Letendre Kenneth, Donnadieu Emmanuel, Moses Melanie E, Cannon Judy L
Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, NM, United States of America; Department of Computer Science, University of New Mexico, Albuquerque, NM, United States of America.
Inserm, U1016, Institut Cochin, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
PLoS One. 2015 May 14;10(5):e0126333. doi: 10.1371/journal.pone.0126333. eCollection 2015.
Two-photon (2P) microscopy provides immunologists with 3D video of the movement of lymphocytes in vivo. Motility parameters extracted from these videos allow detailed analysis of lymphocyte motility in lymph nodes and peripheral tissues. However, standard parametric statistical analyses such as the Student's t-test are often used incorrectly, and fail to take into account confounds introduced by the experimental methods, potentially leading to erroneous conclusions about T cell motility. Here, we compare the motility of WT T cell versus PKCθ-/-, CARMA1-/-, CCR7-/-, and PTX-treated T cells. We show that the fluorescent dyes used to label T cells have significant effects on T cell motility, and we demonstrate the use of factorial ANOVA as a statistical tool that can control for these effects. In addition, researchers often choose between the use of "cell-based" parameters by averaging multiple steps of a single cell over time (e.g. cell mean speed), or "step-based" parameters, in which all steps of a cell population (e.g. instantaneous speed) are grouped without regard for the cell track. Using mixed model ANOVA, we show that we can maintain cell-based analyses without losing the statistical power of step-based data. We find that as we use additional levels of statistical control, we can more accurately estimate the speed of T cells as they move in lymph nodes as well as measure the impact of individual signaling molecules on T cell motility. As there is increasing interest in using computational modeling to understand T cell behavior in in vivo, these quantitative measures not only give us a better determination of actual T cell movement, they may prove crucial for models to generate accurate predictions about T cell behavior.
双光子（2P）显微镜为免疫学家提供了体内淋巴细胞运动的三维视频。从这些视频中提取的运动参数能够对淋巴结和外周组织中的淋巴细胞运动进行详细分析。然而，诸如学生t检验等标准参数统计分析方法常常被错误使用，并且未能考虑到实验方法引入的混杂因素，这可能导致关于T细胞运动的错误结论。在此，我们比较了野生型T细胞与蛋白激酶Cθ基因敲除型、衔接蛋白相关分子1基因敲除型、趋化因子受体7基因敲除型以及经百日咳毒素处理的T细胞的运动情况。我们发现，用于标记T细胞的荧光染料对T细胞运动有显著影响，并且我们证明了使用析因方差分析作为一种能够控制这些影响的统计工具。此外，研究人员常常需要在通过对单个细胞的多个步骤随时间进行平均来使用“基于细胞”的参数（例如细胞平均速度），或者在不考虑细胞轨迹的情况下对细胞群体的所有步骤（例如瞬时速度）进行分组的“基于步骤”的参数之间做出选择。使用混合模型方差分析，我们表明我们能够在不损失基于步骤的数据的统计效力的情况下维持基于细胞的分析。我们发现，随着我们使用更多层次的统计控制，我们能够更准确地估计T细胞在淋巴结中移动时的速度，以及测量单个信号分子对T细胞运动的影响。由于人们越来越关注使用计算模型来理解体内T细胞的行为，这些定量测量不仅能让我们更好地确定实际的T细胞运动，它们对于模型生成关于T细胞行为的准确预测可能也至关重要。
