Kaya Behiye, Goceri Evgin, Becker Aline, Elder Brad, Puduvalli Vinay, Winter Jessica, Gurcan Metin, Otero José Javier
Department of Pathology, Division of Neuropathology, The Ohio State University College of Medicine, Columbus, Ohio, United States of America.
Akdeniz University, Engineering Faculty, Computer Engineering Department, Antalya, Turkey.
PLoS One. 2017 Mar 10;12(3):e0170991. doi: 10.1371/journal.pone.0170991. eCollection 2017.
Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology.
由于存在一些技术障碍，多重免疫荧光检测尚未进入诊断神经病理学领域，其中包括自发荧光。本研究介绍了一种方法的实施，该方法能够通过使用自动数字图像分析克服荧光显微镜在诊断神经病理学中的视觉挑战，其长期目标是为实体组织神经病理学的多重生物标志物提供无偏倚的定量分析。在本研究中，我们验证了一种假定的胶质瘤生物标志物PTBP1，并测试了免疫荧光显微镜结合自动且无偏倚的图像分析在多大程度上能够使PTBP1作为一种生物标志物用于区分具有诊断挑战性的手术活检样本。作为一个范例，我们利用了被诊断为反应性脑改变（假进展）和复发性胶质母细胞瘤（真进展）患者的二次切除样本。我们的图像分析工作流程能够去除背景自发荧光，并允许对DAPI-PTBP1阳性细胞、PTBP1阳性细胞核以及细胞中PTBP1信号的平均强度值进行定量分析。由于专家神经病理学家之间的不一致率高得令人无法接受，传统的病理学解释无法区分不同组。我们的数据表明，与仅显示反应性胶质增生的二次手术切除样本相比，复发性胶质母细胞瘤显示出更多的DAPI-PTBP1阳性细胞和更高的PTBP1信号平均强度值。我们的工作证明了利用自动图像分析克服在诊断神经病理学中应用荧光显微镜所面临挑战的潜力。
