Semi-Automated Digital Image Analysis of Pick's Disease and TDP-43 Proteinopathy
Semi-Automated Digital Image Analysis of Pick's Disease and TDP-43 Proteinopathy
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DOI：
10.1369/0022155415614303
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发表时间：
2016-01-01
影响因子：
3.2
通讯作者：
Trojanowski, John Q.
中科院分区：
文献类型：
--
作者：
Irwin, David J.;Byrne, Matthew D.;Trojanowski, John Q.
组织学切片的数字图像分析为神经病理学研究提供了可靠的高通量方法，但额颞叶变性（FTLD）的数据很少，由于形态学上不同的病理学，这给研究带来了额外的挑战。在这里，我们描述了一种新的方法，半自动数字图像分析FTLD亚型，包括：皮克病（PiD，n=11）与tau阳性细胞内包涵体和神经纤维瘤线程，和TDP-43病理类型C（FTLD-TDPC，n=10），定义为TDP-43阳性聚集体主要在大型营养不良的神经突。为此，我们研究了三个FTLD相关的皮质区域：额中回（MFG），上级颞回（STG）和前扣带回（ACG）的免疫组织化学。我们使用颜色去卷积过程从色原中分离信号，并应用对象检测和强度阈值算法来量化病理负担。我们发现目标检测算法与tau和TDP-43阳性夹杂物的金标准手动定量具有良好的一致性。我们的采样方法在三个独立的研究者中是可靠的，我们在使用开源软件的试点分析中获得了类似的结果。使用这些算法的区域比较发现，使用传统的顺序尺度数据未检测到PiD和FTLD-TDP之间的区域解剖疾病负担的差异，这表明数字图像分析是形态多样的FTLD综合征的临床病理学研究的有力工具。
Digital image analysis of histology sections provides reliable, high-throughput methods for neuropathological studies but data is scant in frontotemporal lobar degeneration (FTLD), which has an added challenge of study due to morphologically diverse pathologies. Here, we describe a novel method of semi-automated digital image analysis in FTLD subtypes including: Pick's disease (PiD, n=11) with tau-positive intracellular inclusions and neuropil threads, and TDP-43 pathology type C (FTLD-TDPC, n=10), defined by TDP-43-positive aggregates predominantly in large dystrophic neurites. To do this, we examined three FTLD-associated cortical regions: mid-frontal gyrus (MFG), superior temporal gyrus (STG) and anterior cingulate gyrus (ACG) by immunohistochemistry. We used a color deconvolution process to isolate signal from the chromogen and applied both object detection and intensity thresholding algorithms to quantify pathological burden. We found object-detection algorithms had good agreement with gold-standard manual quantification of tau- and TDP-43-positive inclusions. Our sampling method was reliable across three separate investigators and we obtained similar results in a pilot analysis using open-source software. Regional comparisons using these algorithms finds differences in regional anatomic disease burden between PiD and FTLD-TDP not detected using traditional ordinal scale data, suggesting digital image analysis is a powerful tool for clinicopathological studies in morphologically diverse FTLD syndromes.