications.Comment: 26 Pages, 11 Figure

    High-throughput design of cultured tissue moulds using a biophysical model

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    The technique presented here identifies tethered mould designs, optimised for growing cultured tissue with very highly-aligned cells. It is based on a microscopic biophysical model for polarised cellular hydrogels. There is an unmet need for tools to assist mould and scaffold designs for the growth of cultured tissues with bespoke cell organisations, that can be used in applications such as regenerative medicine, drug screening and cultured meat. High-throughput biophysical calculations were made for a wide variety of computer-generated moulds, with cell-matrix interactions and tissue-scale forces simulated using a contractile-network dipole-orientation model. Elongated moulds with central broadening and one of the following tethering strategies are found to lead to highly-aligned cells: (1) tethers placed within the bilateral protrusions resulting from an indentation on the short edge, to guide alignment (2) tethers placed within a single vertex to shrink the available space for misalignment. As such, proof-of-concept has been shown for mould and tethered scaffold design based on a recently developed biophysical model. The approach is applicable to a broad range of cell types that align in tissues and is extensible for 3D scaffolds

    Cytotoxic T Lymphocyte–associated Antigen 4 (CTLA-4) Regulates the Unfolding of Autoimmune Diabetes

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    Evidence has been accumulating that shows that insulin-dependent diabetes is subject to immunoregulation. To determine whether cytotoxic T lymphocyte–associated antigen 4 (CTLA-4) is involved, we injected anti–CTLA-4 mAb into a TCR transgenic model of diabetes at different stages of disease. When injected into young mice, months before they would normally become diabetic, anti–CTLA-4 induced diabetes rapidly and essentially universally; this was not the result of a global activation of T lymphocytes, but did reflect a much more aggressive T cell infiltrate in the pancreatic islets. These effects were only observed if anti–CTLA-4 was injected during a narrow time window, before the initiation of insulitis. Thus, engagement of CTLA-4 at the time when potentially diabetogenic T cells are first activated is a pivotal event; if engagement is permitted, invasion of the islets occurs, but remains quite innocuous for months, if not, insulitis is much more aggressive, and diabetes quickly ensues

    Cutting edge: CTLA-4 on effector T cells inhibits in trans,”

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    CTLA-4 is thought to inhibit effector T cells both intrinsically, by competing with CD28 for B7 ligands, and extrinsically, through the action of regulatory T cells (Tregs). We studied in vivo responses of normal and CTLA-4-deficient Ag-specific murine effector CD4 + T cells. We directly demonstrate that effector T cellrestricted CTLA-4 inhibits T cell responses in a cellextrinsic manner. Cotransfer experiments show that CTLA-4 on normal effector CD4 + T cells completely abrogates the dramatically increased expansion normally experienced by their CTLA-4-deficient counterparts. Neither the wild-type nor the CTLA-4-deficient T cells express the Treg transcription factor Foxp3 when transferred alone or together. Thus, cell-extrinsic inhibition of T cell responses by CTLA-4 is not limited to Tregs but is also a function of effector T cells
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