转录组
计算生物学
管道(软件)
可视化
计算机科学
生物
基因
人工智能
遗传学
基因表达
程序设计语言
作者
Jiyuan Yang,Ziqian Zheng,Yun Jiao,Kaiwen Yu,Sheetal Bhatara,Xu Yang,Sivaraman Natarajan,Jiahui Zhang,Qingfei Pan,John Easton,Koon‐Kiu Yan,Junmin Peng,Kaibo Liu,Jiyang Yu
标识
DOI:10.1038/s41592-025-02622-5
摘要
Abstract Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole-tissue sections, but current approaches remain plagued by the challenge of achieving single-cell resolution without sacrificing whole-genome coverage. Here we present Spotiphy (spot imager with pseudo-single-cell-resolution histology), a computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. Spotiphy delivers the most precise cellular proportions in extensive benchmarking evaluations. Spotiphy-derived inferred single-cell profiles reveal astrocyte and disease-associated microglia regional specifications in Alzheimer’s disease and healthy mouse brains. Spotiphy identifies multiple spatial domains and alterations in tumor–tumor microenvironment interactions in human breast ST data. Spotiphy bridges the information gap and enables visualization of cell localization and transcriptomic profiles throughout entire sections, offering highly informative outputs and an innovative spatial analysis pipeline for exploring complex biological systems.
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