An unbiased, automated platform for scoring dopaminergic neurodegeneration in C. elegans

Caenorhabditis elegans (C. elegans) has served as a simple model organism
to study dopaminergic neurodegeneration, as it enables quantitative
analysis of cellular and sub-cellular morphologies in live animals. These
isogenic nematodes have a rapid life cycle and transparent body, making
high-throughput imaging and evaluation of fluorescently tagged neurons
possible. However, the current state-of-the-art method for quantifying
dopaminergic degeneration requires researchers to manually examine images
and score dendrites into groups of varying levels of neurodegeneration
severity, which is time-consuming, subject to bias, and limited in data
sensitivity. We aim to overcome the pitfalls of manual neuron scoring by
developing an automated, unbiased image processing algorithm to quantify
dopaminergic neurodegeneration in C. elegans. The algorithm can
be used on images acquired with different microscopy setups and only
requires two inputs: a maximum projection image of the four cephalic
neurons in the C. elegans head and the pixel size of the user’s camera. We
validate the platform by detecting and quantifying neurodegeneration in
nematodes exposed to rotenone, cold shock, and 6-hydroxydopamine using 63x
epifluorescence, 63x confocal, and 40x epifluorescence microscopy,
respectively. Analysis of tubby mutant worms with altered fat storage
showed that, contrary to our hypothesis, increased adiposity did not
sensitize to stressor-induced neurodegeneration.  We further
verify the accuracy of the algorithm by comparing code-generated,
categorical degeneration results with manually scored dendrites of the
same experiments. The platform, which detects 19 different metrics of
neurodegeneration, can provide comparative insight into how each exposure
affects dopaminergic neurodegeneration patterns.

秀丽隐杆线虫（Caenorhabditis elegans, C. elegans）已作为一种经典简易模式生物，被广泛应用于多巴胺能神经元变性的相关研究，因其可实现活体动物体内细胞及亚细胞形态的定量分析。这类同基因线虫生命周期短促且躯体透明，使得对荧光标记神经元开展高通量成像与评估成为可能。然而，当前用于量化多巴胺能神经元变性的主流方法，仍需研究人员手动检视图像，并将树突按神经元变性严重程度划分为不同组别，该过程耗时耗力、易引入偏倚，且数据灵敏度有限。本研究旨在克服人工神经元评分的诸多缺陷，开发一种自动化、无偏倚的图像处理算法，以实现秀丽隐杆线虫多巴胺能神经元变性的量化分析。该算法可适配不同显微镜成像系统获取的图像，且仅需两个输入参数：秀丽隐杆线虫头部四个头侧神经元的最大投影图像，以及用户所用相机的像素尺寸。我们分别采用63倍落射荧光显微镜（epifluorescence microscopy）、63倍共聚焦显微镜（confocal microscopy）及40倍落射荧光显微镜成像，对暴露于鱼藤酮（rotenone）、冷休克与6-羟基多巴胺（6-hydroxydopamine）的线虫开展变性检测与量化，以此验证该算法平台的有效性。对脂肪存储异常的tubby突变蠕虫开展分析后发现，与我们的初始假说相悖，脂肪含量升高并未增强机体对应激原诱导的神经元变性的易感性。我们进一步将算法生成的分类变性结果与同一实验中人工评分的树突结果进行比对，验证了算法的准确性。该平台可检测19项不同的神经元变性相关指标，能够为阐明各类暴露因素如何影响多巴胺能神经元变性模式提供可比较的研究视角。