Cost-effectiveness of a prosthetic venous valve versus compression therapies and wound care for treatment of infra-inguinal deep vein reflux.

Biology is poised to address a fundamental challenge: describing the molecular features of all cell types in the body at single-cell resolution. But a full understanding of how cells function cannot be achieved without considering how they exist within their microenvironments, as cells of the same subtype may have substantially different properties depending on their niche. Spatial transcriptomics (ST) addresses this challenge by allowing researchers to measure cellular gene expression while preserving spatial context. Earlier capture-based ST platforms used relatively coarse, multi-cell capture spots, but emerging high-resolution methods are now approaching single-cell or even subcellular granularity. High-resolution capture-based ST platforms (e.g., Visium HD, Stereo-seq, Slide-seq) dramatically increase spatial granularity but also introduce analytical challenges. First, the capture elements (spots/pixels/beads/tiles) are not intrinsically co-registered to cell boundaries; many lie in extracellular space and therefore capture few or no transcripts. Second, transcripts from a single cell can be dispersed across several nearby capture elements, and conversely, individual capture elements near cell edges often contain transcripts from multiple neighboring cells. Most existing high-resolution segmentation methods implicitly assume one cell contributes mRNA to one capture element, an assumption that is frequently violated at these resolutions. Thus, there is a need for computational approaches that accurately segment and deconvolve mRNA in capture spots and assign them to single cells. Another obstacle to fully harnessing the power of ST is that most tissues exhibit complex three-dimensional architectures. Many organs—including the intestine, retina, and cerebral cortex—are composed of repeating or curved substructures that can be conceptualized as biological manifolds. Within these manifolds, cells are arranged along defined spatial axes that reflect essential biological gradients, such as differentiation, signaling, and metabolic specialization. However, current analytical tools are poorly suited to interrogate transcriptional organization along these non-Cartesian geometries. This challenge is compounded by tissue deformation and distortion during sample preparation, which obscures native spatial relationships. A computational framework capable of unrolling such complex tissue structures onto standard Cartesian coordinates would therefore enable quantitative analyses of regional gene-expression programs across diverse tissue substructures.