Yichen Si

Abstract: Spatial transcriptomics (ST) enables systematic profiling of whole-transcriptome gene expression in tissues while preserving spatial context. Recent advances in sequencing- and imaging-based ST technologies have ushered in the era of microscopic-resolution ST (μST), allowing transcriptome mapping at cellular and even subcellular scales with unprecedented precision. Despite these advances, μST faces substantial challenges, including sparse transcript discovery per submicron (or micron)-sized spatial units and data fragmentation across platforms, hindering integration and analysis. There is also a growing demand for scalable, segmentation-free, and universally applicable analysis methods, as well as strategies for 3D mapping, multi-omics integration, and artificial intelligence (AI)-driven spatial analysis. In this review, we highlight recent breakthroughs, outline key challenges, and discuss emerging experimental and computational solutions shaping the future of μST.

TL;DR: A coalescent model of imputing rare variants, leveraging the joint genealogy of the sample to be imputed and reference individuals is developed, providing a framework for developing new imputation algorithms and for interpreting rare variant association analyses.

TL;DR: FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed, and its cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.

TL;DR: In this article , a methylation hidden Markov model (MHMM) was proposed to estimate the accumulated germline methylation signature in human population history leveraging two properties: (1) Mutation rates of cytosine to thymine transitions at methylated CG dinucleotides are orders of magnitude higher than that in the rest of the genome.