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Extracellular ATP activates the P2X7 receptor, leading to inflammasome activation and release of pro-inflammatory cytokines in monocytes. However, a detailed analysis of P2X7 receptor expression and function in the human T cell compartment has not been reported. Here, we used a P2X7-specific nanobody to assess cell membrane expression and function of P2X7 on peripheral T lymphocyte subsets. The results show that innate-like T cells, which effectively react to innate stimuli by secreting high amounts of pro-inflammatory cytokines, have the highest expression of P2X7 in the human T cell compartment. Using Tγδ cells as example for an innate-like lymphocyte population, we demonstrate that these cells are more sensitive to P2X7 receptor activation than conventional T cells, affecting fundamental cellular mechanisms like calcium signaling and ATP-induced cell death. The increased susceptibility of innate-like T cells to P2X7-mediated cell death provides a mechanism to control their homeostasis under inflammatory conditions. Understanding the expression and function of P2X7 on human immune cells is essential to assume the benefits and consequences of newly developed P2X7-based therapeutic approaches.

Initial T cell activation is triggered by the formation of highly dynamic, spatiotemporally restricted Ca

Live cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems, but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four data sets: synthetic data for in-depth evaluation of the algorithm behavior; two data sets acquired for analysis of initial Ca2+ microdomains in T cells; and, to illustrate transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

Live-cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes on the subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.