Multispectral Fingerprinting Resolves Dynamics of Nanomaterial Trafficking in Primary Endothelial Cells
Multispectral Fingerprinting Resolves Dynamics of Nanomaterial Trafficking in Primary Endothelial Cells
复制标题
多光谱指纹图谱解析纳米材料在原代内皮细胞中的运输动力学
DOI：
10.1021/acsnano.1c04500
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发表时间：
2021-06-28
期刊：
影响因子：
17.1
通讯作者：
Roxbury, Daniel
中科院分区：
文献类型：
--
作者：
Gravely, Mitchell;Roxbury, Daniel
细胞内囊泡运输涉及一系列复杂的生物学途径，用于分选、回收和降解细胞外组分，包括通过活性内吞过程进入细胞的工程纳米材料（ENM）。最近强调ENM摄取的途径已经建立了指导某些机制的关键物理化学性质，但相对较少的研究已经确定了它们对过去进入和初始亚细胞定位的细胞内运输过程的影响。在这里，我们开发并应用了一种方法，其中单壁碳纳米管（SWCNT）发挥双重作用。ENM进行细胞内处理，除了作为信号转导元件报告这些事件在单个细胞器分辨率。我们使用了特殊的光学性能所表现出的非共价杂交单链DNA和单壁碳纳米管（DNA-SWCNTs），通过近红外（NIR）荧光和共振拉曼散射两个正交的高光谱成像方法报告细胞内处理事件的进展。荧光和G-带强度之间的正相关性被发现在单细胞内，而激子能量转移和最终聚集的DNA-SWCNTs观察到随着内化后的时间增加而缩放。开发了一种分析管道来共定位和去卷积亚细胞感兴趣区域（ROI）的荧光和拉曼光谱，允许以亚微米空间分辨率获得单手性组分光谱。该方法揭示了DNA-SWCNT浓度、介电调制和单个胞内囊泡内的不可逆聚集之间的相关性。免疫荧光测定被设计为直接观察标记的内体囊泡中的DNA-SWCNT，揭示了在内体成熟过程期间细胞器结合的DNA-SWCNT的物理状态与动态管腔条件之间的明显关系。最后，我们训练了一种机器学习算法，使用囊泡结合的DNA-SWCNT的拉曼光谱来预测内体类型，从而使内吞途径中的主要组分能够使用单个细胞内报告子同时可视化。
Intracellular vesicle trafficking involves a complex series of biological pathways used to sort, recycle, and degrade extracellular components, including engineered nanomaterials (ENMs) which gain cellular entry via active endocytic processes. A recent emphasis on routes of ENM uptake has established key physicochemical properties which direct certain mechanisms, yet relatively few studies have identified their effect on intracellular trafficking processes past entry and initial subcellular localization. Here, we developed and applied an approach where single-walled carbon nanotubes (SWCNTs) play a dual role.that of an ENM undergoing intracellular processing, in addition to functioning as the signal transduction element reporting these events in individual cells with single organelle resolution. We used the exceptional optical properties exhibited by noncovalent hybrids of single-stranded DNA and SWCNTs (DNA-SWCNTs) to report the progression of intracellular processing events via two orthogonal hyperspectral imaging approaches of near-infrared (NIR) fluorescence and resonance Raman scattering. A positive correlation between fluorescence and G-band intensities was uncovered within single cells, while exciton energy transfer and eventual aggregation of DNA-SWCNTs were observed to scale with increasing time after internalization. An analysis pipeline was developed to colocalize and deconvolute the fluorescence and Raman spectra of subcellular regions of interest (ROIs), allowing for single-chirality component spectra to be obtained with submicron spatial resolution. This approach uncovered correlations between DNA-SWCNT concentration, dielectric modulation, and irreversible aggregation within single intracellular vesicles. An immunofluorescence assay was designed to directly observe the DNA-SWCNTs in labeled endosomal vesicles, revealing a distinct relationship between the physical state of organelle-bound DNA-SWCNTs and the dynamic luminal conditions during endosomal maturation processes. Finally, we trained a machine learning algorithm to predict endosome type using the Raman spectra of the vesicle-bound DNA-SWCNTs, enabling major components in the endocytic pathway to be simultaneously visualized using a single intracellular reporter.