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Miranda N Hurst, Dr. Robert K DeLong (Nanotechnology Innovation Center of Kansas State (NICKS), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University).

Introduction

Nanotechnology is a rapidly developing field that has caught the interest of the scientific community due to its potential applications in biomedical research. Nanomaterials are typically less than 100 nm in diameter, making them small enough to penetrate mammalian cells. Nanomaterials can be synthesized in many shapes, such as rods, tubes, and particles, as well as in varying elemental compositions such as metals, metal oxides, and combinations of these. Their large surface area to volume ratio makes them suitable for surface functionalization, allowing for attachment of targeting or therapeutic molecules. When nanoparticles are delivered systemically, attached targeting molecules enable detection of certain cell populations, such as tumor cells, while attached therapeutic compounds can act on the targeted cells.

The material a nanoparticle is composed of has a specific band gap, or distance between the ground and excited states of its electrons. Generally, an electron exists in its ground state, or lowest energy state. Upon absorption of photons or light energy, the electron moves to its excited energy state. The distance between the ground state and the excited state is known as the band gap. The nanoparticle material absorbs one or more specific wavelengths, with some of the absorbed energy lost as vibrational energy and the remaining excess energy emitted as fluorescent light, returning the electrons to their ground state. A unique spectral signature can be obtained by plotting the relative fluorescence intensity across a range of different excitation and emission wavelengths.

Currently there are a limited number of techniques available to characterize nanoparticles and their molecular interactions. Here we propose spectral signature analysis as a method to confirm interactions between nanoparticles and surface coating molecules. Upon surface interaction with another molecule, the electronic properties of the nanoparticle material change, resulting in a shift in the peak fluorescence excitation and emission wavelengths, or spectral signature. Comparing the spectral signatures of surface coated nanoparticles and their uncoated counterparts can reveal a spectral signature shift indicative of electrostatic interaction.

Here we show how spectral signature analysis is performed using the SpectraMax^®i3x Multi-Mode Microplate Reader and Spectral Optimization Wizard in SoftMax^®Pro Software.

The Spectral Optimization Wizard enables automatic scanning of a user-defined range of excitation and emission wavelength pairs. The resulting fluorescence values for each wavelength pair are plotted as a heat map with a ‘hot spot’ that indicates the wavelength pair producing the highest signal relative to a control. These hot spots can be compared for different samples to identify a spectral shift.

Figure 1. Diagram of surface functionalization of nanoparticles. Left: original nanoparticle. Right: nanoparticle with surface functionalization allowing the attachment of a variety of targeting molecules including drugs, antibodies, and nucleic acids.

Materials

• Iron (III) oxide nanoparticles (PlasmaChem cat. #PL-FeO)
• Zinc oxide nanopowder, <100 nm size (Sigma Aldrich cat. #544906-10G)
• Methoxy poly(ethylene glycol) (mPEG 5000, Sigma Aldrich cat. #81323-250G)
• Ultra-pure water
• 96-well solid black microplate (Greiner cat. #655076)
• SpectraMax i3xMulti-Mode Microplate Reader

Methods

Nanoparticle preparation

Iron oxide nanoparticles (Fe₂O₃NP) were weighed out on an analytical balance at 2 mg in a microcentrifuge tube and suspended in 1 mL of ultra-pure water to create a final stock concentration of 2 mg/mL. This stock concentration was vortexed to disperse the particles. To obtain a final sample concentration of 1 mg/mL, 100 µL of the stock was transferred to a new microcentrifuge tube, and 100 µL of ultra-pure water was added to make a nanoparticle control sample. This 200-µL sample was vortexed and transferred to one well of a black 96-well microplate.

Zinc oxide nanoparticles (ZnO NP) were weighed out on an analytical balance at 3.5 mg in a microcentrifuge tube and suspended in 1 mL of ultra-pure water to create a stock concentration of 3.5 mg/mL. This solution was vortexed and 57 µL of the stock solution was transferred to a new microcentrifuge tube. To this tube, 143 µL of ultra-pure water was added to bring the volume to 200 µL and the final sample concentration to 1 mg/mL. The sample was vortexed and transferred to one well of a black 96-well microplate as a nanoparticle control.

Nanoparticle surface functionalization

To two new microcentrifuge tubes, either 100 µL of Fe₂O₃NP stock solution or 57 µL of ZnO NP stock solution was added. On an analytical balance, mPEG was weighed out to 4 mg and suspended in 1 mL of ultra-pure water for a final concentration of 4 mg/mL. This stock solution of mPEG was vortexed, and 50 µL was transferred to each microcentrifuge tube. The samples were brought up to a final volume of 200 µL by adding 50 µL of ultra-pure water to the Fe₂O₃NP-mPEG sample and 93 µL of ultra-pure water to the ZnO NP-mPEG sample. The samples were then vortexed and transferred to a black 96-well microplate. Samples were incubated in the microplate for 30 minutes prior to reading to ensure surface coating.

Fluorescence detection

Fluorescence of samples in the 96- well microplate was detected on the SpectraMax i3x Multi-Mode Microplate Reader using the settings shown in Table 1. A preliminary fluorescence spectral scan was performed using an excitation wavelength of 260 nm for Fe₂O₃NP and 350 nm for ZnO NP with and without mPEG. Emission was measured from 295 nm to 750 nm at 5 nm intervals for Fe₂O₃NP samples, and from 375 nm to 750 nm at 5 nm intervals for ZnO NP samples (Table 1). The microplate was shaken for five seconds using the orbital setting at high speed prior to reads.

Table 1. Settings for preliminary spectral scans in SoftMax Pro Software.

Following this initial validation, the Spectral Optimization Wizard (SOW) was used to perform a series of fluorescent reads with user-specified excitation and emission wavelength ranges, from which an optimal wavelength pair was identified for each sample. The SOW was initiated by selecting the settings shown in Table 2. When Read is selected, a new dialog box appears in which the user selects a range of excitation and emission wavelengths to test. The range of excitation wavelengths to scan was set to 250-500 nm, and the range of emission wavelengths to scan was set to 300-700 nm. For Fe₂O₃NP samples, 10 nm steps were selected, and for ZnO NP samples 5 nm steps were used. For all samples, the default read height of 1 mm was used. The setup dialog box is shown in Figure 2.

Table 2. Settings for prompting initiation of the Spectral Optimization Wizard in SoftMax Pro Software.

Figure 2. Spectral Optimization Wizard settings for nanoparticles. Settings used for both Fe₂O₃and ZnO NP samples with and without mPEG. For the ZnO NP samples, a wavelength increment of 5 nm was used.

Data acquisition

The Spectral Optimization Wizard was designed to identify a sample’s optimal excitation and emission wavelengths and then use these wavelengths in a subsequent plate read. The software does not automatically save the heat map and its associated fluorescence values as data. However, while the heat map window is still open, this raw data can be copied and pasted by right-clicking on the heat map and selecting ‘Copy Raw Data’, then pasting the data into the desired software. If the data are pasted into a spreadsheet, the excitation and emission wavelengths used in the spectral optimization must be entered manually, as they are not exported automatically with the raw data values. The original heat map image produced by SoftMax Pro Softwarecan be saved for reference by right clicking it and selecting ‘Save Image As’.

Results

Fe₂O₃NP displayed a spectral signature at an excitation of 260 nm, emission of 580 nm, and a fluorescence intensity of 10.1K relative light units (Figure 3, top). In the presence of mPEG, Fe₂O₃NP exhibited a spectral signature with an excitation of 270 nm, emission of 570 nm, and a fluorescence intensity of 5.1K (Figure 3, bottom). Fe₂O₃NP was used as a negative control, where a 10 nm shift in excitation and emission wavelengths was observed in the presence of mPEG. However, due to the fact that 10 nm steps were used to derive those spectral signatures, the shift is negligible and rather a result of measurement variation. However, the decrease in fluorescence intensity by approximately half suggests some minor interaction, even if it associates and dissociates at equilibrium, causing the fluorescence quenching to take place.

Figure 3. Spectral signature of Fe₂O₃NP (top) and mPEG surface functionalized Fe₂O₃NP (bottom). T he optimal wavelength pair identified by the software appears as a red ‘hot spot’ on each heat map of excitation vs. emission wavelengths. The gray areas of the heat maps represent nonfeasible wavelength combinations that are avoided by the software. Black areas represent readings where fluorescent signal was very close to background values.

Comparatively, ZnO NP showed a spectral signature with an excitation wavelength of 390 nm, emission at 670 nm, and fluorescence intensity of 13K relative light units (Figure 4, top). In the presence of mPEG, ZnO NP gave a spectral signature with an excitation wavelength of 380 nm, emission at 695 nm, and a fluorescence intensity of 95.6K relative light units (Figure 4, bottom). ZnO NP displayed a 25 nm shift in the emission wavelength of the spectral signature in the presence of mPEG when 5 nm steps were used. This is indicative of interaction as the electronic properties of the surface atoms are altered due to binding in the presence of mPEG polymer. The enhanced fluorescence of the coated nanoparticles is indicative of interaction of the polymer with oxygen molecules within the material. Table 3 summarizes the spectral signatures obtained.

Figure 4. Spectral signature of ZnO NP (top) and mPEG surface functionalized ZnO NP (bottom). Here there was a significant shift in the optimized wavelength pair identified for the non-coated vs. coated nanoparticles.

Table 3. Spectral signatures of FeO and ZnO NP with and without mPEG. Optimized wavelength pairs obtained using the Spectral Optimization Wizard in SoftMax Pro Software are shown. With the addition of mPEG, FeO NP only have a small shift in spectral signature, while ZnO NP exhibit a more robust 25 nm shift with mPEG addition.

Conclusion

The 20 nm shift in excitation and emission wavelengths indicates electrostatic interaction and complexation between ZnO NP and mPEG polymer, which was not observable with Fe₂O₃NP in the presence of mPEG. This characterization of nanoparticle and its surface interactions can be detected using the SpectraMax i3x Multi-Mode Microplate Reader and the Spectral Optimization Wizard in SoftMax Pro Software. This technique allows for quality control and assurance when performing surface functionalization of nanoparticles. Additionally, this technique can be extended to assure correct product formation during drug development utilizing nanoparticles as a delivery vehicle of targeting therapeutic compounds.

Learn more about SpectraMax i3x Multi-Mode Detection Platform >>

Miranda N Hurst, Dr. Robert K DeLong (Nanotechnology Innovation Center of Kansas State (NICKS), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University).

简介

鉴于其在生物医学研究的应用潜力，纳 米技术是一个快速发展的领域并受到科 学界的持续关注。纳米材料通常直径小 于100 nm，足够能穿透哺乳动物细胞。同 时，纳米材料合成时不受形状和元素组成 限制。形状上纳米材料可以以杆状，筒状 或颗粒状呈现。不同的元素，如金属，金 属氧化物或者它们的组合都能用于合成纳 米材料。纳米材料具有较大的表面积体积 比，因此适于通过表面功能化偶联靶向或 治疗性分子。在全身给药的情况下，偶联 的靶向分子可完成对特定细胞群体如肿瘤 细胞的标记，而偶联的治疗性化合物则可 以针对标记的细胞群体发挥作用。

纳米粒子的组成材料会有一个特定的带 隙，就是其电子基态和激发态之间的间 距。一般情况下，电子处于基态，也就是 能量最低的状态。在吸收光子或光能量 后， 电子跃迁至激发态，这两个状态之间 的间距被称为带隙。一般单一或多特定波 长的光会被纳米颗粒材料选择性吸收，其 能量一部分会转换成振动能，剩余的以荧 光的形式发射，同时电子返回到基态。因 此通过扫描、比较不同激发、发射波长下 的荧光强度，我们可以获得测试纳米颗粒 的特征光谱。

目前对纳米颗粒和其与分子相互作用的相 关描述和检测手段还十分有限。在此我们 提出特征光谱分析作为一种新方法用于确 定纳米颗粒和表面涂层分子之间的相互作 用。在表面结合到另外一个分子后，纳米 颗粒材料的电子特性会发生变化，引起其 激发光和发射光的荧光峰发生迁移，也就 是特征光谱的变化。因此，对比有无涂层 的纳米颗粒的特征光谱可协助我们确定纳 米颗粒和涂层分子之间是否发生了静电相

互作用。通过使用SpectraMax® i3x多功能 微孔板读板机并结合SoftMax® Pro自带的 光谱优化向导，我们在此展示了如何分析 纳米颗粒的特征光谱。

光谱优化向导允许用户自定义的激发和发 射波长范围组合的自动扫描。扫描结果以 热图的形式展现。热点则代表获得最高信 号时对应的激发发射波长对。通过比较热 点的位置可以发现不同样本间是否出现光 谱特征变化。

***图一：纳米颗粒表面功能化图解。左：**未功能化的纳米颗粒。**右图：*通过表面功能化纳米颗粒可以和 各种靶向分子如化合物，抗体和核酸的结合。

材料

• 氧化铁纳米颗粒 (PlasmaChem cat. #PL-FeO)
• 氧化锌纳米颗粒, <100 nm size (Sigma Aldrich cat. #544906-10G)
• 聚乙二醇甲醚(mPEG5000, Sigma Aldrich cat. #81323-250G)
• 超纯水
• 96孔黑板(Greiner cat. #655076)
• SpectraMax i3x 多功能微孔板读板机

方法

纳米颗粒的制备

用分析天平称取氧化铁纳米颗粒(Fe2O3 NP) 2 mg于微量离心管中，以1 mL超纯水 重悬制成2 mg/mL储液，震荡混悬后备用。 移出100 µL储液于新微量离心管中，加入 100 µL超纯水，得到1 mg/mL的纳米颗粒 对照样品，震荡混悬后移至96孔板黑板中 的选定孔中。

用分析天平称取氧化锌纳米颗粒(ZnO NP) 3.5 mg于微量离心管中，以1 mL超纯水重 悬制成3.7 mg/mL储液，震荡混悬后备用。 移出57 µL储液于新微量离心管中加入143 µL超纯水，得到1 mg/mL的纳米颗粒对照 样品，震荡混悬后移至96孔板黑板中的选 定孔中。

纳米颗粒表面功能化

在两个新微量离心管中分别加入100 µL氧 化铁纳米颗粒储液和57 µL氧化锌储液。用 分析天平秤取4 mg mPEG溶于1 mL超纯 水，获得终浓度为4 mg/mL的储液，震荡 混悬后向每个溶有纳米颗粒的离心管中加 入50 µL mPEG 储液，然后用超纯水补足至 200 µL，震荡混悬后移至96孔黑板中，静 置30分钟以确保完成包被。

荧光检测

按照表1的参数用SpectraMax i3x多功能 微孔板读板机检测样本的荧光值。在初步 扫描中，氧化铁纳米颗粒对照和包被样本 以260 nm激发，以5 nm 步进在295nm至 750 nm范围扫描发射光谱。氧化锌纳米颗 粒样本则以350 nm 激发，5 nm 步进在 375 nm至750 nm范围扫描发射光谱(表1)。

表一： SoftMax Pro 软件初步光谱扫描参数

读板前微孔板以回旋振荡方式高速振荡5 秒钟。完成初步验证后，按照用户定义的 激发光和发射光范围使用光谱优化向导 (Spectral Optimization Wizard，SOW) 进行一系列的荧光值读取，并获得每个样 本的最佳波长组合。SOW可按照表2的设 定进行初始化。点击Read后会弹出一个新 的对话框让用户确定激发和发射波长的测 试范围。此次实验中激发波长的扫描范围 为250到500 nm，发射波长范围为300到 700 nm。对于氧化铁纳米颗粒样本，扫描 步进为10 nm。对于氧化锌纳米颗粒样本 扫描步进则为5 nm。对所有的样本读取高 度为默认1 mm。参数对话框参见图2。

表二： SoftMax Pro 软件提示光谱扫描初始化参数

***图二：纳米颗粒光谱扫描参数。*图中展示了针对氧化铁和氧化锌纳米颗粒(包被样本和对照样本)的光 谱扫描参数。对于氧化锌纳米颗粒样本，波长步进为5 nm。

数据采集

光谱优化向导旨在识别样本的最佳激发和 发射波长组合并以此为基础进行后续的检 测。软件本身不会自动保存分析热图和相 关荧光值数据。但是，我们可以在热图窗 口打开的情况下，右击热图，选择复制原 始数据(Copy Raw Data)，并粘贴到合适 的软件中，做到原始数据的导出。如果原 始数据是导出到电子表格，因为软件没有 自动输出原始数据能力，必须手动输入扫 描中使用的激发和发射波长参数。扫描中 的原始热图可以通过在Softmax Pro软件 中右键，选择图片另存为(Save Image As) 的方式导出。

结果

在260nm波长激发，580nm波长发射组合 下，氧化铁纳米颗粒具有最高的荧光值 (10.1K相对信号强度，图3，顶图)。在 mPEG包被下，氧化铁纳米颗粒的最高荧 光值为5.1K，此时激发波长为270 nm，发 射波长为570nm(图3，底图)。因此，相 较于没有包被的对照样本，mPEG包被引 起最佳激发和发射波长10 nm的迁移。由 于光谱预扫描中采用的是10 nm步进，因 此所观察到的微弱迁移可能是来源于测量 变异。但是近乎一半的荧光强度下降表明 一些微弱的相互作用，即使到了结合解离 平衡阶段，也会引起显著的荧光淬灭。

***图三：氧化铁纳米颗粒(顶图)和mPEG表面功能化的氧化铁纳米颗粒(底图)的光谱特征图。*光谱特征 图以热图形式展现，纵坐标为激发波长，横坐标为发射波长。“红点”指示最佳波长组合。灰色区域 表明软件定义不可行的波长组合。黑色区域标志荧光信号非常接近背景值。

相比下，氧化锌纳米颗粒在390 nm波长激 发，670 nm波长发射下具有最强的荧光值 (13K，图4，顶图)。在mPEG包被下，氧 化锌纳米颗粒最佳波长组合变为380 nm波 长激发，695 nm波长发射，此时荧光值为 95.6K(图4，底图)。因此，在5 nm 扫描步 进下，mPEG包被能引起25 nm最佳发射波 长的迁移，说明mPEG多聚物和氧化锌纳 米颗粒表面存在相互作用。因为mPEG的 结合会改变表面原子的电子性质。同时荧 光信号的增强表明存在多聚物和材料中氧 分子的相互作用。表3总结了上述的光谱 扫描结果。

图四：氧化锌纳米颗粒(顶图)和mPEG表面功能化的氧化锌纳米颗粒(底图)的光谱特征图。。图上可见 包被过程显著改变了最佳波长组合。

表三：mPEG包被与否的氧化铁和氧化锌纳米颗粒的光谱特征。 表中指出了不同情况下Softmax Pro 软件光谱优化向导提供的最佳波长组合。mPEG加入对氧化铁纳米颗粒的光谱迁移影响微弱，然而对 氧化锌纳米颗粒能迅速引起25 nm的波长变化。

总结

测试中观察到的20 nm 激发和发射波长位 移表明氧化锌纳米颗粒和mPEG多聚物之 间出现了静电相互作用，相比下mPEG处 理则不能引起氧化铁纳米颗粒的光谱特征改 变。在此，我们展示如何通过结合SoftMax Pro软件的光谱优化向导，用SpectraMax i3x多功能微孔板读板机进行纳米颗粒的表 征和其表面相互作用的分析。该方法不仅 可以用于质控纳米分析表面功能化过程， 还可以进一步在利用纳米颗粒为载体运送 靶向治疗化合物的药物开发过程中确保产 品的正确包装。

Learn more about SpectraMax i3x Multi-Mode Detection Platform >>

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