Improving the contrast of topographical AFM images by a simple averaging filter

A model for the noninvasive, habitat-inclusive estimation of upper limit abundance for synanthropes, exemplified by M. fascicularis

Accurately estimating population sizes for free-ranging animals through noninvasive methods, such as camera trap images, remains particularly limited by small datasets. To overcome this, we developed a flexible model for estimating upper limit populations and exemplified it by studying a group-living synanthrope, the long-tailed macaque (Macaca fascicularis). Habitat preference maps, based on environmental and GPS data, were generated with a maximum entropy model and combined with data obtained from camera traps, line transect distance sampling, and direct sightings to produce an expected number of individuals. The mapping between habitat preference and number of individuals was optimized through a tunable parameter ρ (inquisitiveness) that accounts for repeated observations of individuals. Benchmarking against published data highlights the high accuracy of the model. Overall, this approach combines citizen science with scientific observations and reveals the long-tailed macaque populations to be (up to 80%) smaller than expected. The model's flexibility makes it suitable for many species, providing a scalable, noninvasive tool for wildlife conservation.

Correction of AFM data artifacts using a convolutional neural network trained with synthetically generated data

AFM microscopy from its nature produces outputs with certain distortions, inaccuracies and errors given by its physical principle. These distortions are more or less well studied and documented. Based on the nature of the individual distortions, different reconstruction and compensation filters have been developed to post-process the scanned images. This article presents an approach based on machine learning - the involved convolutional neural network learns from pairs of distorted images and the ground truth image and then it is able to process pairs of images of interest and produce a filtered image with the artifacts removed or at least suppressed. What is important in our approach is that the neural network is trained purely on synthetic data generated by a simulator of the inputs, based on an analytical description of the physical phenomena causing the distortions. The generator produces training samples involving various combinations of the distortions. The resulting trained network seems to be able to autonomously recognize the distortions present in the testing image (no knowledge of the distortions or any other human knowledge is provided at the test time) and apply the appropriate corrections. The experimental results show that not only is the new approach better or at least on par with conventional post-processing methods, but more importantly, it does not require any operator's input and works completely autonomously. The source codes of the training set generator and of the convolutional neural net model are made public, as well as an evaluation dataset of real captured AFM images.

Subsurface Imaging of Cell Organelles by Force Microscopy

The development of high-resolution, label-free, noninvasive, and subsurface microscopy methods of living cells remains a formidable problem. Force-microscopy-based stiffness measurements contribute to our understanding of single-cell nanomechanics. The elastic properties of the cell's outer structures, such as the plasma membrane and actin cytoskeleton, dominate stiffness measurements, which in turns prevents the imaging of intracellular structures. We propose that the above limitation could be overcome by combining 2D sections of the cell's viscoelastic properties. We show the simultaneous imaging of the outer cell's cytoskeleton and the organelles inside the nucleus. The elastic component of interaction force carries information on the cell's outer elements as the cortex and the actin cytoskeleton. The inelastic component is sensitive to the hydrodynamic drag of the inner structures such the nucleoli.

Automated image segmentation-assisted flattening of atomic force microscopy images

Atomic force microscopy (AFM) images normally exhibit various artifacts. As a result, image flattening is required prior to image analysis. To obtain optimized flattening results, foreground features are generally manually excluded using rectangular masks in image flattening, which is time consuming and inaccurate. In this study, a two-step scheme was proposed to achieve optimized image flattening in an automated manner. In the first step, the convex and concave features in the foreground were automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.

The development of the spatially correlated adjustment wavelet filter for atomic force microscopy data

In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed.

Feeling for Filaments: Quantification of the Cortical Actin Web in Live Vascular Endothelium

Contact-mode atomic force microscopy (AFM) has been shown to reveal cortical actin structures. Using live endothelial cells, we visualized cortical actin dynamics simultaneously by AFM and confocal fluorescence microscopy. We present a method that quantifies dynamic changes in the mechanical ultrastructure of the cortical actin web. We argue that the commonly used, so-called error signal imaging in AFM allows a qualitative, but not quantitative, analysis of cortical actin dynamics. The approach we used comprises fast force-curve-based topography imaging and subsequent image processing that enhances local height differences. Dynamic changes in the organization of the cytoskeleton network can be observed and quantified by surface roughness calculations and automated morphometrics. Upon treatment with low concentrations of the actin-destabilizing agent cytochalasin D, the cortical cytoskeleton network is thinned out and the average mesh size increases. In contrast, jasplakinolide, a drug that enhances actin polymerization, consolidates the cytoskeleton network and reduces the average mesh area. In conclusion, cortical actin dynamics can be quantified in live cells. To our knowledge, this opens a new pathway for conducting quantitative structure-function analyses of the endothelial actin web just beneath the apical plasma membrane.

Identifying dynamic membrane structures with atomic-force microscopy and confocal imaging

Combining the biological specificity of fluorescence microscopy with topographical features revealed by atomic force microscopy (AFM) provides new insights into cell biology. However, the lack of systematic alignment capabilities especially in scanning-tip AFM has limited the combined application approach as AFM drift leads to increasing image mismatch over time. We present an alignment correction method using the cantilever tip as a reference landmark. Since the precise tip position is known in both the fluorescence and AFM images, exact re-alignment becomes possible. We used beads to demonstrate the validity of the method in a complex artificial sample. We then extended this method to biological samples to depict membrane structures in fixed and living human fibroblasts. We were able to map nanoscale membrane structures, such as clathrin-coated pits, to their respective fluorescent spots. Reliable alignment between fluorescence signals and topographic structures opens possibilities to assess key biological processes at the cell surface such as endocytosis and exocytosis.

Large-scale analysis of high-speed atomic force microscopy data sets using adaptive image processing

Modern high-speed atomic force microscopes generate significant quantities of data in a short amount of time. Each image in the sequence has to be processed quickly and accurately in order to obtain a true representation of the sample and its changes over time. This paper presents an automated, adaptive algorithm for the required processing of AFM images. The algorithm adaptively corrects for both common one-dimensional distortions as well as the most common two-dimensional distortions. This method uses an iterative thresholded processing algorithm for rapid and accurate separation of background and surface topography. This separation prevents artificial bias from topographic features and ensures the best possible coherence between the different images in a sequence. This method is equally applicable to all channels of AFM data, and can process images in seconds.

Variational algorithms to remove stationary noise: applications to microscopy imaging

A framework and an algorithm are presented in order to remove stationary noise from images. This algorithm is called variational stationary noise remover. It can be interpreted both as a restoration method in a Bayesian framework and as a cartoon+texture decomposition method. In numerous denoising applications, the white noise assumption fails. For example, structured patterns such as stripes appear in the images. The model described here addresses these cases. Applications are presented with images acquired using different modalities: scanning electron microscope, FIB-nanotomography, and an emerging fluorescence microscopy technique called selective plane illumination microscopy.

AFM feature definition for neural cells on nanofibrillar tissue scaffolds

A diagnostic approach is developed and implemented that provides clear feature definition in atomic force microscopy (AFM) images of neural cells on nanofibrillar tissue scaffolds. Because the cellular edges and processes are on the same order as the background nanofibers, this imaging situation presents a feature definition problem. The diagnostic approach is based on analysis of discrete Fourier transforms of standard AFM section measurements. The diagnostic conclusion that the combination of dynamic range enhancement with low-frequency component suppression enhances feature definition is shown to be correct and to lead to clear-featured images that could change previously held assumptions about the cell-cell interactions present. Clear feature definition of cells on scaffolds extends the usefulness of AFM imaging for use in regenerative medicine.

DeStripe: frequency-based algorithm for removing stripe noises from AFM images

Background

Atomic force microscopy (AFM) is a relatively recently developed technique that shows a promising impact in the field of structural biology and biophysics. It has been used to image the molecular surface of membrane proteins at a lateral resolution of one nanometer or less. An immediate obstacle of characterizing surface features in AFM images is stripe noise. To better interpret structures at a sub-domain level, pre-processing of AFM images for removing stripe noises is necessary. Noise removal can be performed in either spatial or frequency domain. However, denoising processing in the frequency domain is a better solution for preserving edge sharpness.

Results

We have developed a denoising protocol, called DeStripe, for AFM bio-molecular images that are contaminated with heavy and fine stripes. This program adopts a divide-and-conquer approach by dividing the Fourier spectrum of the image into central and off-center regions for noisy pixels detection and intensity restoration; it is also applicable to other images interfered with high-density stripes such as those acquired by the scanning electron microscope. The denoising effect brought by DeStripe provides better visualization for image objects without introducing additional artifacts into the restored image.

Conclusions

The DeStripe denoising effect on AFM images is illustrated in the present work. It allows extracting extended information from the topographic measurements and implicitly enhances the molecular features in the image. All the presented images were processed by DeStripe with the raw image as the only input without any requirement for other prior information. A web service, http://biodev.cea.fr/destripe, is available for running DeStripe.

Atomic force microscopy comes of age

AFM (atomic force microscopy) analysis, both of fixed cells, and live cells in physiological environments, is set to offer a step change in the research of cellular function. With the ability to map cell topography and morphology, provide structural details of surface proteins and their expression patterns and to detect pico-Newton force interactions, AFM represents an exciting addition to the arsenal of the cell biologist. With the explosion of new applications, and the advent of combined instrumentation such as AFM-confocal systems, the biological application of AFM has come of age. The use of AFM in the area of biomedical research has been proposed for some time, and is one where a significant impact could be made. Fixed cell analysis provides qualitative and quantitative subcellular and surface data capable of revealing new biomarkers in medical pathologies. Image height and contrast, surface roughness, fractal, volume and force analysis provide a platform for the multiparameter analysis of cell and protein functions. Here, we review the current status of AFM in the field and discuss the important contribution AFM is poised to make in the understanding of biological systems.

Atomic force microscopy-derived nanoscale chip for the detection of human pathogenic viruses

Native-protein nanolithography (NPNL) was used to fabricate stable bioactive arrays of viral receptor spots. The arrays were specific for the cognate virus and devoid of nonspecific protein and virus adsorption under physiologic conditions. The spot size ranged from 200 nm x 200 nm to 2 microm x 2 microm and up to 3 x 3 spots were arranged per array. With proper force adjustment in the patterning experiments, His(6)-tagged bovine serum albumin (BSA) molecules were selectively removed from the underlying self-assembled monolayer (SAM) while leaving the latter intact. Injection of His(6)-tagged very low density lipoprotein receptor (VLDLR-His(6)) constructs resulted in specific, oriented binding to the Ni(2+)-loaded bis-(nitrolotriacetic acid) (bis-NTA) groups to the re-exposed SAM areas. The arrays of viral receptors were used for the detection of human rhinovirus particles (serotype 2; HRV2) under native conditions by topographical imaging at high signal-to-noise ratio. The kinetic on-rate of the HRV2-VLDLR interaction was derived from the time-dependent binding of the virions to the VLDL receptor spots. No significant binding was observed for the major group virus HRV14 that uses the unrelated receptor ICAM-1.

Imaging single virus particles on the surface of cell membranes by high-resolution scanning surface confocal microscopy

We have developed a high-resolution scanning surface confocal microscopy technique capable of imaging single virus-like particles (VLPs) on the surfaces of cells topographically and by fluorescence. The technique combines recently published single-molecule-resolution ion-conductance microscopy that acquires topographical data with confocal microscopy providing simultaneous fluorescent imaging. In our experiments we have demonstrated that the cell membrane exhibits numerous submicrometer-sized surface structures that could be topographically confused with virus particles. However, simultaneous acquisition of confocal images allows the positions of fluorescently tagged particles to be identified. Using this technique, we have, for the first time, visualized single polyoma VLPs adsorbed onto the cell membrane. Observed VLPs had a mean width of 108 +/- 16 nm. The particles were randomly distributed across the cell membrane, and no specific interactions were seen with cell membrane structures such as microvilli. These experiments demonstrate the utility of this new microscope for imaging the interactions of nanoparticles with the cell surface to provide novel insights into the earliest interactions of viruses and other nanoparticles such as gene therapy vectors with the cell.

Endocytic pathways: combined scanning ion conductance and surface confocal microscopy study

We introduce a novel high resolution scanning surface confocal microscopy technique that enables imaging of endocytic pits in apical membranes of live cells for the first time. The improved topographical resolution of the microscope together with simultaneous fluorescence confocal detection produces pairs of images of cell surfaces sufficient to identify single endocytic pits. Whilst the precise position and size of the pit is detected by the ion conductance microscope, the molecular nature of the pit, e.g. clathrin coated or caveolae, is determined by the corresponding green fluorescent protein fluorescence. Also, for the first time, we showed that flotillin 1 and 2 can be found co-localising with ~200-nm indentations in the cell membrane that supports involvement of this protein in endocytosis.

Dynamic force microscopy imaging of plasmid DNA and viral RNA

Plasmid DNA and viral RNA were imaged in a liquid environment by dynamic force microscopy (DFM) and fine structures of DNA with heights of 1.82+/-0.66 nm were obtained in topographical images. In simultaneously acquired phase images, DNA could be imaged with better contrast at lower imaging forces. By splitting the cantilever oscillation signal into lower and upper parts, the contribution of the adhesion between tip and sample to the topographical images was eliminated, resulting in better signal-to-noise ratio. DFM of the single stranded RNA genome of a human rhinovirus showed loops protruding from a condensed RNA core, 20-50 nm in height. The mechanical rigidity of the RNA was determined by single molecule pulling experiments. From fitting RNA stretching curves to the Worm-Like-Chain (WLC) model a persistence length of 1.0+/-0.17 nm was obtained.