ABSTRACT
Tissues are organized in niches where cell types interact to implement specific functions. Spatial -omics technologies allow to decode the molecular features and spatial interactions that determine such niches. However, computational approaches to process and interpret spatial molecular profiles are challenged by the scale and diversity of this data. Here, we present CellCharter, an algorithmic framework for the identification, characterization, and comparison of cellular niches from heterogeneous spatial transcriptomics and proteomics datasets comprising multiple samples. CellCharter outperformed existing methods, identified biologically meaningful cellular niches in different contexts, and discovered spatial cancer cell states, characterized by cell-intrinsic features and spatial interactions between tumor and immune cells. In non-small cell lung cancer, CellCharter revealed a cellular niche composed of neutrophils and tumor cells expressing markers of hypoxia and cell migration. Expression of these markers determined a spatial gradient associated with cancer cell state transition and neutrophil infiltration. Moreover, CellCharter showed that similar compositions of immune cell types can exhibit remarkably different spatial organizations in different tumors, highlighting the need for exploring spatial cell interactions to decipher intratumor heterogeneity. Overall, CellCharter is a flexible and scalable framework to explore and compare the spatial organization of normal and malignant tissues.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
