Department of Mathematics, 5784University of New Orleans, New Orleans, LA, USA.
Department of Immunology, 5417St. Jude Children's Research Hospital, Memphis, TN, USA.
Stat Methods Med Res. 2022 Aug;31(8):1484-1499. doi: 10.1177/09622802221094133. Epub 2022 Apr 21.
Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy, provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false-positive errors and are restricted to only 2-dimensional images. In this paper, we develop a novel statistical method to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of co-localization of protein light chain 3 and lysosomal-associated membrane protein 1 in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science, and ecology.
高分辨率图像中的空间数据在许多科学领域中都很丰富。例如，单分子定位显微镜，如随机光学重建显微镜，提供超分辨率图像，帮助科学家研究蛋白质的共定位及其在细胞内的相互作用，这是活细胞中的关键事件。然而，用于分析超分辨率图像中共定位的准确方法很少。当前的方法和软件容易产生假阳性错误，并且仅局限于 2 维图像。在本文中，我们开发了一种新颖的统计方法，可有效地解决多个 2 维和 3 维图像数据集的蛋白质共定位的无偏和稳健量化和比较问题。该方法通过在细胞自噬中蛋白质 LC3 和溶酶体相关膜蛋白 1 的共定位的分析以及模拟研究中优异的性能，显著改善了使用超分辨率图像数据的蛋白质共定位分析。此外，该方法可直接应用于其他领域，如诊断成像、流行病学、环境科学和生态学中的共定位分析。
