Project description:Scanning electron microscopy (SEM) is a powerful tool for structural analysis, but it requires biological samples to undergo lengthy, chemically-complex multi-step preparation procedures, arguably altering some features in the sample. Here we report an ultra-rapid and chemical-free technique for visualizing bacterial biofilms at their native state. Our technique minimizes the time interval from culture to imaging to approximately 20 min, while producing high-resolution images that enable the detection of a variety of topographic features such as bacterial chains, and resolving cells from matrix. We analyzed images obtained from Bacillus subtilis biofilms, demonstrate the usefulness of this technique for multiple types of image analysis, and discuss its potential to be improved and adapted to other types of biological samples.
Project description:Scanning electron microscopy (SEM) is a crucial tool for analyzing submicron-scale structures. However, the attainment of high-quality SEM images is contingent upon the high conductivity of the material due to constraints imposed by its imaging principles. For weakly conductive materials or structures induced by intrinsic properties or organic doping, the SEM imaging quality is significantly compromised, thereby impeding the accuracy of subsequent structure-related analyses. Moreover, the unavailability of paired high-low quality images in this context renders the supervised-based image processing methods ineffective in addressing this challenge. Here, an unsupervised method based on Cycle-consistent Generative Adversarial Network (CycleGAN) was proposed to enhance the quality of SEM images for weakly conductive samples. The unsupervised model can perform end-to-end learning using unpaired blurred and clear SEM images from weakly and well-conductive samples, respectively. To address the requirements of material structure analysis, an edge loss function was further introduced to recover finer details in the network-generated images. Various quantitative evaluations substantiate the efficacy of the proposed method in SEM image quality improvement with better performance than the traditional methods. Our framework broadens the application of artificial intelligence in materials analysis, holding significant implications in fields such as materials science and image restoration.
Project description:The presence and configurations of defects are primary components determining materials functionality. Their population and distribution are often nonergodic and dependent on synthesis history, and therefore rarely amenable to direct theoretical prediction. Here, dynamic electron beam-induced transformations in Si deposited on a graphene monolayer are used to create libraries of possible Si and carbon vacancy defects. Deep learning networks are developed for automated image analysis and recognition of the defects, creating a library of (meta) stable defect configurations. Density functional theory is used to estimate atomically resolved scanning tunneling microscopy (STM) signatures of the classified defects from the created library, allowing identification of several defect types across imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically resolved techniques, providing comprehensive insight into defect functionalities.
Project description:The absence of centrosymmetry in chiral and polar crystal structures is the reason for many technical relevant physical properties like optical birefringence or ferroelectricity. Other chirality related properties that are actually intensively investigated are unconventional superconductivity or unusual magnetic ordering like skyrmions in materials with B20 structure. Despite the often close crystal structure - property relation, its detection is often challenging due to superposition of domains with different absolute structure e.g. chirality. Our investigations of high quality CoSi crystals with B20 structure by both complementary methods X- ray (volume sensitive) and electron backscatter diffraction (EBSD) (surface sensitive) results the consistent assignment of the chirality and reveal fundamental differences in their sensitivity to chirality. The analysis of the surface of a CoSi crystal with domains of different chirality show the high spatial resolution of this method which opens the possibility to analyze the chirality in microstructures of technical relevant materials like thin films and catalysts.
Project description:The ability to localize proteins precisely within subcellular space is crucial to understanding the functioning of biological systems. Recently, we described a protocol that correlates a precise map of fluorescent fusion proteins localized using three-dimensional super-resolution optical microscopy with the fine ultrastructural context of three-dimensional electron micrographs. While it achieved the difficult simultaneous objectives of high photoactivated fluorophore preservation and ultrastructure preservation, it required a super-resolution optical and specialized electron microscope that is not available to many researchers. We present here a faster and more practical protocol with the advantage of a simpler two-dimensional optical (Photoactivated Localization Microscopy (PALM)) and scanning electron microscope (SEM) system that retains the often mutually exclusive attributes of fluorophore preservation and ultrastructure preservation. As before, cryosections were prepared using the Tokuyasu protocol, but the staining protocol was modified to be amenable for use in a standard SEM without the need for focused ion beam ablation. We show the versatility of this technique by labeling different cellular compartments and structures including mitochondrial nucleoids, peroxisomes, and the nuclear lamina. We also demonstrate simultaneous two-color PALM imaging with correlated electron micrographs. Lastly, this technique can be used with small-molecule dyes as demonstrated with actin labeling using phalloidin conjugated to a caged dye. By retaining the dense protein labeling expected for super-resolution microscopy combined with ultrastructural preservation, simplifying the tools required for correlative microscopy, and expanding the number of useful labels we expect this method to be accessible and valuable to a wide variety of researchers.
Project description:Human islet primary cilia are vital glucose-regulating organelles whose structure remains uncharacterized. Scanning electron microscopy (SEM) is a useful technique for studying the surface morphology of membrane projections like cilia, but conventional sample preparation does not reveal the submembrane axonemal structure, which holds key implications for ciliary function. To overcome this challenge, we combined SEM with membrane-extraction techniques to examine primary cilia in native human islets. Our data show well-preserved cilia subdomains which demonstrate both expected and unexpected ultrastructural motifs. Morphometric features were quantified when possible, including axonemal length and diameter, microtubule conformations, and chirality. We further describe a ciliary ring, a structure that may be a specialization in human islets. Key findings are correlated with fluorescence microscopy and interpreted in the context of cilia function as a cellular sensor and communications locus in pancreatic islets.
Project description:Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512?×?512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.
Project description:This study describes a novel type of interstitial (stromal) cell - telocytes (TCs) - in the human and mouse respiratory tree (terminal and respiratory bronchioles, as well as alveolar ducts). TCs have recently been described in pleura, epicardium, myocardium, endocardium, intestine, uterus, pancreas, mammary gland, etc. (see www.telocytes.com ). TCs are cells with specific prolongations called telopodes (Tp), frequently two to three per cell. Tp are very long prolongations (tens up to hundreds of μm) built of alternating thin segments known as podomers (≤ 200 nm, below the resolving power of light microscope) and dilated segments called podoms, which accommodate mitochondria, rough endoplasmic reticulum and caveolae. Tp ramify dichotomously, making a 3-dimensional network with complex homo- and heterocellular junctions. Confocal microscopy reveals that TCs are c-kit- and CD34-positive. Tp release shed vesicles or exosomes, sending macromolecular signals to neighboring cells and eventually modifying their transcriptional activity. At bronchoalveolar junctions, TCs have been observed in close association with putative stem cells (SCs) in the subepithelial stroma. SCs are recognized by their ultrastructure and Sca-1 positivity. Tp surround SCs, forming complex TC-SC niches (TC-SCNs). Electron tomography allows the identification of bridging nanostructures, which connect Tp with SCs. In conclusion, this study shows the presence of TCs in lungs and identifies a TC-SC tandem in subepithelial niches of the bronchiolar tree. In TC-SCNs, the synergy of TCs and SCs may be based on nanocontacts and shed vesicles.
Project description:Transmission electron microscopy is a pivotal instrument in materials and biological sciences due to its ability to provide local structural and spectroscopic information on a wide range of materials. However, the electron detectors used in scanning transmission electron microscopy are often unable to provide quantified information, that is the number of electrons impacting the detector, without exhaustive calibration and processing. This results in arbitrary signal values with slow response times that cannot be used for quantification or comparison to simulations. Here we demonstrate and optimise a hardware signal processing approach to augment electron detectors to perform single electron counting.