Munoz Alma Zuniga, Soni Kartik, Li Angela, Lakkundi Vedant, Iyer Arundati, Adler Ari, Kirkendall Kathryn, Petrigliano Frank, Benayoun Bérénice A, Lozito Thomas P, Almada Albert E
bioRxiv. 2024 Dec 22:2024.12.21.629913. doi: 10.1101/2024.12.21.629913.
Stem cells are the key cellular source for regenerating tissues and organs in vertebrate species. Historically, the investigation of stem cell fate decisions has been assessed in tissue sections using immunohistochemistry (IHC), where a trained user quantifies fluorescent signal in multiple randomly selected images using manual counting-which is prone to inaccuracies, bias, and is very labor intensive. Here, we highlight the performance of a recently developed machine-learning (ML)-based image analysis program called Ilastik using skeletal muscle as a model system. Interestingly, we demonstrate that Ilastik accurately quantifies Paired Box Protein 7 (PAX7)-positive muscle stem cells (MuSCs) before and during the regenerative process in whole muscle sections from mice, humans, axolotl salamanders, and short-lived African turquoise killifish, to a precision that exceeds human capabilities and in a fraction of the time. Overall, Ilastik is a free user-friendly ML-based program that will expedite the analysis of stained tissue sections in vertebrate animals.
干细胞是脊椎动物组织和器官再生的关键细胞来源。历史上，干细胞命运决定的研究一直是在组织切片中使用免疫组织化学（IHC）进行评估的，训练有素的使用者通过手动计数在多个随机选择的图像中量化荧光信号，这种方法容易出现不准确、偏差，而且劳动强度很大。在这里，我们以骨骼肌为模型系统，突出展示了一种最近开发的基于机器学习（ML）的图像分析程序Ilastik的性能。有趣的是，我们证明Ilastik能够准确量化来自小鼠、人类、蝾螈和短命的非洲绿松石鳉鱼的全肌肉切片在再生过程之前和期间的配对盒蛋白7（PAX7）阳性肌肉干细胞（MuSCs），其精度超过人类能力，且用时仅为人类的一小部分。总体而言，Ilastik是一个免费的、用户友好的基于ML的程序，它将加快对脊椎动物染色组织切片的分析。
