3D Histology Research Articles

Considerations for analysis of endothelial shear stress and strain in FSI models of atherosclerosis

Atherosclerosis is a lipid driven chronic inflammatory disease that is characterized by the formation of plaques at predilection sites. These predilection sites (side branches, curved segments, and bifurcations) have often been associated with disturbed shear stress profiles. However, in addition to shear stress, endothelial cells also experience artery wall strain that could contribute to atherosclerosis progression. Herein, we describe a method to accurately obtain these shear stress and strain profiles.We developed a fluid–structure interaction (FSI) framework for modelling arteries within a commercially available package (Abaqus, version 6.14) that included known prestresses (circumferential, axial and pressure associated). In addition, we co-registered 3D histology to a micro-CT-derived 3D reconstruction of an atherosclerotic carotid artery from a cholesterol-fed ApoE−/− mouse to include the spatial distribution of lipids within a subject-specific model. The FSI model also incorporated a nonlinear hyperelastic material model with regionally-varying properties that distinguished between healthy vessel wall and plaque.FSI predicted a lower shear stress than CFD (~−12%), but further decreases in plaque regions with softer properties (~-24%) were dependent on the approach used to implement the prestresses in the artery wall. When implemented with our new hybrid approach (zero prestresses in regions of lipid deposition), there was significant heterogeneity in endothelial shear stress in the atherosclerotic artery due to variations in stiffness and, in turn, wall strain.In conclusion, when obtaining endothelial shear stress and strain in diseased arteries, a careful consideration of prestresses is necessary. This paper offers a way to implement them.

The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging.

Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

Local islet remodelling associated with duct lesion-islet complex in adult human pancreas.

Islets are thought to be stably present in the adult human pancreas to maintain glucose homeostasis. However, identification of the pancreatic intraepithelial neoplasia (PanIN)-islet complex in mice and the presence of PanIN lesions in adult humans suggest that similar remodelling of islet structure and environment may occur in the human pancreas. To identify islet remodelling in a clinically related setting, we examine human donor pancreases with 3D histology to detect and characterise the human PanIN-islet complex. Cadaveric donor pancreases (26-65years old, n = 10) were fixed and sectioned (350μm) for tissue labelling, clearing and microscopy to detect local islet remodelling for 3D analysis of the microenvironment. The remodelled microenvironment was subsequently examined via microtome-based histology for clinical assessment. In nine pancreases, we identified the unique peri-lobular islet aggregation associated with the PanIN lesion (16 lesion-islet complexes detected; size: 3.18 ± 1.34mm). Important features of the lesion-islet microenvironment include: (1) formation of intra-islet ducts, (2) acinar atrophy, (3) adipocyte association, (4) inflammation (CD45+), (5) stromal accumulation (α-SMA+), (6) increase in Ki-67 proliferation index but absence of Ki-67+ alpha/beta cells and (7) in-depth and continuous duct-islet cell contacts, forming a cluster. The duct-islet cell cluster and intra-islet ducts suggest likely islet cell neogenesis but not replication. We identify local islet remodelling associated with PanIN-islet complex in the adult human pancreas. The tissue remodelling and the evidence of inflammation and stromal accumulation suggest that the PanIN-islet complex is derived from tissue repair after a local injury.

Connecting structure and function from organisms to molecules in small-animal symbioses through chemo-histo-tomography

Our understanding of metabolic interactions between small symbiotic animals and bacteria or parasitic eukaryotes that reside within their bodies is extremely limited. This gap in knowledge originates from a methodological challenge, namely to connect histological changes in host tissues induced by beneficial and parasitic (micro)organisms to the underlying metabolites. We addressed this challenge and developed chemo-histo-tomography (CHEMHIST), a culture-independent approach to connect anatomic structure and metabolic function in millimeter-sized symbiotic animals. CHEMHIST combines chemical imaging of metabolites based on mass spectrometry imaging (MSI) and microanatomy-based micro-computed X-ray tomography (micro-CT) on the same animal. Both high-resolution MSI and micro-CT allowed us to correlate the distribution of metabolites to the same animal's three-dimensional (3D) histology down to submicrometer resolutions. Our protocol is compatible with tissue-specific DNA sequencing and fluorescence in situ hybridization for the taxonomic identification and localization of the associated micro(organisms). Building CHEMHIST upon in situ imaging, we sampled an earthworm from its natural habitat and created an interactive 3D model of its physical and chemical interactions with bacteria and parasitic nematodes in its tissues. Combining MSI and micro-CT, we present a methodological groundwork for connecting metabolic and anatomic phenotypes of small symbiotic animals that often represent keystone species for ecosystem functioning.

393-P: Combination Treatment with Lisinopril and Empagliflozin Improves Urine and Histological Markers of Diabetic Kidney Disease in Female Renin-AAV UNx db/db Mice

Introduction: Emerging treatments of diabetic kidney disease (DKD) include SGLT2 inhibitors and GLP-1 receptor agonists that are nephroprotective beyond their blood glucose lowering effects. To confirm the translatability of a mouse model for drug discovery in DKD, we tested the efficacy of an ACE inhibitor and a SGLT2 inhibitor in the reninAAV UNx db/db mouse model. Methods: Female db/db mice were injected with a renin-encoding adeno-associated virus construct (reninAAV) to induce hypertension and uninephrectomized (UNx). At 12 weeks of age, dosing with vehicle, lisinopril, empagliflozin, or the combination (combo) was initiated. Plasma and urine markers were measured after 12 weeks of dosing and terminal kidney samples were collected for 3D light sheet microscopy and 2D histology. Results: In reninAAV UNx db/db mice, treatment with empagliflozin and combo reduced fed BG and HbA1c compared to vehicle. Treatment with lisinopril and combo reduced urine ACR and KIM1-to-creatinine compared to vehicle, while treatment with empagliflozin alone worsened urine ACR. Glomerular hypertrophy as assessed by 3D imaging was reduced in combo treated reninAAV UNx db/db mice compared to vehicle, while treatment with empagliflozin alone worsened glomerular hypertrophy. The total number of glomeruli per kidney was unaffected by treatments. Compared to vehicle treatment, lisinopril and combo treatment reduced the fraction of score 3+4 glomeruli, and glomerulosclerosis index (GSI) was reduced by with lisinopril and combo treatment. Treatment with empagliflozin alone worsened GSI. Morphometric analyses showed that lisinopril and combo treatment reduced kidney CD11b and KIM1 load. Conclusion: Responses to the combination treatment with lisinopril and empagliflozin showed improvement of urine and histological markers of DKD. Together, these data confirm the translatability of the reninAAV UNx db/db mouse model of DKD. Disclosure M. V. Østergaard: None. M. Christensen: None. T. Secher: Employee; Self; Gubra, Employee; Spouse/Partner; Novo Nordisk, Stock/Shareholder; Self; Gubra, Stock/Shareholder; Spouse/Partner; Novo Nordisk. J. L. Skytte: None. U. Roostalu: Employee; Self; Gubra. C. G. Salinas: Employee; Self; Gubra, Employee; Spouse/Partner; Novo Nordisk, Stock/Shareholder; Spouse/Partner; Novo Nordisk. F. E. Sembach: None. L. N. Fink: Employee; Self; Gubra, Stock/Shareholder; Self; Novo Nordisk A/S. N. Vrang: Board Member; Self; Gubra, Employee; Self; Gubra, Stock/Shareholder; Self; Gubra.

MO624COMBINATION TREATMENT WITH LISINOPRIL AND EMPAGLIFLOZIN IMPROVES BIOCHEMICAL AND HISTOLOGICAL MARKERS OF DIABETIC NEPHROPATHY IN HYPERTENSIVE UNINEPHRECTOMIZED DB/DB MICE

Abstract Background and Aims Emerging treatments of diabetic kidney disease (DKD) include SGLT2 inhibitors and GLP-1 receptor agonists that are nephroprotective beyond their blood glucose lowering effects. Despite this progress, patients with diabetes are still at risk of developing DKD, and drug discovery remains impeded by the lack of reproducible rodent models exhibiting features of human DKD. To confirm the translatability of an advanced mouse model, we tested standard of care in the setting of type 2 diabetes and hypertension. Method Female db/db mice were injected with a renin-encoding adeno-associated virus construct (reninAAV) to induce hypertension and uninephrectomized (UNx) at 7-8 weeks of age. At 12 weeks of age, daily dosing with vehicle, lisinopril, empagliflozin, or the combination (combo) was initiated. Blood glucose (BG) was measured every third week, while urine albumin-to-creatinine (ACR) and KIM1 were measured in spot urine samples collected before termination at 24 weeks of age. Cystatin C was measured in terminal plasma, while terminal kidney samples were collected for 3D light sheet microscopy and 2D histology: Glomerulosclerosis scoring was performed by AI-assisted automized scoring, whereby glomeruli were given scores from 0 (normal glomerulus) to 4 (glomerulus with global glomerulosclerosis), and morphometric quantification of kidney collagen 3, CD11b, and KIM1 load was performed. Results In reninAAV UNx db/db mice, 12 weeks treatment with empagliflozin and combo reduced fed BG (12.3±1.5 and 13.1±0.9 mM, vehicle: 18.0±1.9 mM) and HbA1c (5.5±0.2 and 5.8±0.2%, vehicle: 7.4±0.03%). Treatment with lisinopril and combo reduced urine ACR (8110±1320 and 2508±346 µg/mg, vehicle: 26,138±1820 µg/mg) and KIM1-to-creatinine (7.5±1.1 and 3.9±0.6 ng/mg, vehicle: 24.8±3.2 ng/mg), while treatment with empagliflozin alone worsened urine ACR (41,523±4670 µg/mg). Glomerular hypertrophy as assessed by 3D imaging was reduced in reninAAV UNx db/db mice combo treated animals compared to vehicle (glomerular volume: 1.46*105±4.3*103 and 1.65*105±4.3*103 µm3), while treatment with empagliflozin alone worsened glomerular hypertrophy (1.83*105 µm3). The total number of glomeruli per kidney was not affected by treatments. Compared to vehicle treatment, lisinopril and combo treatment reduced the fraction of score 3 + 4 glomeruli (% GS 3 + 4 glomeruli: 0.38±0.06 and 0.27±0.03%, vehicle: 0.54±0.05%) Glomerulosclerosis index (GSI) was also reduced by lisinopril and combo treatment (GSI: 2.29±0.10 and 2.04±0.05, vehicle: 2.62±0.09). Treatment with empagliflozin alone worsened GSI (2.95±0.11). Whereas treatments did not significantly affect collagen 3, lisinopril and combo treatment reduced CD11b (total CD11b mass: 0.25±0.03 and 0.24±0.04 mg, vehicle: 0.57±0.07 mg) and KIM1 (total KIM1 mass: 0.25±0.06 and 0.17±0.09 mg, vehicle: 2.01±0.37 mg), whereas empagliflozin did not affect CD11b and KIM1. Conclusion Responses to the combination treatment with lisinopril and empagliflozin showed improvement of urine and histological markers of DKD. Together, these data confirm the translatability of the ReninAAV UNx db/db mouse model of DKD in type 2 diabetes.

Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

Although X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

InShaDe: Invariant Shape Descriptors for visual 2D and 3D cellular and nuclear shape analysis and classification

We present a shape processing framework for visual exploration of cellular nuclear envelopes extracted from microscopic images arising in histology and neuroscience. The framework is based on a novel shape descriptor of closed contours in 2D and 3D. In 2D, it relies on a geodesically uniform resampling of discrete curves to compute unsigned curvatures at vertices and edges based on discrete differential geometry. Our descriptor is, by design, invariant under translation, rotation, and parameterization. We achieve the latter invariance under parameterization shifts by using elliptic Fourier analysis on the resulting curvature vectors. Uniform scale-invariance is optional and is a result of scaling curvature features to z-scores. We further augment the proposed descriptor with feature coefficients obtained through sparse coding of the extracted cellular structures using K-sparse autoencoders. For the analysis of 3D shapes, we compute mean curvatures based on the Laplace-Beltrami operator on triangular meshes, followed by computing a spherical parameterization through mean curvature flow. Finally, we compute the Spherical Harmonics decomposition to obtain invariant energy coefficients. Our invariant descriptors provide an embedding into a fixed-dimensional feature space that can be used for various applications, e.g., as input features for deep and shallow learning techniques or as input for dimension reduction schemes to provide a visual reference for clustering shape collections. We demonstrate the capabilities of our framework in the context of visual analysis and unsupervised classification of 2D histology images and 3D nuclear envelopes extracted from serial section electron microscopy stacks.

Adipose tissue fibrosis assessed by high resolution ex vivo MRI as a hallmark of tissue alteration in morbid obesity.

To determine whether magnetic resonance imaging (MRI) when used in an optimal ex vivo setting can help detecting and quantifying the 3D fibrosis fraction in human subcutaneous adipose tissue (SAT) samples, as compared to histology. This prospective observational study was approved by our institutional review board 3D MRI acquisitions were performed at 4.0 T (Bruker) on XX human SAT samples (around 1 cm3) collected from biopsy in morbidly obese patients. Such acquisitions included saturation-recovery T1 mapping (spatial resolution: 200 µm, acquisition time: ~16 minutes) and DIXON imaging (spatial resolution: 200 µm, acquisition time: ~20 minutes). After MRI, histological quantification of fibrosis was performed using picrosirius staining. T1 maps were clustered based on a k-means algorithm allowing quantification of fibrosis within the adipose tissue and percentage of fibrosis over the entire sample volume was calculated. Fat maps were computed from DIXON in-phase and out-of-phase images. The 3D MRI fibrosis percentage within the SAT samples were comprised between 6% and 15%. Excellent correlations and levels of agreement were observed between single slice MRI and histology (r=0.9, P=0.08) and between 3D MRI and histology in terms fibrosis percentages within SAT samples (r=0.9, P=0.01). High Field ex vivo MRI was able to quantify fibrosis in human SAT samples with high agreement with histology and moreover to provide 3D SAT fibrosis quantification avoiding histological sampling errors.

Abstract PO-017: Annotation-free 3D gland segmentation with generative image-sequence translation for prostate cancer risk assessment

Abstract 1. Purpose We developed an annotation-free image-translation-based approach for 3D segmentation of prostate glands within 3D-histology datasets of whole biopsies stained with a low-cost and rapid-diffusing fluorescent analog of H&E. 2. Introduction The current diagnostic gold standard of histopathology provides a limited 2D view of complex 3D glandular structures in prostate specimens, which contributes to high interobserver variability and reduced prognostic accuracy. We have recently developed nondestructive 3D pathology methods based on open-top light-sheet (OTLS) microscopy. In order to train prognostic models based on 3D glandular morphology, we have developed an objective (biomarker-based) 3D gland-segmentation method that does not rely upon tedious and subjective manual annotations, and which can operate on images of tissue stained with a cheap and fast-diffusing (small molecule) fluorescent analog of H&E. 3. Methods We first convert H&E-analog images into synthetic 3D immunofluorescence (IF) images of cytokeratin-8 (CK8), a biomarker expressed by the luminal epithelial cells that surround all prostate glands. This conversion was performed by treating the 3D data as sequences of 2D images and adapting a generative adversarial network (GAN)-based video-synthesis model to perform image-sequence translation with high depth-wise continuity between frames. Based on the synthetic CK8 images, glands were then objectively segmented in 3D using a thresholding/morphology-based algorithm. This two-step method obviates the need for labor-intensive and subjective manual 3D annotations, as would be needed to train a single-step segmentation model. A 3D structural similarity (SSIM) index was assessed between synthetic and real CK8 images. In addition, based on ground-truth manual annotations of glands in ten 0.2-mm3 regions, we calculated voxel-based Dice coefficients to compare our segmentation accuracy vs. two baseline methods. 4. Results The synthetic CK8 images exhibited high fidelity (3D SSIM = 0.696) and optimal continuity with depth. Our segmentation accuracy outperformed two baseline methods, with Dice coefficients (averaged for 10 samples) of: 0.882 (our method), 0.725 (3D watershed), 0.643 (2D U-net). We are now applying our method to 3D histology datasets of whole biopsies (n > 1000) acquired ex vivo from prostatectomy specimens (N ~ 200), where we are extracting 3D histomorphometric gland features to predict biochemical recurrence (BCR) post-prostatectomy in men with prostate cancer. 5. Conclusions Our annotation-free segmentation method relies upon generative synthetic 3D IF images from H&E-analog images in order to objectively segment the 3D prostate gland network. These accurate 3D segmentations are being extended to whole-biopsy 3D pathology datasets for prostate cancer risk assessment. Citation Format: Weisi Xie, Adam Glaser, Nicholas Reder, Nadia Postupna, Chenyi Mao, Can Koyuncu, Patrick Leo, Robert Serafin, Hongyi Huang, Anant Madabhushi, Lawrence True, Jonathan T.C. Liu. Annotation-free 3D gland segmentation with generative image-sequence translation for prostate cancer risk assessment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-017.

Quantitative measures of bone shape, cartilage morphometry and joint alignment are associated with disease in an ACLT and MMx rat model of osteoarthritis.

Multi-scale, subject-specific quantitative methods to characterize and monitor osteoarthritis in animal models and therapeutic treatments could help reveal causal relationships in disease development and distinguish treatment strategies. In this work, we demonstrate a reproducible and sensitive quantitative image analysis to characterize bone, cartilage and joint measures describing a rat model of post-traumatic osteoarthritis. Eleven 3-month-old male Wistar rats underwent medial anterior cruciate ligament (ACL) transection and medial meniscectomy on the right knee to destabilise the right tibiofemoral joint. They were sacrificed 6weeks post-surgery and a silicon-based micro-bead contrast agent was injected in the joint space, before scanning with micro-computed tomography (microCT). Subsequently, 3D quantitative morphometric analysis (QMA), previously developed for rabbit joints, was performed. This included cartilage, subchondral cortical and epiphyseal bone measures, as well as novel tibiofemoral joint metrics. Semi-quantitative evaluation was performed on matching two-dimensional (2D) histology and microCT images. Reproducibility of the QMA was tested on eleven age-matched additional joints. The results indicate the QMA method is accurate and reproducible and that microCT-derived cartilage measurements are valid for the analysis of rat joints. The pathologic changes caused by transection of the ACL and medial meniscectomy were reflected in measurements of bone shape, cartilage morphology, and joint alignment. Furthermore, we were able to identify model-specific predictive parameters based on morphometric parameters measured with the QMA.

An enhanced loss function simplifies the deep learning model for characterizing the 3D organoid models.

Organization of the organoid models, imaged in 3D with a confocal microscope, is an essential morphometric index to assess responses to stress or therapeutic targets. In fact, differentiating malignant and normal cells is often difficult in monolayer cultures. But in 3D culture, colony organization can provide a clear set of indices for differentiating malignant and normal cells. The limiting factors are delineating each cell in a 3D colony in the presence of perceptual boundaries between adjacent cells and heterogeneity associated with cells being at different cell cycles. In a previous paper, we defined a potential field for delineating adjacent nuclei, with perceptual boundaries, in 2D histology images by coupling three deep networks. This concept is now extended to 3D and simplified by an enhanced cost function that replaces three deep networks with one. Validation includes four cell lines with diverse mutations, and a comparative analysis with the UNet models of microscopy indicates an improved performance with the F1-score of 0.83. All software and annotated images are available through GitHub and Bioinformatics online. The software includes the proposed method, UNet for microscopy that was extended to 3D and report generation for profiling colony organization. Supplementary data are available at Bioinformatics online.

Histologic validation of optical coherence tomography-based three-dimensional morphometric measurements of the human optic nerve head: Methodology and preliminary results

PurposeTo compare the three-dimensional (3D) morphology of the deep load-bearing structures of the human optic nerve head (ONH) as revealed in vivo by spectral domain optical coherence tomography (SDOCT) with ex vivo quantitative 3D histology. MethodsSDOCT imaging of the ONH was performed in six eyes from three brain-dead organ donors on life-support equipment awaiting organ procurement (in vivo conditions). Following organ procurement (ex vivo conditions), the eyes were enucleated and underwent a pars plana vitrectomy followed by pressurization to physiologic IOP and immersion fixation. Ex vivo ONH morphology was obtained from high-fidelity episcopic fluorescent 3D reconstruction. Morphologic parameters of the observed ONH canal geometry and peripapillary choroid, as well as the shape, visibility and depth of the lamina cribrosa were compared between ex vivo and in vivo measurements using custom software to align, scale, and manually delineate the different regions of the ONH. ResultsThere was significant correspondence between in vivo and ex vivo measurements of the depth and shape of the lamina cribrosa, along with the size and shape of Bruch's membrane opening (BMO) and anterior scleral canal opening (ASCO). Weaker correspondence was observed for choroidal thickness; as expected, a thinner choroid was seen ex vivo due to loss of blood volume upon enucleation (−79.9%, p < 0.001). In addition, the lamina was shallower (−32.3%, p = 0.0019) and BMO was smaller ex vivo (−3.38%, p = 0.026), suggesting post mortem shrinkage of the fixed tissue. On average, while highly variable, only 31% of the anterior laminar surface was visible in vivo with SDOCT (p < 0.001). ConclusionsMorphologic parameters by SDOCT imaging of the deep ONH showed promising correspondence to histology metrics. Small but significant shrinkage artifact, along with large effects of exsanguination of the choroid, was seen in the ex vivo reconstructions of fixed tissues that may impact the quantification of ex vivo histoarchitecture, and this should be considered when developing models and biomarkers based on ex vivo imaging of fixed tissue. Lack of visibly of most of the lamina surface in SDOCT images is an important limitation to metrics and biomarkers based on in vivo images of the ONH deep tissues.

New Look at RSV Infection: Tissue Clearing and 3D Imaging of the Entire Mouse Lung at Cellular Resolution.

Background: Respiratory Syncytial Virus (RSV) is the major cause of severe acute respiratory tract illness in young children worldwide and a main pathogen for the elderly and immune-compromised people. In the absence of vaccines or effective treatments, a better characterization of the pathogenesis of RSV infection is required. To date, the pathophysiology of the disease and its diagnosis has mostly relied on chest X-ray and genome detection in nasopharyngeal swabs. The development of new imaging approaches is instrumental to further the description of RSV spread, virus–host interactions and related acute respiratory disease, at the level of the entire lung. Methods: By combining tissue clearing, 3D microscopy and image processing, we developed a novel visualization tool of RSV infection in undissected mouse lungs. Results: Whole tissue analysis allowed the identification of infected cell subtypes, based on both morphological traits and position within the cellular network. Furthermore, 3D imaging was also valuable to detect the cytoplasmic viral factories, also called inclusion bodies, a hallmark of RSV infection. Conclusions: Whole lung clearing and 3D deep imaging represents an unprecedented visualization method of infected lungs to allow insight into RSV pathophysiology and improve the 2D histology analyses.

Morphopathology of myocardial hypoxic-ischemic injuries in newborns at 22-27 weeks' gestation

To determine the parameters of myocardial structural injuries developed in chronic intrauterine hypoxia conditions in newborns at 22-27 weeks' gestation. A battery of morphological techniques, including organometry studies and separate weighing of the heart; 3D histology; morphometry with the determination of the area of cardiomyocyte nuclei, the specific area of the muscular and interstitial components of the right ventricular myocardium; immunohistochemistry with monoclonal antibodies to transforming growth factor βı (TGF-βı), cardiac troponin T (cTnT), and transmission electron microscopy, was used to examine heart samples from 30 deceased newborns at 22-27 weeks' gestation who developed in chronic intrauterine hypoxia conditions. A control group consisted of hearts from 20 extremely low body weight (ELBW) newborns, the main cause of whose death was asphyxia caused by the premature detachment of a normally positioned placenta. Analysis of the organometric parameters of heart samples from newborns at 22-27 weeks' gestation, who had experienced chronic intrauterine hypoxia, revealed right ventricular hypertrophy with increases in the area of cardiomyocyte nuclei and in the specific area of the muscle component compared to the control group. Impaired myocardial microcirculation and destructive changes in cardiomyocytes were diagnosed in conjunction with the decreased troponin T and increased TGF-β1 expressions. Incomplete differentiation of cardiomyocytes and their myofibrillar component was revealed at the myocardial ultrastructural level in ELBW newborns who had experienced chronic intrauterine hypoxia. The parameters of myocardial structural rearrangement in ELBW newborns who had experienced chronic intrauterine hypoxia are compensatory right ventricular hypertrophy, microcirculatory disorders, destructive changes in cardiomyocytes, decreased cTnT and increased TGF-β1 expressions in conjunction with impaired cardiomyocyte differentiation.

Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images.

Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg.

Targeted 2D histology and ultrastructural bone analysis based on 3D microCT anatomical locations

Histological processing of mineralised tissue (e.g. bone) allows examining the anatomy of cells and tissues as well as the material properties of the tissue. However, resin-embedding offers limited control over the specimen position for cutting. Moreover, specific anatomical planes (coronal, sagittal) or defined landmarks are often missed with standard microtome sectioning. Here we describe a method to precisely locate a specific anatomical 2D plane or any anatomical feature of interest (e.g. bone lesions, newly formed bone, etc.) using 3D micro computed tomography (microCT), and to expose it using controlled-angle microtome cutting. The resulting sections and corresponding specimen's block surface offer correlative information of the same anatomical location, which can then be analysed using multiscale imaging. Moreover, this method can be combined with immunohistochemistry (IHC) to further identify any component of the bone microenvironment (cells, extracellular matrix, proteins, etc.) and guide subsequent in-depth analysis. Overall, this method allows to:•Cut your specimens in a consistent position and precise manner using microCT-based controlled-angle microtome sectioning.•Locate and expose a specific anatomical plane (coronal, sagittal plane) or any other anatomical landmarks of interest based on microCT.•Identify any cell or tissue markers based on IHC to guide further in-depth examination of those regions of interest.

Diagnosing 12 prostate needle cores within an hour of biopsy via open-top light-sheet microscopy.

.Significance: Processing and diagnosing a set of 12 prostate biopsies using conventional histology methods typically take at least one day. A rapid and accurate process performed while the patient is still on-site could significantly improve the patient’s quality of life.Aim: We develop and assess the feasibility of a one-hour-to-diagnosis (1Hr2Dx) method for processing and providing a preliminary diagnosis of a set of 12 prostate biopsies.Approach: We developed a fluorescence staining, optical clearing, and 3D open-top light-sheet microscopy workflow to enable 12 prostate needle core biopsies to be processed and diagnosed within an hour of receipt. We analyzed 44 biopsies by the 1Hr2Dx method, which does not consume tissue. The biopsies were then processed for routine, slide-based 2D histology. Three pathologists independently evaluated the 3D 1Hr2Dx and 2D slide-based datasets in a blinded, randomized fashion. Turnaround times were recorded, and the accuracy of our method was compared with gold-standard slide-based histology.Results: The average turnaround time for tissue processing, imaging, and diagnosis was 44.5 min. The sensitivity and specificity of 1Hr2Dx in diagnosing cancer were both .Conclusions: The 1Hr2Dx method has the potential to improve patient care by providing an accurate preliminary diagnosis within an hour of biopsy.

'Light-Guided Sectioning' for Precise Localization and 3D Visualization of In Situ Optical Implants

In the last two decades, there has been a significant increase in the use of implanted optical devices to control and monitor neuronal activity in vivo. However, post hoc examination of the implant location is commonly achieved via 2D histology, which often suffers from distortion and loss during tissue processing. To address this, we developed a 3D method that allows post hoc labeling and clearing of tissue that preserves the size of the tissue while leaving the optical implant unperturbed. By coupling the implant to a light-emitting diode, the implant itself can serve as a light guide for removal of redundant tissue (light-guided sectioning, LiGS). The residual tissue, including the tissue–implant interface, is then processed as a whole for further investigation. This enables the precise location of the optical implant to be determined as well as the identification of tissue changes at the implant site. LiGS can also be used for accurate cell registration of ex vivo histology with single-cell two-photon calcium images obtained through gradient-index (GRIN) lenses. We anticipate that LiGS will provide valuable additional information in any experiment that uses optical implants and will increase reproducibility through well-defined fiber-to-target localization.

Human Electrophysiology, Neuroimaging and 3D Histology Define a Target to Modulate Eating Behavior Within the Human Hippocampal Region

Online date: November 16, 2020 This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)