ALOA, a pipeline for preliminary analysis of spatial profiling imaging data

Abstract

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.

Keywords

• Spatial imaging
• Tumor microenvironment
• Bioinformatic tool
• Multiplex immunofluorescence
• Single cell