3D Microscopy Images Research Articles

In vitro and in silico approaches to engineering three-dimensional biological tissues and organoids

<i>In vitro</i> and <i>in silico</i> approaches to engineering three-dimensional biological tissues and organoids

Super-resolution fluorescence blinking imaging using compressive sensing

Super-resolution optical fluctuation imaging (SOFI) is an extensively used super-resolution (SR) imaging technique. The sample condition and imaging system are simpler than most SR systems, and the cusp-artifacts limit the resolution of high-order SOFI. To improve the resolution of SOFI, an alternative method is to improve the reconstruction algorithm. Here, compressive sensing (CS) is used in SOFI to improve its resolution. The detailed CS reconstruction algorithm is chosen as multiple measurement vector model sparse Bayesian learning (MSBL). We demonstrate that MSBL can achieve higher than threefold resolution improvement in simulation under certain conditions. The SOFI experiment analysis demonstrates that MSBL can improve the resolution about 2.5-fold compared with the diffraction limit. All results prove that MSBL has a higher resolving ability compared with other algorithms. Our results prove that CS is a broad applicability tool for most of the existing SR microscopy techniques and can be applied in 3D and live-cell SR fluorescence microscopy imaging in the future.

An Optical and Chemiluminescence Assay for Assessing the Cytotoxicity of Balamuthia mandrillaris against Human Neurospheroids.

A spheroid is a cell aggregate in a three-dimensional context; thereby, it recapitulates the cellular architecture in human tissue. However, the utility of spheroids as an assay for host–parasite interactions remains unexplored. This study demonstrates the potential use of neurospheroids for assessing the cytotoxicity of the life-threatening pathogenic amoeba Balamuthia mandrillaris. The neuroblastoma SH-SY5Y cells formed a spheroid in a hanging drop of culture medium. Cellular damage caused by B. mandrillaris trophozoites on human neuronal spheroids was observed using microscopic imaging and ATP detection. B. mandrillaris trophozoites rapidly caused a decrease in ATP production in the spheroid, leading to loss of neurospheroid integrity. Moreover, 3D confocal microscopy imaging revealed interactions between the trophozoites and SH-SY5Y neuronal cells in the outer layer of the neurospheroid. In conclusion, the neurospheroid allows the assessment of host cell damage in a simple and quantitative manner.

Synthetic Generation of 3D Microscopy Images using Generative Adversarial Networks.

Fluorescence microscopy images of cell organelles enable the study of various complex biological processes. Recently, deep learning (DL) models are being used for the accurate automatic analysis of these images. DL models present state-of-the-art performance in many image analysis tasks such as object classification, segmentation and detection. However, to train a DL model a large manually annotated dataset is required. Manual annotation of 3D microscopy images is a time-consuming task and must be performed by specialists in the area. Thus, only a few images with annotations are typically available. Recent advances in generative adversarial networks (GANs) have allowed the translation of images with some conditions into realistic looking synthetic images. Therefore, in this work we explore approaches based on GANs to create synthetic 3D microscopy images. We compare four approaches that differ in the conditions of the input image. The quality of the generated images was assessed visually and using a quantitative objective GAN evaluation metric. The results showed that the GAN is able to generate synthetic images similar to the real ones. Hence, we have presented a method based on GANs to overcome the issue of small annotated datasets in the biomedical imaging field.

SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images.

Image-based cell counting is a fundamental yet challenging task with wide applications in biological research. In this paper, we propose a novel unified deep network framework designed to solve this problem for various cell types in both 2D and 3D images. Specifically, we first propose SAU-Net for cell counting by extending the segmentation network U-Net with a Self-Attention module. Second, we design an extension of Batch Normalization (BN) to facilitate the training process for small datasets. In addition, a new 3D benchmark dataset based on the existing mouse blastocyst (MBC) dataset is developed and released to the community. Our SAU-Net achieves state-of-the-art results on four benchmark 2D datasets - synthetic fluorescence microscopy (VGG) dataset, Modified Bone Marrow (MBM) dataset, human subcutaneous adipose tissue (ADI) dataset, and Dublin Cell Counting (DCC) dataset, and the new 3D dataset, MBC. The BN extension is validated using extensive experiments on the 2D datasets, since GPU memory constraints preclude use of 3D datasets. The source code is available at https://github.com/mzlr/sau-net.

Abstract 3650: Trace'n'Seq: Assessing neuronal infiltration and its impact in pancreatic ductal carcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive tumor entity with dismal prognosis. PDAC is characterized by an extensive desmoplastic stroma and the tumor microenvironment (TME) has been recognized to play crucial roles in tumor development, therapy resistance, progression and metastasis. Although different cell types of the TME have been molecularly analyzed, tumor-infiltrating neurons could not be considered so far, because their cell bodies are located in adjacent ganglia of the peripheral nervous system and thus they are not part of the tumor mass (Demir, Thiel et al, Nature Cancer 2020; Demir, Thiel et al. Cancer Cell 2020). One of the unique features of PDAC is the strong infiltration by nerve fibers which correlates directly with tumor malignancy. In recent years it has been shown that either neurotransmitters secreted by neurons, or modulators of neuronal infiltration secreted by cancer cells can influence tumor growth. Here we use 3D light sheet fluorescence microscopy imaging (LSFM) of full tissue cleared PDAC specimen of tumors growing in genetically engineered mouse models or patient-derived xenograft models. Our data showthat these PDACs are innervated by a complex array of sensory and sympathetic neurons. To molecularly analyse neurons innervating the tumor mass, we have developed a novel approach we termed Trace’n’seq. This combines retrograde axonal tracing and FACS analysis with single cell RNA sequencing with the aim to identify, isolate and analyse hundreds of traced neurons infiltrating tumors or their respective healthy organs. Thereby, we have identified previously undescribed neuronal cell types and provide a landscape of sympathetic and sensory nerves innervating the healthy pancreas as well as PDAC. Our single cell transcriptomic analysis revealed signs of reprogramming of neuronal behaviour orchestrated by the tumor cells. These datasets combined with single cell profiling of PDAC cells and its associated stroma provide a first complete, unbiased insight into neurons infiltrating both healthy pancreas and PDAC. Based on a comprehensive interactome analysis of the interplay between tumor cells, stroma and the neurons, we have identified various candidate genes involved in tumor-nerve signalling. We are currently performing gain-and loss- of function approaches to dissect the role of individual factors. In summary, with Trace'n'seq, we have developed a novel technology pipeline to isolate tissue innervating neurons to investigate the role of neuronal infiltration in the development and progression of PDAC. Citation Format: Vera Thiel, Simon Renders, Jasper Panten, Julian Mochayedi, Rienk Offringa, Martin Sprick, Andreas Trumpp. Trace'n'Seq: Assessing neuronal infiltration and its impact in pancreatic ductal carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3650.

Unbiased analysis of mouse brain endothelial networks from two- or three-dimensional fluorescence images.

.Significance: A growing body of research supports the significant role of cerebrovascular abnormalities in neurological disorders. As these insights develop, standardized tools for unbiased and high-throughput quantification of cerebrovascular structure are needed.Aim: We provide a detailed protocol for performing immunofluorescent labeling of mouse brain vessels, using thin () or thick (50 to ) tissue sections, followed respectively by two- or three-dimensional (2D or 3D) unbiased quantification of vessel density, branching, and tortuosity using digital image processing algorithms.Approach: Mouse brain sections were immunofluorescently labeled using a highly selective antibody raised against mouse Cluster of Differentiation-31 (CD31), and 2D or 3D microscopy images of the mouse brain vasculature were obtained using optical sectioning. An open-source toolbox, called Pyvane, was developed for analyzing the imaged vascular networks. The toolbox can be used to identify the vasculature, generate the medial axes of blood vessels, represent the vascular network as a graph, and calculate relevant measurements regarding vascular morphology.Results: Using Pyvane, vascular parameters such as endothelial network density, number of branching points, and tortuosity are quantified from 2D and 3D immunofluorescence micrographs.Conclusions: The steps described in this protocol are simple to follow and allow for reproducible and unbiased analysis of mouse brain vascular structure. Such a procedure can be applied to the broader field of vascular biology.

GIANI - open-source software for automated analysis of 3D microscopy images.

ABSTRACTThe study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a limited number of open-source software packages to analyse such images. Here, we describe General Image Analysis of Nuclei-based Images (GIANI; https://djpbarry.github.io/Giani), new software for the analysis of 3D images. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images, and comes with scripting and command line tools. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in confocal images of mouse early embryos and by segmenting nuclei from light-sheet microscopy images of the flour beetle embryo. We also validate the performance of the software using simulated data. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.

Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images.

Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. In this paper, we propose a novel method called SPE-DNR that combines spherical-patches extraction (SPE) and deep-learning for neuron reconstruction (DNR). Based on 2D Convolutional Neural Networks (CNNs) and the intensity distribution features extracted by SPE, it determines the tracing directions and classifies voxels into foreground or background. This way, starting from a set of seed points, it automatically traces the neurite centerlines and determines when to stop tracing. To avoid errors caused by imperfect manual reconstructions, we develop an image synthesizing scheme to generate synthetic training images with exact reconstructions. This scheme simulates 3D microscopy imaging conditions as well as structural defects, such as gaps and abrupt radii changes, to improve the visual realism of the synthetic images. To demonstrate the applicability and generalizability of SPE-DNR, we test it on 67 real 3D neuron microscopy images from three datasets. The experimental results show that the proposed SPE-DNR method is robust and competitive compared with other state-of-the-art neuron reconstruction methods.

Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.

Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficient non-DL method named MARS. The DL methods were trained on a common dataset of 3D cellular confocal microscopy images. Their segmentation accuracies were also tested in the presence of different image artifacts. A specific method for segmentation quality evaluation was adopted, which isolates segmentation errors due to under- or oversegmentation. This is complemented with a 3D visualization strategy for interactive exploration of segmentation quality. Our analysis shows that the DL pipelines have different levels of accuracy. Two of them, which are end-to-end 3D and were originally designed for cell boundary detection, show high performance and offer clear advantages in terms of adaptability to new data.

Microstructural Defects of Epoxy–Phenolic Polymers on Metal Substrates during Acidic Corrosion

This study uses confocal microscopy and image processing to investigate the microstructural changes of coating–metal systems immersed in a heated acidic bath. Unlike standard optical microscopy techniques, 3D confocal microscopy images can quantitatively reveal microscopic defects formed at early stages of cathodic delamination. The coatings are made of fluorescent epoxy–phenolic resins cured at high temperatures onto tinplate (T23) and tin-free steel (TFS) substrates. When the coated metal substrates are immersed in acetic acid, a series of microscopic corrosion events occur at the polymer–metal interface. These events are quantified by changes in the thickness distribution of the degraded samples relative to that of intact coatings. The degradation rate is highest for epoxy–phenolic polymers on TFS substrates, represented by multiple orders of magnitude increase in the number density of defect sites. Higher molecular weight coatings provide slightly better resistance against delamination. The coating thickness dictates the rate of oxygen diffusion and ion transport along the polymer–coating interface, where raised asperities serve as localized sites for metal oxidation and formation of alkaline species, leading to subsequent delamination of the cured polymers from the surfaces. The results show that the metal surface topology is as important as chemistry when designing the corrosion resistance of products containing acidic liquids, and that confocal microscopy is useful in quality control through early detection of mesoscale polymer failure.

Structural Characterization of the Full-Length Anti-CD20 Antibody Rituximab.

Rituximab, a murine–human chimera, is the first monoclonal antibody (mAb) developed as a therapeutic agent to target CD20 protein. Its Fab domain and its interaction with CD20 have been extensively studied and high-resolution atomic models obtained by X-ray diffraction or cryo-electron microscopy are available. However, the structure of the full-length antibody is still missing as the inherent protein flexibility hampers the formation of well-diffracting crystals and the reconstruction of 3D microscope images. The global structure of rituximab from its dilute solution is here elucidated by small-angle X-ray scattering (SAXS). The limited data resolution achievable by this technique has been compensated by intensive computational modelling that led to develop a new and effective procedure to characterize the average mAb conformation as well as that of the single domains. SAXS data indicated that rituximab adopts an asymmetric average conformation in solution, with a radius of gyration and a maximum linear dimension of 52 Å and 197 Å, respectively. The asymmetry is mainly due to an uneven arrangement of the two Fab units with respect to the central stem (the Fc domain) and reflects in a different conformation of the individual units. As a result, the Fab elbow angle, which is a crucial determinant for antigen recognition and binding, was found to be larger (169°) in the more distant Fab unit than that in the less distant one (143°). The whole flexibility of the antibody has been found to strongly depend on the relative inter-domain orientations, with one of the Fab arms playing a major role. The average structure and the amount of flexibility has been studied in the presence of different buffers and additives, and monitored at increasing temperature, up to the complete unfolding of the antibody. Overall, the structural characterization of rituximab can help in designing next-generation anti-CD20 antibodies and finding more efficient routes for rituximab production at industrial level.

Advances in studying whole mouse brain vasculature using high-resolution 3D light microscopy imaging.

.SignificanceThe cerebrovasculature has become increasingly recognized as a major player in overall brain health and many brain disorders. Although there have been several landmark studies to understand details of these crucially important structures in an anatomically defined area, brain-wide examination of the whole cerebrovasculature, including microvessels, has been challenging. However, emerging techniques, including tissue processing and three-dimensional (3D) microscopy imaging, enable neuroscientists to examine the total vasculature in the entire mouse brain.AimHere, we aim to highlight advances in these high-resolution 3D mapping methods including block-face imaging and light sheet fluorescent microscopy.ApproachWe summarize latest mapping tools to understand detailed anatomical arrangement of the cerebrovascular network and the organizing principles of the neurovascular unit (NVU) as a whole. ResultsWe discuss biological insights gained from studies using these imaging methods and how these tools can be used to advance our understanding of the cerebrovascular network and related cell types in the entire brain.ConclusionsThis review article will help to understand recent advance in high-resolution NVU mapping in mice and provide perspective on future studies.

Structure-Guided Segmentation for 3D Neuron Reconstruction.

Digital reconstruction of neuronal morphologies in 3D microscopy images is critical in the field of neuroscience. However, most existing automatic tracing algorithms cannot obtain accurate neuron reconstruction when processing 3D neuron images contaminated by strong background noises or containing weak filament signals. In this paper, we present a 3D neuron segmentation network named Structure-Guided Segmentation Network (SGSNet) to enhance weak neuronal structures and remove background noises. The network contains a shared encoding path but utilizes two decoding paths called Main Segmentation Branch (MSB) and Structure-Detection Branch (SDB), respectively. MSB is trained on binary labels to acquire the 3D neuron image segmentation maps. However, the segmentation results in challenging datasets often contain structural errors, such as discontinued segments of the weak-signal neuronal structures and missing filaments due to low signal-to-noise ratio (SNR). Therefore, SDB is presented to detect the neuronal structures by regressing neuron distance transform maps. Furthermore, a Structure Attention Module (SAM) is designed to integrate the multi-scale feature maps of the two decoding paths, and provide contextual guidance of structural features from SDB to MSB to improve the final segmentation performance. In the experiments, we evaluate our model in two challenging 3D neuron image datasets, the BigNeuron dataset and the Extended Whole Mouse Brain Sub-image (EWMBS) dataset. When using different tracing methods on the segmented images produced by our method rather than other state-of-the-art segmentation methods, the distance scores gain 42.48% and 35.83% improvement in the BigNeuron dataset and 37.75% and 23.13% in the EWMBS dataset.

Elastic 3D–2D Image Registration

We propose a method to nonrigidly align a three-dimensional (3D) volumetric image with a two-dimensional (2D) planar image representing a projection of the deformed volume. The application in mind comes from biological studies in which 2D intravital microscopy videos of living tissue are recorded, after which the tissue is excised and a more detailed 3D volume microscopy is performed. Coregistration of both data sets allows to combine the temporal (but 2D) information with more detailed spatial 3D information. Our approach is variational and uses a hyperelastic deformation regularization, as is appropriate for biological material. As a particular feature, the out-of-plane deformation is estimated based on the out-of-focus blur inside the 2D microscopy image. The approach becomes computationally feasible through the use of a coarse-to-fine optimization strategy and higher-order optimization methods.

Microglia phenotypes are associated with subregional patterns of concomitant tau, amyloid-\u03b2 and \u03b1-synuclein pathologies in the hippocampus of patients with Alzheimer\u2019s disease and dementia with Lewy bodies

The cellular alterations of the hippocampus lead to memory decline, a shared symptom between Alzheimer’s disease (AD) and dementia with Lewy Bodies (DLB) patients. However, the subregional deterioration pattern of the hippocampus differs between AD and DLB with the CA1 subfield being more severely affected in AD. The activation of microglia, the brain immune cells, could play a role in its selective volume loss. How subregional microglia populations vary within AD or DLB and across these conditions remains poorly understood. Furthermore, how the nature of the hippocampal local pathological imprint is associated with microglia responses needs to be elucidated. To this purpose, we employed an automated pipeline for analysis of 3D confocal microscopy images to assess CA1, CA3 and DG/CA4 subfields microglia responses in post-mortem hippocampal samples from late-onset AD (n = 10), DLB (n = 8) and age-matched control (CTL) (n = 11) individuals. In parallel, we performed volumetric analyses of hyperphosphorylated tau (pTau), amyloid-β (Aβ) and phosphorylated α-synuclein (pSyn) loads. For each of the 32,447 extracted microglia, 16 morphological features were measured to classify them into seven distinct morphological clusters. Our results show similar alterations of microglial morphological features and clusters in AD and DLB, but with more prominent changes in AD. We identified two distinct microglia clusters enriched in disease conditions and particularly increased in CA1 and DG/CA4 of AD and CA3 of DLB. Our study confirms frequent concomitance of pTau, Aβ and pSyn loads across AD and DLB but reveals a specific subregional pattern for each type of pathology, along with a generally increased severity in AD. Furthermore, pTau and pSyn loads were highly correlated across subregions and conditions. We uncovered tight associations between microglial changes and the subfield pathological imprint. Our findings suggest that combinations and severity of subregional pTau, Aβ and pSyn pathologies transform local microglia phenotypic composition in the hippocampus. The high burdens of pTau and pSyn associated with increased microglial alterations could be a factor in CA1 vulnerability in AD.

Mass fractal dimension from 2D microscopy images via an aggregation model with variable compactness.

Microscopy-image analysis provides precious information on size and structure of colloidal aggregates and agglomerates. The structure of colloids is often characterized using the mass fractal dimension , which is different from the two-dimensional (2D) fractal dimension that can be computed from microscopy images. In this work, we propose to use a recent morphological aggregation model to find a relationship between 2D image fractal dimension and 3D mass fractal dimension of aggregates and agglomerates. Our case study is represented by scanning transmission electron microscopy images of boehmite colloidal suspensions. The behaviour of the computed at different acid and base concentration shows a fair agreement with the results of small angle x-ray scattering and with the literature, enabling to use the versus relationship to study the impact of the composition of the colloidal suspension on the density of colloidal aggregates andagglomerates.

Three-dimensional microscopic single-pixel imaging with chaotic light

Three-dimensional (3D) microscopic imaging is fundamentally important for microscale observations and manipulations. Here, we show that single-pixel imaging (SPI) is available for realizing superior 3D microscopic imaging, which relies on the near-field illumination with chaotic light. Like considering a two-point object to investigate two-dimensional microscopic imaging, we theoretically and experimentally studied the image formation process for a 3D object with two slices, demonstrating the excellent capability of the SPI for 3D microscopic imaging. The proposed 3D microscopic imaging via SPI would be useful for applications such as biological imaging and micromanipulations.

Descriptor-based reconstruction of three-dimensional microstructures through gradient-based optimization

Microstructure reconstruction is an important cornerstone to the inverse materials design concept. In this work, a general algorithm is developed to reconstruct a three-dimensional microstructure from given descriptors. Based on two-dimensional (2D) micrographs, this reconstruction algorithm allows valuable insight through spatial visualization of the microstructure and in silico studies of structure-property linkages. The formulation ensures computational efficiency by casting microstructure reconstruction as a gradient-based optimization problem. Herein, the descriptors can be chosen freely, such as spatial correlations or Gram matrices, as long as they are differentiable with respect to the microstructure. Because real microstructure samples are commonly available as 2D microscopy images only, the desired descriptors for the reconstruction process are prescribed on orthogonal 2D slices. This adds a source of noise, which is handled in a new, superior and interpretable manner. The efficiency and applicability of this formulation is demonstrated by various numerical experiments.

Stochastic reconstruction of 3D microstructures from 2D cross-sectional images using machine learning-based characterization

The availability of high-quality three-dimensional (3D) microstructures is an essential prerequisite to understanding the macroscopic behaviours of heterogeneous media. Considering the high cost of 3D microscopy imaging, it is an economical way to derive large numbers of virtual 3D microstructures from limited morphological information of 2D cross-sectional images. This paper presents an efficient method that incorporates machine learning-based characterization of 2D images to statistically reconstruct 3D microstructures. The latent stochasticity of 2D images is mastered by fitting supervised machine learning models, which essentially characterize the local morphological statistics. A morphology integration scheme is developed to project the 2D morphological statistics into the 3D space, and new equivalent 3D microstructures can then be synthesized by sequentially generating voxel values from probability sampling. The new method is tested on a series of heterogeneous media with distinct morphologies, and it is also compared with two classical methods (i.e. stochastic optimization-based reconstruction and Gaussian random field transformation) in terms of reconstruction accuracy and efficiency. Besides, various microstructural descriptors are used to quantify the discrepancies between the reconstructed and target microstructures. The results confirm that the proposed method provides a highly cost-effective and widely applicable way to reproduce 3D realizations that precisely preserve the statistical characteristics, geometrical irregularities, long-distance connectivity and anisotropy that exist in 2D cross-sectional images.