Nanoscale compositional mapping of cells, tissues, and polymers with ringing mode of atomic force microscopy

On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition

Atomic force microscopy (AFM or SPM) imaging is one of the best matches with machine learning (ML) analysis among microscopy techniques. The digital format of AFM images allows for direct utilization in ML algorithms without the need for additional processing. Additionally, AFM enables the simultaneous imaging of distributions of over a dozen different physicochemical properties of sample surfaces, a process known as multidimensional imaging. While this wealth of information can be challenging to analyze using traditional methods, ML provides a seamless approach to this task. However, the relatively slow speed of AFM imaging poses a challenge in applying deep learning methods broadly used in image recognition. This prospective is focused on ML recognition/classification when using a relatively small number of AFM images, aka small database. We discuss ML methods other than popular deep-learning neural networks. The described approach has already been successfully used to analyze and classify the surfaces of biological cells. It can be applied to recognize medical images, specific material processing, in forensic studies, even to identify the authenticity of arts. A general template for ML analysis specific to AFM is suggested, with a specific example of the identification of cell phenotype. Special attention is given to the analysis of the statistical significance of the obtained results, an important feature that is often overlooked in papers dealing with machine learning. A simple method for finding statistical significance is also described.

Cell Surface Parameters for Accessing Neutrophil Activation Level with Atomic Force Microscopy

In this study, we examine the topography and adhesion images of the cell surface of neutrophils during the activation process. Our analysis of cell surface parameters indicates that the most significant changes in neutrophils occur within the first 30 min of activation, suggesting that reactive oxygen species may require approximately this amount of time to activate the cells. Interestingly, we observed surface granular structure as early as 10 min after neutrophil activation when examining atomic force microscopy images. This finding aligns with the reorganization observed within the cells under confocal laser scanning microscopy. By analyzing the cell surface images of adhesion, we identified three spatial surface parameters that correlate with the activation time. This finding enables us to estimate the degree of activation by using atomic force microscopy maps of the cell surface.

Enhanced feedback performance in off-resonance AFM modes through pulse train sampling

Dynamic atomic force microscopy (AFM) modes that operate at frequencies far away from the resonance frequency of the cantilever (off-resonance tapping (ORT) modes) can provide high-resolution imaging of a wide range of sample types, including biological samples, soft polymers, and hard materials. These modes offer precise and stable control of vertical force, as well as reduced lateral force. Simultaneously, they enable mechanical property mapping of the sample. However, ORT modes have an intrinsic drawback: a low scan speed due to the limited ORT rate, generally in the low-kilohertz range. Here, we analyze how the conventional ORT control method limits the topography tracking quality and hence the imaging speed. The closed-loop controller in conventional ORT restricts the sampling rate to the ORT rate and introduces a large closed-loop delay. We present an alternative ORT control method in which the closed-loop controller samples and tracks the vertical force changes during a defined time window of the tip-sample interaction. Through this, we use multiple samples in the proximity of the maximum force for the feedback loop, rather than only one sample at the maximum force instant. This method leads to improved topography tracking at a given ORT rate and therefore enables higher scan rates while refining the mechanical property mapping.

Effects of Temperature and Humidity on Energy Dissipation between Human Corneocytes and Nanoasperity Sliding Contacts

Human haptic perception relies on the ability of sensory receptors underneath the skin corneocyte layer to sense external load, where adhesion and friction play an essential role in nanoscale solid-solid contact. Energy dissipation present at the surface interface due to the change of separation distance during sliding contact was uncovered, but the energy dissipation of human finger skin cell-nanoprobe contact under humidity and temperature conditions has not been investigated yet. In this paper, the energy dissipation of skin corneocyte-nanoprobe interface under variation of both humidity, 0.05-80%RH, and temperature ranging from 25 to 40 °C is directly measured by atomic force microscopy (AFM). Analytical models of dissipation energy for this nanomaterial interface mechanism are developed, and the results are compared to the measured values. AFM measurements of dissipation energy reveal that the amount of dissipated energy caused by water meniscus stretching monotonically increases with humidity and temperature, resulting in adhesion and friction decreases. The purposed analytical model represents that dissipation energy trend.

Scanning Probe Microscopy Techniques for Studying the Cell Glycocalyx

The glycocalyx is a brush-like layer that covers the surfaces of the membranes of most cell types. It consists of a mixture of carbohydrates, mainly glycoproteins and proteoglycans. Due to its structure and sensitivity to environmental conditions, it represents a complicated object to investigate. Here, we review studies of the glycocalyx conducted using scanning probe microscopy approaches. This includes imaging techniques as well as the measurement of nanomechanical properties. The nanomechanics of the glycocalyx is particularly important since it is widely present on the surfaces of mechanosensitive cells such as endothelial cells. An overview of problems with the interpretation of indirect data via the use of analytical models is presented. Special insight is given into changes in glycocalyx properties during pathological processes. The biological background and alternative research methods are briefly covered.

Non-contact non-resonant atomic force microscopy method for measurements of highly mobile molecules and nanoparticles

Atomic force microscopy (AFM) is nowadays indispensable versatile scanning probe method widely employed for fundamental and applied research in physics, chemistry, biology as well as industrial metrology. Conventional AFM systems can operate in various environments such as ultra-high vacuum, electrolyte solutions, or controlled gas atmosphere. Measurements in ambient air are prevalent due to their technical simplicity; however, there are drawbacks such as formation of water meniscus that greatly increases attractive interaction (adhesion) between the tip and the sample, reduced spatial resolution, and too strong interactions leading to tip and/or sample modifications. Here we show how the attractive forces in AFM under ambient conditions can be used with advantage to probe surface properties in a very sensitive way even on highly mobile molecules and nanoparticles. We introduce a stable non-contact non-resonant (NCNR) AFM method which enables to reliably perform measurements in the attractive force regime even in air by controlling the tip position in the intimate surface vicinity without touching it. We demonstrate proof-of-concept results on helicene-based macrocycles, DNA on mica, and nanodiamonds on SiO2. We compare the results with other conventional AFM regimes, showing NCNR advantages such as higher spatial resolution, reduced tip contamination, and negligible sample modification. We analyze principle physical and chemical mechanisms influencing the measurements, discuss issues of stability and various possible method implementations. We explain how the NCNR method can be applied in any AFM system by a mere software modification. The method thus opens a new research field for measurements of highly sensitive and mobile nanoscale objects under air and other environments.

Identification of Geometrical Features of Cell Surface Responsible for Cancer Aggressiveness: Machine Learning Analysis of Atomic Force Microscopy Images of Human Colorectal Epithelial Cells

It has been recently demonstrated that atomic force microscopy (AFM) allows for the rather precise identification of malignancy in bladder and cervical cells. Furthermore, an example of human colorectal epithelial cells imaged in AFM Ringing mode has demonstrated the ability to distinguish cells with varying cancer aggressiveness with the help of machine learning (ML). The previously used ML methods analyzed the entire cell image. The problem with such an approach is the lack of information about which features of the cell surface are associated with a high degree of aggressiveness of the cells. Here we suggest a machine-learning approach to overcome this problem. Our approach identifies specific geometrical regions on the cell surface that are critical for classifying cells as highly or lowly aggressive. Such localization gives a path to colocalize the newly identified features with possible clustering of specific molecules identified via standard bio-fluorescence imaging. The biological interpretation of the obtained information is discussed.

Review: Advanced Atomic Force Microscopy Modes for Biomedical Research

Visualization of biomedical samples in their native environments at the microscopic scale is crucial for studying fundamental principles and discovering biomedical systems with complex interaction. The study of dynamic biological processes requires a microscope system with multiple modalities, high spatial/temporal resolution, large imaging ranges, versatile imaging environments and ideally in-situ manipulation capabilities. Recent development of new Atomic Force Microscopy (AFM) capabilities has made it such a powerful tool for biological and biomedical research. This review introduces novel AFM functionalities including high-speed imaging for dynamic process visualization, mechanobiology with force spectroscopy, molecular species characterization, and AFM nano-manipulation. These capabilities enable many new possibilities for novel scientific research and allow scientists to observe and explore processes at the nanoscale like never before. Selected application examples from recent studies are provided to demonstrate the effectiveness of these AFM techniques.

In operando nanomechanical mapping of PEDOT:PSS thin films in electrolyte solutions with bimodal AFM

Understanding the interplay between the nanomechanical properties of organic electronic materials and their electronic properties is central to developing sensors and transducers for applications ranging from immunosensing to e-skin. Controlling organic device operations in aqueous electrolyte solutions and their mechanical compliance with the host tissue or living systems, as for instance in active implants for the recording or stimulation of neural signals, is still largely unexplored. Here, we implemented bimodal AFM to map the nanomechanical and structural properties of thin films made from poly(3,4-ethylenedioxythiophene):poly(styrene-sulfonate) (PEDOT:PSS), the most widely used conducting polymer blend, during operation as a microelectrode in an electrolyte solution. Nanomechanical maps showed the film consisting of a granular structure made from PEDOT:PSS regions embedded in the PSS matrix. The film swelled upon immersion in an aqueous solution. In operando bimodal AFM data obtained by applying a sequence of doping/de-doping bias cycles showed a significant decrease in the modulus (70%) that saturated after about 10 cycles. A similar sequence of biases at the opposite polarity did not significantly influence the mechanical behaviour of PEDOT:PSS. The decrease in the modulus was explained by the development of persistent hydration, which was enhanced by the cations trapped inside the organic electronic material.

Acceleration of imaging in atomic force microscopy working in sub-resonance tapping mode

Sub-resonance tapping (SRT) mode of atomic force microscopy (AFM) enables researchers to image surfaces with well-controlled load forces and to collect maps of multiple physical properties of samples. The major bottleneck of this mode is a relatively low scan speed compared to other scanning modes. This paper presents a novel control algorithm that substantially improves the scanning speed over the standard SRT. We propose naming the new modality Trajectory Tracking SRT (TT-SRT). In contrast with the standard SRT control, TT-SRT uses the feedback within every single touch of the sample by the AFM probe. To demonstrate the advantage of TT-SRT, we conduct scans on a variety of samples with differing topologies, roughnesses, and mechanical properties. Each sample region is scanned with both standard SRT and TT-SRT at the same set of speeds. The control gains are tuned before each scan for maximum performance in each mode. Performance is evaluated by selecting a given level of image quality and finding the maximum speed that can be achieved by each algorithm. We find that with increased demand for data quality, the utility of TT-SRT becomes more apparent; for example, the speed of TT-SRT can be ten times faster or more than standard SRT for a reasonable expectation of data quality.

Nanomechanical mapping of soft materials with the atomic force microscope: methods, theory and applications

Fast, high-resolution, non-destructive and quantitative characterization methods are needed to develop materials with tailored properties at the nanoscale or to understand the relationship between mechanical properties and cell physiology. This review introduces the state-of-the-art force microscope-based methods to map at high-spatial resolution the elastic and viscoelastic properties of soft materials. The experimental methods are explained in terms of the theories that enable the transformation of observables into material properties. Several applications in materials science, molecular biology and mechanobiology illustrate the scope, impact and potential of nanomechanical mapping methods.

Nanorheology of living cells measured by AFM-based force-distance curves

Mechanobiology aims to establish functional relationships between the mechanical state of a living a cell and its physiology. The acquisition of force-distance curves with an AFM is by far the dominant method to characterize the nanomechanical properties of living cells. However, theoretical simulations have shown that the contact mechanics models used to determine the Young's modulus from a force-distance curve could be off by a factor 5 from its expected value. The semi-quantitative character arises from the lack of a theory that integrates the AFM data, a realistic viscoelastic model of a cell and its finite-thickness. Here, we develop a method to determine the mechanical response of a cell from a force-distance curve. The method incorporates bottom-effect corrections, a power-law rheology model and the deformation history of the cell. It transforms the experimental data into viscoelastic parameters of the cell as a function of the indentation frequency. The quantitative agreement obtained between the experiments performed on living fibroblast cells and the analytical theory supports the use of force-distance curves to measure the nanorheological properties of cells.

Few-cycle Regime Atomic Force Microscopy

Traditionally, dynamic atomic force microscopy (AFM) techniques are based on the analysis of the quasi-steady state response of the cantilever deflection in terms of Fourier analysis. Here we describe a technique that instead exploits the often disregarded transient response of the cantilever through a relatively modern mathematical tool, which has caused important developments in several scientific fields but that is still quite unknown in the AFM context: the wavelet analysis. This tool allows us to localize the time-varying spectral composition of the initial oscillations of the cantilever deflection when an impulsive excitation is given (as in the band excitation method), a mode that we call the few-cycle regime. We show that this regime encodes very meaningful information about the tip-sample interaction in a unique and extremely sensitive manner. We exploit this high sensitivity to gain detailed insight into multiple physical parameters that perturb the dynamics of the AFM probe, such as the tip radius, Hamaker constant, sample's elastic modulus and height of an adsorbed water layer. We validate these findings with experimental evidence and computational simulations and show a feasible path towards the simultaneous retrieval of multiple physical parameters.

Mechanics of Brain Tissues Studied by Atomic Force Microscopy: A Perspective

Tissue morphology and mechanics are crucial to the regulation of organ function. Investigating the exceptionally complex tissue of the brain at the sub-micron scale is challenging due to the complex structure and softness of this tissue, despite the large interest of biologists, medical engineers, biophysicists, and others in this topic. Atomic force microscopy (AFM) both as an imaging and as a mechanical tool provides an excellent opportunity to study soft biological samples such as live brain tissues. Here we review the principles of AFM, the performance of AFM in tissue imaging and mechanical mapping of cells and tissues, and finally opening the prospects and challenges of probing the biophysical properties of brain tissue using AFM.

Noninvasive diagnostic imaging using machine-learning analysis of nanoresolution images of cell surfaces: Detection of bladder cancer

We report an approach in diagnostic imaging based on nanoscale-resolution scanning of surfaces of cells collected from body fluids using a recent modality of atomic force microscopy (AFM), subresonance tapping, and machine-leaning analysis. The surface parameters, which are typically used in engineering to describe surfaces, are used to classify cells. The method is applied to the detection of bladder cancer, which is one of the most common human malignancies and the most expensive cancer to treat. The frequent visual examinations of bladder (cytoscopy) required for follow-up are not only uncomfortable for the patient but a serious cost for the health care system. Our method addresses an unmet need in noninvasive and accurate detection of bladder cancer, which may eliminate unnecessary and expensive cystoscopies. The method, which evaluates cells collected from urine, shows 94% diagnostic accuracy when examining five cells per patient's urine sample. It is a statistically significant improvement (P < 0.05) in diagnostic accuracy compared with the currently used clinical standard, cystoscopy, as verified on 43 control and 25 bladder cancer patients.

AFM Monitoring the Influence of Selected Cryoprotectants on Regeneration of Cryopreserved Cells Mechanical Properties

Cryopreservation of cells (mouse embryonic fibroblasts) is a fundamental task for wide range of applications. In practice, cells are protected against damage during freezing by applications of specific cryoprotectants and freezing/melting protocols. In this study by using AFM and fluorescence microscopy we showed how selected cryoprotectants (dimethyl sulfoxide and polyethylene glycol) affected the cryopreserved cells mechanical properties (stiffness) and how these parameters are correlated with cytoskeleton damage and reconstruction. We showed how cryopreserved (frozen and thawed) cells' stiffness change according to type of applied cryoprotectant and its functionality in extracellular or intracellular space. We showed that AFM can be used as technique for investigation of cryopreserved cells surfaces state and development ex vivo. Our results offer a new perspective on the monitoring and characterization of frozen cells recovery by measuring changes in elastic properties by nanoindentation technique. This may lead to a new and detailed way of investigating the post-thaw development of cryopreserved cells which allows to distinguish between different cell parts.

Imaging of Soft and Biological Samples Using AFM Ringing Mode

Ringing mode of atomic force microscopy (AFM) enables imaging the surfaces of biological samples, cells, tissue, biopolymers, etc. to obtain unique information, such as the size of molecules pulled by the AFM probe from the sample surface, heights of the sample at different load forces, etc. (up to eight different imaging channels can be recorded simultaneously, which is in addition to five channels already available in other rival modes). The imaging can be done in both air (gases) and liquid (buffers). In addition, the images obtained in ringing mode do not have several common artifacts and can be collected up to 20× faster compared to the rival imaging modes. Here we describe a step-by-step approach to collect images in ringing mode applied to biological and soft materials in general. Technical details, potential difficulties, and points of special attention are described.