Host-Guest Self-Assembled Interfacial Nanoarrays for Precise Metabolic Profiling.
Host-Guest Self-Assembled Interfacial Nanoarrays for Precise Metabolic Profiling.
复制标题
DOI：
10.1002/smll.202207190
复制
发表时间：
2023-01
期刊：
影响因子：
13.3
通讯作者：
Yuning Wang;Yu Liu;Shouzhi Yang;Jia Yi;Xiao-Yan Xu;Kun Zhang;Baohong Liu;Kun Qian
中科院分区：
文献类型：
--
作者：
Yuning Wang;Yu Liu;Shouzhi Yang;Jia Yi;Xiao-Yan Xu;Kun Zhang;Baohong Liu;Kun Qian
迫切需要准确、快速的脑脊液 (CSF) 代谢分析，但对于中枢神经系统疾病的临床诊断和生物标志物发现仍然具有挑战性。基质辅助激光解吸电离质谱 (MALDI-MS) 有望用于代谢分析。然而，其信号重现性低，严重限制了临床实践中定量 MS 数据的采集。在此，开发了一种基于多功能自组装 AuNP 阵列 (MSANA) 的 LDI-MS 平台，用于对患者脑脊液样本进行直接氨基酸分析和代谢分析。 MSANA 具有高度有序和紧密堆积的二维纳米结构，允许通过 LDI-MS 捕获和直接分析芳香族氨基酸，具有高选择性和微摩尔灵敏度。同时，基于 MSANA 的 LDI-MS 平台表现出出色的重现性 (RSD < 10%)，大大优于目前广泛使用的直接基质点样方法 (RSD < 44%)。该平台已成功用于在几分钟内对 CSF (1 µL) 进行代谢分析，以区分髓母细胞瘤患者和非肿瘤对照。总而言之，基于 MSANA 的 LDI-MS 平台显示出对大规模代谢诊断和致病机制研究的潜在临床价值。
Accurate and rapid metabolic profiling of cerebrospinal fluid (CSF) is urgently needed but remains challenging for clinical diagnosis of central nervous system diseases and biomarker discovery. Matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) holds promise for metabolic analysis. Its low signal reproducibility, however, severely restricts acquisition of quantitative MS data in clinical practice. Herein, a multifunctional self-assembled AuNPs array (MSANA)-based LDI-MS platform for direct amino acids analysis and metabolic profiling in patient CSF samples is developed. MSANA featuring a highly ordered and closely packed two-dimensional nanostructure permits capture and direct analysis of aromatic amino acids by LDI-MS with high selectivity and micromolar sensitivity. Meanwhile, the MSANA-based LDI-MS platform exhibits excellent reproducibility (RSD < 10%), largely outperforming the direct matrix spotting approach widely used now (RSD < 44%). The platform is successfully used in metabolic profiling of CSF (1 µL) within minutes for discrimination of medulloblastoma patients from non-tumor controls. Taken together, the MSANA-based LDI-MS platform shows potential clinical values toward large-scale metabolic diagnostics and pathogenic mechanism study.