Ravikumar K, Voigt Sven P, Kalidindi Surya R, Basu Bikramjit
Materials Research Centre, Indian Institute of Science, Bangalore 560012, India.
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America.
Biomed Mater. 2021 Mar 3;16(3). doi: 10.1088/1748-605X/abcf5e.
Quantitative image analysis is an important tool in understanding cell fate processes through the study of cell morphological changes in terms of size, shape, number, and orientation. In this context, this work explores systematically the main challenges involved in the quantitative analysis of fluorescence microscopy images and also proposes a new protocol while comparing its outcome with the widely used ImageJ analysis. It is important to mention that fluorescence microscopy is by far most widely used in biocompatibility analysis (observing cell fate changes) of implantable biomaterials. In this study, we employed two different image analyses toolsets: (a) the conventionally employed ImageJ software, and (b) a recently developed automated digital image analyses framework, called ImageMKS. While ImageJ offers a powerful toolset for image analyses, it requires sophisticated user expertise to design and iteratively refine the analyses workflow. This workflow primarily comprises a sequence of image transformations that typically involve de-noising and labeling of features. On the other hand, ImageMKS automates the image analyses protocol to a large extent, and thereby mitigates the influence of the user bias on the final results. This aspect is addressed using a case study of C2C12 mouse myoblast cells grown on poly(vinylidene difluoride) (PVDF) based polymeric substrates. In particular, we used a number of fluorescence microscopy images of these mouse myoblasts grown on PVDF-based nanobiocomposites under the influence of electric field. In addition to the MKS workflows requiring much less user time because of their automation, it was observed that ImageMKS workflows consistently produced more reliable results that correlated better with the previously reported experimental studies.
定量图像分析是通过研究细胞在大小、形状、数量和方向方面的形态变化来理解细胞命运过程的重要工具。在此背景下，本研究系统地探讨了荧光显微镜图像定量分析中涉及的主要挑战，并提出了一种新的方案，同时将其结果与广泛使用的ImageJ分析进行比较。值得一提的是，荧光显微镜目前在可植入生物材料的生物相容性分析（观察细胞命运变化）中应用最为广泛。在本研究中，我们采用了两种不同的图像分析工具集：（a）传统使用的ImageJ软件，以及（b）最近开发的名为ImageMKS的自动化数字图像分析框架。虽然ImageJ为图像分析提供了强大的工具集，但它需要复杂的用户专业知识来设计和迭代优化分析工作流程。该工作流程主要包括一系列图像变换，通常涉及特征的去噪和标记。另一方面，ImageMKS在很大程度上实现了图像分析协议的自动化，从而减轻了用户偏差对最终结果的影响。通过对在聚偏二氟乙烯（PVDF）基聚合物基材上生长的C2C12小鼠成肌细胞的案例研究来探讨这一方面。具体而言，我们使用了一些在电场影响下在基于PVDF的纳米生物复合材料上生长的这些小鼠成肌细胞的荧光显微镜图像。除了MKS工作流程由于自动化而所需的用户时间少得多之外，还观察到ImageMKS工作流程始终产生更可靠的结果，这些结果与先前报道的实验研究相关性更好。
