Interview Questions for Biophysical Techniques (e.g., AFM, SPR)

Atomic Force Microscopy (AFM) is a powerful technique used to image surfaces at the nanoscale. Imagine having a tiny, incredibly sharp needle on a cantilever (a tiny beam). This needle, or tip, scans across a sample’s surface. As it encounters bumps and valleys, the cantilever bends. A laser beam reflects off the back of the cantilever, and its deflection is measured by a photodetector. This deflection is directly proportional to the forces between the tip and the sample surface. The signal from the photodetector is then used to generate a three-dimensional image of the sample’s topography.

The principle is based on the detection of these tiny forces, allowing for incredibly high-resolution images, often down to the atomic level. It’s like feeling the surface with your fingertip, but at a scale many orders of magnitude smaller.

AFM offers a variety of imaging modes, each providing different information about the sample.

• Contact Mode: The tip is in constant contact with the surface. This mode is simple but can be damaging to soft samples. It’s great for hard, rigid samples like silicon wafers.
• Tapping Mode (Intermittent Contact): The cantilever oscillates, and the tip briefly touches the surface at the bottom of each oscillation. This minimizes damage to soft samples, making it ideal for biological samples like proteins or cells.
• Non-Contact Mode: The tip oscillates above the surface, and the changes in oscillation frequency are measured. This mode is the gentlest and best for very fragile samples, but the resolution is typically lower.
• Force Modulation Microscopy (FMM): The cantilever oscillates at a specific frequency, and changes in the amplitude and phase are measured. This provides information about the sample’s mechanical properties, such as stiffness and elasticity. This is commonly used to characterize variations in material properties.

The choice of imaging mode depends heavily on the sample’s properties and the information desired. For instance, you’d use tapping mode for delicate biological cells to avoid damaging them, but contact mode could be appropriate for a hard material analysis.

While AFM is a powerful tool, it has limitations:

• Scan Speed: High-resolution imaging can be time-consuming, making it unsuitable for dynamic processes requiring real-time observation.
• Tip Wear: The AFM tip can wear down over time, affecting image quality and requiring frequent tip replacement.
• Sample Preparation: Samples need to be properly prepared, which can be complex and challenging depending on the sample type. Improper preparation can lead to artefacts.
• Artifacts: The images can sometimes be affected by artifacts, requiring careful data analysis and interpretation.
• Lateral Resolution Limitations: While vertical resolution can be extremely high (sub-angstrom), lateral resolution can be limited by tip shape and size.

Understanding these limitations is crucial for proper experimental design and interpretation of results.

Sample preparation for AFM is critical for obtaining high-quality images and avoiding artifacts. The procedure varies depending on the sample but generally involves:

• Cleaning: The sample surface must be clean and free of contaminants. This often involves sonication in appropriate solvents and rinsing with ultrapure water.
• Substrate Selection: A suitable substrate must be chosen to support the sample and provide a stable surface for scanning. This is often mica or silicon wafers, depending on the application.
• Mounting: The sample needs to be securely mounted onto the substrate, ensuring it’s flat and stable during scanning. This can be done through various methods such as spin-coating, drop-casting, or micro-manipulation, depending on the sample type.
• Dehydration (for biological samples): Biological samples often require dehydration to prevent damage to the structure.

For example, imaging a protein would require preparing a highly diluted solution to deposit the protein on a mica substrate.

A noisy AFM image can be caused by several factors. Troubleshooting involves systematic investigation. Here’s a step-by-step approach:

1. Check the environment: Vibrations are a major source of noise. Ensure the AFM is on a stable surface, away from sources of vibration (e.g., traffic, equipment).
2. Inspect the cantilever: A damaged or dirty cantilever can produce noise. Replace the cantilever if necessary.
3. Optimize scan parameters: Incorrect scan parameters (scan rate, setpoint, gain) can lead to noise. Adjust these parameters to minimize noise while maintaining resolution. This usually involves trial and error based on experience.
4. Check the sample: A poorly prepared or unstable sample can contribute to noise. Ensure the sample is properly mounted and the surface is clean.
5. Check electronics: Ensure all connections are secure and there are no problems with the AFM’s electronics.

If the problem persists after these checks, consulting the AFM’s manual or contacting technical support is advised. Sometimes, it’s a combination of these factors; for instance, a dirty tip combined with vibrations might produce a really poor image.

Surface Plasmon Resonance (SPR) is a label-free optical technique used to study biomolecular interactions in real-time. Imagine a thin metallic layer (usually gold) on a glass prism. When light shines onto this layer at a specific angle, it excites surface plasmons – electrons oscillating at the metal-dielectric interface. This excitation causes a dip in the reflected light intensity.

When molecules bind to the surface, it changes the refractive index at the interface, shifting the angle of minimum reflection. This shift is directly proportional to the amount of bound molecules. By monitoring this change in the reflected light intensity over time, we can measure the kinetics of the binding process—how fast molecules bind and dissociate.

Think of it like a tiny scale measuring the mass of molecules as they bind to the surface—but instead of mass, it measures the change in refractive index, which is proportional to the amount of bound material.

SPR has several advantages over other biophysical techniques:

• Label-free detection: No labeling of the molecules is required, preserving their native state and avoiding potential artifacts.
• Real-time monitoring: Binding kinetics (association and dissociation rates) can be measured in real-time.
• High sensitivity: It can detect very small changes in concentration.
• Versatility: Applicable to a wide range of biomolecular interactions.

However, it also has some disadvantages:

• Mass transport limitations: The rate of binding can be limited by the diffusion of molecules to the surface, especially for large molecules.
• Surface effects: The surface properties can influence the binding kinetics, making it crucial to optimize surface chemistry.
• Limited information: Provides information about binding affinity and kinetics but not about the structure of the interacting molecules.

Compared to techniques like ELISA (enzyme-linked immunosorbent assay), SPR offers real-time data and is label-free. Compared to AFM, it’s less suited for high-resolution imaging but provides superior kinetic data of interactions. The choice of technique depends on the specific research question.

A typical SPR experiment measuring protein-protein interaction involves immobilizing one protein (the ligand) onto a sensor chip’s surface. Then, a solution containing the other protein (the analyte) is flowed over the surface. As the analyte binds to the ligand, it changes the refractive index near the surface. This change is detected as a shift in the resonance angle, which is then monitored over time. The resulting sensorgram provides information on the kinetics and affinity of the interaction.

For example, imagine studying the interaction between an enzyme and its inhibitor. We’d immobilize the enzyme on the chip and then flow different concentrations of the inhibitor across the surface. The resulting sensorgrams would show how quickly the inhibitor binds (association rate) and how quickly it dissociates (dissociation rate), providing crucial information about the inhibitor’s effectiveness.

SPR data analysis involves fitting the sensorgram data to a kinetic model, most commonly a 1:1 Langmuir model, to determine the rate constants for association (k_a) and dissociation (k_d). These rate constants are then used to calculate the equilibrium dissociation constant (K_D), which represents the binding affinity. Software packages are available to perform this fitting process. A good fit will minimize the difference between the experimental data and the model’s prediction. It is important to carefully select the appropriate model for the interaction, as more complex models (e.g., two-state binding models) may be needed for intricate interactions.

Visual inspection of the sensorgram is also vital. Non-specific binding, which shows as a slow increase in signal even in the absence of specific binding, needs to be assessed and subtracted to avoid erroneous kinetic parameters. Various methods exist for correcting for non-specific binding, such as using a control flow cell.

SPR instrument calibration is crucial for accurate measurements. It involves several steps:

• Flow cell alignment and baseline stabilization: Ensuring the flow cells are properly aligned and the baseline signal is stable before commencing any experiments.
• Sensor chip surface preparation: Properly preparing the sensor chip surface, such as cleaning and functionalization with appropriate ligands, is critical for consistent immobilization.
• Reference surface: Measuring the response from a reference flow cell lacking the immobilized ligand helps correct for non-specific interactions and bulk refractive index changes.
• Concentration series: Running a concentration series of a known analyte (with known affinity) helps to validate the instrument’s response and sensitivity. This allows verification of the instrument response.
• Regular maintenance: Proper cleaning and maintenance to prevent any contaminants from interfering with results is essential.

Without proper calibration, the sensorgram readings would be inaccurate, leading to errors in kinetic and affinity determinations.

Several types of SPR sensors exist, differing primarily in the material and method used for surface plasmon resonance excitation. Common types include:

• Gold-coated sensors: The most prevalent type, where a thin gold layer is used to generate surface plasmons.
• Silver-coated sensors: Similar to gold-coated sensors, but with potentially different sensitivities and detection ranges.
• Biacore sensors: This refers to a specific brand of SPR instruments often employing specialized sensor chips and surface chemistries.
• LSPR (Localized Surface Plasmon Resonance) sensors: Utilizing nanoparticles or nanostructures, these sensors offer advantages in miniaturization and potentially higher sensitivity in certain applications.

The choice of sensor depends on the specific application and the nature of the analytes and ligands being studied.

The resonance angle in SPR is the angle of incident light at which the maximum absorption of light occurs due to the excitation of surface plasmons. Surface plasmons are electron oscillations at the interface between a metal (usually gold) and a dielectric (usually water). When light hits this interface at a specific angle (the resonance angle), it excites these surface plasmons, leading to a significant drop in reflected light intensity. Any change in the refractive index near this interface, such as that caused by molecular binding, alters the resonance angle. This change is what SPR instruments detect.

Think of it like this: Imagine throwing a ball at a specific angle to make it bounce perfectly back to you. If you change the surface you’re throwing the ball on, like adding a layer of sand, you’ll have to change your throwing angle to get it to bounce back the same way. The change in the throwing angle corresponds to the change in the resonance angle in SPR.

Temperature significantly impacts SPR measurements. It affects both the binding kinetics and the bulk refractive index. Increasing temperature usually increases the rate of association and dissociation, leading to faster binding and unbinding. Therefore, precise temperature control is crucial for obtaining reliable and reproducible results. Changes in bulk refractive index due to temperature fluctuations can also affect the resonance angle, leading to errors in the measurement. The effects of temperature are often described using the Arrhenius equation, linking the rate constants to temperature and activation energies.

In practice, a temperature-controlled environment is essential during the experiment. Variations in temperature can lead to errors in the determination of kinetic and thermodynamic parameters. It is important to maintain a consistent temperature throughout the experiment.

The buffer plays a critical role in SPR experiments. It maintains the pH, ionic strength, and overall environment surrounding the interaction. The choice of buffer is crucial because it affects the stability, conformation, and binding affinity of the biomolecules involved. Inappropriate buffers can lead to denaturation, aggregation, or non-specific binding. A well-chosen buffer ensures that the molecules are in their native state and interact in a physiologically relevant manner.

For instance, using a buffer with high ionic strength might shield electrostatic interactions between the molecules, leading to a weaker apparent affinity. Selecting a buffer with a pH that is too acidic or alkaline could denature the proteins. Therefore, careful buffer selection is crucial for accurate and reliable SPR results. Often, researchers will test a range of buffers before deciding on the optimal choice for their specific experiment.

A sensorgram in Surface Plasmon Resonance (SPR) is a graph plotting the change in resonance angle or resonance unit (RU) over time. It visually represents the interaction between an analyte (a molecule in solution) and a ligand (a molecule immobilized on the sensor chip). The x-axis represents time, and the y-axis represents the response, usually in resonance units (RU), which is directly proportional to the mass bound to the sensor surface.

Interpreting a sensorgram involves analyzing several key features:

• Association Phase: This shows the increase in RU as the analyte binds to the ligand. The slope reflects the association rate constant (ka).
• Dissociation Phase: After the analyte injection stops, this shows the decrease in RU as the analyte dissociates from the ligand. The slope reflects the dissociation rate constant (kd).
• Equilibrium Phase: The plateau reached during the association phase (if it exists) indicates the equilibrium between association and dissociation. From this, we can determine the equilibrium dissociation constant (Kd), a measure of binding affinity (Kd = kd/ka). A lower Kd indicates stronger binding.

For example, a steep association phase and a slow dissociation phase would suggest high affinity binding. Conversely, a shallow association phase and a rapid dissociation phase would indicate weak binding. Careful analysis, often involving fitting the data to appropriate models (e.g., 1:1 Langmuir model), is crucial for extracting quantitative binding parameters.

Both Atomic Force Microscopy (AFM) and Surface Plasmon Resonance (SPR) are powerful biophysical techniques used to study biomolecular interactions, but they differ significantly in their principles and applications.

• AFM is a high-resolution imaging technique that uses a sharp tip to scan a surface. It provides topographical information, allowing visualization of individual molecules and their interactions. AFM can also be used in force spectroscopy mode to measure the forces involved in molecular interactions. It offers nanoscale resolution and direct visualization.
• SPR is an optical technique that measures changes in the refractive index at a sensor surface. These changes are caused by the binding of molecules to the surface. It provides information about the kinetics and affinity of biomolecular interactions, but it doesn’t directly image the molecules. SPR provides label-free detection, high throughput capability and good sensitivity.

In summary:

• AFM: High-resolution imaging, force measurements, nanoscale resolution, but limited throughput.
• SPR: Kinetic and affinity measurements, label-free, high throughput, but no direct imaging.

Therefore, the choice between AFM and SPR depends on the specific research question. If you need high-resolution images and force measurements, AFM is preferred. If you need to measure the kinetics and affinity of interactions in a high-throughput manner, SPR is more suitable. In some cases, a combination of both techniques provides a comprehensive understanding of the system.

My experience encompasses a wide range of data analysis software for AFM and SPR. For AFM, I’m proficient in using software like Gwyddion (for image processing and analysis), SPIP (for quantitative analysis of AFM images and force curves), and NanoScope Analysis (for data acquisition and basic analysis from Bruker AFMs). These packages allow me to process raw AFM data, including image flattening, particle size analysis, and force curve fitting.

With SPR, I am highly experienced with software such as BIAevaluation (from Biacore), SPRAssist, and Scrubber. These programs facilitate sensorgram analysis, including fitting data to various kinetic models (Langmuir, bivalent, etc.), determining kinetic parameters (ka, kd, Kd), and assessing the quality of binding curves. I regularly use these tools to quantitatively assess binding interactions and extract meaningful biological information. I’m comfortable scripting and automating data analysis tasks within these packages to improve efficiency.

Both AFM and SPR experiments are susceptible to various artifacts that can affect data quality.

• In AFM: Common artifacts include tip convolution (the tip shape distorting the image), cantilever drift (slow, unwanted movement of the cantilever), and surface contamination (affecting image quality and force measurements). I mitigate these through careful sample preparation (clean surfaces, appropriate imaging environments), tip selection (appropriate geometry and sharpness), and careful calibration of the instrument. Image processing techniques like plane fitting and background subtraction can also help correct for drift and other artifacts.
• In SPR: Artifacts like non-specific binding (analyte binding to non-target sites on the chip), mass transport limitations (analyte diffusion limiting the binding rate), and bulk refractive index changes (causing inaccurate signal) are frequently encountered. I address these by using appropriate blocking agents to minimize non-specific binding, optimizing flow rates to mitigate mass transport limitations, and employing careful referencing and data subtraction techniques to correct for bulk refractive index changes. Proper experimental design (e.g., concentration series, control experiments) is crucial for minimizing and identifying these artifacts.

Rigorous quality control and the use of appropriate controls are fundamental to addressing artifacts in both techniques. Careful attention to detail and a methodical approach are essential for obtaining reliable and accurate data.

Force spectroscopy using AFM involves measuring the force between the AFM tip and the sample as a function of distance. The AFM tip, often functionalized with a specific molecule, is brought into contact with a surface containing another molecule of interest. The force required to separate the two molecules provides insights into their interaction strength and characteristics.

In a typical experiment, the tip approaches the surface, establishing contact, and then retracts. The force curve shows how the force changes with distance. Key features of the force curve include:

• Approach curve: Shows the force as the tip approaches the surface.
• Contact point: The point where the tip makes contact with the surface.
• Adhesion force: The force required to separate the tip from the surface, reflecting the strength of the interaction.
• Retraction curve: Shows the force as the tip retracts from the surface.

From analyzing these features, one can quantify the strength of molecular interactions (adhesion forces), determine the length scale of the interactions, and even identify the presence of different types of interactions (e.g., electrostatic, hydrophobic, hydrogen bonding). Force spectroscopy is invaluable in studying the mechanics of individual molecular interactions, protein unfolding, and other biological processes at the nanoscale.

Determining the binding affinity of a molecule using SPR involves analyzing the sensorgram obtained during a binding experiment. The most common approach uses the equilibrium dissociation constant (Kd), which represents the ratio of the dissociation rate constant (kd) to the association rate constant (ka).

Steps involved:

1. Perform SPR experiment: Inject various concentrations of the analyte over the ligand-immobilized sensor surface and monitor the response in RU over time.
2. Fit the data to a kinetic model: Using dedicated SPR analysis software (e.g., BIAevaluation), fit the association and dissociation phases of the sensorgrams to an appropriate kinetic model (e.g., 1:1 Langmuir model, which assumes a single binding site). The software will then calculate ka and kd values.
3. Calculate Kd: From the fitted parameters, calculate the Kd using the formula: Kd = kd/ka. A lower Kd indicates a higher binding affinity.
4. Assess data quality: Evaluate the goodness-of-fit (e.g., chi-squared values) to ensure that the chosen model accurately reflects the data.
5. Error Analysis: Perform appropriate statistical analysis to calculate confidence intervals for the fitted parameters and Kd to evaluate the reliability of the results.

For example, if you obtain a Kd of 1 nM, it suggests a much stronger binding than a Kd of 1 μM. The Kd value provides a quantitative measure of the strength of the interaction and is crucial in various applications like drug discovery and protein engineering.

My experience covers a variety of AFM tips, each tailored for different applications. The choice of tip depends heavily on the sample and the type of measurement being performed.

• Silicon tips: These are the most common type, offering good mechanical properties and relatively low cost. They come in various shapes (e.g., pyramidal, conical, rectangular) and are available with different coating for increased conductivity or chemical functionalities.
• Silicon nitride tips: These are often preferred for imaging delicate samples due to their greater flexibility and reduced risk of sample damage. They are also commonly used in liquid environments.
• li>Carbon nanotubes (CNT) tips: These offer exceptional sharpness and high aspect ratio, enabling high-resolution imaging and force measurements. However, their fabrication is more complex, and they can be more expensive.
• Functionalized tips: For specific applications, such as studying molecular recognition, tips can be functionalized with specific ligands or antibodies to selectively interact with the target molecules. The choice of functionalization is crucial for ensuring specific binding and obtaining meaningful results.

In my work, I carefully select tips based on factors such as the desired resolution, the sample’s mechanical properties, the imaging environment (air or liquid), and the specific application. For example, when imaging soft biological samples, I would choose a more compliant silicon nitride tip to avoid damaging the sample. If very high resolution is required, a CNT tip might be preferred. For specific binding studies, functionalized tips are essential for selectivity and reliable quantitative measurements.

Surface functionalization in AFM and SPR is crucial for creating a specific interaction between the surface and the biomolecules of interest. Think of it like preparing a dance floor for a specific type of dance – you need the right surface to enable the right interactions. We modify the surface chemistry to either covalently attach or passively adsorb molecules that will specifically bind to our target, creating a selective interaction.

For example, in SPR, we might functionalize a gold surface with a self-assembled monolayer (SAM) of thiolated molecules, such as alkanethiols, to which we then attach a capture ligand (e.g., an antibody) specific to our target protein. In AFM, we might functionalize a substrate (mica, silicon) with a protein or ligand to image its interaction with other molecules. The choice of functionalization strategy depends greatly on the target molecule, the required specificity, and the specific technique used.

• Common techniques include self-assembled monolayers (SAMs), plasma treatment, and covalent coupling using chemical linkers.
• Important considerations include surface density, stability of the attached molecules, and non-specific binding.

To investigate the effect of a drug on a protein using SPR, I would design an experiment involving surface plasmon resonance measurements of the protein-drug interaction. Imagine it like a microscopic ‘tug-of-war’: We measure the strength of the interaction by observing the change in resonance angle.

1. Surface preparation: First, I would immobilize the target protein onto a sensor chip surface (e.g., gold) using appropriate functionalization strategies (as discussed above). The choice of ligand is key. For example, if the protein is an enzyme, we might need to choose a ligand that doesn’t interfere with the active site.
2. Drug injection: Next, different concentrations of the drug would be injected over the protein-coated surface. The interaction between the protein and the drug will cause a change in the refractive index near the surface, detectable as a change in the SPR signal.
3. Data acquisition and analysis: The sensorgram, which is the plot of response (resonance units or RU) versus time, will be generated. We will then analyze the sensorgram to determine kinetic parameters like association rate constant (ka), dissociation rate constant (kd), and equilibrium dissociation constant (Kd), which quantifies the strength of the interaction. Fitting the data to appropriate kinetic models will provide quantitative insights into the interaction. Control experiments with buffer only will also be necessary to establish baselines and ensure reproducibility.
4. Controls: Crucial controls include surface blank (no protein), negative controls (an irrelevant drug) to identify and remove non-specific binding and background signal.

This approach allows for a quantitative assessment of the drug’s binding affinity, kinetics, and potentially its effect on protein conformation or activity.

Ethical considerations in using biophysical techniques are primarily focused on the responsible use of biological materials and data, and the well-being of any involved organisms.

• Source of biological materials: Ethical sourcing of biological materials is paramount, especially for animal or human-derived samples. This includes ensuring compliance with all relevant regulations and guidelines, obtaining appropriate consent, and minimizing harm to animals. For example, if using cell lines, making sure they are ethically sourced and handled correctly is crucial.
• Data integrity and reproducibility: Maintaining data integrity and reproducibility is essential to avoid any misrepresentation of results. Rigorous documentation, transparent methods, and open data sharing promote transparency and facilitate scientific scrutiny.
• Data privacy and security: If dealing with human samples, data privacy and security must be maintained to protect sensitive patient information. This means adhering to all relevant regulations and guidelines like HIPAA or GDPR.
• Responsible use of resources: The use of resources (chemicals, energy, equipment) should be carefully considered with the environment in mind, seeking to minimize waste and maximize efficiency.

Troubleshooting a malfunctioning SPR instrument requires a systematic approach. It’s like diagnosing a car problem; you need to check the basics first.

1. Check the obvious: Begin by verifying basic parameters, like proper fluidics connections, correct buffer solutions and concentrations, and sufficient flow rates. Are there any leaks? Is the instrument properly powered on?
2. Inspect the sensor chip: Ensure the sensor chip is properly mounted and clean. Damaged sensor chips can lead to poor signals or high background noise.
3. Analyze the sensorgram: Examine the sensorgram for irregularities. High background noise, unusually high drift, or complete lack of signal all suggest issues that must be investigated. For example, high noise may indicate problems with the detector or the flow system. A large drift may indicate temperature instability or inadequate buffer flow.
4. Test the system: Carry out a system diagnostic test, usually available in the instrument’s software. This can detect hardware problems or software errors.
5. Consult the manual: If the problem persists, consult the instrument’s operating manual for troubleshooting guidance. It usually contains comprehensive instructions on the likely causes of various error messages.
6. Contact technical support: If you cannot resolve the problem using the steps above, contact the instrument’s manufacturer for technical support.

Maintaining and calibrating AFM and SPR instruments requires meticulous attention to detail and regular preventative maintenance. It’s a bit like servicing a complex machine.

For AFM, regular maintenance includes cleaning the cantilever and ensuring the system’s alignment, which is critical to achieving high-resolution images. Calibration might involve using standard calibration samples with known dimensions to ensure accuracy.

For SPR, regular maintenance involves cleaning the flow cells and sensor chips, checking the fluidics system for leaks, and regular calibration using known standards (e.g., a solution of known refractive index). Accurate calibration is crucial to obtaining reliable kinetic data. I regularly carry out system checks to ensure optimal instrument performance. For both instruments, meticulous record keeping is crucial, documenting maintenance activities and calibration results for both traceability and troubleshooting.

Ensuring data quality and reproducibility in AFM and SPR experiments requires careful planning and execution.

• Instrument calibration: Regular and proper instrument calibration is essential. Regular calibration ensures the accuracy and precision of measurements.
• Control experiments: Appropriate controls (e.g., blanks, positive and negative controls) must be included in all experiments. These controls establish baselines and help to identify and eliminate artifacts or non-specific interactions.
• Sample preparation and handling: Consistent and appropriate sample preparation and handling procedures must be used to ensure data reproducibility. Proper purification and concentration of biomolecules are critical. Using the same batch of reagents and avoiding cross contamination will also enhance reproducibility.
• Data analysis: Rigorous data analysis techniques and appropriate statistical analysis are necessary to draw meaningful conclusions from the experimental data. Using appropriate fitting procedures and error analysis are critical here. Repeating experiments independently multiple times are also crucial for establishing reliability.
• Documentation: Detailed experimental protocols and documentation are essential to ensure reproducibility of the experiments. This documentation should include information on all aspects of the experiment. This allows for re-examination and verification of the experiment.

AFM and SPR are constantly evolving, with exciting new applications in biophysics emerging regularly.

• AFM: Single-molecule studies are becoming increasingly important in understanding molecular machines and mechanisms. AFM is being applied to investigate interactions and conformational changes in single biomolecules with greater accuracy. High-speed AFM is enabling the study of dynamic processes such as protein folding and enzyme catalysis in real-time.
• SPR: SPR is being used in drug discovery for high-throughput screening and the development of novel therapeutics. Improvements in sensitivity and miniaturization are expanding the range of applications to include early-stage diagnostics and point-of-care devices. Label-free detection capabilities make SPR particularly valuable for studying biomolecular interactions in their native states.

Beyond these, both techniques are being combined with other biophysical methods (e.g., fluorescence microscopy, electrophysiology) to provide a more comprehensive understanding of biological systems.