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Apr 2018
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Unraveling the Physicochemical Determinants of Protein Liquid-liquid Phase Separation by Nanoscale Infrared Vibrational Spectroscopy
利用纳米尺度红外振动光谱揭示蛋白质液-液相分离的理化决定因素   

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Abstract

The phenomenon of reversible liquid-liquid phase separation of proteins underlies the formation of membraneless organelles, which are crucial for cellular processes such as signalling and transport. In addition, it is also of great interest to uncover the mechanisms of further irreversible maturation of the functional dense liquid phase into aberrant insoluble assemblies due to its implication in human disease. Recent advances in methods based on atomic force microscopy (AFM) have made it possible to study protein condensates at the nanometer level, providing unprecedented information on the nature of the intermolecular interactions governing phase separation. Here, we provide an in-depth description of a protocol for the characterisation of the morphology, stiffness, and chemical properties of protein condensates using infrared nanospectroscopy (AFM-IR).

Keywords: Liquid-liquid phase separation (液-液分离阶段), Infrared nanospectroscopy (红外纳米光谱), Atomic force microscopy (原子力显微镜), Single-molecule biophysics (单分子生物物理技术)

Background

Liquid-liquid phase separation (LLPS, Figure 1) is a fundamental process resulting in the formation of membraneless subcellular bodies, such as nucleoli and germ granules (Hyman et al., 2014; Banani et al., 2017). This reversible transition, during which proteins condense into a dense liquid phase or droplet within the cell, is involved in many aspects of cellular organisation and function, such as mRNA processing and signalling (Murakami et al., 2015). It has been realised that most of the proteins that undergo LLPS contain intrinsically disordered domains (IDRs), which are involved in networks of multivalent interactions that underpin the condensation process (Li et al., 2012). However, dysregulation of this process can lead to the formation of solid hydrogel-like inclusions, which are implicated in human disease. Such is the case of fused in sarcoma (FUS), an RNA-binding protein implicated in the onset of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) (Patel et al., 2015).


The LLPS behaviour of protein condensates has typically been studied using optical and fluorescence microscopy methods, as well as bulk characterisation of properties in solution, such as turbidity measurements (Alberti et al., 2019). These approaches have shown the effect of external factors on the modulation of phase separation, revealing the influence of protein concentration, pH, salt type and concentration, and small molecules (Hyman et al., 2014; Murakami et al., 2015; Patel et al., 2017; Ruggeri et al., 2018; Alberti et al., 2019). A further understanding of the process of LLPS, in particular the fundamental molecular interactions determining protein condensation and the inner structure of the individual protein condensates, is hampered by their intrinsic transient nature and sub-micrometer scale dimensions. However, conventional approaches, which are limited by their spatial resolution and the need for highly concentrated samples, cannot probe molecular mechanisms and internal factors determining LLPS at the individual condensate and sub-condensate levels.



Figure 1. Schematic of protein aggregation vs. liquid-liquid phase separation and liquid-to-solid transition


In our previous and current work (Müller et al., 2014; Ruggeri et al., 2015, 2016a, 2018, 2020a, and 2020b; Ramer et al., 2018; Lipiec et al., 2019), we have demonstrated that infrared nanospectroscopy (AFM-IR) can provide new and complementary information, as compared with bulk methods, regarding the phenomenon of protein LLPS at the sub-condensate nanoscale level. This information can then be successfully exploited to address fundamental knowledge gaps in the physicochemical determinants of this process (Müller et al., 2014; Ruggeri et al., 2015, 2016a, 2016b, 2018, 2020a; Dazzi and Prater, 2017; Lipiec et al., 2019). AFM-IR combines the capabilities of infrared spectroscopy (IR) and atomic force microscopy (AFM) to allow for the simultaneous characterisation of morphology, stiffness, and chemical properties of individual droplets, and even of different regions within the same droplet, thereby providing mechanistic insight into their inner biophysical properties and formation.


The AFM-IR system bases its function on scanning probe microscopy (Figure 2). Briefly, during morphology acquisition, the tip and cantilever in contact with the sample can be used to acquire nanomechanical and nanospectroscopy information. The relative stiffness of the sample is evaluated by considering the tip and the sample in contact as a system of coupled springs. The contact resonance frequency of these springs is monotonically related to the intrinsic stiffness of the sample (Volpatti et al., 2016; Qamar et al., 2018). A stiffer sample would cause an increase in contact resonance, which we can term ‘frequency shift’; thus, the mechanical properties at the sub-droplet nanoscale level can be measured (Qamar et al., 2018; Shen et al., 2020). To retrieve chemical information in the form of nanoscale-resolved maps and localised spectra, the tip is used as a detector of photothermal expansion. If a sample absorbs IR light at a specific wavelength, the chemical bonds absorbing the light will vibrate; this vibration is dissipated as thermal heat, producing a sample expansion that can be measured by the AFM tip in contact with the sample. The response of the cantilever is proportional to the IR absorption (Ruggeri et al., 2016b). Localised IR spectra can be acquired by holding the tip at a fixed position on the sample while sweeping the frequency of the IR light. Similarly, IR maps of whole droplets can also be acquired by measuring the thermal expansion at a fixed wavelength during the measurement of sample morphology. AFM-IR is capable of acquiring chemical information down to the single protein molecule scale (Ruggeri et al., 2020a). In the case of LLPS of proteins, it is relevant to investigate the vibrations associated with the amide band I, which is related to C=O stretching (80%) and is therefore sensitive to the conformation and hydrogen bonding of the protein backbone. Thus, the shape of the amide band I is studied to reveal secondary and quaternary structural information (Ruggeri et al., 2015; 2016a and 2020a).



Figure 2. AFM-IR nanoscale chemical imaging and spectroscopy principles for a liquid-liquid phase-separated droplet. Absorbed IR light causes thermal expansion of the sample exciting the AFM cantilever, whose thermomechanical response is proportional to the absorbed light. Scanning the cantilever on the sample while fixing the laser wavelength enables one to simultaneously obtain nanoscale-resolved a) morphology, b) chemical (IR absorption), and c) mechanical maps. d) nanoscale-localised (red cross) IR spectra are then obtained by sweeping the laser wavelength while fixing the position of the cantilever.


AFM-IR has recently been successfully exploited to study protein droplets for drug delivery purposes, to evaluate the effect of post-translational modifications, specifically methylation, and to investigate the interactions and phase-separation behaviour, in addition to the effect of shear, on liquid phase-separated condensates (Müller et al., 2014; Volpatti et al., 2016). In an initial study involving the application of AFM-IR to phase-separated droplets, it was shown that a change in methylation state alters the structural and biophysical state of condensates of FUS, the dysfunction of which impairs neuronal function. The study quantitatively compared the structural state of the methylated and hypomethylated condensates, showing an increased content of antiparallel β-sheet in the latter. Hypomethylated FUS also showed heterogeneity with the coexistence of liquid and gel-like states within a single condensate, suggesting a possible interconversion between the two states (Qamar et al., 2018). A more recent study reported a liquid-to-solid transition through the application of shear, which led to the formation of stable fibres that were more anistropically ordered and had a higher β-sheet content and intermolecular hydrogen bonding compared with spherical liquid-liquid phase-separated condensates (Shen et al., 2020). In this study, infrared nanopolarimetry was applied to study the internal structural organisation of the shear-formed FUS fibrils.


Here, we describe the protocol followed for the controlled, in-depth characterisation of the morphology, stiffness, and chemical properties of phase-separated condensates using AFM-IR.

Materials and Reagents

  1. FUS sample at a concentration of 1-10 μM (the production and purification of FUS is discussed in previously published protocols (Qamar et al., 2018)

  2. ZnSe window (Thorlabs, catalog number: WG70530)

  3. MilliQ water

Equipment

  1. AFM-IR with thermomechanical detection: nanoIR1, nanoIR2, or nanoIR3 (Anasys Instruments, Bruker, USA)

  2. PR-EX-nIR2 AFM probes: silicon, gold-coated, with a nominal radius of 30 nm and a spring constant of 0.2 N/m (for contact mode measurements; Anasys, Bruker, USA)

  3. P10 Pipette (Starlabs, UK)

Software

  1. In-built nanoIR software (Bruker, USA)

  2. SPIP (Image Metrology, Denmark, https://www.imagemet.com/products/spip/)

  3. OriginPRO (Origin Labs, USA, https://www.originlab.com/origin)

Procedure

  1. Sample preparation for AFM-IR measurements

    1. Pipette 10 μl sample onto the hydrophobic ZnSe crystal. Incubate for 1 min to allow for physisorbtion. Longer deposition times are possible, but this may induce artefacts due to artificial self-organisation on the surface.

    2. Rinse three times with 1 ml MilliQ water to remove the excess salt.

    3. Dry under a gentle flow of nitrogen.


  2. Infrared nanospectroscopy measurements of liquid-liquid phase-separated droplets

    1. Turn on the AFM-IR system and the infrared (IR) laser. This should be done 30-60 min before measurement to allow the system to reach thermal equilibrium.

    2. Open the in-built software to control the AFM-IR (Figure 3). Select file > new to open a new nanoIR file. Press the initialise button to start the AFM-IR system.



      Figure 3. An overview of the nanoIR software. In the controls window, imaging parameters are given as input. In the microscope window, the relevant information is presented as imaging proceeds.


    3. Open the instrument cover and mount the probe (Figure 4).



      Figure 4. A photo of the AFM head. The cantilever is mounted on the AFM head (red box). Then, the knobs on the AFM head are used to adjust the position of the detection laser.


    4. Now, focus the optical camera on the cantilever. Select AFM Probe > Load > Next. Under focus on probe, click on the arrows to change the focus. Under sample XY movement, use the arrows to position the crosshair on the cantilever.

    5. Focus the detection laser on the end of the cantilever using the knobs. The value of the sum measured by the four-quadrant photodiode should be at least 3 V. Use the deflection knob in the AFM head and adjust the cantilever deflection to -1 V and then click next.

    6. Close the cover of the instrument.

    7. Focus the camera on the sample using the arrows under focus on sample (Figure 5).



      Figure 5. Illustration of Steps B4-B7. The cantilever is loaded, and the optical camera and laser focused on the end of the cantilever. Then, the camera is focused on the sample surface. Arrows can be used to navigate around the sample.


    8. To select a region of interest on the sample, select sample XY movement. Use the arrows to move around the sample surface. When you have identified the region of interest, click on approach and engage.

    9. In this context of the study of protein droplets, the information on morphology, IR absorption, and stiffness are of particular interest. To measure these parameters, select the display channels under the microscope window. Choose Height for morphology, Amplitude 2 for IR absorption, and PLL frequency to map tip-sample contact resonance (Figures 6 and 7).

      Note: During morphology acquisition, the tip and the sample in contact can be considered a system of coupled springs. The contact resonance of these springs is related to the intrinsic stiffness of the sample. A stiffer sample causes an increase in contact resonance, which we can term ‘frequency shift.’



      Figure 6. Demonstration of Step B9, in which display channels are selected according to the parameters of interest. In this case, they are morphology (Height), tip-sample contact resonance frequency (PLL Frequency), and IR absorption (Amp2).



      Figure 7. The contact resonance between the tip and the sample is related monotonically to the intrinsic sample stiffness. A stiffer sample causes a shift to a higher frequency, which can be used to investigate the relative stiffness of heterogeneous samples.


    10. Set the imaging parameters for morphology measurements in the AFM scan section of the controls window (Figure 8).

      Note: It is important to consider the sample properties when selecting imaging parameters. For example, choosing a lower scan rate will decrease the lateral force exerted on the sample, which is relevant for biological samples; however, this choice will increase the image acquisition time. A higher number of pixels will increase the image resolution but will similarly increase the image acquisition time. The gain values should be selected considering the sample roughness (i.e., a higher integral gain will allow the system to respond to changes in sample height but can also introduce noise). The following are the recommended range of values: scan rate 0.1-1.0 Hz and 256 × 256-1024 × 1024 pixels. The appropriate range for gain values are as follows: integral I = 1-10 and proportional P = 5-30. The value of the gains should be chosen as the minimal values to have overlapping trace and retrace scan lines.



      Figure 8. A demonstration of Step B10, in which the imaging parameters are input for morphology mapping. The parameters that should be considered first are the scan rate, image size, resolution (pixels), setpoint, and gain (integral and proportional).


    11. Select scan to acquire a morphology map. Representative morphology maps and contact resonance values of FUS droplets are shown in Figures 9 and 10.

      Note: The morphology can be similarly measured in dynamic tapping mode, but AFM-IR measurements are performed here in contact mode to have higher signal-to-noise ratios during infrared spectra and map acquisition.

    12. When the mapping of morphology is finished (Figure 9), set up the instrument to perform the IR and contact resonance (Figure 10) measurements. In the microscope window, click on the height map to position the probe on top of one droplet. Then in the nanoIR section of the controls window, click on start IR to illuminate the sample with the IR laser.



      Figure 9. A representative image of the morphology of a FUS droplet. The 3D morphology of a droplet is represented as a 2D colour map, with lighter colours indicating increasing height.



      Figure 10. A representative contact resonance map of a FUS droplet. A contact frequency map is simultaneously acquired during morphology acquisition. Tip-sample contact resonance provides information regarding sample stiffness. A stiffer sample would cause an increase in contact resonance, which we can term ‘frequency shift.’


    13. To focus the infrared laser on the cantilever, click on optimisation. In this window, write your chosen wavenumber click add.

      Note: The wavenumber should be chosen such that your sample will have significant absorbance. If the sample absorbs IR light at a specific wavelength, chemical bonds will vibrate, causing a thermal expansion of the sample that can be detected by the AFM tip in contact with the droplet. A typical value for protein samples is 1,655 cm-1, which corresponds to the amide I band. The response of the cantilever is proportional to the absorption of IR light by the sample; thus, differences in IR absorption can be mapped and correlated to the corresponding morphology map.

    14. Select scan to find the IR laser position and then update to align its position with the cantilever (Figure 11). Close the window.



      Figure 11. A demonstration of Steps B13 and B14, in which the wavenumber of interest, in this case 1,655 cm-1, is entered. Then select scan to find the IR laser position and update to align its position with the cantilever.


    15. Select general in the nanoIR settings. Input a wavenumber where IR absorption is expected. Then, deactivate the Band Pass Filter option and look at the meter reading and fast Fourier transform (FFT) of the cantilever response. In the FFT window, move the green cursor to read the resonance frequency of the cantilever (Figure 12). A typical value for the FFT of the resonance of the cantilevers is around 300 kHz. Write the found resonance frequency value in the general section of the freq. centre field and use a freq. window of 50 kHz. Select a laser power that is low enough to avoid saturating the meter reading, distorting the cantilever response, and overheating the sample.



      Figure 12. A demonstration of B15, in which the parameters of the IR laser are set up. On the left-hand side, input the parameters as described above. On the right-hand side, the meter reading displays the thermomechanical response of the cantilever. Use the cursors to centre the green course at the peak of the resonance frequency. Once the value for the resonance frequency has been determined, input it in the Freq. Centre on the left-hand side.


    16. Click on the laser pulse tune window to use the resonance enhanced mode. Choose a frequency centre of 300 kHz, a tune range of 50 kHz, and a duty cycle of the laser of 5% (Duty cycle is the fraction time during which there is laser pulse emission). Click on acquire to sweep the pulse rate of the laser. Use the cursor in the graph to tune the laser pulse to the frequency of the mechanical response of the thermal expansion of the sample absorbing the IR light (Figure 13).



      Figure 13. A demonstration of laser tuning (Steps B16 and B17). In the laser pulse tune window, input the parameters as described in Step B19. Click on acquire to display the contact resonance frequency and phase. Under the auto-tune window, select PLL to track the contact resonance frequency of the resonance enhanced mode.


    17. To avoid cross-talk between the chemical and mechanical information in the sample, it is important to track the contact resonance frequency in resonance enhanced mode by a phase locked loop (PLL). Select PLL, then click the zero button in the PLL window and tick enable. Choose an integral gain I = 0.5 and proportional gain P = 10.

    18. Open the optimisation window again and identify the position of the IR laser for at least 3 wavelengths corresponding to the major absorbance bands of the sample. For a protein sample, this should be amide bands I, II, and III. Moreover, identify the position of at least one wavelength for each chip of the laser.

    19. Click on Tools > IR Background Calibration > New (Figure 14). In the window, select the spectroscopic region of interest, which is 1,800-1,200 cm-1 in the case of a protein sample. Choose the same pulse rate as determined in Step B16 and a duty cycle of 5%.



      Figure 14. A demonstration of the steps for background calibration (Steps B19-B21). Input the parameters as described above. Then, select acquire to generate a spectrum of the background (i.e., the substrate).


    20. Select fast acquisition and choose the laser speed. For a quantum cascade laser, a typical range is between 20-5,000 cm-1/s. The background spectrum will be used for normalisation of the measured nanoscale localised spectra. Close the window.

    21. If a fast laser and resonance-enhanced mode is not available, skip Step B16 and select stepped spectra instead of fast in both the background and IR spectra acquisition windows. This will still allow for chemical characterisation. While this sensitivity is not sufficient to reach the single -molecule level, it still allows for full characterisation of liquid-liquid phase-separated droplets with a typical size ranging from hundreds of nanometers to micrometers.

    22. Now, measure a nanoscale-localized IR spectrum in the protein range (1,800-1,200 cm-1) (Figure 15). In the IR spectra settings, choose an IR spectrum resolution of 1-4 cm-1. For the number of co-averages, choose at least 64. Select acquire.

    23. Additionally, a nanoscale-resolved chemical map can be acquired (Figures 15 and 16). To do this, select the IR imaging option, choose a wavenumber of interest (e.g., 1,655 cm-1 for amide band I), and click on scan in the AFM scan window.

      Note: The principles of acquiring localised IR spectra and IR maps are generally the same. When acquiring IR maps, the tip is scanned across the sample while maintaining a fixed laser wavenumber, so that the absorption of the sample and surface is mapped at that wavenumber. When acquiring a localised spectrum, the tip is held in a fixed position on the sample, and the IR laser wavenumber is varied to obtain absorption across a range of values. This provides a full IR spectrum that can then be used to provide specific information regarding the secondary structural contributions (discussed further below).



      Figure 15. Representative IR map and localised IR spectra of FUS droplets. An IR absorption map can be measured simultaneously during morphology mapping (together with the contact resonance map). The localised IR spectra can also be acquired at a single point on the surface of a droplet (indicated by the blue cross).



      Figure 16. An example of completed raw morphology and IR maps, as well as typical raw localised IR spectra. The blue cross indicates where the localised IR spectra were recorded.


    24. Our advice is to acquire multiple localised spectra within one droplet to enable appropriate statistical significance for studying its heterogeneity. Repeat Step B22 multiple times within each droplet.


  3. Data processing and analysis

    1. Open an imaging processing software, such as SPIP, to process AFM maps.

    2. First-order flatten AFM maps by selecting General > Plane Correction > Plane Correction Set-Up.

    3. Under Global Correction, select Average Profile Fit.

    4. Select Degree to 0, then Apply.

    5. Repeat for Degree = 1.

    6. Zero-order flatten IR and stiffness-related maps by repeating the sample flattening procedure (excluding Step C2).

    7. To evaluate the relative sample stiffness, consider the distribution of the values for the pixels of the contact resonance frequency to calculate its average and standard deviation inside each region of interest in your sample (Figure 8). Then, plot these averages as a normalised ratio (Figure 17). Due to the complicated nature of determining the absolute stiffness of soft droplets, the stiffness of the droplets should be considered relative to the ZnSe substrate. To prove that the measured mechanical differences are real, the mechanical differences should be evaluated for independent tips and samples. Any software of your choice can be used.



      Figure 17. Measuring sample stiffness. A. An example of plotted average frequency shifts for each droplet measured. B. Relative stiffness of the droplets can be determined by representing the data as a ratio of the average frequency shifts.


    8. Next, IR spectra can be processed by the OriginPro software to have normalised smoothed spectra and their second derivatives (Figure 18).



      Figure 18. The second derivative of the IR spectrum provides information on the secondary structure of the droplets. A. Representative processed IR spectrum of the droplets in the protein range (1,800-1,400 cm-1). B. A second derivative operation was performed to identify the secondary structure.


    9. For each spectrum acquired, subtract the spectrum acquired in a location close to the droplet on the ZnSe substrate to eliminate buffer absorption or tip contamination contributions. Select Analysis > Data Manipulation > Reference Data. Select the Input1 as your droplet spectrum and the reference data input as your ZnSe substrate spectrum. Select Okay.

    10. Then for each droplet or for a specific location on its area, calculate an average spectrum by averaging spectra taken at different positions within the droplet. To reduce the level of instrumental noise in the spectra, at least 5 spectra should be averaged. Select Analysis > Mathematics > Average Multiple Curves > Open Dialogue.

    11. For Method, select Average; for Averaged X, select Common X Range.

    12. Select additional output parameters as desired and select Okay.

    13. Smooth the average spectrum by applying a Savitzky-Golay filter (second order, 9 points). Select Analysis > Signal Processing > Smooth > Open Dialogue.

    14. From the drop-down list for Method, select Savitzky-Golay.

    15. For Points of Window, input 9.

    16. For Polynomial Order, select 2. Select Okay.

    17. Next, normalise the spectrum. Select Analysis > Mathematics > Normalise Curves > Open Dialogue.

    18. For Normalize Methods, select Normalize to [0,1], then Okay.

    19. Take the second derivative of the smoothed normalised spectrum. Select Analysis > Mathematics > Differentiate.

    20. For Derivative Order, select 2.

    21. Under Smooth, check the box for Savitzky-Golay Smooth. For Polynomial Order, select 2; for Points of Window, select 9.

    22. Tick the box for Plot Derivative Curve and select Okay.

      Note: The amide I band contains information on the secondary structure, as C=O stretching is influenced by the backbone conformation. Deconvoluting the spectrum by taking the second derivative provides quantitative information on the structural contribution (i.e., provides information on the content of α-helices, β-sheets, and random coils). An example of this is shown in Figure 18.

Acknowledgments

The authors acknowledge funding from Darwin College, University of Cambridge (FSR) and Emmanuel College, University of Cambridge (AMM). This protocol was largely derived from previous work presented in Qamar et al. (2018).

Competing interests

The authors have no competing interests to declare.

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简介

[摘要]蛋白质的可逆的液-液相分离的现象underlies无膜细胞器,它的形成是对细胞过程例如信令和传输的关键。此外,由于其对人类疾病的影响,揭示功能性致密液相进一步不可逆成熟为异常不溶性组装体的机制也很有趣。基于原子力显微镜 (AFM) 的方法的最新进展使得在纳米水平上研究蛋白质缩合物成为可能,提供了有关控制相分离的分子间相互作用性质的前所未有的信息。在这里,我们提供了一个协议,为形态学,刚度的表征进行了深入的描述,并使用红外nanospectroscopy(AFM-IR)蛋白质缩合物的化学性质。


[背景]液LIQ UID相分离(LLPS,图URE 1)是导致无膜的亚细胞机构,例如核仁和胚芽颗粒的形成的基本过程(海曼等人,2014; Banani 。等人,2017 )。这种可逆转变,在此期间蛋白质在细胞内凝结成致密的液相或液滴,涉及细胞组织和功能的许多方面,例如 mRNA 加工和信号传导(Murakami等,2015 )。人们已经意识到,大多数经历 LLPS 的蛋白质都包含内在无序结构域 (IDR),这些结构域涉及支持缩合过程的多价相互作用网络(Li等人,2012 年)。然而,这个过程的失调会导致固体水凝胶样包裹体的形成,这与人类疾病有关。这就是融合肉瘤 (FUS)的情况,这是一种 RNA 结合蛋白,与肌萎缩侧索硬化 (ALS) 和额颞叶痴呆 (FTD) 的发病有关(Patel等人,2015 年)。

蛋白质缩合物的 LLPS 行为通常使用光学和荧光显微镜方法以及溶液中特性的整体表征进行研究,例如浊度测量(Alberti等人,2019 年)。这些方法显示了外部因素对相分离调节的影响,揭示了蛋白质浓度、pH、盐类和浓度以及小分子的影响(Hyman et al. , 2014; Murakami et al. , 2015 ; Patel et al. , al., 2017; Ruggeri et al. , 2018; Alberti et al. , 2019 ) 。对 LLPS 过程的进一步理解,特别是决定蛋白质凝聚和单个蛋白质凝聚物内部结构的基本分子相互作用,受到其内在瞬态性质和亚微米尺度尺寸的阻碍。然而,常规方法中,其通过它们的空间分辨率和需要限制用于高LY浓缩样品,C ANNOT探针分子机制和内部因素,确定在个人冷凝和分冷凝LLPS水平。


图1 。示意图p rotein一个ggregation与升iquid-升iquid p HASE小号eparation和升iquid -以-s奥利德牛逼ransition


在OU R上和目前的工作(穆勒等人。2014;鲁杰里等人,2015年,2016a,2018,2020A ,和2020B;拉默等人,2018; Lipiec 。等,2019) ,我们已经表明,红外nanospectroscopy(AFM-IR)可以提供新的和补充信息,作为比较用散装方法,关于蛋白质的LLPS在子缩合物纳米级的现象水平。Ť他的信息然后可以成功地利用来在这个过程的物理化学因素地址基本知识缺口(穆勒等人,2014;鲁杰。等人,2015年,2016 A,2016B ,2018 ,2020A; Dazzi和普拉特,2017; Lipiec等人,2019 年)。AFM-IR结合红外光谱(IR)和原子力显微镜(AFM)的能力,以允许形态,刚度的同时表征,和相同的液滴内的单个液滴,甚至不同区域中的化学性质,从而提供机械深入了解它们的内部生物物理特性和形成。

所述AFM-IR系统立足其上扫描探针显微术(图函数URE 2)。简而言之,在形态采集过程中,与样品接触的尖端和悬臂可用于采集纳米力学和纳米光谱信息。通过将接触的尖端和样品视为耦合弹簧系统来评估样品的相对刚度。这些弹簧的接触共振频率与样品的固有刚度单调相关(Volpatti等人,2016 年;Qamar等人,2018 年)。较硬的样品将导致增加在接触共振,由此我们可以术语“频移”; 因此,可以测量亚液滴纳米级的机械性能(Qamar等人,2018 年;Shen等人,2020 年)。为了以纳米级分辨图和局部光谱的形式检索化学信息,尖端被用作光热膨胀的检测器。如果样品吸收IR在特定波光长度,化学键吸收光会振动; Ť他的振动耗散为热的热,从而产生可以由AFM针尖与样品接触要测量的样品的扩展。悬臂梁的响应与 IR 吸收成正比(Ruggeri等,2016b )。局部IR光谱可以通过按住尖端来获取在样品上的固定位置,而清扫IR光的频率。类似地,整个液滴的IR图可以也可以通过测量在热膨胀获取一个固定波长的样品的形态测量期间。AFM-IR 能够获取低至单个蛋白质分子尺度的化学信息(Ruggeri等人,2020a )。在蛋白质的 LLPS 的情况下,研究与酰胺带 I相关的振动是相关的,它与 C=O 拉伸 (80%) 相关,因此对蛋白质骨架的构象和氢键敏感。因此,研究了酰胺带 I 的形状以揭示二级和四级结构信息(Ruggeri等,2015;2016a 和 2020a )。


图2 。AFM-IR Ñ anoscale Ç hemical我maging和小号pectroscopy p rinciples用于升iquid -升iquid p HASE -s eparated d roplet 。吸收的 IR 光导致样品的热膨胀,激发 AFM 悬臂,其热机械响应与吸收的光成正比。在样品上扫描所述悬臂同时固定激光波长使人们能够同时获得纳米级-解决的)形态,B)化学(IR吸收),以及c)机械地图。d) 然后通过扫描激光波长同时固定悬臂的位置来获得纳米级局部(红十字)红外光谱。


AFM-IR公顷小号最近已成功地利用来研究蛋白质的液滴用于药物递送的目的,以评估翻译后修饰,特别是甲基化的效果,并研究的相互作用和相分离行为,除了剪切的效果,关于液相分离的冷凝物(Müller等人,2014 年;Volpatti等人,2016 年)。在涉及AFM-IR的相位的应用的初始研究-分开的液滴,它表明一个变化中甲基化状态会改变FUS的缩合物的结构和生物物理状态,所述功能障碍其也妨碍神经元功能。该研究定量比较了甲基化和低甲基化缩合物的结构状态,显示后者中反平行β-折叠的含量增加。低甲基化 FUS 还表现出异质性,液体和凝胶状状态在单个冷凝物中共存,表明这两种状态之间可能存在相互转化(Qamar等人,2018 年)。更近期的研究报告液体- -通过施加剪切固体过渡,这导致了稳定的纤维的形成该被更各向异性地有序并具有一个比较高β折叠含量和分子间的氢键键合与球形液-液相-分离的冷凝物(Shen等人,2020 年)。在第是研究中,红外线nanopolarimetry被应用于研究剪切形成FUS的内部结构组织原纤维。

在这里,我们描述了协议遵循形态,刚度的控制,深入表征,并相的化学性质-用AFM-IR分离缩合物。

关键字:液-液分离阶段, 红外纳米光谱, 原子力显微镜, 单分子生物物理技术

材料和试剂
 
1.在浓FUS样品entration的1-10       μ M(FUS 的生产和纯化在之前发布的协议中进行了讨论(Qamar等人,2018 年)
2. ZnSe 窗口(Thorlabs,目录号:WG70530)      
3. MilliQ 水      
 
设备
 
AFM-IR与热检测:nanoIR1,nanoIR2 ,或nanoIR3(ANASYS仪器,布鲁克,USA)
PR-EX-nIR2 AFM 探针:硅,镀金,标称半径为 30 nm,弹簧常数为0.2 N/m(用于接触模式测量;Anasys, Bruker, USA)
P10 移液器(Starlabs,英国)
 
软件
 
内置 nanoIR 软件,(美国布鲁克)
SPIP(丹麦图像计量学,https: //www.imagemet.com/products/spip/ )
OriginPRO(美国Origin Labs,https: //www.originlab.com/origin )
 
程序
 
AFM-IR 测量的样品制备
移液管10 μ升样品到疏水ZnSe晶体。孵育 1分钟以进行物理吸附。更长的沉积时间是可能的,但这可能会由于表面上的人工自组织而导致伪影。
用1个冲洗三次毫升中号illiQ水以除去所述过量的盐。
在温和的氮气流下干燥。
 
液-液相的红外线nanospectroscopy测量-分离液滴
打开 AFM-IR 系统和红外 (IR) 激光。这应该在测量前30 - 60 分钟完成,以使系统达到热平衡。
打开内置软件来控制AFM-IR (图URE 3) 。选择file>new打开一个新的nanoIR文件。按初始化按钮启动 AFM-IR 系统。
 
 
图 3. nanoIR 软件概述。在控制窗口中,成像参数作为输入给出。在显微镜窗口中,相关信息随着成像的进行而呈现。
 
打开仪器盖和安装探头(图URE 4) 。
 
 
图 4. AFM 头的照片。悬臂安装在 AFM 头(红色框)上。然后,AFM 头上的旋钮用于调整检测激光的位置。
 
现在,将光学相机聚焦在悬臂上。选择AFM 探针 >加载> 下一步。在探头焦点下,单击箭头以更改焦点。在示例 XY 移动下,使用箭头将十字准线定位在悬臂上。
使用旋钮将检测激光聚焦在悬臂的末端。四象限光电二极管测量的总和值应至少为 3 V。使用AFM 头中的偏转旋钮,将悬臂偏转调整为 -1 V,然后单击下一步。
关闭仪器的盖子。
将照相机聚焦在使用下的箭头样品焦点上的样品(图URE 5) 。
 
 
图5插图小号TEPS乙4-乙7.悬臂加载和光学相机和激光聚焦在悬臂的端部。然后,相机聚焦在样品表面上。箭头可用于在样本中导航。
 
要选择样本上的感兴趣区域,请选择样本 XY 移动。使用箭头在样品表面周围移动。当您确定了感兴趣的区域后,单击方法并参与。
在蛋白质的液滴,形态学的信息,IR吸收研究中的这种情况下,和刚度是特别感兴趣的。要测量这些参数,请选择显微镜窗口下的显示通道。选择高度为形态,振幅2为IR吸收,和PLL频率映射针尖-样品接触共振(图URE小号6和7 )。
注意:在形态采集过程中,接触的尖端和样品可以被认为是一个耦合弹簧系统。这些弹簧的接触共振与样品的固有刚度有关。较硬的样品引起的增加在接触共振,由此我们可以术语“频移。'
 
 
图6.示范小号TEP乙9 ,其中显示频道根据感兴趣的参数中选择。在这种情况下,它们的形态(高度),尖端-样本接触共振频率(PLL频率),以及IR吸收(器Amp2)。
 
 
图 7. 尖端和样品之间的接触共振与固有样品刚度单调相关。较硬的样品引起的移位至一个较高的频率,其可被用于研究异质样品的相对刚度。
 
在控制窗口的AFM 扫描部分设置形态测量的成像参数(图 8)。
注意:在选择成像参数时考虑样品特性很重要。例如,选择较低的扫描速率将减少施加在样品上的侧向力,这与生物样品相关;^ h H但是,这种选择将增加的图像采集时间。甲ħ像素igher数目将增加的图像分辨率,但同样会增加的图像获取时间。应根据样品粗糙度选择增益值(即,较高的积分增益将使系统能够响应样品高度的变化,但也会引入噪声)。以下是推荐的值范围:扫描速率 0.1 - 1.0 Hz和256 × 256 - 1024 × 1024 像素。增益值的适当范围如下:积分 I = 1 - 10 和比例 P = 5 - 30。增益值应选择为具有重叠跟踪和回扫扫描线的最小值。
 
 
图8.示范小号TEP乙10 ,其中成像参数是用于形态学映射输入。参数即应当首先考虑的是扫描速度,图像尺寸,分辨率(像素),给定值,并且增益(积分和比例)。
 
选择扫描以获取形态图。FUS 液滴的代表性形态图和接触共振值如图9 和10 所示。
注意:形态可在动态轻敲模式可以类似地测量,但AFM-IR测量在这里进行接触模式具有更高的信号-到-红外光谱和地图获取期间信噪比。
当形态的映射结束(图URE 9) ,设置为执行所述IR仪器和接触共振(图URE 10)测量。在显微镜窗口中,单击高度图以将探头放在一个液滴的顶部。然后在控制窗口的nanoIR部分,单击启动 IR以使用 IR 激光照亮样品。
 
 
图 9. FUS 液滴形态的代表性图像。液滴的 3D 形态表示为 2D 颜色图,颜色较浅表示高度增加。
 
 
图 10. FUS 液滴的代表性接触共振图。在形态学采集期间同时采集接触频率图。尖端-样品接触共振提供有关样品刚度的信息。较硬的样品将导致增加在接触共振,由此我们可以术语“频移。'
 
要将红外激光聚焦在悬臂上,请单击优化。在这个窗口中,写下你选择的波数,点击添加。
注意:应选择波数,以便您的样品具有显着的吸光度。如果样品在特定波长下吸收IR光,化学键会振动,从而导致样品的热膨胀的是可以通过在与液滴接触针尖来检测。甲典型值˚F或蛋白质样品是1 ,655厘米-1 ,这对应于酰胺I带。悬臂梁的响应与样品对红外光的吸收成正比;因此,红外吸收的差异可以被映射并与相应的形态图相关联。
选择扫描以找到IR激光位置,然后更新对准其与悬臂位置(图URE 11) 。关闭窗口。
 
 
图11.一种示范步骤B13和B14 ,其中所关注的波数,在这种情况下1 ,655厘米-1 ,被输入。然后选择扫描以找到红外激光位置并更新以将其位置与悬臂对齐。
 
              选择一般在该nanoIR设置小号。输入预期 IR 吸收的波数。然后,停用带通滤波器选项并查看悬臂梁响应的仪表读数和快速傅立叶变换 (FFT)。在 FFT 窗口中,移动绿色光标以读取悬臂梁的共振频率(图 12)。悬臂梁共振的典型值 f或FFT 约为 300 kHz。写在找到的共振频率值一般部分的的频率。中心场并使用频率。50 kHz 的窗口。选择激光功率是足够低,以避免饱和的电表读数,扭曲悬臂响应,和样品过热。
 
 
图 12 。B15 的演示,其中设置了红外激光器的参数。在左侧,输入如上所述的参数。在右手侧,仪表读取显示器š悬臂的热机械响应。使用光标将绿色路线置于共振频率的峰值处。确定谐振频率的值后,将其输入Freq. 以左侧为中心。
 
单击激光脉冲调谐窗口以使用共振增强模式。选择300kHz的频率中心,50kHz的调范围,和占空比为5%的激光的(占空比是在其期间存在分数时间激光脉冲发射)。点击上获取扫激光的脉冲速率。使用图中的光标将激光脉冲调整到吸收红外光的样品热膨胀的机械响应频率(图 13)。
 
 
激光调谐的图13.示范(小号TEPS B16和B17)。在激光脉冲调窗口,如在输入的参数小号TEP B19 。单击获取以显示接触共振频率和相位。在自动调谐窗口下,选择PLL以跟踪共振增强模式的接触共振频率。
 
为了避免样品中化学和机械信息之间的串扰,重要的是通过锁相环 (PLL)在共振增强模式下跟踪接触共振频率。选择PLL,然后单击PLL 窗口中的零按钮并勾选enable 。选择积分增益 I = 0.5 和比例增益 P = 10。
打开优化再次窗口并识别IR激光器的位置至少3米波的长度对应于所述样品的主要吸收谱带。用于蛋白质样品,这应该是酰胺频带I,II ,和III。此外,识别至少一个波的位置的长度为激光的每个芯片。
点击工具>红外背景校正>新建(图URE 14)。在窗口中,选择感兴趣的光谱区域,在蛋白质样品的情况下为 1 , 800 - 1 , 200 cm -1 。选择相同的脉冲率在确定的小号TEP乙16和5%的占空比。
 
 
 
 
图14.一种示范的步骤为背景校准(步骤小号B19 - B21)。如上所述输入参数。然后,选择获取以生成背景光谱(即底物)。
 
选择快速采集并选择激光速度。对于量子级联激光器,典型范围在 20 - 5 , 000 cm -1 /s 之间。背景光谱将用于normali小号的通货膨胀的测量纳米级locali小号编光谱。关闭窗口。
如果快速激光和共振-增强模式不可用,请跳过步骤 B16并在背景和红外光谱采集窗口中选择阶梯光谱而不是快速。这仍将允许进行化学表征。W¯¯往往微不足道这种敏感性不足以达到单-分子的水平,它仍允许用于满characteris的通货膨胀液-液相-具有典型尺寸范围从几百纳米到微米的分离液滴。
现在,测量纳米级-在蛋白质范围局部IR谱(1 ,800 - 1 ,200厘米- 1 )(图1 5 )。在IR光谱的设置,选择的IR光谱分辨率的1 - 4厘米-1 。对于共同平均数,至少选择 64。选择获取。
此外,纳米级分辨化学地图可以被获取(图小号1 5和16 )。要做到这一点,选择的红外成像选项,选择感兴趣的波数(例如,1 ,655厘米-1的酰胺带我),然后点击扫描在AFM扫描窗口。
注意:获取局部红外光谱和红外图的原理大体相同。当获取IR地图,所述尖端在样品,同时保持固定的激光的波数,因此扫描该样品和表面的吸收在该波数作图。当获取的局部频谱,尖端我小号HEL d在上样品和波数是变化的IR激光的固定位置获得跨越一定范围的值的吸收。这提供了完整的 IR 光谱,然后可用于提供有关二级结构贡献的特定信息(下面进一步讨论)。
 
 
图 15. FUS 液滴的代表性红外图和局部红外光谱。IR 吸收图可以在形态映射期间同时测量(连同接触共振图)。局部红外光谱也可以在液滴表面的单个点上获得(由蓝色十字表示)。
 
 
图 16. 完整的原始形态和红外图以及典型的原始局部红外光谱示例。蓝色横指示瓦特ħ埃雷局部IR光谱瓦特ERE记录。
 
我们的建议是在一个液滴内获取多个局部光谱,以便为研究其异质性提供适当的统计意义。重复小号TEP B22每滴内多次。
 
数据处理与分析
打开一个成像处理软件,如 SPIP,来处理 AFM 地图。
通过选择General>Plane Correction>Plane Correction Set-Up一阶展平 AFM 图。
在全局校正下,选择平均轮廓拟合。
选择度数为 0,然后应用。
重复度数=1。
零级通过重复压平IR和刚度相关的地图荷兰国际集团的样品平坦化过程(不含小号TEP C2) 。
为了评估所述相对样品刚度,考虑值的分布为的像素的接触共振频率来计算您样品(图8)中的每个感兴趣区域内的平均和标准偏差。然后,将这些平均值绘制为归一化比率(图 1 7 )。由于确定的复杂性的绝对软液滴的刚度,液滴的刚度应被视为相对于ZnSe衬底。为了证明测得的机械差异是真实的,应对独立的吸头和样品评估机械差异。可以使用您选择的任何软件。
 
 
图 17 。测量样品刚度。一个。绘制的每个测量液滴的平均频移示例。乙。的相对刚度的液滴能够通过表示数据作为平均频率偏移的比例来确定。
 
接着,红外光谱可以通过被处理的OriginPro软件已归一化平滑光谱和它们的二阶导数(图URE 18 )。
 
 
图18.红外光谱的二阶导数提供了有关信息的二级结构的液滴。一个。的代表性处理IR光谱的蛋白质中的范围的液滴(1 ,800 - 1 ,400厘米-1 )。乙。进行二阶导数运算以鉴定二级结构。
 
对于取得的各光谱,减去在一个位置靠近获取的光谱,以在ZnSe基片上液滴以消除缓冲器吸收或尖端污染贡献小号。选择分析>数据操作>参考数据。选择Input1作为您的液滴光谱和参考数据输入作为您的 ZnSe 衬底光谱。选择好的。
然后,对于每个液滴或在其区域中的特定位置,计算由averag平均光谱ING在液滴内的不同位置截取光谱。为了降低光谱中的仪器噪声水平,至少应平均 5 个光谱。选择分析>数学>平均多条曲线>打开对话。
对于方法,选择平均值;对于平均 X ,选择通用 X 范围。
根据需要选择其他输出参数并选择Okay 。
通过应用 Savitzky-Golay 滤波器(二阶,9 分)平滑平均频谱。选择分析>信号处理>平滑>打开对话。
神父Ø米为下拉列表方法,选择Savitzky -格雷。
对于窗点,输入9 。
对于多项式阶数,选择2 。选择好的。
接下来,对频谱进行归一化。选择Analysis>Mathematics>Normalise Curves>Open Dialogue 。
对于Normalize Methods ,选择Normalize to [0,1 ] ,然后选择OK 。
取的第二导数的平滑化归一化的光谱。选择分析>数学>微分。
对于衍生顺序,选择2 。
在平滑下,选中Savitzky-Golay 平滑框。对于多项式阶数,选择2 ;对于窗口点选择9 。
勾选Plot Derivative Curve框并选择Okay 。
注:酰胺I带包含所述二级结构信息,如C = O拉伸是影响d由骨架构象。通过取第二微分的去卷积谱提供了关于结构的贡献(即定量信息,提供关于α-直升机的内容的信息的CES ,β-折叠s和无规卷曲s )。这方面的一个例子如图18所示。
 
致谢
 
作者承认达尔文学院的资助, 剑桥大学(FSR) 和剑桥大学伊曼纽尔学院 (AMM)。该协议主要源自Qamar等人先前提出的工作。(2018)。
 
利益争夺
 
作者没有要声明的竞争利益。
 
参考
 
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引用:Ruggeri, F. S., Miller, A. M., Vendruscolo, M. and Knowles, T. P. J. (2021). Unraveling the Physicochemical Determinants of Protein Liquid-liquid Phase Separation by Nanoscale Infrared Vibrational Spectroscopy. Bio-protocol 11(16): e4122. DOI: 10.21769/BioProtoc.4122.
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