髓系白血病
计算生物学
融合基因
计算机科学
基因分型
生物
癌症研究
基因
遗传学
基因型
作者
Yuanyuan Huang,Zixin Zhang,Tiantian Yang,Yangli Zhang,Xiaoxue Cheng,Yuexi Kang,Guang Ye,Yuting Zou,Xiaoying Zhang,Zewei Luo,Junman Chen,Wei Cheng
标识
DOI:10.1002/smtd.202500194
摘要
Abstract RNA small fragment aberrances are associated with diseases by mediating a range of pathogenesis and pathological processes. DNA assembly‐based barcoding and amplification technologies are currently being actively explored for RNA in situ analysis. However, these modular integrated DNA assembly processes are inevitably accompanied with false positive signals caused by unexpected misassembly. Completely avoiding this phenomenon through simple and universal methods is challenging. Here, a novel dual‐input to dual‐output in situ analysis paradigm is proposed, aiming to improve target specificity through co‐recognition (dual‐input) and to eliminate false positive misassembly through fluorescent signal co‐localization (dual‐output). Based on this paradigm, Gemini molecular assembly co‐localization (GOAL) in situ imaging system is launched to accurately distinguish the fusion gene subtypes associated with chronic myeloid leukemia (CML), and to precisely report the proportion of minimum residual cancer cells in clinical samples by intelligent co‐localization counting and sorting. GOAL achieves highly sensitive and accurate genotyping recognition of 0.01% CML tumor cells and realizes fully automatic rapid diagnosis with a customized Intelligent Cell Image Sorter (iCis). iCis‐assisted GOAL represents an innovative and versatile molecular toolkit for accurate, rapid, user‐friendly, and professional‐independent profiling of cancer cells with RNA small fragment aberrances, providing efficient clinical decision support for disease diagnosis.
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