Imaging Of Biological Structures Research Articles

2D and 3D visualization of herbaceous plant–plant contact zones using high‐resolution X‐ray computed tomography (HRXCT)

Societal Impact StatementParasitic plants that deprive crops of water and nutrients are an increasingly concerning food security issue, affecting the livelihood of millions of subsistence, small‐ and mid‐scale farmers. An in‐depth understanding of parasite–host interactions is required to develop species‐specific and ecologically sustainable parasite management methods. The non‐invasive visualization of herbaceous contact zones, applicable to diverse parasite–host pathosystems presented in this study, brings methodological advance to the research of biotic interactions between crops and plant parasites belonging to the most devastating parasitic plant family (Orobanchaceae). This work also provides first insights into how the parasites' feeding organ displaces host tissue beyond the direct parasite–host interface.SummaryHigh‐resolution X‐ray computed tomography (HRXCT) enables sectioning‐free two‐dimensional imaging of biological structures and reconstruction of three‐dimensional objects. Although its application is common in many areas of biomedicine and despite its flexibility regarding resolution levels, the technology remains underutilized in the plant sciences. Here, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution. We tested various sample preparation methods and contrast stains for their efficiency to improve the imaging of haustorium samples. In doing so, we achieved cellular resolution with the visible cellular organization of haustorial structures, especially of the vascular system. Fresh stained and dehydrated sample preparation of soft haustoria enables the highest spatial resolution with fine‐cellular discrimination of haustorium versus host cells. Application of cell‐level resolved HRXCT to five pathosystems: Alectra‐cowpea, Phelipanche‐tomato, Phtheirospermum‐tomato, Rhamphicarpa‐tomato, and Striga‐sorghum highlighted a life history‐specific organization and uncovered an as yet undescribed internal displacement of host tissue at parasite–host interfaces. Following image‐based training, our HRXCT approach could invoke AI‐based cell recognition for automated parasite cell–host cell differentiation. Superseding extensive microsectioning for 3D imaging, the newly established HRXCT protocol for 2D‐ and 3D‐visualization of herbaceous plant–plant contact zones and the first insights gained from it, is useful for mid‐throughput, comparative studies of parasitic plant–host interactions.

Aggregation-Induced Emission (AIE), Life and Health.

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health.

Fluorescence microscopy imaging of a neurotransmitter receptor and its cell membrane lipid milieu.

Hampered by the diffraction phenomenon, as expressed in 1873 by Abbe, applications of optical microscopy to image biological structures were for a long time limited to resolutions above the ∼200nm barrier and restricted to the observation of stained specimens. The introduction of fluorescence was a game changer, and since its inception it became the gold standard technique in biological microscopy. The plasma membrane is a tenuous envelope of 4nm-10nm in thickness surrounding the cell. Because of its highly versatile spectroscopic properties and availability of suitable instrumentation, fluorescence techniques epitomize the current approach to study this delicate structure and its molecular constituents. The wide spectral range covered by fluorescence, intimately linked to the availability of appropriate intrinsic and extrinsic probes, provides the ability to dissect membrane constituents at the molecular scale in the spatial domain. In addition, the time resolution capabilities of fluorescence methods provide complementary high precision for studying the behavior of membrane molecules in the time domain. This review illustrates the value of various fluorescence techniques to extract information on the topography and motion of plasma membrane receptors. To this end I resort to a paradigmatic membrane-bound neurotransmitter receptor, the nicotinic acetylcholine receptor (nAChR). The structural and dynamic picture emerging from studies of this prototypic pentameric ligand-gated ion channel can be extrapolated not only to other members of this superfamily of ion channels but to other membrane-bound proteins. I also briefly discuss the various emerging techniques in the field of biomembrane labeling with new organic chemistry strategies oriented to applications in fluorescence nanoscopy, the form of fluorescence microscopy that is expanding the depth and scope of interrogation of membrane-associated phenomena.

Carbazole-based fluorescent probe with a Large Stokes Shift for three-dimensional dynamic ultra-structures to monitor mitochondria in living cells

In the past few years, super‐resolution microscopy techniques have already allowed imaging of biological structures at nanometer scale resolution. Nevertheless, due to the strict requirements of super-resolution optical conditions, fluorescent probes targeting specific mitochondrial structures, such as cristae, are still lacking, which constitutes a major challenge in studying mitochondrial biology. In this paper, we rationally constructed a pyridine-carbazole-based fluorescent probe that targets mitochondria with high specificity without compromising fluorescence under live, fixation and oxidative phosphorylation condition. In addition, its excellent optical properties (Large Stokes Shift, mitochondria-specific targeting under different conditions) allowed to monitor mitochondrial morphological changes in living cells using SIM super-resolution nanoscopy.

Optimized single-step optical clearing solution for 3D volume imaging of biological structures

Various optical clearing approaches have been introduced to meet the growing demand for 3D volume imaging of biological structures. Each has its own strengths but still suffers from low transparency, long incubation time, processing complexity, tissue deformation, or fluorescence quenching, and a single solution that best satisfies all aspects has yet been developed. Here, we develop OptiMuS, an optimized single-step solution that overcomes the shortcomings of the existing aqueous-based clearing methods and that provides the best performance in terms of transparency, clearing rate, and size retention. OptiMuS achieves rapid and high transparency of brain tissues and other intact organs while preserving the size and fluorescent signal of the tissues. Moreover, OptiMuS is compatible with the use of lipophilic dyes, revealing DiI-labeled vascular structures of the whole brain, kidney, spleen, and intestine, and is also applied to 3D quantitative and comparative analysis of DiI-labeled vascular structures of glomeruli turfs in normal and diseased kidneys. Together, OptiMuS provides a single-step solution for simple, fast, and versatile optical clearing method to obtain high tissue transparency with minimum structural changes and is widely applicable for 3D imaging of various whole biological structures.

Laboratory for biomolecular structure (LBMS): a DOE-funded national cryo-EM center

To advance our understanding of biological processes and complexity, cryo-electron microscopy (Cryo-EM) is becoming a preferred method for studying structures of biological macromolecules and high-order machinery. However, for many institutions and research groups, acquiring and operating a state-of-the-art cryo-EM facility remain cost prohibitive. With the joint support from the New York State and the Department of Energy (DOE), the Laboratory for BioMolecular Structure (LBMS) at Brookhaven National Laboratory, a national cryo-EM center, provides cutting-edge instruments and operations for imaging biological structures and processes.

A New Framework for Accelerating Magnetic Resonance Imaging using Deep Learning along with HPC Parallel Computing Technologies

MRI (magnetic resource imaging) has played a vital role in emerging technologies because of its non-invasion principle. MR equipment is traditional procedure being used for imaging biological structures. In medical domain, MRI is a most important tool being used for staging in clinical diagnosis that has ability to furnish rich physiological and functional information and radiation and non-ionizing nature. However, MRI is highly demanding in several clinical applications. In this paper, we have proposed a novel deep learning based method that accelerates MRI using a huge number of MR images. In proposed method, we used supervised learning approach that performs network training of given datasets. It determines the required network parameters that afford an accurate reconstruction of under-sampled acquisitions. We also designed offline based neural network (NN) that was trained to discover the relationship between MR images and K-space. All the experiments were performed over advanced NVIDIA GPUs (Tesla k80 and GTX Titan) based computers. It was observed that the proposed model outperformed and attained <0.2% error rate. With our best knowledge, our method is the best approach that can be considered as leading model in future.

Single-molecule localization microscopy.

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM.

Image-Based Polygonal Lattices for Mechanical Modeling of Biological Materials: 2D Demonstrations.

Understanding the structure-property relationship of biological materials, such as bones, teeth, cells, and biofilms, is critical for diagnosing diseases and developing bioinspired materials and structures. The intrinsic multiphase heterogeneity with interfaces places great challenges for mechanical modeling. Here, we develop an image-based polygonal lattice model for simulating the mechanical deformation of biological materials with complicated shapes and interfaces. The proposed lattice model maintains the uniform meshes inside the homogeneous phases and restricts the irregular polygonal meshes near the boundaries or interfaces. This approach significantly simplifies the mesh generation from images of biological structures with complicated geometries. The conventional finite element simulations validate this polygonal lattice model. We further demonstrate that the image-based polygonal lattices generate meshes from images of composite structures with multiple inclusions and capture the nonlinear mechanical deformation. We conclude the paper by highlighting a few future research directions that will benefit from the functionalities of polygonal lattice modeling.

Optical coherence tomography's current clinical medical and dental applications: a review.

Optical coherence tomography (OCT) is a non-invasive investigative technique that is used to obtain high-resolution three-dimensional (3D) images of biological structures. This method is useful in diagnosing diseases of specific organs like the eye, where a direct biopsy cannot be conducted. Since its inception, significant advancements have been made in its technology. Apart from its initial application in ophthalmology for retinal imaging, substantial technological innovations in OCT brought by the research community have enabled its utilization beyond its original scope and allowed its application in many new clinical areas. This review presents a summary of the clinical applications of OCT in the field of medicine (ophthalmology, cardiology, otology, and dermatology) and dentistry (tissue imaging, detection of caries, analysis of dental polymer composite restorations, imaging of root canals, and diagnosis of oral cancer). In addition, potential advantages and disadvantages of OCT are also discussed.

A novel constrained reconstruction model towards high-resolution subtomogram averaging.

Electron tomography (ET) offers a unique capacity to image biological structures in situ. However, the resolution of ET reconstructed tomograms is not comparable to that of the single-particle cryo-EM. If many copies of the object of interest are present in the tomograms, their structures can be reconstructed in the tomogram, picked, aligned and averaged to increase the signal-to-noise ratio and improve the resolution, which is known as the subtomogram averaging. To date, the resolution improvement of the subtomogram averaging is still limited because each reconstructed subtomogram is of low reconstruction quality due to the missing wedge issue. In this article, we propose a novel computational model, the constrained reconstruction model (CRM), to better recover the information from the multiple subtomograms and compensate for the missing wedge issue in each of them. CRM is supposed to produce a refined reconstruction in the final turn of subtomogram averaging after alignment, instead of directly taking the average. We first formulate the averaging method and our CRM as linear systems, and prove that the solution space of CRM is no larger, and in practice much smaller, than that of the averaging method. We then propose a sparse Kaczmarz algorithm to solve the formulated CRM, and further extend the solution to the simultaneous algebraic reconstruction technique (SART). Experimental results demonstrate that CRM can significantly alleviate the missing wedge issue and improve the final reconstruction quality. In addition, our model is robust to the number of images in each tilt series, the tilt range and the noise level. The codes of CRM-SIRT and CRM-SART are available at https://github.com/icthrm/CRM. Supplementary data are available at Bioinformatics online.

Label-free CARS microscopy through a multimode fiber endoscope.

Multimode fibres have recently been employed as high-resolution ultra-thin endoscopes, capable of imaging biological structures deep inside tissue in vivo. Here, we extend this technique to label-free non-linear microscopy with chemical contrast using coherent anti-Stokes Raman scattering (CARS) through a multimode fibre endoscope, which opens up new avenues for instant and in-situ diagnosis of potentially malignant tissue. We use a commercial 125 µm diameter, 0.29 NA GRIN fibre, and wavefront shaping on an SLM is used to create foci that are scanned behind the fibre facet across the sample. The chemical selectivity is demonstrated by imaging 2 µm polystyrene and 2.5 µm PMMA beads with per pixel integration time as low as 1 ms for epi-detection.

Superresolution microscopy reveals distinct localisation of full length IRSp53 and its I-BAR domain protein within filopodia

Superresolution microscopy offers the advantage of imaging biological structures within cells at the nano-scale. Here we apply two superresolution microscopy techniques, specifically 3D structured illumination microscopy (3D-SIM) and direct stochastic optical reconstruction microscopy (dSTORM), a type of single molecule localisation microscopy, to localise IRSp53 protein and its I-BAR domain in relation to F-actin within filopodia. IRSp53 generates dynamic (extending and retracting) filopodia 300 nm wide with a distinct gap between IRSp53 and F-actin. By contrast, protrusions induced by the I-BAR domain alone are non-dynamic measuring between 100–200 nm in width and exhibit a comparatively closer localisation of the I-BAR domain with the F-actin. The data suggest that IRSp53 membrane localisation is spatially segregated to the lateral edges of filopodia, in contrast to the I-BAR domain is uniformly distributed throughout the membranes of protrusions. Modeling of fluorescence recovery after photobleaching (FRAP) data suggests that a greater proportion of I-BAR domain is associated with membranes when compared to full length IRSp53. The significance of this new data relates to the role filopodia play in cell migration and its importance to cancer.

Self-referenced axial chromatic dispersion measurement in multiphoton microscopy through 2-color third-harmonic generation imaging.

With tunable excitation light, multiphoton microscopy is widely used for imaging biological structures at subcellular resolution. Axial chromatic dispersion, present in virtually every transmissive optical system including the multiphoton microscope, leads to focal (and the resultant image) plane separation. Here, we experimentally demonstrate a technique to measure the axial chromatic dispersion in a multiphoton microscope, using simultaneous 2-color third-harmonic generation imaging excited by a 2-color soliton source with tunable wavelength separation. Our technique is self-referenced, eliminating potential measurement error when 1-color tunable excitation light is used which necessitates reciprocating motion of the mechanical translation stage. Using this technique, we demonstrate measured axial chromatic dispersion with 2 different objective lenses in a multiphoton microscope. Further measurement in a biological sample also indicates that this axial chromatic dispersion, in combination with 2-color imaging, may open up opportunity for simultaneous imaging of 2 different axial planes.

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization.

Medical image registration establishes a correspondence between images of biological structures, and it is at the core of many applications. Commonly used deformable image registration methods depend on a good preregistration initialization. We develop a learning-based method to automatically find a set of robust landmarks in three-dimensional MR image volumes of the head. These landmarks are then used to compute a thin plate spline-based initialization transformation. The process involves two steps: (1)identifying a set of landmarks that can be reliably localized in the images and (2)selecting among them the subset that leads to a good initial transformation. To validate our method, we use it to initialize five well-established deformable registration algorithms that are subsequently used to register an atlas to MR images of the head. We compare our proposed initialization method with a standard approach that involves estimating an affine transformation with an intensity-based approach. We show that for all five registration algorithms the final registration results are statistically better when they are initialized with the method that we propose than when a standard approach is used. The technique that we propose is generic and could be used to initialize nonrigid registration algorithms for other applications.

Cryo-electron microscopy images battery dendrites

An analytical technique famous for imaging biological structures in exquisite detail can do a heck of a job when adapted to energy storage materials, according to a study. A research team led by Stanford University materials scientists Yuzhang Li, Yanbin Li, and Yi Cui applied cryo-transmission electron microscopy (cryo-TEM), the subject of the 2017 Nobel Prize in Chemistry, to study formation of needlelike lithium dendrites inside lithium-ion batteries (Science 2017, DOI: 10.1126/science.aam6014). Deposition of lithium during charging cycles can lead to dendrites that grow large enough to pierce a battery’s insulating separator and make contact with both of its electrodes. That process short-circuits the battery, causing it to fail and occasionally burst into flames. Standard methods for loading a specimen into a microscope for TEM analysis expose the sample to air. In the case of room-temperature lithium dendrites, that process causes corrosion, which irreversibly changes their structure and composition. To

Automatic selection of landmarks in T1-weighted head MRI with regression forests for image registration initialization.

Medical image registration establishes a correspondence between images of biological structures and it is at the core of many applications. Commonly used deformable image registration methods are dependent on a good preregistration initialization. The initialization can be performed by localizing homologous landmarks and calculating a point-based transformation between the images. The selection of landmarks is however important. In this work, we present a learning-based method to automatically find a set of robust landmarks in 3D MR image volumes of the head to initialize non-rigid transformations. To validate our method, these selected landmarks are localized in unknown image volumes and they are used to compute a smoothing thin-plate splines transformation that registers the atlas to the volumes. The transformed atlas image is then used as the preregistration initialization of an intensity-based non-rigid registration algorithm. We show that the registration accuracy of this algorithm is statistically significantly improved when using the presented registration initialization over a standard intensity-based affine registration.

Statistical dispersion relation for spatially broadband fields.

The dispersion relation is fundamental to a physical phenomenon that develops in both space and time. This equation connects the spatial and temporal frequencies involved in the dynamic process through the material constants. Electromagnetic plane waves propagating in homogeneous media are bound by simple dispersion relation, which sets the magnitude of the spatial frequency, k, as being proportional to the temporal frequency, ω, with the proportionality constant dependent on the refractive index, n, and the speed of light in vacuum, c. Here we show that, for spatially broadband fields, an analog dispersion relation can be derived, except in this case the k-vector variance is connected with the temporal frequency through the statistics of the refractive index fluctuations in the medium. We discuss how this relationship can be used to retrieve information about refractive index distributions in biological tissues. This result is particularly significant in measurements of angular light scattering and quantitative phase imaging of biological structures.

Innovative molecular-based fluorescent nanoparticles for multicolor single particle tracking in cells

Based on an original molecular-based design, we present bright and photostable fluorescent organic nanoparticles (FONs) showing excellent colloidal stability in various aqueous environments. Complementary near-infrared emitting and green emitting FONs were prepared using a simple, fast and robust protocol. Both types of FONs could be simultaneously imaged at the single-particle level in solution as well as in biological environments using a monochromatic excitation and a dual-color fluorescence microscope. No evidence of acute cytotoxicity was found upon incubation of live cells with mixed solutions of FONs, and both types of nanoparticles were found internalized in the cells where their motion could be simultaneously tracked at video-rate up to minutes. These fluorescent organic nanoparticles open a novel non-toxic alternative to existing nanoparticles for imaging biological structures, compatible with live-cell experiments and specially fitted for multicolor single particle tracking.

Analyzing Macromolecular Complexes in Situ Using Cellular Cryo‐Electron Microscopy

Cryo‐electron tomography (cryo‐ET) is a powerful technique for imaging biological structures in their native state and in an unperturbed cellular environment. We integrate high resolution imaging by either cryo‐ET and sub‐tomogram averaging or TYGRESS (Tomography‐Guided 3D Reconstruction of Subcellular Structures), with comparative genetics, biochemical methods and EM‐visible labeling to deconstruct the in situ 3D structure and functional organization of macromolecular complexes. We use cilia and flagella as model systems to advance techniques and approaches for high‐resolution imaging of complex cellular structures. Cilia and flagella are ubiquitous organelles with important biological roles in motility and sensation; defects in their assembly or function cause severe human diseases. The normal beating of cilia requires the precise coordination of thousands of dynein motors, but much remains to be learned about dynein's motility and regulation. We are in the process of generating a molecular blueprint of major ciliary complexes to gain a better understanding of the inner workings of these intriguing nano‐machines.