Department of Electrical and Electronic Engineering, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Department of Physiological Sciences, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
PLoS One. 2018 Aug 29;13(8):e0201965. doi: 10.1371/journal.pone.0201965. eCollection 2018.
Although modern fluorescence microscopy produces detailed three-dimensional (3D) datasets, colocalization analysis and region of interest (ROI) selection is most commonly performed two-dimensionally (2D) using maximum intensity projections (MIP). However, these 2D projections exclude much of the available data. Furthermore, 2D ROI selections cannot adequately select complex 3D structures which may inadvertently lead to either the exclusion of relevant or the inclusion of irrelevant data points, consequently affecting the accuracy of the colocalization analysis. Using a virtual reality (VR) enabled system, we demonstrate that 3D visualization, sample interrogation and analysis can be achieved in a highly controlled and precise manner. We calculate several key colocalization metrics using both 2D and 3D derived super-resolved structured illumination-based data sets. Using a neuronal injury model, we investigate the change in colocalization between Tau and acetylated α-tubulin at control conditions, after 6 hours and again after 24 hours. We demonstrate that performing colocalization analysis in 3D enhances its sensitivity, leading to a greater number of statistically significant differences than could be established when using 2D methods. Moreover, by carefully delimiting the 3D structures under analysis using the 3D VR system, we were able to reveal a time dependent loss in colocalization between the Tau and microtubule network as an early event in neuronal injury. This behavior could not be reliably detected using a 2D based projection. We conclude that, using 3D colocalization analysis, biologically relevant samples can be interrogated and assessed with greater precision, thereby better exploiting the potential of fluorescence-based image analysis in biomedical research.
尽管现代荧光显微镜可以生成详细的三维（3D）数据集，但共定位分析和感兴趣区域（ROI）的选择通常是使用最大强度投影（MIP）在二维（2D）上进行的。然而，这些 2D 投影排除了大部分可用数据。此外，2D ROI 选择无法充分选择复杂的 3D 结构，这可能会无意中排除相关数据点或包含不相关的数据点，从而影响共定位分析的准确性。使用虚拟现实（VR）启用系统，我们证明可以以高度可控和精确的方式实现 3D 可视化、样本询问和分析。我们使用 2D 和 3D 衍生的超分辨率结构光照数据集中计算了几个关键的共定位度量。使用神经元损伤模型，我们研究了在对照条件下、6 小时后和 24 小时后 Tau 和乙酰化α-微管蛋白之间共定位的变化。我们证明，在 3D 中进行共定位分析可以提高其灵敏度，从而导致比使用 2D 方法建立的更多的统计学显著差异。此外，通过使用 3D VR 系统仔细限定正在分析的 3D 结构，我们能够揭示 Tau 和微管网络之间共定位的时间依赖性丧失，这是神经元损伤的早期事件。这种行为使用基于 2D 的投影无法可靠地检测到。我们得出结论，使用 3D 共定位分析可以更精确地询问和评估生物学上相关的样本，从而更好地利用荧光基图像分析在生物医学研究中的潜力。
