TopoStats - A program for automated tracing of biomolecules from AFM images

Multiscale topological analysis of kinetoplast DNA via high-resolution AFM

Kinetoplast DNA is a complex nanoscale network, naturally assembled from thousands of interconnected DNA circles within the mitochondrion of certain parasites. Despite the relevance of this molecule to parasitology and the recent discovery of tuneable mechanics, its topology remains highly contested. Here we present a multiscale analysis into the structure of kDNA using a combination of high-resolution atomic force microscopy and custom-designed image analysis protocols. By capturing a notably large set of high-resolution images, we are able to look beyond individual kDNA variations and quantify population properties throughout several length scales. Within the sample, geometric fluctuations of area and mean curvature are observed, corresponding with previous in vitro measurements. These translate to localised variations in density, with a sample-wide decrease in DNA density from the outer rim of the molecule to the centre and an increase in pore size. Nodes were investigated in a single molecule study, and their estimated connectivity significantly exceeded mean valence, with a high dependence on their position in the network. While node separation was approximately half the minicircle circumference, it followed a strong bimodal distribution, suggesting more complex underlying behaviour. Finally, upon selective digestion of the network, breakdown of the fibril-cap heterogeneity was observed, with molecules expanding less upon immobilisation on the mica surface. Additionally, preferential digestion was seen in localised areas of the network, increasing pore size disproportionately. Overall, the combination of high-resolution AFM and single molecule image analysis provides a promising method to the continued investigation of complex nanoscale structures. These findings support the ongoing characterisation of kDNA topology to aid understanding of its biological and mechanical phenomena.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy Topographic Images

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

PyFMLab: Open-source software for atomic force microscopy microrheology data analysis

Background

Atomic force microscopy (AFM) is one of the main techniques used to characterize the mechanical properties of soft biological samples and biomaterials at the nanoscale. Despite efforts made by the AFM community to promote open-source data analysis tools, standardization continues to be a significant concern in a field that requires common analysis procedures. AFM-based mechanical measurements involve applying a controlled force to the sample and measure the resulting deformation in the so-called force-distance curves. These may include simple approach and retract or oscillatory cycles at various frequencies (microrheology). To extract quantitative parameters, such as the elastic modulus, from these measurements, AFM measurements are processed using data analysis software. Although open tools exist and allow obtaining the mechanical properties of the sample, most of them only include standard elastic models and do not allow the processing of microrheology data. In this work, we have developed an open-source software package (called PyFMLab, as of python force microscopy laboratory) capable of determining the viscoelastic properties of samples from both conventional force-distance curves and microrheology measurements.

Methods

PyFMLab has been written in Python, which provides an accessible syntax and sufficient computational efficiency. The software features were divided into separate, self-contained libraries to enhance code organization and modularity and to improve readability, maintainability, testability, and reusability. To validate PyFMLab, two AFM datasets, one composed of simple force curves and another including oscillatory measurements, were collected on HeLa cells.

Results

The viscoelastic parameters obtained on the two datasets analysed using PyFMLab were validated against data processing proprietary software and against validated MATLAB routines developed before obtaining equivalent results.

Conclusions

Its open-source nature and versatility makes PyFMLab an open-source solution that paves the way for standardized viscoelastic characterization of biological samples from both force-distance curves and microrheology measurements.

The Paradoxical Behavior of Rough Colloids at Fluid Interfaces

Colloidal particles adsorb and remain trapped at immiscible fluid interfaces due to strong interfacial adsorption energy with a contact angle defined by the chemistry of the particle and fluid phases. An undulated contact line may appear due to either particle surface roughness or shape anisotropy, which results in a quadrupolar interfacial deformation and strong long-range capillary interaction between neighboring particles. While each effect has been observed separately, here we report the paradoxical impact of surface roughness on spherical and anisotropic ellipsoidal polymer colloids. Using a seeded emulsion polymerization technique, we synthesize spherical and ellipsoidal particles with controlled roughness magnitudes and topography (convex/concave). Via in situ measurement of the interfacial deformation around colloids at an air-water interface, we find that while surface roughness strengthens the quadrupolar deformation in spheres as expected by theory, in stark contrast, it weakens the same in ellipsoids. As roughness increases, particles of both shapes become more hydrophilic, and their apparent contact angle decreases. Using numerical predictions, we show that this partially explains the decreased interfacial deformation and capillary interactions between the ellipsoids. Therefore, particle surface engineering has the potential to decrease the capillary deformation by asymmetric particles via changing their capillary pinning, as well as wetting behavior at fluid interfaces.

Atomic force microscopy 3D structural reconstruction of individual particles in the study of amyloid protein assemblies

Recent developments in atomic force microscopy (AFM) image analysis have made three-dimensional (3D) structural reconstruction of individual particles observed on 2D AFM height images a reality. Here, we review the emerging contact point reconstruction AFM (CPR-AFM) methodology and its application in 3D reconstruction of individual helical amyloid filaments in the context of the challenges presented by the structural analysis of highly polymorphous and heterogeneous amyloid protein structures. How individual particle-level structural analysis can contribute to resolving the amyloid polymorph structure-function relationships, the environmental triggers leading to protein misfolding and aggregation into amyloid species, the influences by the conditions or minor fluctuations in the initial monomeric protein structure on the speed of amyloid fibril formation, and the extent of the different types of amyloid species that can be formed, are discussed. Future perspectives in the capabilities of AFM-based 3D structural reconstruction methodology exploiting synergies with other recent AFM technology advances are also discussed to highlight the potential of AFM as an emergent general, accessible and multimodal structural biology tool for the analysis of individual biomolecules.

NanoLocz: Image Analysis Platform for AFM, High-Speed AFM, and Localization AFM

Atomic Force Microscopy (AFM), High-Speed AFM (HS-AFM) simulation AFM, and Localization AFM (LAFM) enable the study of molecules and surfaces with increasingly higher spatiotemporal resolution. However, effective and rapid analysis of the images and movies produced by these techniques can be challenging, often requiring the use of multiple image processing software applications and scripts. Here, NanoLocz, an open-source solution that offers advanced analysis capabilities for the AFM community, is presented. Integration and continued development of AFM analysis tools is essential to improve access to data, increase throughput, and open new analysis opportunities. NanoLocz efficiently leverages the rich data AFM has to offer by incorporating and combining existing and newly developed analysis methods for AFM, HS-AFM, simulation AFM, and LAFM seamlessly. It facilitates and streamlines AFM analysis workflows from import of raw data, through to various analysis workflows. Here, the study demonstrates the capabilities of NanoLocz and the new methods it enables including single-molecule LAFM, time-resolved LAFM, and simulation LAFM.

Simulating structurally variable nuclear pore complexes for microscopy

MOTIVATION

The nuclear pore complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and an important reference standard in microscopy: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudoatomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single-molecule localization microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However, the true picture of this variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from variability of the underlying structure.

RESULTS

We introduce CIR4MICS ('ceramics', Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that synthesizes ground truth datasets of structurally variable NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. Further, we provide annotated reference datasets of simulated human NPCs, which facilitate a side-by-side comparison with real data. To demonstrate, we synthetically replicate a geometric analysis of real NPC radii and reveal that a range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to test the capabilities of image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies.

AVAILABILITY AND IMPLEMENTATION

Code: https://github.com/uhlmanngroup/cir4mics. Simulated data: BioStudies S-BSST1058.

Autophagy receptor NDP52 alters DNA conformation to modulate RNA polymerase II transcription

NDP52 is an autophagy receptor involved in the recognition and degradation of invading pathogens and damaged organelles. Although NDP52 was first identified in the nucleus and is expressed throughout the cell, to date, there is no clear nuclear functions for NDP52. Here, we use a multidisciplinary approach to characterise the biochemical properties and nuclear roles of NDP52. We find that NDP52 clusters with RNA Polymerase II (RNAPII) at transcription initiation sites and that its overexpression promotes the formation of additional transcriptional clusters. We also show that depletion of NDP52 impacts overall gene expression levels in two model mammalian cells, and that transcription inhibition affects the spatial organisation and molecular dynamics of NDP52 in the nucleus. This directly links NDP52 to a role in RNAPII-dependent transcription. Furthermore, we also show that NDP52 binds specifically and with high affinity to double-stranded DNA (dsDNA) and that this interaction leads to changes in DNA structure in vitro. This, together with our proteomics data indicating enrichment for interactions with nucleosome remodelling proteins and DNA structure regulators, suggests a possible function for NDP52 in chromatin regulation. Overall, here we uncover nuclear roles for NDP52 in gene expression and DNA structure regulation.

Protein dynamics by the combination of high-speed AFM and computational modeling

High-speed atomic force microscopy (HS-AFM) allows direct observation of biological molecules in dynamic action. However, HS-AFM has no atomic resolution. This article reviews recent progress of computational methods to infer high-resolution information, including the construction of 3D atomistic structures, from experimentally acquired resolution-limited HS-AFM images.

BioAFMviewer software for simulation atomic force microscopy of molecular structures and conformational dynamics

Atomic force microscopy (AFM) and high-speed scanning have significantly advanced real time observation of biomolecular dynamics, with applications ranging from single molecules to the cellular level. To facilitate the interpretation of resolution-limited imaging, post-experimental computational analysis plays an increasingly important role to understand AFM measurements. Data-driven simulation of AFM, computationally emulating experimental scanning, and automatized fitting has recently elevated the understanding of measured AFM topographies by inferring the underlying full 3D atomistic structures. Providing an interactive user-friendly interface for simulation AFM, the BioAFMviewer software has become an established tool within the Bio-AFM community, with a plethora of applications demonstrating how the obtained full atomistic information advances molecular understanding beyond topographic imaging. This graphical review illustrates the BioAFMviewer capacities and further emphasizes the importance of simulation AFM to complement experimental observations.

DNA conformational equilibrium enables continuous changing of curvatures

Assembly of complex structures from a small set of tiles is a common theme in biology. For example, many copies of identical proteins make up polyhedron-shaped, viral capsids and tubulin can make long microtubules. This inspired the development of tile-based DNA self-assembly for nanoconstruction, particularly for structures with high symmetries. In the final structure, each type of motif will adopt the same conformation, either rigid or with defined flexibility. For structures that have no symmetry, their assembly remains a challenge from a small set of tiles. To meet this challenge, algorithmic self-assembly has been explored driven by computational science, but it is not clear how to implement this approach to one-dimensional (1D) structures. Here, we have demonstrated that a constant shift of a conformational equilibrium could allow 1D structures to evolve. As shown by atomic force microscopy imaging, one type of DNA tile successfully assembled into DNA spirals and concentric circles, which became less and less curved from the structure's center outward. This work points to a new direction for tile-based DNA assembly.

Circular Polycatenanes: Supramolecular Structures with Topologically Tunable Properties

Polycatenanes, macrochains of topologically interlocked rings with unique physical properties have recently gained considerable interest in supramolecular chemistry, biology, and soft matter. Most of the work has been, so far, focused on linear chains and on their variety of conformational properties compared to standard polymers. Here we go beyond the linear case and show that, by circularizing such macrochains, one can exploit the topology of the local interlockings to store twist in the system, significantly altering its metric and local properties. Moreover, by properly defining the twist (Tw) and writhe (Wr) of these macrorings we show the validity of a relation equivalent to the Călugăreanu-White-Fuller theorem Tw+Wr=const, originally proved for ribbonlike structures such as double stranded DNA. Our results suggest that circular polycatenanes with storable and tunable twist can form a new category of highly designable multiscale structures with potential applications in supramolecular chemistry and material science.

Rapid DNA origami nanostructure detection and classification using the YOLOv5 deep convolutional neural network

The intra-image identification of DNA structures is essential to rapid prototyping and quality control of self-assembled DNA origami scaffold systems. We postulate that the YOLO modern object detection platform commonly used for facial recognition can be applied to rapidly scour atomic force microscope (AFM) images for identifying correctly formed DNA nanostructures with high fidelity. To make this approach widely available, we use open-source software and provide a straightforward procedure for designing a tailored, intelligent identification platform which can easily be repurposed to fit arbitrary structural geometries beyond AFM images of DNA structures. Here, we describe methods to acquire and generate the necessary components to create this robust system. Beginning with DNA structure design, we detail AFM imaging, data point annotation, data augmentation, model training, and inference. To demonstrate the adaptability of this system, we assembled two distinct DNA origami architectures (triangles and breadboards) for detection in raw AFM images. Using the images acquired of each structure, we trained two separate single class object identification models unique to each architecture. By applying these models in sequence, we correctly identified 3470 structures from a total population of 3617 using images that sometimes included a third DNA origami structure as well as other impurities. Analysis was completed in under 20 s with results yielding an F1 score of 0.96 using our approach.

AI-based atomic force microscopy image analysis allows to predict electrochemical impedance spectra of defects in tethered bilayer membranes

Atomic force microscopy (AFM) image analysis of supported bilayers, such as tethered bilayer membranes (tBLMs) can reveal the nature of the membrane damage by pore-forming proteins and predict the electrochemical impedance spectroscopy (EIS) response of such objects. However, automated analysis involving pore detection in such images is often non-trivial and can require AI-based object detection techniques. The specific object-detection algorithm we used to determine the defect coordinates in real AFM images was a convolutional neural network (CNN). Defect coordinates allow to predict the EIS response of tBLMs populated by the pore-forming toxins using finite element analysis (FEA) modeling. We tested if the accuracy of the CNN algorithm affected the EIS spectral features sensitive to defect densities and other physical parameters of tBLMs. We found that the EIS spectra can be predicted sufficiently well, however, systematic errors of characteristic spectral points were observed and need to be taken into account. Importantly, the comparison of predicted EIS curves with experimental ones allowed to estimate important physical parameters of tBLMs such as the specific resistance of submembrane reservoir. This reservoir separates phospholipid bilayer from the solid support. We found that the specific resistance of the reservoir amounts to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{4.25 \pm 0.10}$$\end{document}104.25±0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Omega \cdot cm$$\end{document}Ω·cm which is approximately two orders of a magnitude higher compared to the specific resistance of the buffer bathing tBLMs studied in this work. We hypothesize that such effect may be related in part due to decreased concentration of ionic carriers in the submembrane due to decreased relative dielectric permittivity in this region.

Phase separation in the outer membrane of Escherichia coli

Gram-negative bacteria are surrounded by a protective outer membrane (OM) with phospholipids in its inner leaflet and lipopolysaccharides (LPS) in its outer leaflet. The OM is also populated with many β-barrel outer-membrane proteins (OMPs), some of which have been shown to cluster into supramolecular assemblies. However, it remains unknown how abundant OMPs are organized across the entire bacterial surface and how this relates to the lipids in the membrane. Here, we reveal how the OM is organized from molecular to cellular length scales, using atomic force microscopy to visualize the OM of live bacteria, including engineered Escherichia coli strains and complemented by specific labeling of abundant OMPs. We find that a predominant OMP in the E. coli OM, the porin OmpF, forms a near-static network across the surface, which is interspersed with barren patches of LPS that grow and merge with other patches during cell elongation. Embedded within the porin network is OmpA, which forms noncovalent interactions to the underlying cell wall. When the OM is destabilized by mislocalization of phospholipids to the outer leaflet, a new phase appears, correlating with bacterial sensitivity to harsh environments. We conclude that the OM is a mosaic of phase-separated LPS-rich and OMP-rich regions, the maintenance of which is essential to the integrity of the membrane and hence to the lifestyle of a gram-negative bacterium.

Atomic force microscopy-A tool for structural and translational DNA research

Atomic force microscopy (AFM) is a powerful imaging technique that allows for structural characterization of single biomolecules with nanoscale resolution. AFM has a unique capability to image biological molecules in their native states under physiological conditions without the need for labeling or averaging. DNA has been extensively imaged with AFM from early single-molecule studies of conformational diversity in plasmids, to recent examinations of intramolecular variation between groove depths within an individual DNA molecule. The ability to image dynamic biological interactions in situ has also allowed for the interaction of various proteins and therapeutic ligands with DNA to be evaluated-providing insights into structural assembly, flexibility, and movement. This review provides an overview of how innovation and optimization in AFM imaging have advanced our understanding of DNA structure, mechanics, and interactions. These include studies of the secondary and tertiary structure of DNA, including how these are affected by its interactions with proteins. The broader role of AFM as a tool in translational cancer research is also explored through its use in imaging DNA with key chemotherapeutic ligands, including those currently employed in clinical practice.

The role of convolutional neural networks in scanning probe microscopy: a review

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

Correlative approaches in single-molecule biophysics: A review of the progress in methods and applications

Here, we discuss a collection of cutting-edge techniques and applications in use today by some of the leading experts in the field of correlative approaches in single-molecule biophysics. A key difference in emphasis, compared with traditional single-molecule biophysics approaches detailed previously, is on the emphasis of the development and use of complex methods which explicitly combine multiple approaches to increase biological insights at the single-molecule level. These so-called correlative single-molecule biophysics methods rely on multiple, orthogonal tools and analysis, as opposed to any one single driving technique. Importantly, they span both in vivo and in vitro biological systems as well as the interfaces between theory and experiment in often highly integrated ways, very different to earlier traditional non-integrative approaches. The first applications of correlative single-molecule methods involved adaption of a range of different experimental technologies to the same biological sample whose measurements were synchronised. However, now we find a greater flora of integrated methods emerging that include approaches applied to different samples at different times and yet still permit useful molecular-scale correlations to be performed. The resultant findings often enable far greater precision of length and time scales of measurements, and a more nuanced understanding of the interplay between different processes in the same cell. Many new correlative single-molecule biophysics techniques also include more complex, physiologically relevant approaches as well as an increasing number that combine of approaches advanced computational methods and mathematical analysis with experimental tools. Here, we review the motivation behind the development of correlative single-molecule microscopy methods, its history and recent progress in the field.

Base-pair resolution analysis of the effect of supercoiling on DNA flexibility and major groove recognition by triplex-forming oligonucleotides

In the cell, DNA is arranged into highly-organised and topologically-constrained (supercoiled) structures. It remains unclear how this supercoiling affects the detailed double-helical structure of DNA, largely because of limitations in spatial resolution of the available biophysical tools. Here, we overcome these limitations, by a combination of atomic force microscopy (AFM) and atomistic molecular dynamics (MD) simulations, to resolve structures of negatively-supercoiled DNA minicircles at base-pair resolution. We observe that negative superhelical stress induces local variation in the canonical B-form DNA structure by introducing kinks and defects that affect global minicircle structure and flexibility. We probe how these local and global conformational changes affect DNA interactions through the binding of triplex-forming oligonucleotides to DNA minicircles. We show that the energetics of triplex formation is governed by a delicate balance between electrostatics and bonding interactions. Our results provide mechanistic insight into how DNA supercoiling can affect molecular recognition, that may have broader implications for DNA interactions with other molecular species.