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使用时间分辨分析揭示炎性小体读数之间的动力学差异。

Exposing kinetic disparities between inflammasome readouts using time-resolved analysis.

作者信息

Herring Matthew, Persson Alexander, Potter Ryan, Karlsson Roger, Särndahl Eva, Ejdebäck Mikael

机构信息

School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.

Inflammatory Response and Infection Susceptibility Centre (iRiSC), Örebro University, Örebro, Sweden.

出版信息

Heliyon. 2024 May 29;10(11):e32023. doi: 10.1016/j.heliyon.2024.e32023. eCollection 2024 Jun 15.

Abstract

The NLRP3 inflammasome is an intracellular multiprotein complex described to be involved in both an effective host response to infectious agents and various diseases. Investigation into the NLRP3 inflammasome has been extensive in the past two decades, and often revolves around the analysis of a few specific readouts, including ASC-speck formation, caspase-1 cleavage or activation, and cleavage and release of IL-1β and/or IL-18. Quantification of these readouts is commonly undertaken as an endpoint analysis, where the presence of each positive outcome is assessed independently of the others. In this study, we apply time-resolved analysis of a human macrophage model (differentiated THP-1-ASC-GFP cells) to commonly accessible methods. This approach yields the additional quantifiable metrics time-resolved absolute change and acceleration, allowing comparisons between readouts. Using this methodological approach, we reveal (potential) discrepancies between inflammasome-related readouts that otherwise might go undiscovered. The study highlights the importance of time-resolved data in general and may be further extended as well as incorporated into other areas of research.

摘要

NLRP3炎性小体是一种细胞内多蛋白复合物,据描述它既参与宿主对病原体的有效反应,也参与多种疾病。在过去二十年中,对NLRP3炎性小体的研究广泛,且常常围绕少数几个特定的检测指标展开分析,包括ASC斑点形成、半胱天冬酶-1的切割或激活,以及白细胞介素-1β和/或白细胞介素-18的切割和释放。这些检测指标的量化通常作为终点分析进行,其中每个阳性结果的存在是独立于其他结果进行评估的。在本研究中,我们将人类巨噬细胞模型(分化的THP-1-ASC-GFP细胞)的时间分辨分析应用于常用方法。这种方法产生了额外的可量化指标——时间分辨绝对变化和加速度,从而能够对检测指标进行比较。使用这种方法,我们揭示了炎性小体相关检测指标之间(潜在的)差异,否则这些差异可能未被发现。该研究总体上突出了时间分辨数据的重要性,并且可能会进一步扩展并纳入其他研究领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d37/11168392/79b7ed723a86/gr1.jpg

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