Parrillo C, Persiani F, Mantini G, Cellini B, D'Amati A, Lucchetti D, Scambia G, Sgambato A, Giacò L
Bioinformatics Research Core Facility, Gemelli Science and Technology Park (GSTeP), Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Catholic University of the Sacred Heart, Rome, Italy.
Comput Struct Biotechnol J. 2024 Nov 14;23:4143-4147. doi: 10.1016/j.csbj.2024.11.029. eCollection 2024 Dec.
In the last decade, it has been recognized that tumors do not exist in isolation but interact with surrounding cells, blood vessels, immune cells, and extracellular matrix components. This understanding has shifted the focus from tumor cells alone to the broader context in which they exist, known as tumor microenvironment (TME). The TME is highly heterogeneous, consisting of various cell types, mainly cancer cells, immune cells, and stromal cells. The interactions among different cell types in the TME significantly influence tumor progression, immune evasion, and response to therapy. Spatial profiling helps to map these interactions, providing insights into how cells communicate and influence each other, analyzing them in their spatial context. However, there is a lack of tools capable of efficiently analyzing this type of data. As a matter of fact, the most commonly used tool, phenoptr, is time consuming, lacks automation, and is often not user friendly. In this scenario, ALOA (Analysis spatiaL prOfiling imAging), represents a tool that, starting from inForm™ data, provides a complete and accurate analysis along with accompanying graphs and statistical analysis. Of note, ALOA is specifically designed to handle spatial coordinates and image-based data derived from multiplexed immunohistochemistry (IHC) and immunofluorescence (IF). Therefore, it is not suited to work with single cell transcriptomics or non-spatial single cell transcriptomics data, which require specific tools for handling high-dimensional gene expression information. We integrated Phenoimager multiplexed tissue imaging with the ALOA modeling algorithm.
在过去十年中，人们已经认识到肿瘤并非孤立存在，而是与周围细胞、血管、免疫细胞和细胞外基质成分相互作用。这种认识将关注点从单纯的肿瘤细胞转移到了肿瘤细胞所处的更广泛背景，即肿瘤微环境（TME）。肿瘤微环境高度异质性，由多种细胞类型组成，主要包括癌细胞、免疫细胞和基质细胞。肿瘤微环境中不同细胞类型之间的相互作用显著影响肿瘤进展、免疫逃逸和对治疗的反应。空间分析有助于描绘这些相互作用，深入了解细胞如何相互交流和影响，并在空间背景下对其进行分析。然而，缺乏能够有效分析这类数据的工具。事实上，最常用的工具phenoptr耗时、缺乏自动化，而且通常对用户不友好。在这种情况下，ALOA（空间分析成像）是一种工具，它从inForm™数据出发，提供完整准确的分析以及附带的图表和统计分析。值得注意的是，ALOA专门设计用于处理源自多重免疫组织化学（IHC）和免疫荧光（IF）的空间坐标和基于图像的数据。因此，它不适用于处理单细胞转录组学或非空间单细胞转录组学数据，这些数据需要特定工具来处理高维基因表达信息。我们将Phenoimager多重组织成像与ALOA建模算法进行了整合。
