Clustering and visualization of single-cell RNA-seq data using path metrics.

Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.

Topological data analysis reveals a core gene expression backbone that defines form and function across flowering plants.

Since they emerged approximately 125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests that there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

Saturated fatty acids dampen the immunogenicity of cancer by suppressing STING.

Oncogenes destabilize STING in epithelial cell-derived cancer cells, such as head and neck squamous cell carcinomas (HNSCCs), to promote immune escape. Despite the abundance of tumor-infiltrating myeloid cells, HNSCC presents notable resistance to STING stimulation. Here, we show how saturated fatty acids in the microenvironment dampen tumor response to STING stimulation. Using single-cell analysis, we found that obesity creates an IFN-I-deprived tumor microenvironment with a massive expansion of suppressive myeloid cell clusters and contraction of effector T cells. Saturated fatty acids, but not unsaturated fatty acids, potently inhibit the STING-IFN-I pathway in HNSCC cells. Myeloid cells from obese mice show dampened responses to STING stimulation and are more suppressive of T cell activation. In agreement, obese hosts exhibited increased tumor burden and lower responsiveness to STING agonist. As a mechanism, saturated fatty acids induce the expression of NLRC3, depletion of which results in a T cell inflamed tumor microenvironment and IFN-I-dependent tumor control.