CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity.

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.

CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers.

Tumor microenvironments (TMEs) influence cancer progression but are complex and often differ between patients. Considering that microenvironment variations may reveal rules governing intratumoral cellular programs and disease outcome, we focused on tumor-to-tumor variation to examine 52 head and neck squamous cell carcinomas. We found that macrophage polarity-defined by CXCL9 and SPP1 (CS) expression but not by conventional M1 and M2 markers-had a noticeably strong prognostic association. CS macrophage polarity also identified a highly coordinated network of either pro- or antitumor variables, which involved each tumor-associated cell type and was spatially organized. We extended these findings to other cancer indications. Overall, these results suggest that, despite their complexity, TMEs coordinate coherent responses that control human cancers and for which CS macrophage polarity is a relevant yet simple variable.

Exploring chromatin conformation and gene co-expression through graph embedding.

MOTIVATION: The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships. RESULTS: To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.