Abstract 7409: SpatialCells: Automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data

Abstract Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using spatially resolved multiplexed single-cell data. We have provided detailed documents and tutorials at https: //github. com/SemenovLab/SpatialCells. SpatialCells is featured by its capability to computationally define regions of interest based on any group of cells and subsequently conduct region-based analyses. SpatialCells incorporates several modules to facilitate its functionality, such as the spatial module and the measurements module. Our workflow starts with developing the spatial module, including functions to establish regional boundaries and check the region in which a cell is located. In particular, SpatialCells supports macro-region and micro-region analyses. In the macro-region analysis, users can employ SpatialCells to divide the whole tissue into several subregions, such as tumor, tumor border, stroma border, and stroma. In the micro-region analysis, SpatialCells identifies small regions within an area, such as within the tumor area, enabling a finer-grained understanding of the cellular landscape. The measurements module contains functions to extract the properties of tumor cells, immune cells, and tumor-immune cell interactions, such as multivariate tumor proliferation index, immune infiltration score, and tumor-immune cell distances. These properties can be assessed for the whole tissue or local regions. Importantly, SpatialCells can efficiently process datasets containing millions of cells. In summary, SpatialCells is a novel software solution for spatially analyzing the tumor microenvironment and automatically extracting quantitative features of cells using multiplexed imaging data in a streamlined fashion with the capacity to process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions at scale, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis. Citation Format: Guihong Wan. SpatialCells: Automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 7409.