Accelerated Exosomal Metabolic Profiling Enabled by Robust On‐Target Array Sintering with Metal–Organic Frameworks

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL Small Methods Pub Date : 2024-09-12 DOI:10.1002/smtd.202401238
Yun Wu, Yiming Qiao, Chenyu Yang, Yueying Chen, Xizhong Shen, Chunhui Deng, Qunyan Yao, Nianrong Sun
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Abstract

Pancreatic cancer is highly lethal, and survival chances improve only with early detection at a precancerous stage. However, there remains a significant gap in developing tools for large‐scale, rapid screening. To this end, a high‐throughput On‐Target Array Extraction Platform (OTAEP) by direct sintering of a series of metal–organic frameworks (MOFs) for dual in situ extraction, encompassing both exosomes and their metabolic profiles, is developed. Based on the principle of geometry‐dependent photothermal conversion efficiency and standard testing, the appropriate MOF functional unit is identified. This unit enables exosome enrichment within 10 min and metabolic fingerprint extraction in under 1 s of laser irradiation, with over five reuse. To further accelerate and enhance the quality of metabolic profile analysis, the application of Surrogate Variable Analysis to eliminate hidden confounding factors within the profiles is proposed, and five biomarkers demonstrated by MS/MS experiments are identified. These biomarkers enable early diagnosis, risk stratification, and staging of pancreatic cancer simultaneously, with sensitivity of 94.1%, specificity of 98.8%, and precision of 94.9%. This work represents a breakthrough for overcoming throughput challenges in large‐scale testing and for addressing confounding factors in big data analysis.

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利用金属有机框架进行可靠的靶上阵列烧结,加速外泌体代谢谱分析
胰腺癌的致死率很高,只有在癌前病变阶段及早发现,才能提高生存几率。然而,在开发大规模快速筛查工具方面仍存在巨大差距。为此,我们开发了一种高通量靶向阵列提取平台(OTAEP),通过直接烧结一系列金属有机框架(MOFs)来实现双重原位提取,包括外泌体及其代谢特征。根据光热转换效率取决于几何形状的原理和标准测试,确定了合适的 MOF 功能单元。该单元可在 10 分钟内富集外泌体,并在激光照射 1 秒钟内提取代谢指纹,重复使用次数超过五次。为了进一步加快和提高代谢图谱分析的质量,提出了应用替代变量分析法来消除图谱中隐藏的混杂因素,并确定了通过 MS/MS 实验证明的五个生物标记物。这些生物标记物可同时实现胰腺癌的早期诊断、风险分层和分期,灵敏度达 94.1%,特异性达 98.8%,精确度达 94.9%。这项工作为克服大规模检测中的通量挑战和解决大数据分析中的混杂因素带来了突破。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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