We found abnormalities in the magnitude, pattern, and spatial distribution of [Ca2+]i signaling in T cells from SF of patients with chronic inflammatory arthritis. A reduction in the number of responding SF T cells may partly explain some of our observations. However, we propose that the observed redistribution of SF Ca2+ stores may underlie the altered [Ca2+]i signaling, thus making these cells hyporesponsive to mitogen. The inflammatory environment of the joint and the late stage of differentiation of SF T cells are both likely to contribute to these changes in [Ca2+]i signaling, resulting in aberrant T cell function and promotion of disease chronicity.

Summary Stimulation of T lymphocytes results in the calcium cells and showed a difference in the numbers of cells giving a transient, rather than sustained, calcium signal. The increase in oscillating cells in the CD4+ CD45RO + population may reflect the heterogeneity of this population, particularly in terms of cytokine production. The changing patterns of calcium responses in T cells as they differentiate may explain variation in the cellular response to activation at different stages in their lifespan and emphasize the importance of the both the quantity and the quality of the calcium signal in determining the outcome of T cell activation.

The gene activities in T lymphocytes that regulate immune responses are influenced by Ca2+ ([Ca2+]i). The intracellular calcium signals are highly heterogeneous and vitally important in determining the immune outcome. The signals in individual cells can be measured using fluorescence microscopy but to group the cells into classes with similar signal kinetics is currently laborious. Here, we demonstrate a method for the automated classification of the responses into four categories formerly identified by an expert's inspection. This method comprises characterising the response by a second-order model, performing frequency analysis, and using derived features as inputs to two multilayer perceptron neural networks (NNs). We compare the algorithm's performance on an example data set against the human classification: it was found to classify identically more than 70% of the data, despite small sample sizes in two categories and significant overlap between the other two classes. The group characterized by an oscillating signal showed the presence of a number of frequencies, which may be important in determining gene activation. A classification threshold enables the automatic identification of patterns with a low-classification certainty. Future refinement of the algorithm may allow the identification of more classes, which may be important in different immune responses associated with disease.