Characterization of Amyloid Fibril Networks by Atomic Force Microscopy

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Materials Science and Engineering C
Oct 2017



Dense networks of amyloid nanofibrils fabricated from common globular proteins adsorbed to solid supports can improve cell adhesion, spreading and differentiation compared to traditional flat, stiff 2D cell culture substrates like Tissue Culture Polystyrene (TCPS). This is due to the fibrous, nanotopographic nature of the amyloid fibril networks and the fact that they closely mimic the mechanical properties and architecture of the extracellular matrix (ECM). However, precise cell responses are strongly dependent on the nanostructure of the network at the cell culture interface, thus accurate characterization of the immobilized network is important. Due to its exquisite lateral resolution and simple sample preparation techniques, Atomic Force Microscopy (AFM) is an ideal technique to characterize the fibril network morphology. Thus, here we describe a detailed protocol, for the characterization of amyloid fibril networks by tapping mode AFM.

Keywords: Amyloid nanofibrils (淀粉样蛋白纳米纤维), Atomic force microscopy (原子力显微镜检查), Self-assembly (自组装), Roughness analysis (粗糙度分析), Protein aggregation (蛋白质聚集), Biomaterials (生物材料)


Networks of non-toxic amyloid fibrils assembled from common globular proteins (Jung et al., 2008; Lara et al., 2011) adsorbed to solid supports have applications in a wide variety of fields (Dharmadana et al., 2017; Wei et al., 2017). Particularly interesting is their applications in eukaryotic cell culture and biomaterials in general (Reynolds et al., 2013, 2014 and 2015; Gilbert et al., 2017b). This is largely due to the fact that networks of amyloid fibrils have morphologies and mechanical properties that closely resemble the local microenvironment of many cell types (the ECM). Such amyloid fibril networks have the added attraction that they are simple to fabricate, inexpensive and possess well-defined chemistries that can be easily reproduced.

As expected, the response of cells grown on these amyloid fibril networks is highly dependent on the nanoscale properties of the fibril network itself. For instance, small changes in fibril diameter, nanoscale roughness, surface coverage and fibril morphology have been shown to affect cell attachment and spreading (Reynolds et al., 2014 and 2015). Thus, it is important to accurately characterize the nanotopography and surface roughness of the immobilized networks before using them for cell culture applications. AFM is a powerful technique to perform this analysis as it requires little sample preparation, possesses nanoscale lateral resolution and sub-nanometer vertical resolution (Reynolds et al., 2014 and 2015; Gilbert et al., 2017a and 2017b; Reynolds et al., 2017). Additionally, parameters such as nanoscale roughness can be extracted by post imaging analysis. In this protocol, we will describe the process of imaging a dense network of amyloid fibrils (fabricated from the protein Hen Egg White Lysozyme) on solid (mica) substrates by AFM. We will also describe the most common steps of post-processing analysis, namely flattening (removal of sample tilt and bowing artefacts) and roughness analysis.

Materials and Reagents

  1. AFM Metal Specimen discs Diameter 15 mm (ProSciTech, catalog number: GA530-15 )
  2. Muscovite Mica disks, grade V-1 diameter 12.5 mm (ProSciTech, catalog number: G51-12 )
  3. STKYDOT adhesive pads (Bruker Nano, catalog number: STKYDOT )


  1. Cole-Parmer Precision Tweezer Set, Stainless Steel (Cole-Parmer Instrument, catalog number: 07387-16 )
  2. Multimode 8 Atomic Force Microscope (AFM) with Nanoscope V controller (Bruker Nano, model: Multimode 8 )
  3. Tapping Mode AFM tips (Approx. Resonant Frequency = 300 kHz, force constant 40 N/m) (Bruker Nano, model: RTESPA-300 )


  1. Nanoscope Analysis Software (Bruker Version 1.7)


Note: All procedures here are described for a Bruker Multimode AFM, for different models and brands of AFM the protocol will need to be adapted according to the manufactures instructions.

  1. Loading the sample and setting up the AFM
    Modern AFM instruments have a multitude of different imaging modes to choose from including contact mode, non-contact mode and tapping mode. For delicate soft materials or biomaterials such as this, it is important to minimize the force exerted on the substrate by the AFM tip to prevent the surface being damaged. Tapping mode AFM (TM-AFM) achieves this by intermediately tapping the substrate, and not dragging the hard, sharp tip of the AFM cantilever across the substrate as in contact mode AFM. For this reason, all imaging on these amyloid fibril networks should be performed in TM-AFM.
    1. Amyloid fibril networks are fabricated by exposing globular proteins (typically β-lactoglobulin or hen egg white lysozyme) to high temperatures and low pH which causes the proteins to be hydrolyzed into peptide fragments.Over time the fragments self-assemble into amyloid fibrils (see Charnley et al. [2018] for a detailed protocol on fabricating these networks). The freshly prepared amyloid fibril networks can be deposited on mica via a simple drop-casting protocol (see Charnley et al. [2018]) and can now be attached to the magnetic AFM stub using one of the double-sided sticky dots. Carefully mount the sample onto the AFM (Figure 1a) ensuring not to damage the mirror in the top left of the sample loading area.

      Figure 1. Bruker Multimode 8 AFM with important controls labelled. a) Magnetic sample holder (for samples attached to magnetic AFM stubs; b) Knobs to manipulate x-y stage; c) Knobs to manipulate laser position; d)vert Knob to manipulate the vertical displacement of the photodiode; d)hor Knob to manipulate the horizontal displacement of the photodiode; e) Knob to secure and unsecure the cantilever holder; f) Lever to adjust angle of mirror directing laser onto the photodiode; g) Cable and connector for the laser source (ensure it is plugged in).

    2. Mount the AFM cantilever into its holder, ensuring that the tip is not damaged (Video 1), and that the base of the cantilever sits flush with the back of the cantilever holder (Figure 2a). Now load the AFM cantilever holder into the AFM, ensuring that the sample is not too high, which would cause the cantilever to crash into the sample. Ensure that the AFM is in AFM mode (not STM or TM-AFM).

      Video 1. Loading AFM cantilever into the cantilever holder

    3. Focus the optical microscope on the AFM to the cantilever and ensure that it is mounted straight and not at an angle (Figures 2b and 2c). Refocus the camera on the surface of the mica sheet.
      Note: Take care that you are focussed on the uppermost surface, to prevent crashing the cantilever into the substrate.
    4. Move the sample upwards (or AFM tip downwards depending on the AFM model) so that it is close to the mica substrate, the distance from the substrate can be judged by looking at the separation between the AFM tip and its reflection (Figure 2d).
    5. Focus the AFM laser spot on the cantilever (Figure 1c), maximizing the obtained Sum (it should be around 7 for an RTESPA300 tip), but also ensuring that the laser spot is close to the tip of the cantilever and not significantly over one edge (Figure 2e).
      Note: To maximize the Sum both the laser position (Figure 1c) and the angle of the mirror (Figure 1f) that focusses the laser onto the 4 quadrant photodiode within the AFM may need to be adjusted.
    6. After correctly positioning the laser spot onto the AFM cantilever, the 4 quadrant photodiode should be adjusted so that both the vertical and horizontal deflection reads as zero (Figure 1dvert and 1dhor). Once correctly adjusted the AFM should be switched to TM-AFM mode (using the switch on the base of the instrument) and the horizontal deflection reset to zero if required.
      Note: Upon switching to TM-AFM the vertical deflection will have disappeared and have been replaced with the RMS value.

      Figure 2. Cantilever and laser alignment. a) Correctly mounted RTESPA300 cantilever in a standard Bruker multimode cantilever holder; b) Optical microscopy image of correctly aligned cantilever; c) Optical microscopy image of poorly aligned cantilever; d) Optical microscope image of cantilever approaching the surface, tip-sample separation can be monitored by observing the gap between the image of the cantilever and its reflection; e) When the cantilever is sufficiently close to the surface the image of the cantilever and its reflection cannot be distinguished from each other, at this point the rest of the approach should be performed by the software (using the engage command).

    7. Now the cantilever is correctly mounted and the laser aligned, all that remains before imaging is to select the resonant frequency of the cantilever. This can be done in the setup menu of the AFM software (within the soft tapping mode experimental pre-set). As this imaging is being performed in air, the autotune software (Figure 3) works well however, the user should ensure that autotune is set up to scan across the correct frequency range (200-400 kHz works well for the RTESPA300 cantilevers) and that the target amplitude is sufficient (500 mV is a good place to start) (see Figure 3).
      Note: When tuning the cantilever to find the precise resonant frequency ensure that the cantilever is sufficiently far away from the surface so that the resonant frequency is not influenced by sample-tip interactions.
      Upon clicking execute in the autotune menu, the software should correctly identify the resonant frequency of the cantilever (likely to be somewhere between 270-330 kHz for RTESPA300 cantilevers) and fix the amplitude to the chosen target amplitude (see Figure 3). If the software cannot identify a resonant frequency or it looks suspicious (i.e., > ± 50 kHz from the quoted resonant frequency, and/or not obviously the most intense peak) then Steps A2-A5 may need to be repeated or the cantilever may be damaged.

      Figure 3. Cantilever tuning. The resonant frequency and phase shift should look approximately like the examples given above with one major peak around (± 50 kHz) the quoted resonant frequency of the cantilever (in this case 300 kHz). Particular attention should be given to the highlighted parameters, the start and end frequency should include the expected resonant frequency (300 kHz) and the desired target amplitude should be selected (for amyloid fibrils 500-1,000 mV works well).

  2. Imaging the amyloid fibril networks
    Maximum available resolution in the AFM will depend on a large variety of factors (tip quality, instrument noise, external noise, the piezo scanner used etc.). This protocol described uses a relatively high resolution ‘e’ scanner, which sacrifices available scan size for increased lateral resolution, thus maximum scan sizes are around 10 μm. Other piezo scanners and other models of AFM will allow for bigger scan sizes (up to hundreds of microns in some cases).
    1. Ensure that the AFM cantilever is close to the substrate interface (Figure 2e), select a scan rate (~1 Hz), scan size (1-10 μm) and an image resolution (512 x 512 pixels for publication quality images) and engage the cantilever from the scan menu in the software. The sample will now automatically be moved (initially via a stepper motor, and later by a piezo motor), until the cantilever and substrate come into contact. Occasionally a false engage occurs when the cantilever fails to come into contact with the surface, if this happens multiple times then Steps A2-A5 above may need to be repeated or the tip may be damaged.
    2. The most important parameter to be optimised once the cantilever in contact with the substrate is the amplitude setpoint. The amplitude setpoint approximates to the force exerted by the tip on the sample (lower voltage = higher force exerted), and is automatically set to an initial value of 50% of the target amplitude. Once scanning has commenced the amplitude setpoint can be adjusted so that the tip closely tracks overall features on the surface. This can be assessed by looking at the trace and retrace line scans in the topography channel (Figure 4b). These should be exactly overlapping, if they are not then the amplitude setpoint should be reduced. An optimum amplitude setpoint is achieved when the AFM is just tracking the surface (in trace and retrace directions), but the force exerted on the sample is minimized (Figure 4). At this stage, if required, the drive amplitude and drive frequency can also be optimized to further improve the quality of the image.
    3. Finally adjust the integral (IG) and proportional gain (PG) so that the noise in all of the channels is minimised. The amplitude error channel (channel 2 by default) most clearly shows noise therefore this channel should be used to fine tune the IG and PG. Typically as the gains are increased a reduction in noise is observed until an upper limit is reached, beyond which additional electrical noise is introduced into the image. As a rule of thumb, the PG should always be 2-3 greater than the IG (Figure 4c). Once satisfied with the image quality, restart the scan, set the file name and directory and turn on capture to record the image. Examples of both good and bad scanning parameters are shown in Figure 4.

      Figure 4. Good (left) and bad (right) quality AFM imaging of amyloid fibril networks. a) Example of amyloid fibril network image being recorded accurately; b) Trace and retrace scan lines overlapping confirming quality of the image; c) Typical scan settings used to record the above image; d) Poor imaging parameters resulting in the AFM cantilever not closely tracking the surface hence the blurry images; e) Trace and retrace scan lines not well overlapping due to high amplitude setpoint; f) Example of non-optimised scan settings.

Data analysis

Basic image manipulation and roughness analysis
Here we will describe in detail the most basic post image processing used for the majority of AFM images. Note all processing and analysis protocols were performed in the Nanoscope Analysis software (Bruker v1.7), protocols may vary for other AFM analysis software.

  1. The first stage of image processing in all topographic AFM images is to correct for deviations from planarity (tilt due to samples not being mounted in the AFM perfectly horizontally). This is performed through the flatten tool in the Nanoscope Software (Figure 5). For images that scan a small percentage of the total possible distance of the piezo scanner, a first order flatten that simply corrects for tilt is sufficient. In the authors experience first order plane fits are appropriate up to approximately < 40% of the total available scanning distance, in the case of the multimode ‘e’ scanner this is roughly equal to 4-5 μm. For larger scans 2nd or 3rd order flatten algorithms may be required to remove both sample tilt and bowing artifacts that occur when the piezo travels over the majority of its working distance.

    Figure 5. Screen Shots from the flattening tool in the Nanoscope Analysis Software. a) Pre-flattened image with multiple artifacts due to tilt in sample. b) 1st order flattened image removing tilt only. For larger scans 2nd or 3rd order flattening may be required to remove bowing effects caused by the piezo traveling a large percentage of its total possible distance.

  2. In order to calculate both arithmetical and root mean square roughness (termed Ra and Rq respectively in the Nanoscope Analysis software), the roughness tool can be used by clicking on the roughness icon in the software (Figure 6i). The table that is generated as a result displays the Ra and Rq values for the entire image (Figure 6ii) and also for a specific region of interest (Figure 6iii) if selected by drawing a box on the image.
  3. There are many additional AFM analysis tools that can be used to pull out a wealth of information on amyloid fibril networks such as those described above, however it is beyond the scope of this protocol to describe them here. For a detailed description of all the tools available in Nanoscope Analysis see the help files associated with the software. In addition, for fibrillar materials, the authors recommend the free software FiberApp (Usov and Mezzenga, 2015) for more complex statistical analysis methods.

    Figure 6. Screen Shots from the Roughness Analysis tool in the Nanoscope Analysis software. i) The roughness analysis icon; ii) highlighted in red are the Rq and Ra values for the entire of the image; iii) Highlighted in green are the Rq and Ra values for the selected region of the image.


The quality of the images obtained is highly dependent on the quality and cleanliness of the AFM cantilevers used, for results of publication quality we always recommend using pristine cantilevers and not cantilevers that have previously been used for imaging. Additionally, the quality of cantilevers from different manufacturers can vary massively, the authors note that often buying cantilevers from cheaper manufactures turns out to be economically unsound, as the quality is reduced to the point where many of the cantilevers are not usable.


All the recipes for preparing the amyloid nanofibril networks imaged here are detailed in Charnley et al. (2018).


This work was performed in part at the ANFF-Vic node of the Australian National Fabrication Facility, a company established under the National Collaborative Research Infrastructure Strategy to provide nano-and micro-fabrication facilities for Australia’s researchers. JG acknowledges the Australian Government Department of Education and Training for an Endeavour Scholarship and the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1333468. NPR and JG acknowledge the ARC Training Centre for Biodevices at Swinburne University of Technology (IC140100023) for funding (NPR) and for hosting for the duration of his scholarship (JG). MC acknowledges support from the Swiss National Science Foundation (SNSF) (grants PA00P3_142120 and P300P3_154664). JG and OGJ acknowledge further funding support from USDA Hatch Act funds (IND0-1162). The authors declare no conflict of interest or competing interests.


  1. Charnley, M., Gilbert, J., Jones, O. G. and Reynolds, N. P. (2018). Preparation of amyloid fibril networks. Bio-protocol 8(4): e2733
  2. Dharmadana, D., Reynolds, N. P., Conn, C. E. and Valery, C. (2017). Molecular interactions of amyloid nanofibrils with biological aggregation modifiers: implications for cytotoxicity mechanisms and biomaterial design. Interface Focus 7(4): 20160160.
  3. Gilbert, J., Charnley, M., Cheng, C., Reynolds, N. P. and Jones, O. G. (2017a). Quantifying Young’s moduli of protein fibrils and particles with bimodal force spectroscopy. Biointerphases 12(4): 041001.
  4. Gilbert, J., Reynolds, N. P., Russell, S. M., Haylock, D., McArthur, S., Charnley, M. and Jones, O. G. (2017b). Chitosan-coated amyloid fibrils increase adipogenesis of mesenchymal stem cells. Mater Sci Eng C 79: 363-371.
  5. Jung, J. M., Savin, G., Pouzot, M., Schmitt, C. and Mezzenga, R. (2008). Structure of heat-induced beta-lactoglobulin aggregates and their complexes with sodium-dodecyl sulfate. Biomacromolecules 9(9): 2477-2486.
  6. Lara, C., Adamcik, J., Jordens, S. and Mezzenga, R. (2011). General self-assembly mechanism converting hydrolyzed globular proteins into giant multistranded amyloid ribbons. Biomacromolecules 12(5): 1868-1875.
  7. Reynolds, N. P., Adamcik, J., Berryman, J. T., Handschin, S., Zanjani, A. A. H., Li, W., Liu, K., Zhang, A. and Mezzenga, R. (2017). Competition between crystal and fibril formation in molecular mutations of amyloidogenic peptides. Nature Communications 8: 1338.
  8. Reynolds, N. P., Charnley, M., Bongiovanni, M. N., Hartley, P. G. and Gras, S. L. (2015). Biomimetic topography and chemistry control cell attachment to amyloid fibrils. Biomacromolecules 16(5): 1556-1565.
  9. Reynolds, N. P., Charnley, M., Mezzenga, R. and Hartley, P. G. (2014). Engineered lysozyme amyloid fibril networks support cellular growth and spreading. Biomacromolecules 15(2): 599-608.
  10. Reynolds, N. P., Styan, K. E., Easton, C. D., Li, Y., Waddington, L., Lara, C., Forsythe, J. S., Mezzenga, R., Hartley, P. G. and Muir, B. W. (2013). Nanotopographic surfaces with defined surface chemistries from amyloid fibril networks can control cell attachment. Biomacromolecules 14(7): 2305-2316.
  11. Usov, I. and Mezzenga, R. (2015). FiberApp: An open-source software for tracking and analyzing polymers, filaments, biomacromolecules, and fibrous objects. Macromolecules 48: 1269-1280.
  12. Wei, G., Su, Z., Reynolds, N. P., Arosio, P., Hamley, I. W., Gazit, E. and Mezzenga, R. (2017). Self-assembling peptide and protein amyloids: from structure to tailored function in nanotechnology. Chem Soc Rev 46(15): 4661-4708.


与传统的扁平,僵硬的2D细胞培养底物如组织培养聚苯乙烯(TCPS)相比,由普通球形蛋白质吸附至固体支持物制备的淀粉样蛋白纳米原纤维的密集网络可以改善细胞粘附,扩散和分化。 这是由于淀粉样蛋白原纤维网络的纤维性,纳米形貌性质以及它们模仿细胞外基质(ECM)的机械性质和结构的事实。 然而,精确的细胞响应强烈依赖于细胞培养界面处的网络的纳米结构,因此固定网络的准确表征是重要的。 原子力显微镜(AFM)由于其精确的横向分辨率和简单的样品制备技术,是表征原纤维网络形态的理想技术。 因此,在这里我们描述了一个详细的协议,用于表征模式AFM的淀粉样原纤维网络。

【背景】吸附到固体载体上的由常见球状蛋白组装的无毒淀粉状蛋白原纤维网络(Jung等人,2011; Lara等人,2011)具有广泛的应用(Dharmadana等人,2017; Wei等人,2017)。特别有意义的是它们在真核细胞培养和一般生物材料中的应用(Reynolds等人,2013,2014和2015; Gilbert等人,2017b)。这主要是由于淀粉样原纤维网络具有与许多细胞类型(ECM)的局部微环境非常相似的形态学和机械性质。这样的淀粉样蛋白原纤维网络具有附加的吸引力,即它们制造简单,价格便宜并且具有可以容易地复制的明确的化学物质。

正如所料,生长在这些淀粉样蛋白原纤维网络上的细胞的反应高度依赖于原纤维网络本身的纳米级特性。例如,原纤维直径,纳米级粗糙度,表面覆盖度和原纤维形态的微小变化已经显示出影响细胞附着和扩散(Reynolds等人,2014和2015)。因此,在将其用于细胞培养应用之前,准确地表征固定化网络的纳米形貌和表面粗糙度是重要的。原子力显微镜是一种强大的技术来执行这种分析,因为它只需要很少的样品制备,具有纳米级的横向分辨率和亚纳米级的垂直分辨率(Reynolds等人,2014和2015; Gilbert等人。 ,2017a和2017b; Reynolds ,2017)。另外,诸如纳米粗糙度的参数可以通过后成像分析来提取。在该协议中,我们将描述通过AFM在固体(云母)基底上成像淀粉样蛋白原纤维的稠密网络(由蛋白质鸡蛋白溶菌酶制造)的过程。我们还将描述后处理分析的最常见步骤,即展平(去除样品倾斜和弯曲伪像)和粗糙度分析。

关键字:淀粉样蛋白纳米纤维, 原子力显微镜检查, 自组装, 粗糙度分析, 蛋白质聚集, 生物材料


  1. 原子力显微镜金属试样圆盘直径15毫米(ProSciTech,目录号:GA530-15)
  2. 莫斯科云母盘,等级V-1直径12.5毫米(ProSciTech,目录号:G51-12)
  3. STKYDOT粘性垫(布鲁克纳米,目录号:STKYDOT)


  1. Cole-Parmer精密镊子套装,不锈钢(Cole-Parmer仪器,目录号:07387-16)
  2. 带有纳米级V控制器的多模式8原子力显微镜(AFM)(Bruker Nano,型号:Multimode 8)
  3. 轻敲模式AFM尖端(约谐振频率= 300kHz,力常数40N / m)(Bruker Nano,型号:RTESPA-300)


  1. 纳米分析软件(Bruker版本1.7)



  1. 加载样品并设置AFM
    1. 通过将球形蛋白质(通常是β-乳球蛋白或蛋清溶菌酶)暴露于高温和低pH下,使蛋白质水解成肽片段,制备淀粉样蛋白原纤维网络。随着时间片段自组装成淀粉样原纤维(参见Charnley关于制造这些网络的详细协议,请参见 [2018])。新鲜制备的淀粉状蛋白原纤维网络可以通过简单的滴铸流程(参见Charnley等人[2018])沉积在云母上,现在可以使用双重侧面的粘点。小心地将样品安装到AFM上(图1a),确保不会损坏样品装载区域左上方的反射镜。

      图1.带有重要对照标记的Bruker Multimode 8 AFM。a)磁性样品架(用于连接磁性AFM柱的样品; b)操作x-y台的旋钮; c)旋钮操纵激光位置; d)旋钮操纵光电二极管的垂直位移; d)控制光电二极管的水平位移; e)旋钮来固定和解除悬臂支架的固定; f)用杠杆调整反射镜对光电二极管的角度; g)激光源的电缆和连接器(确保插入)。

    2. 将AFM悬臂安装到其支架上,确保针尖没有损坏(视频1),并且悬臂底座与悬臂支架背面齐平(图2a)。现在将原子力显微镜悬臂支架放入原子力显微镜,确保样品不会太高,导致悬臂撞击样品。确保AFM处于AFM模式(非STM或TM-AFM)。


    3. 将AFM上的光学显微镜聚焦到悬臂上,并确保它安装得笔直而不是倾斜(图2b和2c)。将相机重新对准云母片的表面。
    4. 向上移动样品(或AFM尖端取决于AFM模型),使其靠近云母基底,通过观察AFM尖端与其反射之间的间隔(图2d),可以判断与基底的距离。
    5. 将AFM激光光斑聚焦在悬臂上(图1c),使所得到的Sum最大化(对于RTESPA300尖端应该是7左右),但是也要确保激光光斑接近悬臂尖端,而不是明显超过一个边缘(图2e)。
      注意:为了最大化总和,可能需要调整激光位置(图1c)和将激光聚焦到AFM内的4象限光电二极管上的反射镜角度(图1f)。 br />
    6. 在将激光点正确定位在AFM悬臂上之后,应该调整四象限光电二极管,使得垂直和水平偏转读数为零(图1d和1dh) )。一旦正确调整,应该将AFM切换到TM-AFM模式(使用仪器底座上的开关),如果需要,水平偏转复位为零。

      a)将RTESPA300悬臂正确安装在标准Bruker多模悬臂支架上; b)正确对准的悬臂的光学显微镜图像; c)对准不佳的悬臂的光学显微镜图像; d)悬臂接近表面的光学显微镜图像,可以通过观察悬臂图像与其反射之间的间隙来监测尖端 - 样品分离; e)当悬臂足够靠近表面时,悬臂和其反射的图像不能相互区分,此时其余的方法应该由软件(使用啮合命令)执行。 >
    7. 现在悬臂被正确地安装并且激光对准,所有在成像之前保留的是选择悬臂的共振频率。这可以在AFM软件的设置菜单中完成(在轻敲模式实验预设中)。由于这种成像是在空中进行的,所以自动调谐软件(图3)运行良好,但是用户应该确保自动调谐设置为在正确的频率范围内扫描(200-400 kHz适用于RTESPA300悬臂)目标幅度是足够的(500 mV是一个很好的起点)(见图3)。
      在自动调谐菜单中单击执行后,软件应正确识别悬臂的谐振频率(可能在RTESPA300悬臂的270-330 kHz之间),并将幅度固定为选定的目标幅度(见图3)。如果软件无法识别谐振频率,或者看起来可疑(即,>从引用的谐振频率±50kHz,和/或不明显是最强的峰值),则步骤A2-A5可能需要重复或悬臂可能会损坏。

      图3.悬臂式调谐谐振频率和相移大致类似于上面给出的示例,其中悬臂的引用谐振频率(在这种情况下为300 kHz)附近有一个主峰(±50 kHz) )。应特别注意突出显示的参数,起始和结束频率应包括预期的谐振频率(300 kHz),并应选择所需的目标振幅(对于500-1,000 mV的淀粉样原纤维效果良好)。

  2. 成像淀粉样原纤维网络
    1. 确保AFM悬臂靠近基底界面(图2e),选择扫描速率(〜1 Hz),扫描尺寸(1-10μm)和图像分辨率(512 x 512像素用于出版质量的图像),并从事软件中扫描菜单的悬臂。样品现在将自动移动(最初通过步进电机,后来由压电马达),直到悬臂和基底接触。当悬臂未能与表面接触时偶尔发生错误的接合,如果发生多次,则可能需要重复上述步骤A2-A5或尖端可能被损坏。
    2. 一旦悬臂与基板接触,最重要的参数是振幅设定点。振幅设定值近似于尖端施加在样品上的力(较低的电压=施加的较高的力),并自动设定为目标振幅的50%的初始值。一旦开始扫描,振幅设定值可以调整,以便尖端紧密跟踪表面的整体特征。这可以通过查看地形通道中的追踪和回扫线扫描来评估(图4b)。这些应该是完全重叠的,如果它们不是那么应该减小振幅设定点。当原子力显微镜正好跟踪表面(在轨迹线和回程方向)时,获得最佳振幅设定点,但施加在样品上的力最小化(图4)。在这个阶段,如果需要的话,还可以优化驱动幅度和驱动频率,以进一步提高图像的质量。
    3. 最后调整积分(IG)和比例增益(PG),使所有通道的噪声最小化。幅度误差通道(默认通道2)最清楚地显示噪声,因此应该使用该通道来微调IG和PG。典型地,随着增益的增加,观察到噪声的降低,直到达到上限,超过该上限,将额外的电噪声引入到图像中。根据经验,PG应该总是比IG大2-3(图4c)。一旦满意图像质量,重新启动扫描,设置文件名称和目录,然后打开捕获以记录图像。好的和坏的扫描参数的例子如图4所示。

      图4.淀粉样蛋白原纤维网络的良好(左)和不良(右)AFM成像质量a)准确记录淀粉样蛋白原纤维网络图像的例子; b)跟踪和回扫重叠确认图像质量的扫描线; c)用于记录上述图像的典型扫描设置; d)成像参数差导致原子力显微镜悬臂梁不能紧密跟踪表面,因此模糊的图像; e)由于高振幅设定值,扫描线和扫描线不能很好地重叠; f)非优化扫描设置的例子。


这里我们将详细描述大多数AFM图像所使用的最基本的后期图像处理。注意所有的处理和分析协议都在Nanoscope Analysis软件(Bruker v1.7)中进行,对于其他AFM分析软件,协议可能会有所不同。

  1. 在所有地形AFM图像中的图像处理的第一阶段是为了校正与平面的偏差(由于样品未被完全水平安装在AFM中而导致的倾斜)。这是通过Nanoscope软件中的扁平工具(图5)执行的。对于扫描压电式扫描仪总可能距离的一小部分的图像来说,只需校正倾斜的一阶平坦就足够了。在作者的经验中,一阶平面拟合是合适的,总扫描距离的40%,在多模“e”扫描仪的情况下,大约等于4-5微米。对于较大的扫描,可能需要2阶或3阶扁平算法来去除压电元件在其大部分工作距离上行进时发生的样品倾斜和弯曲伪影。

    图5. Nanoscope分析软件中展平工具的屏幕截图。 a)由于样品中的倾斜,具有多个伪影的预先平坦的图像。 b)仅有1阶平坦图像去除倾斜。对于较大的扫描,可能需要2阶或3阶平坦化来消除因压电传播其总可能距离的大部分而导致的弯曲效应。

  2. 为了计算算术平均粗糙度和均方根粗糙度(在Nanoscope分析软件中分别称为Ra和Rq),粗糙度工具可以通过点击软件中的粗糙度图标来使用(图6i)。作为结果生成的表格显示整个图像(图6ii)以及特定感兴趣区域(图6iii)的Ra和Rq值(如果通过在图像上绘制一个框进行选择)。
  3. 还有许多额外的AFM分析工具可以用来提取淀粉样纤维网络的大量信息,如上所述,但是在这里描述它们超出了本协议的范围。有关Nanoscope Analysis中所有可用工具的详细说明,请参阅与软件相关的帮助文件。此外,对于纤维材料,作者推荐免费的软件FiberApp(Usov和Mezzenga,2015)来获得更复杂的统计分析方法。

    图6. Nanoscope分析软件中粗糙度分析工具的屏幕截图。 i)粗糙度分析图标; ii)用红色突出显示整个图像的Rq和Ra值; iii)以绿色突出显示图像所选区域的Rq和Ra值。






这项工作部分在澳大利亚国家制造设施的ANFF-Vic节点进行,澳大利亚国家制造设施是一家根据国家合作研究基础设施战略建立的公司,为澳大利亚研究人员提供纳米和微制造设施。 JG承认澳大利亚政府教育和培训部为奋进奖学金和国家科学基金会研究生研究奖学金项目,编号为DGE-1333468。 NPR和JG在Swinburne科技大学(IC140100023)向ARC生物设备培训中心(IC140100023)表示感谢,感谢他的奖学金(JG)资助(NPR)和主办。 MC承认瑞士国家科学基金会(SNSF)的支持(授予PA00P3_142120和P300P3_154664)。 JG和OGJ承认美国农业部Hatch Act基金进一步资助(IND0-1162)。作者声明不存在利益冲突或利益冲突。


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引用:Charnley, M., Gilbert, J., Jones, O. G. and Reynolds, N. P. (2018). Characterization of Amyloid Fibril Networks by Atomic Force Microscopy. Bio-protocol 8(4): e2732. DOI: 10.21769/BioProtoc.2732.