CNRS, Inserm, AP-HP, Inria, Paris Brain Institute-ICM, Sorbonne University, 75013, Paris, France.
Inserm, CNRS, AP-HP, Institut du Cerveau, ICM, Sorbonne Université, 75013, Paris, France.
Sci Rep. 2024 Mar 18;14(1):6482. doi: 10.1038/s41598-024-56081-7.
Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative diseases. However, measuring rapid cell interactions and distinguishing cells from background make this task very challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce an end-to-end, scalable, and versatile real-time framework for quantifying and analyzing phagocytic activity. Our proposed pipeline is able to process large data-sets and includes a data quality verification module to counteract potential perturbations such as microscope movements and frame blurring. We also propose an explainable cell segmentation module to improve the interpretability of deep learning methods compared to black-box algorithms. This includes two interpretable deep learning capabilities: visual explanation and model simplification. We demonstrate that interpretability in deep learning is not the opposite of high performance, by additionally providing essential deep learning algorithm optimization insights and solutions. Besides, incorporating interpretable modules results in an efficient architecture design and optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant cells are larger and more aggressive than control cells. The method has been tested and validated on several public benchmarks by generating state-of-the art performances. To stimulate translational approaches and future studies, we release an open-source end-to-end pipeline and a unique microglial cells phagocytosis dataset for immune system characterization in neurodegenerative diseases research. This pipeline and the associated dataset will consistently crystallize future advances in this field, promoting the development of efficient and effective interpretable algorithms dedicated to the critical domain of neurodegenerative diseases' characterization. https://github.com/ounissimehdi/PhagoStat .
量化动态、未染色细胞的吞噬作用对于评估神经退行性疾病至关重要。然而，在处理延时相差显微镜的视频时，测量快速的细胞相互作用并将细胞与背景区分开来是一项极具挑战性的任务。在这项研究中，我们引入了一个端到端的、可扩展的、通用的实时框架，用于量化和分析吞噬活性。我们提出的流水线能够处理大数据集，并包括一个数据质量验证模块，以抵消潜在的干扰，如显微镜运动和帧模糊。我们还提出了一个可解释的细胞分割模块，与黑盒算法相比，提高了深度学习方法的可解释性。这包括两个可解释的深度学习功能：可视化解释和模型简化。我们证明了深度学习中的可解释性并不是高性能的对立面，同时提供了必要的深度学习算法优化见解和解决方案。此外，纳入可解释模块可以实现高效的架构设计和优化的执行时间。我们将该流水线应用于量化和分析额颞叶痴呆（FTD）中的小胶质细胞吞噬作用，并获得了具有统计学意义的可靠结果，表明 FTD 突变细胞比对照细胞更大、更具攻击性。该方法已经在几个公共基准上进行了测试和验证，生成了最先进的性能。为了促进转化方法和未来的研究，我们发布了一个开源的端到端流水线和一个独特的小胶质细胞吞噬数据集，用于神经退行性疾病研究中的免疫系统特征化。这个流水线和相关数据集将不断推动该领域的未来发展，促进开发高效、有效的可解释算法，专门用于神经退行性疾病特征化的关键领域。https://github.com/ounissimehdi/PhagoStat。
