Beam Scanning Electron Microscopy Tomography Research Articles

Tuning α Precipitation via Post-Heat Treatments in Direct Energy Deposited Metastable β Ti-5Al-5Mo-5V-3Cr Alloy and Its Impact on Mechanical Properties

In this study, the strength and ductility of a direct energy deposited (DEDed) metastable β Ti-5Al-5Mo-5V-3Cr (wt.%, Ti-5553) alloy was explored by tuning the formation of various α microstructures through post-heat treatments. The microstructure of DEDed Ti-5553 with and without post-heat treatments was systematically studied using scanning electron microscopy, three-dimensional (3D) focused ion beam-scanning electron microscopy tomography, transmission electron microscopy, scanning transmission electron microscopy, and atom probe tomography. Nanoscale ω phase and O′ phase particles were observed in the as-built DEDed Ti-5553 without post-heat treatment for the first time. By altering the aging temperatures and heating rates during post-heat treatments, various microstructural evolution pathways were activated, leading to α microstructures with different sizescales, morphologies, and number densities. By fast heating and isothermal aging at 600°C for 2hours, refined α microstructure was formed through the pseudospinodal decomposition mechanism. While slow heating (at the heating rates of 5°C/min or 0.5°C/min) to 600°C and isothermal aging for 2hours, super-refined α microstructures were produced respectively, primarily via ω-assisted and O′-assisted α precipitation mechanisms. By fast heating to and isothermal aging at 700°C, fine-scaled intragranular α microstructure was formed, dramatically different in morphology and sizescale from the coarse α microstructure formed in the casted Ti-5553, which is suspected to be related to the indirect influence of nanoscale isothermal ω phase particles formed during the DED process and the excess oxygen introduced during the DED process. With fast heating to and isothermal aging at 800°C, coarse α microstructure forms through the classical nucleation and growth mechanism. Various α microstructures led to a drastic change of mechanical properties with improvements up to a 55% increase in hardness and 92% increase in tensile yield strength, compared with as-built DEDed Ti-5553. Our work indicates the feasibility of achieving tailored mechanical properties in DEDed metastable β-Ti alloys by tuning α microstructures through selectively activating various phase transformation mechanisms via post-heat treatments.

Mapping the composite nature of clay matrix in mudstones: integrated micromechanics profiling by high-throughput nanoindentation and data analysis

AbstractMudstones and shales serve as natural barrier rocks in various geoenergy applications. Although many studies have investigated their mechanical properties, characterizing these parameters at the microscale remains challenging due to their fine-grained nature and susceptibility to microstructural damage introduced during sample preparation. This study aims to investigate the micromechanical properties of clay matrix composite in mudstones by combining high-speed nanoindentation mapping and machine learning data analysis. The nanoindentation approach effectively captured the heterogeneity in high-resolution mechanical property maps. Utilizing machine learning-based k-means clustering, the mechanical characteristics of matrix clay, brittle minerals, as well as measurements on grain boundaries and structural discontinuities (e.g., cracks) were successfully distinguished. The classification results were validated through correlation with broad ion beam-scanning electron microscopy images. The resulting average reduced elastic modulus (Er) and hardness (H) values for the clay matrix were determined to be 16.2 ± 6.2 and 0.5 ± 0.5 GPa, respectively, showing consistency across different test settings and indenter tips. Furthermore, the sensitivity of indentation measurements to various factors was investigated, revealing limited sensitivity to indentation depth and tip geometry (when comparing Cube corner and Berkovich tip in a small range of indentation depth variations), but decreased stability at lower loading rates. Box counting and bootstrapping methods were applied to assess the representativeness of parameters determined for the clay matrix. A relatively small dataset (indentation number = 60) is needed to achieve representativeness, while the main challenges is to cover a representative mapping area for clay matrix characterization. Overall, this study demonstrates the feasibility of high-speed nanoindentation mapping combined with data analysis for micromechanical characterization of the clay matrix in mudstones, paving the way for efficient analysis of similar fine-grained sedimentary rocks.

Editors’ Choice—Visualizing the Impact of the Composite Cathode Microstructure and Porosity on Solid-State Battery Performance

The kinetics of composite cathodes for solid-state batteries (SSBs) relies heavily on their microstructure. Spatial distribution of the different phases, porosity, interface areas, and tortuosity factors are important descriptors that need accurate quantification for models to predict the electrochemistry and mechanics of SSBs. In this study, high-resolution focused ion beam-scanning electron microscopy tomography was used to investigate the microstructure of cathodes composed of a nickel-rich cathode active material (NCM) and a thiophosphate-based inorganic solid electrolyte (ISE). The influence of the ISE particle size on the microstructure of the cathode was visualized by 3D reconstruction and charge transport simulation. By comparison of experimentally determined and simulated conductivities of composite cathodes with different ISE particle sizes, the electrode charge transport kinetics is evaluated. Porosity is shown to have a major influence on the cell kinetics and the evaluation of the active mass of electrochemically active particles reveals a higher fraction of connected NCM particles in electrode composites utilizing smaller ISE particles. The results highlight the importance of homogeneous and optimized microstructures for high performance SSBs, securing fast ion and electron transport.

Pt-Black-Modified (Hemi)spherical AFM Sensors: In Situ Imaging of Light-Driven Hydrogen Peroxide Evolution.

In this work, we present (hemi)spherical atomic force microscopy (AFM) sensors for the detection of hydrogen peroxide. Platinum-black (Pt-B) was electrodeposited onto conductive colloidal AFM probes or directly at recessed microelectrodes located at the end of a tipless cantilever, resulting in electrocatalytically active cantilever-based sensors that have a small geometric area but, due to the porosity of the films, exhibit a large electroactive surface area. Focused ion beam-scanning electron microscopy tomography revealed the porous 3D structure of the deposited Pt-B. Given the accurate positioning capability of AFM, these probes are suitable for local in situ sensing of hydrogen peroxide and at the same time can be used for (electrochemical) force spectroscopy measurements. Detection limits for hydrogen peroxide in the nanomolar range (LOD = 68 ± 7 nM) were obtained. Stability test and first in situ proof-of-principle experiments to achieve the electrochemical imaging of hydrogen peroxide generated at a microelectrode and at photocatalytically active structured poly(heptazine imide) films are demonstrated. Force spectroscopic data of the photocatalyst films were recorded in ambient conditions, in solution, and by applying a potential, which demonstrates the versatility of these novel Pt-B-modified spherical AFM probes.

Interactive Machine Learning Improves Accuracy of Coal Porosity Segmentation in Focused Ion Beam–Scanning Electron Microscopy Images

Interactive Machine Learning Improves Accuracy of Coal Porosity Segmentation in Focused Ion Beam–Scanning Electron Microscopy Images

Ascertaining Uncertain Nanopore Boundaries in 2D Images of Porous Materials for Accurate 3D Microstructural Reconstruction and Heat Transfer Performance Prediction

A precise three-dimensional (3D) microstructure is crucial in porous material-based sciences and technologies, whereas accurate and efficient microstructure reconstruction using two-dimensional (2D) images remains challenging. Here, the strategy of ascertaining nanopore boundaries in focused ion beam-scanning electron microscopy images using experimentally guided image segmentation and deep learning-based reconstruction is proposed for precise reconstruction and heat transfer performance prediction. We demonstrate that uncertain boundaries in 2D images and pore characteristics in reconstructed 3D microstructures can be mathematically linked through area proportion (AP)-determined image segmentation. By calibrating the AP value once using reliable experimental data, accurate reconstruction of porous materials with different pore structures can be achieved with pore characteristic errors of <3%. Compared to randomly generated models, microstructures reconstructed using the proposed method have a better description in the connectivity of solid particles. Porosity-related transport property simulations of reconstructed microstructures reveal the remarkable boundary-identifying ability of the proposed strategy. Benefiting from ascertained porous structures, thermal conductivities in different directions could be predicted using 2D images with accuracy >99% and efficiency <1 s.

Local ageing effects of polymer electrolyte fuel cell membrane electrode assemblies due to accelerated durability testing

AbstractAccelerated durability test (ADT) protocols are useful tools to reduce testing time and development costs of automotive polymer electrolyte fuel cell stacks. Established accelerated stress tests allow comparing individual cell components. However, such tests in general do not allow drawing reliable conclusions on the expected lifetime for a complete stack. In this work, we examine the influence of combined stressors on the ageing behavior of individual cell components operated on stack level. We combine known main stressors for mobile fuel‐cell operation such as dynamic load, temperature and humidity cycling or hydrogen/air fronts during start‐up to develop a new accelerated test protocol. It was applied to an automotive 5‐cell short stack for 460 operating hours (OpH). A second stack was operated in a reference long‐term test for 2300 OpH. Several in situ characterization techniques, such as cyclic voltammetry, and recording the local current density distribution were employed. In addition, post‐mortem analyses, such as focused ion beam scanning electron microscopy imaging, was applied in order to better understand the degradation mechanisms. The results presented here provide an excellent basis for the further development of a new ADT protocol, which will accelerate ageing processes in the fuel cell in a realistic way, i.e. leading to similar degradation characteristics as in long term testing.

AGR-2 irradiated TRISO particle IPyC/SiC interface analysis using FIB-SEM tomography

The morphology in the interface region between the inner pyrolytic carbon layer (IPyC) and silicon carbide (SiC) layers in tristructural-isotropic (TRISO) particle fuel from the AGR-2 irradiation experiment were studied using focused ion beam-scanning electron microscopy tomography. This work quantitatively described the interface and corresponding relevant metrics to understand how the microstructural features at the IPyC/SiC interface may influence actinide and fission product interactions with the SiC layer. Particles were selected with varied 110mAg retention rates, and their volumes were reconstructed and analyzed for distributions of pores, fission product/actinide features, SiC, and IPyC. It was found that porosity accommodates fission products in the interface and SiC layers. The largest fission product/actinide precipitates were found in the interface region. This was also where the largest number fraction of fission products/actinides was found, consistent with SEM showing fission product/actinide pileup along selected areas of the interface region.

High-Resolution Numerical Simulation of Microfiltration of Oil-in-Water Emulsion Permeating through a Realistic Membrane Microporous Structure Generated by Focused Ion Beam Scanning Electron Microscopy Images.

Owing to the limitations of visualization techniques in experimental studies and low-resolution numerical models based on computational fluid dynamics (CFD), the detailed behavior of oil droplets during microfiltration is not well understood. Hence, a high-resolution CFD model based on an in-house direct numerical simulation (DNS) code was constructed in this study to analyze the detailed dynamics of an oil-in-water (O/W) emulsion using a microfiltration membrane. The realistic microporous structure of commercial ceramic microfiltration membranes (mullite and α-alumina membranes) was obtained using an image processing technique based on focused ion beam scanning electron microscopy (FIB-SEM). Numerical simulations of microfiltration of O/W emulsions on the membrane microstructure obtained by FIB-SEM were performed, and the effects of different parameters, including contact angle, transmembrane pressure, and membrane microporous structure, on filtration performance were studied. Droplet deformation had a strong impact on filtration behavior because coalesced droplets with diameters larger than the pore diameter permeated the membrane pores. The permeability, oil hold-up fraction inside the pores, and rejection were considerably influenced by the contact angle, while the transmembrane pressure had a little impact on the permeability and oil hold-up fraction. The membrane structure, especially the pore size distribution, also had a significant effect on the microfiltration behavior and performance.

Technology Focus: Digital Data Acquisition (January 2022)

This third installment of the Digital Data Acquisition Technology Focus will focus on computer vision (CV) for improved data acquisition. CV is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos. The field of computer vision has seen significant growth in recent years because of the convergence of multiple events, such as advancements in machine-learning technology (e.g., deep learning), an exponential increase in availability of visual data from mobile phones and platforms such as YouTube, and the growth of new use cases such as autonomous driving. As such, it is no wonder that CV is also having a major effect on the oil and gas industry, reflected in the number of papers this year on this subject alone. The oil and gas industry is awash with image and video data such as core images, seismic profiles, maps, video feeds for monitoring remote operations, and, these days, even drone images. The traditional approach to interpreting and acting on this data has been highly manual, which can be extremely time-consuming and prone to bias and error. CV technology intends to automate these processes and significantly improve the turnaround time from data to decisions. The papers chosen this year include application of CV for reservoir characterization, such as source-rock reconstruction, facies identification, and seismic data enhancement; and safety applications such as rig-state identification and automated corrosion mapping. Given the versatility of CV technology, its application across the oil and gas industry clearly will continue to grow. Recommended additional reading at OnePetro: www.onepetro.org. SPE 204216 - Deep-Learning-Based Vuggy Facies Identification From Borehole Images by Jiajun Jiang, Baylor University, et al. SPE 202710 - Machine-Learning-Based Seismic Data Enhancement Toward Overcoming Geophysical Limitations by Shotaro Nakayama, INPEX, et al. SPE 205347 - Machine-Learning-Assisted Segmentation of Focused Ion Beam Scanning Electron Microscopy Images With Artifacts for Improved Void-Space Characterization of Tight Reservoir Rocks by Andrey Kazak, Skolkovo Institute of Science and Technology, et al.

Microstructure Evolution in a Solid Oxide Fuel Cell Stack Quantified with Interfacial Free Energy

Degradation of electrode microstructure is one of the key factors affecting long term performance of Solid Oxide Fuel Cell systems. Evolution of a multiphase system can be described quantitatively by the change in its interfacial energy. In this paper, we discuss free energy of a microstructure to showcase the anisotropy of its evolution during a long-term performance experiment involving an SOFC stack. Ginzburg Landau type functional is used to compute the free energy, using diffuse phase distributions based on Focused Ion Beam Scanning Electron Microscopy images of samples taken from nine different sites within the stack. It is shown that the rate of microstructure evolution differs depending on the position within the stack, similar to phase anisotropy. However, the computed spatial relation does not correlate with the observed distribution of temperature.

Surfactant Protein B Promotes Cytosolic SiRNA Delivery by Adopting a Virus-like Mechanism of Action.

RNA therapeutics are poised to revolutionize medicine. To unlock the full potential of RNA drugs, safe and efficient (nano)formulations to deliver them inside target cells are required. Endosomal sequestration of nanocarriers represents a major bottleneck in nucleic acid delivery. Gaining more detailed information on the intracellular behavior of RNA nanocarriers is crucial to rationally develop delivery systems with improved therapeutic efficiency. Surfactant protein B (SP-B) is a key component of pulmonary surfactant (PS), essential for mammalian breathing. In contrast to the general belief that PS should be regarded as a barrier for inhaled nanomedicines, we recently discovered the ability of SP-B to promote gene silencing by siRNA-loaded and lipid-coated nanogels. However, the mechanisms governing this process are poorly understood. The major objective of this work was to obtain mechanistic insights into the SP-B-mediated cellular delivery of siRNA. To this end, we combined siRNA knockdown experiments, confocal microscopy, and focused ion beam scanning electron microscopy imaging in an in vitro non-small-cell lung carcinoma model with lipid mixing assays on vesicles that mimic the composition of (intra)cellular membranes. Our work highlights a strong correlation between SP-B-mediated fusion with anionic endosomal membranes and cytosolic siRNA delivery, a mode of action resembling that of certain viruses and virus-derived cell-penetrating peptides. Building on these gained insights, we optimized the SP-B proteolipid composition, which dramatically improved delivery efficiency. Altogether, our work provides a mechanistic understanding of SP-B-induced perturbation of intracellular membranes, offering opportunities to fuel the rational design of SP-B-inspired RNA nanoformulations for inhalation therapy.

Machine-Learning-Assisted Segmentation of Focused Ion Beam-Scanning Electron Microscopy Images with Artifacts for Improved Void-Space Characterization of Tight Reservoir Rocks

SummaryThe modern focused ion beam-scanning electron microscopy (FIB-SEM) allows imaging of nanoporous tight reservoir-rock samples in 3D at a resolution up to 3 nm/voxel. Correct porosity determination from FIB-SEM images requires fast and robust segmentation. However, the quality and efficient segmentation of FIB-SEM images is still a complicated and challenging task. Typically, a trained operator spends days or weeks in subjective and semimanual labeling of a single FIB-SEM data set. The presence of FIB-SEM artifacts, such as porebacks, requires developing a new methodology for efficient image segmentation. We have developed a method for simplification of multimodal segmentation of FIB-SEM data sets using machine-learning (ML)-based techniques.We study a collection of rock samples formed according to the petrophysical interpretation of well logs from a complex tight gas reservoir rock of the Berezov Formation (West Siberia, Russia). The core samples were passed through a multiscale imaging workflow for pore-space-structure upscaling from nanometer to log scale. FIB-SEM imaging resolved the finest scale using a dual-beam analytical system. Image segmentation used an architecture derived from a convolutional neural network (CNN) in the DeepUNet (Ronneberger et al. 2015) configuration. We implemented the solution in the Pytorch® (Facebook, Inc., Menlo Park, California, USA) framework in a Linux environment. Computation exploited a high-performance computing system.The acquired data included three 3D FIB-SEM data sets with a physical size of approximately 20 × 15 × 25 µm with a voxel size of 5 nm. A professional geologist manually segmented (labeled) a fraction of slices. We split the labeled slices into training, validation, and test data. We then augmented the training data to increase its size. The developed CNN delivered promising results. The model performed automatic segmentation with the following average quality indicators according to test data: accuracy of 86.66%, precision of 54.93%, recall of 83.76%, and F1 score of 55.10%. We achieved a significant boost in segmentation speed of 14.5 megapixel (MP)/min. Compared with 0.18 to 1.45 MP/min for manual labeling, this yielded an efficiency increase of at least 10 times.The presented research work improves the quality of quantitative petrophysical characterization of complex reservoir rocks using digital rock imaging. The development allows the multiphase segmentation of 3D FIB-SEM data complicated with artifacts. It delivers correct and precise pore-space segmentation, resulting in little turn-around-time saving and increased porosity-data quality. Although image segmentation using CNNs is mainstream in the modern ML world, it is an emerging novel approach for reservoir-characterization tasks.

Multiple length-scale microstructural characterisation of four grades of fine-grained graphite

Multiple length-scale microstructural characterisation has been conducted on four different grades of unirradiated fine-grained graphite: CL 2020, IG 430, SGL R7650 and POCO ZXF-5Q, which are the potential candidates as high-power proton beam intercepting materials (secondary particle-production targets). Micrometre and sub-micrometre sized porosity structures have been characterised by X-ray micro-computed tomography (µXCT) and focused ion beam-scanning electron microscopy tomography (FIB-SEM tomography). Results show that these four grades of graphite have distinct porosity structures with a substantial amount of interconnected pores; POCO-ZXF-5Q has the most uniform porosity size and spatial distribution out of the four at these length scales examined. As a direct measurement of the total number and volume of nano-sized Mrozowski cracks is currently not readily feasible, the characteristic crystalline size La reflecting the coherent scattering length at nano-scale was evaluated using micro-Raman spectroscopy. It has been found that a significant reduction in average characteristic crystalline size had been induced by sample preparation. Further measurements on freshly fractured sample surfaces excluding these damages suggested that IG430 graphite has the largest average crystallite size (~290 nm) while POCO-ZXF-5Q has the smallest average crystallite size (~80 nm) with a narrow distribution. These results from un-irradiated graphite were used to further understand the proton irradiated behaviour of these four grades of graphite.

Simulating permeability reduction by clay mineral nanopores in a tight sandstone by combining computer X-ray microtomography and focussed ion beam scanning electron microscopy imaging

Abstract. Computer X-ray microtomography (µXCT) represents a powerful tool for investigating the physical properties of porous rocks. While calculated porosities determined by this method typically match experimental measurements, computed permeabilities are often overestimated by more than 1 order of magnitude. This effect increases towards smaller pore sizes, as shown in this study, in which nanostructural features related to clay minerals reduce the permeability of tight reservoir sandstone samples. Focussed ion beam scanning electron microscopy (FIB-SEM) tomography was applied to determine the permeability effects of illites at the nanometre scale, and Navier–Stokes equations were applied to calculate the permeability of these domains. With these data, microporous domains (porous voxels) were defined using microtomography images of a tight reservoir sample. The distribution of these domains could be extrapolated by calibration against size distributions measured in FIB-SEM images. For this, we assumed a mean permeability for the dominant clay mineral (illite) in the rock and assigned it to the microporous domains within the structure. The results prove the applicability of our novel approach by combining FIB-SEM with X-ray tomographic rock core scans to achieve a good correspondence between measured and simulated permeabilities. This methodology results in a more accurate representation of reservoir rock permeability in comparison to that estimated purely based on µXCT images.

Correlation of organelle dynamics between light microscopic live imaging and electron microscopic 3D architecture using FIB-SEM

Correlative light and electron microscopy (CLEM) methods combined with live imaging can be applied to understand the dynamics of organelles. Although recent advances in cell biology and light microscopy have helped in visualizing the details of organelle activities, observing their ultrastructure or organization of surrounding microenvironments is a challenging task. Therefore, CLEM, which allows us to observe the same area as an optical microscope with an electron microscope, has become a key technique in cell biology. Unfortunately, most CLEM methods have technical drawbacks, and many researchers face difficulties in applying CLEM methods. Here, we propose a live three-dimensional CLEM method, combined with a three-dimensional reconstruction technique using focused ion beam scanning electron microscopy tomography, as a solution to such technical barriers. We review our method, the associated technical limitations and the options considered to perform live CLEM.

Multi-scale characterization of ceramic inert-substrate-supported and co-sintered solid oxide fuel cells

Understanding cell performance is essential for selecting cell components and the processing parameters for solid oxide fuel cells. The scale of relevant microstructural features in electrodes, electrolyte and supporting substrate covers several orders of magnitude. This contribution will demonstrate how advanced correlative multi-scale tomography can be used to identify those parameters: ranging from millimeter to nanometer scale. We employ optical microscopy, X-ray computed tomography (μ-CT), focused ion beam-scanning electron microscopy tomography and energy-dispersive X-ray spectroscopy–scanning transmission electron microscopy. Additional investigations by selected area electron diffraction allow a determination of the underlying crystal structures. An SOFC design based on the co-sintering of an inert substrate with various functional layers on top is used as a blueprint, allowing further methodological development. The effect of interdiffusion between phases and development of secondary phases on microstructure and chemical composition will be shown. Furthermore, porosity and tortuosity extracted individually from all porous layers will allow modeling of gas diffusion loss contributions within the co-fired cell structure. This exemplifies how correlative tomography helps to understand specific contributions to overall cell performance.

Tomographic Reconstruction and Analysis of a Silver CO2 Reduction Cathode

AbstractTomographic reconstruction has been well established as a valuable tool in the analysis of polymer electrolyte fuel cell (PEMFC) electrodes. While forays have been made into applying it to polymer electrolyte water electrolyzer (PEMWE) electrodes, CO2 electrolyzer electrodes are still new ground. Here a tomographic analysis of an electrochemical CO2 reduction gas diffusion electrode by means of focused ion beam scanning electron microscope tomography is presented. The reconstruction shows a porosity of 68%. While most of the porosity is on the nanoscale, a broad tail of micropores is observed in the distribution. The spatial distribution of the pores is nonuniform. The large pores are concentrated in the center of the layer in the through‐plane direction. From the reconstruction, an effective diffusivity factor of 0.5 for the catalyst layer is calculated. The Knudsen number of 0.19 obtained from the later shows that the diffusion is mostly in the bulk regime. Flooding of the catalyst layer is likely to decrease the effective diffusivity factor substantially.

Increased intracellular survival of Salmonella Typhimurium ST313 in HIV-1-infected primary human macrophages is not associated with Salmonella hijacking the HIV compartment.

Non-typhoidal Salmonella (NTS) causes a severe invasive syndrome (iNTS disease) described in HIV-positive adults. The impact of HIV-1 on Salmonella pathogenesis and the molecular basis for the differences between these bacteria and classical diarrhoeal S. Typhimurium remains unclear. Here, we show that iNTS-associated S. Typhimurium Sequence Type 313 (ST313) bacteria show greater intracellular survival in primary human macrophages, compared with a 'classical' diarrhoeal S. Typhimurium ST19 isolate. The increased intracellular survival phenotype of ST313 is more pronounced in HIV-infected macrophages. We explored the possibility that the bacteria take advantage of the HIV-associated viral-containing compartments created in human macrophages that have low pH. Confocal fluorescence microscopy and focussed ion beam-scanning electron microscopy tomography showed that Salmonella did not co-localise extensively with HIV-positive compartments. The capacity of ST313 bacteria to survive better than ST19 bacteria within primary human macrophages is enhanced in cells pre-infected with HIV-1. Our results indicate that the ST313 bacteria do not directly benefit from the niche created by the virus in HIV-1-infected macrophages, and that they might take advantage from a more globally modified host cell. A better understanding of the interplay between HIV-1 and Salmonella is important not only for these bacteria but also for other opportunistic pathogens.

FIB-SEM-based analysis of Borrelia intracellular processing by human macrophages.

Borrelia burgdorferi is the causative agent of Lyme disease, a multisystemic disorder affecting primarily skin, joints and nervous system. Successful internalization and intracellular processing of borreliae by immune cells, like macrophages, is decisive for the outcome of a respective infection. Here, we use, for the first time, focused ion beam scanning electron microscopy tomography (FIB-SEM tomography) to visualize the interaction of borreliae with primary human macrophages with high resolution. We report that interaction between macrophages and the elongated and highly motile borreliae can lead to formation of membrane tunnels that extend deeper into the host cytoplasm than the actual phagosome, most probably as a result of partial extrication of captured borreliae. We also show that membrane tubulation at borreliae-containing phagosomes, a process suggested earlier as a mechanism leading to phagosome compaction but hard to visualize in live-cell imaging, is apparently a frequent phenomenon. Finally, we demonstrate that the endoplasmic reticulum (ER) forms multiple STIM1-positive contact sites with both membrane tunnels and phagosome tubulations, confirming the important role of the ER during uptake and intracellular processing of borreliae.