Institute of Bioengineering and Nanotechnology, 31 Biopolis Way, the Nanos, #04-01, 138669, Singapore.
BioData Min. 2008 Nov 14;1(1):10. doi: 10.1186/1756-0381-1-10.
Dysfunction in the endolysosome, a late endosomal to lysosomal degradative intracellular compartment, is an early hallmark of some neurodegenerative diseases, in particular Alzheimer's disease. However, the subtle morphological changes in compartments of affected neurons are difficult to quantify quickly and reliably, making this phenotype inaccessible as either an early diagnostic marker, or as a read-out for drug screening.
We present a method for automatic detection of fluorescently labeled endolysosomes in degenerative neurons in situ. The Drosophila blue cheese (bchs) mutant was taken as a genetic neurodegenerative model for direct in situ visualization and quantification of endolysosomal compartments in affected neurons. Endolysosomal compartments were first detected automatically from 2-D image sections using a combination of point-wise multi-scale correlation and normalized correlation operations. This detection algorithm performed well at recognizing fluorescent endolysosomes, unlike conventional convolution methods, which are confounded by variable intensity levels and background noise. Morphological feature differences between endolysosomes from wild type vs. degenerative neurons were then quantified by multivariate profiling and support vector machine (SVM) classification based on compartment density, size and contrast distribution. Finally, we ranked these distributions according to their profiling accuracy, based on the backward elimination method.
This analysis revealed a statistically significant difference between the neurodegenerative phenotype and the wild type up to a 99.9% confidence interval. Differences between the wild type and phenotypes resulting from overexpression of the Bchs protein are detectable by contrast variations, whereas both size and contrast variations distinguish the wild type from either of the loss of function alleles bchs1 or bchs58. In contrast, the density measurement differentiates all three bchs phenotypes (loss of function as well as overexpression) from the wild type.
Our model demonstrates that neurodegeneration-associated endolysosomal defects can be detected, analyzed, and classified rapidly and accurately as a diagnostic imaging-based screening tool.
内溶酶体（晚期内体到溶酶体的降解性细胞内隔室）功能障碍是一些神经退行性疾病（尤其是阿尔茨海默病）的早期标志。然而，受影响神经元的隔室的细微形态变化难以快速且可靠地定量，因此，该表型无法作为早期诊断标志物或药物筛选的读出器。
我们提出了一种用于自动检测变性神经元中荧光标记的内溶酶体的方法。采用果蝇蓝奶酪（bchs）突变体作为遗传神经退行性模型，直接对受影响神经元中的内溶酶体隔室进行可视化和定量。使用点式多尺度相关和归一化相关操作的组合，从 2-D 图像切片自动检测内溶酶体隔室。与传统的卷积方法不同，该检测算法在识别荧光内溶酶体方面效果良好，因为传统的卷积方法会受到强度水平和背景噪声变化的影响。然后，通过基于隔室密度、大小和对比度分布的多元分析和支持向量机（SVM）分类来量化来自野生型与变性神经元的内溶酶体之间的形态特征差异。最后，我们根据后向消除方法，根据分析准确性对这些分布进行排序。
该分析在 99.9%置信区间内揭示了神经退行性表型与野生型之间的统计学显著差异。Bchs 蛋白过表达导致的表型与野生型之间的差异可以通过对比度变化来检测，而大小和对比度变化都可以将野生型与失活功能等位基因 bchs1 或 bchs58 区分开来。相比之下，密度测量可以将所有三种 bchs 表型（失活功能以及过表达）与野生型区分开来。
我们的模型表明，神经退行性相关的内溶酶体缺陷可以作为基于诊断成像的筛选工具快速且准确地检测、分析和分类。
