Inferring histology-associated gene expression gradients in spatial transcriptomic studies.

Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.

Sialic acid metabolism orchestrates transcellular connectivity and signaling in glioblastoma.

BACKGROUND: In glioblastoma (GBM), the effects of altered glycocalyx are largely unexplored. The terminal moiety of cell coating glycans, sialic acid, is of paramount importance for cell-cell contacts. However, sialic acid turnover in gliomas and its impact on tumor networks remain unknown. METHODS: We streamlined an experimental setup using organotypic human brain slice cultures as a framework for exploring brain glycobiology, including metabolic labeling of sialic acid moieties and quantification of glycocalyx changes. By live, 2-photon and high-resolution microscopy we have examined morphological and functional effects of altered sialic acid metabolism in GBM. By calcium imaging we investigated the effects of the altered glycocalyx on a functional level of GBM networks. RESULTS: The visualization and quantitative analysis of newly synthesized sialic acids revealed a high rate of de novo sialylation in GBM cells. Sialyltrasferases and sialidases were highly expressed in GBM, indicating that significant turnover of sialic acids is involved in GBM pathology. Inhibition of either sialic acid biosynthesis or desialylation affected the pattern of tumor growth and lead to the alterations in the connectivity of glioblastoma cells network. CONCLUSIONS: Our results indicate that sialic acid is essential for the establishment of GBM tumor and its cellular network. They highlight the importance of sialic acid for glioblastoma pathology and suggest that dynamics of sialylation have the potential to be targeted therapeutically.