CD28 Negative T cells: is their loss our gain?
By Danny Mou, J. Espinosa, Denise J Lo, A. Kirk. 2014
123 citations recorded by the source corpus.
CD28 is a primary costimulation molecule for T cell activation. However, during the course of activation some T cells lose this molecule and assume a CD28‐independent existence. These CD28− T cells are generally antigen‐experienced and highly differentiated. CD28− T cells are functionally heterogeneous. Their characteristics vary largely on the context in which they are found and range from having enhanced cytotoxic abilities to promoting immune regulation. Thus, CD28 loss appears to be more of a marker for advanced differentiation regardless of the cytotoxic or regulatory function being conducted by the T cell. CD28− T cells are now being recognized as playing significant roles in several human diseases. Various functional CD28− populations have been characterized in inflammatory conditions, infections and cancers. Of note, the recent introduction of costimulation blockade‐based therapies, particularly those that inhibit CD28–B7 interactions, has made CD28 loss particularly relevant for solid organ transplantation. Certain CD28− T cell populations seem to promote allograft tolerance whereas others contribute to alloreactivity and costimulation blockade resistant rejection. Elucidating the interplay between these populations and characterizing the determinants of their ultimate function may have relevance for clinical risk stratification and personal determination of optimal posttransplant immune management.
Subfields: Immunology, Oncology
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Citations in the corpus
- Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, background): “In the CD4 + subset, opt-SNE visualization revealed two groups of CD4 + CD28 − cells likely representing terminally differentiated memory CD4 + T cells 27 with different levels of CCR7 expression that may re fl ect distinct differentiation states of the two populations.”
- Automated Optimal Parameters for T-Distributed Stochastic Neighbor Embedding Improve Visualization and Allow Analysis of Large Datasets (2018, background): “While opt-SNE and t-SNE both capture the macro-structure illustrated by gene overlays, several Louvain clusters (3, 18, 19, 25, 28) appear partially or completely overlapped by other clusters in standard t-SNE but not in the opt-SNE embedding.”
- Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types (2017, background): “5a), including a small CD28 − CD4 + T-cell memory population (25,398 cells; 0.5% of total), most likely representing terminally differentiated cells 26 .”