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Publications

Journal Article Epublish

PubMed: 39640532 DOI: 10.1016/j.csbj.2024.11.029

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

Comput Struct Biotechnol J 23:4143-4147

Authors: C Parrillo, F Persiani, G Mantini, B Cellini, A D'Amati, D Lucchetti, G Scambia, A Sgambato, L Giacò

Affiliations

  • 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.
  • Unit of Human Anatomy and Histology, Department of Translational Biomedicine and Neuroscience, School of Medicine, University of Bari "Aldo Moro", Bari 70124, Italy.
  • Neuropathology Unit, div of Histopathology, University of the Sacred Heart, Rome, Italy.
  • General Pathology, Department of Translational Medicine and Surgery, Catholic University of the Sacred Heart, Rome, Italy.
  • Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
  • Department of Woman, Child and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy.
  • Institute of Obstetrics and Gynecology, Catholic University of the Sacred Heart, Rome, Italy.

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.

MeSH

Cells, Genes, Neoplasms