Flores Molina Manuel, Fabre Thomas, Cleret-Buhot Aurélie, Soucy Geneviève, Meunier Liliane, Abdelnabi Mohamed N, Belforte Nicolas, Turcotte Simon, Shoukry Naglaa H
Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM); Département de Microbiologie, Infectiologie et Immunologie, Faculté de Médecine, Université de Montréal.
Centre de Recherche du Centre hospitalier de l'Université de Montréal (CRCHUM).
J Vis Exp. 2020 Mar 25(157). doi: 10.3791/60740.
The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.
肿瘤微环境（TME）的免疫格局是癌症进展和对治疗反应的决定性因素。具体而言，TME中免疫细胞的密度和位置具有重要的诊断和预后价值。TME的多组学分析极大地增进了我们对众多调节肿瘤发生和进展的细胞及分子网络的理解。然而，这些技术无法提供有关细胞空间组织或细胞间相互作用的信息。需要价格合理、易于获取且易于执行的多重技术，以实现组织切片中免疫细胞的空间分辨率，从而补充基于单细胞的高通量技术。在此，我们描述了一种整合连续成像、顺序标记和图像对齐的策略，以生成整个组织切片的虚拟多参数玻片。随后使用用户定义的协议以自动化方式分析虚拟玻片，这些协议能够识别、量化和绘制感兴趣的细胞群体。在这种情况下，使用分析模块Tissuealign、Author和HISTOmap进行图像分析。我们展示了一个将该策略成功应用于一个临床标本的示例，最大限度地从有限的组织样本中获取信息，并提供整个组织切片中TME的无偏视图。
