利用机器学习驱动的智能表面增强拉曼光谱载玻片实时跟踪细胞对纳米塑料及其载流子效应的响应

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

摘要

有关纳米塑料(NP)毒性及其对人体健康的 "载体效应 "的研究仍处于起步阶段,尤其是对实时、原位监测活细胞代谢反应的研究。在此,我们利用循环离心增强静电装载(CCEL)方法开发了智能表面增强拉曼光谱(SERS)载玻片,以方便跟踪活细胞代谢信号。设计的内嵌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了 mPS 诱导的代谢变化,区分了膜相互作用、内吞过程、内体聚集和细胞凋亡的不同阶段。此外,我们还利用这一平台对裸NPs及其 "载体效应 "诱导的细胞周期变化进行了实时、原位比较,结果显示NPs延长了BEAS-2B细胞的S期和G2期,而 "载体效应 "则进一步延长了G2期,破坏了S期的进展。此外,我们还整合了机器学习算法,以准确预测与 mPS 及其 "载体效应 "相关的细胞周期影响。这项研究提供了一种无标记、原位、实时的方法来监测 NP 诱导的活细胞代谢变化,为进一步研究细胞毒性行为和减轻 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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