Abstract
Background 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.
Results This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data.
Conclusions SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.
Availability of code and materials https://github.com/SemenovLab/SpatialCells.
Competing Interest Statement
YRS is an advisory board member/consultant and has received honoraria from Incyte Corporation, Castle Biosciences, Galderma, Pfizer, and Sanofi outside of the submitted work.
Footnotes
↵** This work was jointly supervised by Peter K. Sorger and Yevgeniy R. Semenov
List of abbreviations
- TME
- Tumor Microenvironment
- CODEX
- Co-detection by indexing: a method for multiplexed tissue imaging
- CyCIF
- Cyclic immunofluorescence: a method for highly multiplexed tissue imaging
- SCIMAP
- A toolkit for analyzing spatial molecular data.
- HALO
- A toolkit for quantitative image analysis
- ROI
- Region of interest
- AJCC
- American Joint Committee on Cancer
- UICC
- Union for International Cancer Control
- MPI
- Multivariate proliferation index
