Protocol for enhancing visualization clarity for categorical spatial datasets using Spaco.

In categorical data visualization, appropriate color arrangements can avoid perceptual ambiguity and help perceive underlying data patterns. We introduce a protocol to assign contrastive colors to neighboring categories using both Python and R packages. We describe steps for calculating the interlacement between clusters and generating a proper color palette and calculating color contrast. We then detail procedures for aligning cluster interlacement and color contrast to get an optimized cluster-color assignment, achieving clear categorical visualization. For complete details on the use and execution of this protocol, please refer to Jing et al.1.

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution.

Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.

An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor progression.

Dissecting and understanding the cancer ecosystem, especially that around the tumor margins, which have strong implications for tumor cell infiltration and invasion, are essential for exploring the mechanisms of tumor metastasis and developing effective new treatments. Using a novel tumor border scanning and digitization model enabled by nanoscale resolution-SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we identified a 500 µm-wide zone centered around the tumor border in patients with liver cancer, referred to as "the invasive zone". We detected strong immunosuppression, metabolic reprogramming, and severely damaged hepatocytes in this zone. We also identified a subpopulation of damaged hepatocytes with increased expression of serum amyloid A1 and A2 (referred to collectively as SAAs) located close to the border on the paratumor side. Overexpression of CXCL6 in adjacent malignant cells could induce activation of the JAK-STAT3 pathway in nearby hepatocytes, which subsequently caused SAAs' overexpression in these hepatocytes. Furthermore, overexpression and secretion of SAAs by hepatocytes in the invasive zone could lead to the recruitment of macrophages and M2 polarization, further promoting local immunosuppression, potentially resulting in tumor progression. Clinical association analysis in additional five independent cohorts of patients with primary and secondary liver cancer (n = 423) showed that patients with overexpression of SAAs in the invasive zone had a worse prognosis. Further in vivo experiments using mouse liver tumor models in situ confirmed that the knockdown of genes encoding SAAs in hepatocytes decreased macrophage accumulation around the tumor border and delayed tumor growth. The identification and characterization of a novel invasive zone in human cancer patients not only add an important layer of understanding regarding the mechanisms of tumor invasion and metastasis, but may also pave the way for developing novel therapeutic strategies for advanced liver cancer and other solid tumors.

Facial Skin Microbiota-Mediated Host Response to Pollution Stress Revealed by Microbiome Networks of Individual.

Urban living has been reported to cause various skin disorders. As an integral part of the skin barrier, the skin microbiome is among the key factors associated with urbanization-related skin alterations. The role of skin microbiome in mediating the effect of urban stressors (e.g., air pollutants) on skin physiology is not well understood. We generated 16S sequencing data and constructed a microbiome network of individual (MNI) to analyze the effect of pollution stressors on the microbiome network and its downstream mediation effect on skin physiology in a personalized manner. In particular, we found that the connectivity and fragility of MNIs significantly mediated the adverse effects of air pollution on skin health, and a smoking lifestyle deepened the negative effects of pollution stress on facial skin microbiota. This is the first study that describes the mediation effect of the microbiome network on the skin's physiological response toward environmental factors as revealed by our newly developed MNI approach and conditional process analysis. IMPORTANCE The association between the skin microbiome and skin health has been widely reported. However, the role of the skin microbiome in mediating skin physiology remains a challenging and yet priority subject in the field. Through developing a novel MNI method followed by mediation analysis, we characterized the network signature of the skin microbiome at an individual level and revealed the role of the skin microbiome in mediating the skin's responses toward environmental stressors. Our findings may shed new light on microbiome functions in skin health and lay the foundation for the design of a microbiome-based intervention strategy in the future.