Melgoza Itzel Paola, Chenna Srish S, Tessier Steven, Zhang Yejia, Tang Simon Y, Ohnishi Takashi, Novais Emanuel José, Kerr Geoffrey J, Mohanty Sarthak, Tam Vivian, Chan Wilson C W, Zhou Chao-Ming, Zhang Ying, Leung Victor Y, Brice Angela K, Séguin Cheryle A, Chan Danny, Vo Nam, Risbud Makarand V, Dahia Chitra L
Orthopedic Soft Tissue Research Program Hospital for Special Surgery New York City New York USA.
Department of Orthopaedic Surgery Sidney Kimmel Medical College, Thomas Jefferson University Philadelphia Pennsylvania USA.
JOR Spine. 2021 Jul 17;4(2):e1164. doi: 10.1002/jsp2.1164. eCollection 2021 Jun.
Mice have been increasingly used as preclinical model to elucidate mechanisms and test therapeutics for treating intervertebral disc degeneration (IDD). Several intervertebral disc (IVD) histological scoring systems have been proposed, but none exists that reliably quantitate mouse disc pathologies. Here, we report a new robust quantitative mouse IVD histopathological scoring system developed by building consensus from the spine community analyses of previous scoring systems and features noted on different mouse models of IDD. The new scoring system analyzes 14 key histopathological features from nucleus pulposus (NP), annulus fibrosus (AF), endplate (EP), and AF/NP/EP interface regions. Each feature is categorized and scored; hence, the weight for quantifying the disc histopathology is equally distributed and not driven by only a few features. We tested the new histopathological scoring criteria using images of lumbar and coccygeal discs from different IDD models of both sexes, including genetic, needle-punctured, static compressive models, and natural aging mice spanning neonatal to old age stages. Moreover, disc sections from common histological preparation techniques and stains including H&E, SafraninO/Fast green, and FAST were analyzed to enable better cross-study comparisons. Fleiss's multi-rater agreement test shows significant agreement by both experienced and novice multiple raters for all 14 features on several mouse models and sections prepared using various histological techniques. The sensitivity and specificity of the new scoring system was validated using artificial intelligence and supervised and unsupervised machine learning algorithms, including artificial neural networks, -means clustering, and principal component analysis. Finally, we applied the new scoring system on established disc degeneration models and demonstrated high sensitivity and specificity of histopathological scoring changes. Overall, the new histopathological scoring system offers the ability to quantify histological changes in mouse models of disc degeneration and regeneration with high sensitivity and specificity.
小鼠越来越多地被用作临床前模型,以阐明治疗椎间盘退变(IDD)的机制并测试治疗方法。已经提出了几种椎间盘(IVD)组织学评分系统,但尚无能够可靠地量化小鼠椎间盘病变的系统。在此,我们报告了一种新的、强大的小鼠IVD组织病理学定量评分系统,该系统是通过对先前评分系统的脊柱社区分析以及在不同IDD小鼠模型中观察到的特征达成共识而开发的。新的评分系统分析了来自髓核(NP)、纤维环(AF)、终板(EP)以及AF/NP/EP界面区域的14个关键组织病理学特征。每个特征都进行了分类和评分;因此,量化椎间盘组织病理学的权重是均匀分布的,而不是仅由少数特征驱动。我们使用来自不同性别、不同IDD模型(包括基因、针刺、静态压缩模型以及从新生到老年阶段的自然衰老小鼠)的腰椎和尾椎椎间盘图像,测试了新的组织病理学评分标准。此外,还分析了来自常见组织学制备技术和染色(包括苏木精和伊红染色、番红O/固绿染色以及FAST染色)的椎间盘切片,以便进行更好的跨研究比较。Fleiss多评分者一致性检验表明,经验丰富和新手评分者在使用各种组织学技术制备的几种小鼠模型和切片上,对所有14个特征都有显著的一致性。新评分系统的敏感性和特异性通过人工智能以及监督和无监督机器学习算法(包括人工神经网络、K均值聚类和主成分分析)进行了验证。最后,我们将新评分系统应用于已建立的椎间盘退变模型,并证明了组织病理学评分变化具有高敏感性和特异性。总体而言,新的组织病理学评分系统能够以高敏感性和特异性量化椎间盘退变和再生小鼠模型中的组织学变化。