Carr Laura M, Guglietti Bianca, Wee Ing Chee, Musgrave Ian F, Mustafa Sanam, Corrigan Frances, Collins-Praino Lyndsey E
School of Biomedicine, The University of Adelaide, Adelaide, South Australia, Australia.
Australian Research Council Centre of Excellence for Nanoscale Biophotonics, The University of Adelaide, Adelaide, South Australia, Australia.
Brain Pathol. 2025 Aug 19:e70036. doi: 10.1111/bpa.70036.
Phenotypic changes in microglia have been linked to multiple neurological conditions, such as dementia, Parkinson's disease, stroke and traumatic brain injury. Consistent identification and classification of microglia is essential in understanding potential links with neurological diseases. Currently, there are several ways by which the microglial population and morphology are assessed, including manually or using open-source image analysis platforms, such as ImageJ. A microglial classification module for the HALO digital pathology platform has been developed for this purpose but has not yet been validated within the literature. The current study therefore conducted a comparison of the performance of this HALO module to manual microglial analysis and to automated analysis via ImageJ using both human and rat brain tissue. In 5 μm thick human tissue, total and activated microglia/mm counted by HALO showed strong positive correlations with both manual and ImageJ counts. HALO did not differ from the other methods for total microglia counts; however, Halo did differ from both manual and ImageJ methods in the number of activated microglia detected within the substantia nigra. In 20 μm rat tissue, total counts derived from HALO showed moderate positive correlations with both manual and ImageJ counting; however, activated counts on Halo were not positively correlated with any method. To our knowledge, this is the first study to systematically compare the Halo module to other common methods of microglia analysis. When applied to 5 μm tissue, the Halo module is comparable to manual counting and to automated analysis on ImageJ. However, when analyzing thicker tissue, Halo struggles to perform in line with these other methods, particularly for counts of activated microglia, likely due to increased cell density and the morphological complexity of microglia. These results highlight the importance of carefully tailoring image analysis parameters on automated counting methods to suit the needs of the tissue.
小胶质细胞的表型变化与多种神经系统疾病有关，如痴呆、帕金森病、中风和创伤性脑损伤。对小胶质细胞进行一致的识别和分类对于理解其与神经系统疾病的潜在联系至关重要。目前，评估小胶质细胞群体和形态的方法有多种，包括手动评估或使用开源图像分析平台，如图像J。为此，已经开发了用于HALO数字病理学平台的小胶质细胞分类模块，但尚未在文献中得到验证。因此，本研究比较了该HALO模块与手动小胶质细胞分析以及通过图像J对人和大鼠脑组织进行自动分析的性能。在5μm厚的人体组织中，HALO计数的每平方毫米总小胶质细胞和活化小胶质细胞与手动计数和图像J计数均呈强正相关。HALO在总小胶质细胞计数方面与其他方法没有差异；然而，在黑质中检测到的活化小胶质细胞数量上，HALO与手动和图像J方法均不同。在20μm厚的大鼠组织中，HALO得出的总计数与手动计数和图像J计数均呈中度正相关；然而，HALO上的活化计数与任何方法均无正相关。据我们所知，这是第一项系统比较HALO模块与其他常见小胶质细胞分析方法的研究。当应用于5μm组织时，HALO模块与手动计数和图像J自动分析相当。然而，在分析较厚组织时，HALO难以与这些其他方法保持一致，特别是在活化小胶质细胞计数方面，这可能是由于细胞密度增加和小胶质细胞形态复杂性所致。这些结果凸显了根据组织需求精心调整自动计数方法的图像分析参数的重要性。
