Atomic Resolution Transmission Electron Microscopy

Kirkland, Angus I.; Chang, Shery L. Y.; Hutchison, J. L.

doi:10.1007/978-3-030-00069-1_1

Cited by 7 publications

(4 citation statements)

References 225 publications

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“…1, the sum is over individual lipid atoms, ρ i ( w ) is the atom’s time-averaged atomic number density profile (units of Å -3 ) calculated from the MD simulation trajectory, and V i is the spatially integrated, shielded coulomb potential for an isolated neutral atom ( V i = 25, 130, 108, 97, and 267 V Å 3 for H, C, N, O and P, respectively). The phase shift experienced by an electron wave passing through the bilayer is given by: where σ e accounts for the dependence of the electron phase on the projected potential and is equal to 0.65 mrad V -1 Å -1 for 300 keV electrons (38). Following Wang et al, we refer to g ( w ) as the electron ‘scattering profile’ of the flat simulated bilayer; as Φ has units of V , the scattering profile has units of mrad Å -1 .…”

Section: Methodsmentioning

confidence: 99%

Cryo-EM images of phase separated lipid bilayer vesicles analyzed with a machine learning approach

Sharma,

Doktorova,

Waxham

et al. 2024

Preprint

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Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a liquid-disordered (Ld) phase coexists with a liquid-ordered (Lo) phase. Using such models, we recently showed that cryogenic electron microscopy (cryo-EM) can detect phase separation in lipid vesicles based on differences in bilayer thickness. However, the considerable noise within cryo-EM data poses a significant challenge for accurately determining the membrane phase state at high spatial resolution. To this end, we have developed an image processing pipeline that utilizes machine learning (ML) to predict the bilayer phase in projection images of lipid vesicles. Importantly, the ML method exploits differences in both the thickness and molecular density of Lo compared to Ld, which leads to improved phase identification. To assess accuracy, we used artificial images of phase-separated lipid vesicles generated from all-atom molecular dynamics simulations of Lo and Ld phases. Synthetic ground truth datasets mimicking a series of compositions along a tieline of Ld+Lo coexistence were created and then analyzed with various ML models. For all tieline compositions, we find that the ML approach can correctly identify the bilayer phase at 5 nm lateral resolution with > 90% accuracy, thus providing a means to isolate the intensity profiles of coexisting Ld and Lo phases, as well as accurately determine domain size distributions, number of domains, and phase area fractions. The method described here provides a framework for characterizing nanoscopic lateral heterogeneities in membranes and paves the way for a more detailed understanding of raft properties in biological contexts.

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Section: Methodsmentioning

confidence: 99%

Cryo-EM images of phase separated lipid bilayer vesicles analyzed with a machine learning approach

Sharma,

Doktorova,

Waxham

et al. 2024

Preprint

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“…Additionally, the fibers once washed with water to remove the PEO core and the TEM analysis of the ensuing hollow structure can confirm the complete removal of the PEO core while the chitosan shell maintains its physical structure [75]. High-resolution transmission electron microscopy (HRTEM) is a variant of TEM with resolutions below 0.5 Å [159] which facilitates the imaging of specimens at an atomic scale and enables the analysis of the atomic structure of the samples [159]. HRTEM has been an invaluable asset in the analysis of biopolymer-assisted formation of nanoparticles including the study of their crystal planes [160][161][162], the crystal structure of cellulose [163], nanocomposites [164,165], and even in the molecular orientations of biopolymers [166].…”

Section: Transmission Electron Microscopymentioning

confidence: 99%

“…HRTEM has been an invaluable asset in the analysis of biopolymer-assisted formation of nanoparticles including the study of their crystal planes [160][161][162], the crystal structure of cellulose [163], nanocomposites [164,165], and even in the molecular orientations of biopolymers [166]. TEM with resolutions below 0.5 Å [159] which facilitates the imaging of specimens at an atomic scale and enables the analysis of the atomic structure of the samples [159]. HRTEM has been an invaluable asset in the analysis of biopolymer-assisted formation of nanoparticles including the study of their crystal planes [160][161][162], the crystal structure of cellulose [163], nanocomposites [164,165], and even in the molecular orientations of biopolymers [166].…”

Section: Transmission Electron Microscopymentioning

confidence: 99%

Microscopic Techniques for the Analysis of Micro and Nanostructures of Biopolymers and Their Derivatives

et al. 2020

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Natural biopolymers, a class of materials extracted from renewable sources, is garnering interest due to growing concerns over environmental safety; biopolymers have the advantage of biocompatibility and biodegradability, an imperative requirement. The synthesis of nanoparticles and nanofibers from biopolymers provides a green platform relative to the conventional methods that use hazardous chemicals. However, it is challenging to characterize these nanoparticles and fibers due to the variation in size, shape, and morphology. In order to evaluate these properties, microscopic techniques such as optical microscopy, atomic force microscopy (AFM), and transmission electron microscopy (TEM) are essential. With the advent of new biopolymer systems, it is necessary to obtain insights into the fundamental structures of these systems to determine their structural, physical, and morphological properties, which play a vital role in defining their performance and applications. Microscopic techniques perform a decisive role in revealing intricate details, which assists in the appraisal of microstructure, surface morphology, chemical composition, and interfacial properties. This review highlights the significance of various microscopic techniques incorporating the literature details that help characterize biopolymers and their derivatives.

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“…In addition, modern microscopes are equipped with additional analytical accessories, such as X-ray spectroscopy (both wavelength and energy dispersion), electron backscatter diffraction (EBSD), Auger electron spectroscopy (AE), or cathodoluminescence (CL), to allow a deeper characterization of the sample and to provide further information on the chemical content of the samples. Although instrumental development has brought resolution to the nanometric level for SEM and even beyond for TEM [1], electron microscopy remains essentially a visualization technique, where the spatial arrangement of the constituents is represented in a two-dimensional image. The possibility of a three-dimensional (3D) reconstruction in the micrometer range required the long-awaited integration of computer-assisted methods with the imaging capability of electron microscopes.…”

Section: Introductionmentioning

confidence: 99%

Advances in Focused Ion Beam Tomography for Three-Dimensional Characterization in Materials Science

Mura,

Cognigni,

Ferroni

et al. 2023

Materials

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Over the years, FIB-SEM tomography has become an extremely important technique for the three-dimensional reconstruction of microscopic structures with nanometric resolution. This paper describes in detail the steps required to perform this analysis, from the experimental setup to the data analysis and final reconstruction. To demonstrate the versatility of the technique, a comprehensive list of applications is also summarized, ranging from batteries to shale rocks and even some types of soft materials. Moreover, the continuous technological development, such as the introduction of the latest models of plasma and cryo-FIB, can open the way towards the analysis with this technique of a large class of soft materials, while the introduction of new machine learning and deep learning systems will not only improve the resolution and the quality of the final data, but also expand the degree of automation and efficiency in the dataset handling. These future developments, combined with a technique that is already reliable and widely used in various fields of research, are certain to become a routine tool in electron microscopy and material characterization.

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Atomic Resolution Transmission Electron Microscopy

Cited by 7 publications

References 225 publications

Cryo-EM images of phase separated lipid bilayer vesicles analyzed with a machine learning approach

Cryo-EM images of phase separated lipid bilayer vesicles analyzed with a machine learning approach

Microscopic Techniques for the Analysis of Micro and Nanostructures of Biopolymers and Their Derivatives

Advances in Focused Ion Beam Tomography for Three-Dimensional Characterization in Materials Science

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