Department of Cell, Developmental, and Cancer Biology, Oregon Health & Science University, Portland, Oregon, USA.
Department of Otolaryngology-Head & Neck Surgery, Oregon Health & Science University, Portland, Oregon, USA.
Cytometry A. 2019 Apr;95(4):389-398. doi: 10.1002/cyto.a.23726. Epub 2019 Feb 4.
Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare T 17 cells, further enabling sub-population analysis into T 1-like and T 2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of T 2-like T 17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, T 2-like T 17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration. © 2019 International Society for Advancement of Cytometry.
图像细胞术能够在保持组织架构的情况下对细胞进行定量特征分析；因此，它在推动基于免疫的生物标志物监测的多重免疫组化（IHC）和数字图像分析方面得到了突出的发展，而这些监测与癌症免疫治疗有关。然而，目前图像细胞术方法学中的一个挑战是在分割细胞核和细胞成分方面存在技术限制，特别是在异质染色的癌症组织图像中。为了提高在苏木精染色图像中单细胞分割的检测和特异性（这可用于最近报道的 12 种生物标志物显色顺序多重 IHC），我们对以前为苏木精和曙红染色图像开发的分割算法进行了改进，其中基于 Gabor 滤波提取形态特征，然后将图像像素堆叠到 n 维特征空间，并对单个像素进行无监督聚类。与标准分割方法相比，我们提出的方法显示出了更高的灵敏度和特异性。在多重 IHC/图像细胞术分析中用我们的方法替换以前提出的方法，我们观察到包括相对罕见的 T17 细胞在内的细胞谱系的检测率更高，进一步基于 T-bet 和 GATA3 表达将亚群分析为 T1 样和 T2 样表型。有趣的是，T2 样 T17 细胞的优势与头颈部鳞状细胞癌的人乳头瘤病毒（HPV）阴性状态相关，与 HPV 阳性状态相比，这是一种预后不良的亚型。此外，HPV 阴性头颈部癌症组织中的 T2 样 T17 细胞与 CD66b 粒细胞在时空上相关，推测与免疫抑制微环境有关。我们的用于多重 IHC/图像细胞术的细胞分割方法可能有助于深入的免疫分析和空间关联，从而进一步探索基于组织的生物标志物。©2019 国际细胞分析学会。
