GE Research, 1 Research Circle, Niskayuna, NY 12309, USA.
Integrated Research Facility at Fort Detrick, National Institute of Allergy and Infectious Diseases, National Institutes of Health, B-8200 Research Plaza, Frederick, MD 21702, USA.
Viruses. 2020 Jul 23;12(8):787. doi: 10.3390/v12080787.
Over the last 15 years, advances in immunofluorescence-imaging based cycling methods, antibody conjugation methods, and automated image processing have facilitated the development of a high-resolution, multiplexed tissue immunofluorescence (MxIF) method with single cell-level quantitation termed Cell DIVE. Originally developed for fixed oncology samples, here it was evaluated in highly fixed (up to 30 days), archived monkeypox virus-induced inflammatory skin lesions from a retrospective study in 11 rhesus monkeys to determine whether MxIF was comparable to manual H-scoring of chromogenic stains. Six protein markers related to immune and cellular response (CD68, CD3, Hsp70, Hsp90, ERK1/2, ERK1/2 pT202_pY204) were manually quantified (H-scores) by a pathologist from chromogenic IHC double stains on serial sections and compared to MxIF automated single cell quantification of the same markers that were multiplexed on a single tissue section. Overall, there was directional consistency between the H-score and the MxIF results for all markers except phosphorylated ERK1/2 (ERK1/2 pT202_pY204), which showed a decrease in the lesion compared to the adjacent non-lesioned skin by MxIF vs an increase via H-score. Improvements to automated segmentation using machine learning and adding additional cell markers for cell viability are future options for improvement. This method could be useful in infectious disease research as it conserves tissue, provides marker colocalization data on thousands of cells, allowing further cell level data mining as well as a reduction in user bias.
在过去的 15 年中,基于免疫荧光成像的循环方法、抗体偶联方法和自动化图像处理的进步,促进了高通量、多重组织免疫荧光(MxIF)方法的发展,该方法具有单细胞水平定量能力,称为 Cell DIVE。最初是为固定的肿瘤样本开发的,在这里,它在高度固定(长达 30 天)的存档猴痘病毒诱导的炎症性皮肤病变中进行了评估,该病变来自 11 只恒河猴的回顾性研究,以确定 MxIF 是否可与显色染色的手动 H 评分相媲美。六个与免疫和细胞反应相关的蛋白质标记物(CD68、CD3、Hsp70、Hsp90、ERK1/2、ERK1/2 pT202_pY204)由病理学家从连续切片上的显色免疫组化双染色中手动量化(H 评分),并与 MxIF 对同一标记物的自动单细胞定量进行比较,这些标记物在单个组织切片上进行了多重化。总体而言,除磷酸化 ERK1/2(ERK1/2 pT202_pY204)外,所有标记物的 H 评分和 MxIF 结果均具有方向性一致性,MxIF 显示病变与相邻非病变皮肤相比降低,而 H 评分显示增加。使用机器学习改进自动分割以及添加用于细胞活力的额外细胞标记物是未来改进的选择。该方法在传染病研究中可能很有用,因为它可以保存组织,提供数千个细胞的标记物共定位数据,允许进一步进行细胞水平的数据挖掘,并减少用户偏见。