利用机器学习驱动的智能表面增强拉曼光谱幻灯片实时跟踪细胞对纳米塑料的反应及其载体效应

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Ruili Li, Shuting Huang, Yuyang Hu, Xiaotong Sun, Zhipeng Zhang, Zaixuan Yang, Qi Liu* and Xiaoqing Chen*, 
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引用次数: 0

摘要

纳米塑料(NP)毒性及其对人类健康的“载体效应”的研究仍处于起步阶段,特别是对活细胞代谢反应的实时、原位监测。在此,我们开发了智能表面增强拉曼光谱(SERS)载玻片,使用循环离心增强静电加载(CCEL)方法来方便地跟踪活细胞代谢信号。所设计的嵌入拉曼探针的核壳聚苯乙烯NPs (mPS)通过一个明显的拉曼沉默峰成功地识别了细胞内的积累。智能SERS玻片在分子水平上有效监测mPS诱导的代谢变化,区分膜相互作用、内吞过程、内体聚集和细胞凋亡的不同阶段。此外,利用该平台对裸NPs诱导的细胞周期变化及其“载体效应”进行了实时原位比较,发现NPs延长了BEAS-2B细胞的S期和G2期,而“载体效应”进一步延长了G2期并破坏了S期进程。此外,我们整合了机器学习算法来准确预测与mPS及其“载体效应”相关的细胞周期影响。本研究提供了一种无标记、原位、实时监测NP诱导的活细胞代谢变化的方法,为进一步研究NP毒性行为和减轻NP毒性的策略奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Cell Response to Nanoplastics and Their Carrier Effects Tracked Real-Timely with Machine Learning-Driven Smart Surface-Enhanced Raman Spectroscopy Slides

Cell Response to Nanoplastics and Their Carrier Effects Tracked Real-Timely with Machine Learning-Driven Smart Surface-Enhanced Raman Spectroscopy Slides

Research on nanoplastic (NP) toxicity and their “carrier effects” on human health remains nascent, especially for real-time, in situ monitoring of metabolic reactions in live cells. Herein, we developed smart surface-enhanced Raman spectroscopy (SERS) slides using a cyclic centrifugation-enhanced electrostatic loading (CCEL) method to facilitatively track live-cell metabolic signals. The designed core–shell polystyrene NPs (mPS) with embedded Raman probes successfully identified intracellular accumulation via a distinct Raman-silent peak. The smart SERS slide effectively monitored the metabolic changes induced by mPS at the molecular level, distinguishing different stages of membrane interaction, the endocytosis process, endosomal aggregation, and cell apoptosis. Besides, this platform was employed to perform a real-time, in situ comparison of cell cycle alterations induced by bare NPs and their “carrier effects”, revealing that NPs extended both the S and G2 phases in BEAS-2B cells, while the “carrier effects” further prolonged G2 and disrupted S-phase progression. Additionally, we integrated machine learning algorithms to accurately predict the cell cycle impacts associated with mPS and their “carrier effects”. This study provides a label-free, in situ, real-time method for monitoring NP-induced metabolic changes in live cells, laying the groundwork for further investigation into cytotoxic behaviors and strategies to mitigate NP toxicity.

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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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