Automated Spatial Omics Landscape Analysis Approach Reveals Novel Tissue Architectures in Ulcerative Colitis.

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Separating gene clustering in the rare mucopolysaccharidosis disease.

Rare disease datasets are typically structured such that a small number of patients (cases) are represented by multidimensional feature vectors. In this report, we considered a rare disease, mucopolysaccharidosis (MPS). This disease is divided into 11 types and subtypes, depending on the genetic defect, type of deficient enzyme, and nature of accumulated glycosaminoglycan(s). Among them, 7 types are known as possibly neuronopathic and 4 are non-neuronopathic, and in the case of the former group, prediction of the course of the disease is crucial for patient's treatment and the management. Here, we have used transcriptomic data available for one patient from each MPS type/subtype. The approach to gene grouping considered by us was based on the minimization of the perceptron criterion in the form of convex and piecewise linear function (CPL). This approach allows designing complexes of linear classifiers on the basis of small samples of multivariate vectors. As a result, distinguishing neuronopathic and non-neuronopathic forms of MPS was possible on the basis of bioinformatic analysis of gene expression patterns where each MPS type was represented by only one patient. This approach can be potentially used also for assessing other features of patients suffering from rare diseases, for which large body of data (like transcriptomic data) is available from only one or a few representatives.