LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies

Abstract

Motivation Recent advances in highly multiplexed imaging have provided unprecedented insights into the complex cellular organization of tissues, with many applications in translational medicine. However, downstream analyses of multiplexed imaging data face several technical limitations, and although some computational methods and bioinformatics tools are available, deciphering the complex spatial organisation of cellular ecosystems remains a challenging problem. Results To mitigate this problem, we develop a novel computational tool, LOCATOR (anaLysis Of CAncer Tissue micrOenviRonment), for spatial analysis of cancer tissue microenvironments using data acquired from mass cytometry imaging (MCI) technologies. LOCATOR introduces a graph-based representation of tissue images to describe features of the cellular organisation and deploys downstream analysis and visualisation utilities that can be used for data-driven patient risk stratification. Our case studies using MCI data from two well-annotated breast cancer cohorts re-confirmed that the spatial organisation of the tumour-immune microenvironment is strongly associated with the clinical outcome in breast cancer. In addition, we report interesting potential associations between the spatial organization of macrophages and patients’ survival. Our work introduces an automated and versatile analysis tool for MCI data with many applications in future cancer research projects. Availability and implementation Datasets and codes of LOCATOR are publicly available at https://github.com/RezvanEhsani/LOCATOR. Supplementary information Supplementary data are available at Bioinformatics Advances online

Development of a high dimensional imaging mass cytometry panel to investigate spatial organization of tissue microenvironment in formalin-fixed archival clinical tissues

“…Thanks to the recent advances in single-cell research, a variety of pipelines are available utilizing different analysis platforms (python, R, MATLAB etc.). In addition, various computational methods can be used to extract and perform spatial analysis of IMC data such as “analysis of cancer tissue microenvironment” (LOCATOR) approach [ 44 ] or monkeybread [ 45 ]. We have used functionalities from Cytomapper package and adopted them in R to investigate the spatial location of single-cells and visualize the tissue structures [ 27 ].…”

Automated cell type annotation and exploration of single-cell signaling dynamics using mass cytometry

“…Our results demonstrate that it is possible to incorporate single-cell omics in combination with “standard” clinical and genetic characteristics. For example, in future studies, state-of-the-art classification systems for leukemia such as the European LeukemiaNet (ELN) risk classification system for acute myeloid leukemia 32 or the PAM50 33 classification algorithm for breast cancer could be complemented with information from CyTOF, imaging mass cytometry (IMC), 34 or spatial transcriptomic datasets that capture the cellular heterogeneity of patients as well as aberrant signaling states that are disease specific. In addition, with different antibody panels (e.g., surface phenotypic markers and/or signaling phosphoproteins), we anticipate that the proposed computational framework could easily be extended to investigate different cell types and signaling proteins.…”