Integrating X-ray phase-contrast imaging and histology for comparative evaluation of breast tissue malignancies in virtual histology analysis.

Detecting breast tissue alterations is essential for cancer diagnosis. However, inherent bidimensionality limits histological procedures' effectiveness in identifying these changes. Our study applies a 3D virtual histology method based on X-ray phase-contrast microtomography (PhC µ CT), performed at a synchrotron facility, to investigate breast tissue samples including different types of lesions, namely intraductal papilloma, micropapillary intracystic carcinoma, and invasive lobular carcinoma. One-to-one comparisons of X-ray and histological images explore the clinical potential of 3D X-ray virtual histology. Results show that PhC µ CT technique provides high spatial resolution and soft tissue sensitivity, while being non-destructive, not requiring a dedicated sample processing and being compatible with conventional histology. PhC µ CT can enhance the visualization of morphological characteristics such as stromal tissue, fibrovascular core, terminal duct lobular unit, stromal/epithelium interface, basement membrane, and adipocytes. Despite not reaching the (sub) cellular level, the three-dimensionality of PhC µ CT images allows to depict in-depth alterations of the breast tissues, potentially revealing pathologically relevant details missed by a single histological section. Compared to serial sectioning, PhC µ CT allows the virtual investigation of the sample volume along any orientation, possibly guiding the pathologist in the choice of the most suitable cutting plane. Overall, PhC µ CT virtual histology holds great promise as a tool adding to conventional histology for improving efficiency, accessibility, and diagnostic accuracy of pathological evaluation.

AI-based Apoptosis Cell Classification Using Phase-contrast Images of K562 Cells.

BACKGROUND/AIM: This study aimed to automate the classification of cells, particularly in identifying apoptosis, using artificial intelligence (AI) in conjunction with phase-contrast microscopy. The objective was to reduce reliance on manual observation, which is often time-consuming and subject to human error. MATERIALS AND METHODS: K562 cells were used as a model system and apoptosis was induced following administration of gamma-secretase inhibitors. Fluorescence staining was applied to detect DNA fragmentation and caspase activity. Cell images were obtained using both phase-contrast and fluorescence microscopy. Two AI models, Lobe(R) and a server-based ResNet50, were trained using these images and evaluated using F-values through five-fold cross-validation. RESULTS: Both AI models demonstrated effectively categorized individual cells into three groups: caspase-negative/no DNA fragmentation, caspase-positive/no DNA fragmentation, and caspase-positive/DNA fragmentation. Notably, the AI models' ability to differentiate cells relied on subtle variations in phase-contrast images, potentially linked to changes in refractive indices during apoptosis progression. Both AI models exhibited high accuracy, with the server-based ResNet50 model showing improved performance through repeated training. CONCLUSION: This study demonstrates the potential of AI-assisted phase-contrast microscopy as a powerful tool for automating cell classification, especially in the context of apoptosis research and the discovery of anticancer substances. By reducing the need for manual labor and enhancing classification accuracy, this approach holds promise for expediting high-throughput cell screening, significantly contributing to advancements in medical diagnostics and drug development.

Single fiber curvature for muscle impairment assessment: Phase contrast imaging of stroke-induced animals.

There are technical challenges in imaging studies that can three-dimensionally (3D) analyze a single fiber (SF) to observe the functionality of the entire muscle after stroke. This study proposes a 3D assessment technique that only segments the SF of the right stroke-induced soleus muscle of a gerbil using synchrotron radiation x-ray microcomputed tomography (SR-µCT), which is capable of muscle structure analysis. Curvature damage in the SF of the left soleus muscle (impaired) progressed at 7-day intervals after the stroke in the control; particularly on the 7 days (1 week) and 14 days (2 weeks), as observed through visualization analysis. At 2 weeks, the SF volume was significantly reduced in the control impaired group (p = .033), and was significantly less than that in the non-impaired group (p = .009). We expect that animal post-stroke studies will improve the basic field of rehabilitation therapy by diagnosing the degree of SF curvature. RESEARCH HIGHLIGHTS: Muscle evaluation after ischemic stroke using synchrotron radiation x-ray microcomputed tomography (SR-µCT). Curvature is measured by segmenting a single fiber (SF) in the muscle. Structural changes in the SF of impaired gerbils at 7-day intervals were assessed.

Deep learning for asbestos counting.

The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untreated airborne samples using standard Mixed Cellulose Ester (MCE) filters. Several samples with a mix of chrysotile and crocidolite with different concentration loads have been prepared. Using a 20x objective lens coupled with a backlight illumination system a number of 140 images were collected from these samples, which along with additional 13 highly fibre loaded artificial images constituted the database. About 7500 fibres were manually recognised and annotated following the National Institute for Occupational Safety and Health (NIOSH) fibre counting Method 7400 as input for the training and validation of the model. The best trained model provides a total precision of 0.84 with F1-Score of 0.77 at a confidence of 0.64. A further post-detection refinement to ignore detected fibres < 5 µm in length improves the final precision. This method can be considered as a reliable and competent alternative to conventional PCM.

Multiscale optical phase fluctuations link disorder strength and fractal dimension of cell structure.

Optical methods for examining cellular structure based on endogenous contrast rely on analysis of refractive index changes to discriminate cell phenotype. These changes can be visualized using techniques such as phase contrast microscopy, detected by light scattering, or analyzed numerically using quantitative phase imaging. The statistical variations of refractive index at the nanoscale can be quantified using disorder strength, a metric seen to increase with neoplastic change. In contrast, the spatial organization of these variations is typically characterized using a fractal dimension, which is also seen to increase with cancer progression. Here, we seek to link these two measurements using multiscale measurements of optical phase to calculate disorder strength and in turn to determine the fractal dimension of the structures. First, quantitative phase images are analyzed to show that the disorder strength metric changes with resolution. The trend of disorder strength with length scales is analyzed to determine the fractal dimension of the cellular structures. Comparison of these metrics is presented for different cell lines with varying phenotypes including MCF10A, MCF7, BT474, HT-29, A431, and A549 cell lines, in addition to three cell populations with modified phenotypes. Our results show that disorder strength and fractal dimension can both be obtained with quantitative phase imaging and that these metrics can independently distinguish between different cell lines. Furthermore, their combined use presents a new approach for better understanding cellular restructuring during different pathways.

Quantitative assessment of mesothelioma and lung cancer risk based on Phase Contrast Microscopy (PCM) estimates of fibre exposure: an update of 2000 asbestos cohort data.

An earlier meta-analysis of mortality studies of asbestos-exposed worker populations, quantified excess mesothelioma and lung cancer risks in relation to cumulative exposure to the three main commercial asbestos types. The aim of this paper was to update these analyses incorporating new data based on increased follow-up of studies previously included, as well as studies of worker populations exposed predominantly to single fibre types published since the original analysis. Mesothelioma as a percentage of expected mortality due to all causes of death, percentage excess lung cancer and mean cumulative exposure were abstracted from available mortality studies of workers exposed predominantly to single asbestos types. Average excess mesothelioma and lung cancer per unit of cumulative exposure were summarised for groupings of studies by fibre type; models for pleural and peritoneal mesothelioma risk and lung cancer risk in terms of cumulative exposure for the different fibre types were fitted using Poisson regression. The average mesothelioma risks (per cent of total expected mortality) per unit cumulative exposure (f/cc.yr), RM, were 0.51 for crocidolite, 0.12 for amosite, and 0.03 for the Libby mixed amphiboles cohort. Significant heterogeneity was present for cohorts classed as chrysotile, with RM values of 0.01 for chrysotile textiles cohorts and 0.0011 for other chrysotile-exposed cohorts. Average percentage excess lung cancer risks per unit cumulative exposure, RL, were 4.3 for crocidolite and amosite combined, 0.82 for Libby. Very significant heterogeneity was present for chrysotile-exposed cohorts with RL values spanning two orders of magnitude from 0.053 for the Balangero mine to 4.8 for the South Carolina textiles cohort. Best fitting models suggest a non-linear exposure-response in which the peritoneal mesothelioma risk is proportional to approximately the square of cumulative exposure. Pleural mesothelioma and lung cancer risk were proportion to powers of cumulative exposure slightly less than one and slightly higher than one respectively.

Observation of the same asbestos body by both phase contrast microscope and analytical transmission electron microscope.

The amount of asbestos body (AB) in the human lungs is used as an index to assess asbestos lung cancer (ALC). This study reports a new method to observe the same AB previously observed by analytical transmission electron microscope (ATEM) by phase contrast microscope (PCM) or the contrary order. Four kinds of specimens were prepared from the lung tissue of an asbestos related worker: ordinary PCM specimen (A); PCM specimen (B) of which the cover glass was stripped off and ashed at a low temperature; transmission electron microscope (TEM) specimen (C); and PCM specimen (D) covered a TEM specimen (C) with immersion liquid and cover glass. These specimens were all observed by PCM, and the specimen (C) by analytical TEM (ATEM). The results showed that the TEM specimen (C) is transparent in visible light and we can also see the particles by PCM. The image by PCM of the TEM specimen (C) showed very similar features to that of PCM specimens (A) and (B). Accordingly, we could observe various same particles by both ATEM and PCM. In conclusion, the method observing the same AB by both PCM and ATEM will contribute to standardize the recognition of AB for PCM analysts.

Characterization of drug effects on cell cultures from phase-contrast microscopy images.

In this work, we classify chemotherapeutic agents (topoisomerase inhibitors) based on their effect on U-2 OS cells. We use phase-contrast microscopy images, which are faster and easier to obtain than fluorescence images and support live cell imaging. We use a convolutional neural network (CNN) trained end-to-end directly on the input images without requiring for manual segmentations or any other auxiliary data. Our method can distinguish between tested cytotoxic drugs with an accuracy of 98%, provided that their mechanism of action differs, outperforming previous work. The results are even better when substance-specific concentrations are used. We show the benefit of sharing the extracted features over all classes (drugs). Finally, a 2D visualization of these features reveals clusters, which correspond well to known class labels, suggesting the possible use of our methodology for drug discovery application in analyzing new, unseen drugs.

3D shape reconstruction of normal and cancerous red blood cells using digital holographic tomography: Combination of angular spectrum method and multiplicative technique.

Since the red blood cell shape affects the oxygen transport, so a robust method to reconstruct the 3D shape of an RBC from different projections is presented. A robust one-piece polarizing holographic microscope setup is used to record inline holograms of normal and cancerous red blood cells (RBCs) with high stability. The inline holograms are corrected by flat fielding and windowed Fourier filtering methods to mitigate the zero-order and the defocused twin image due to the inline recording configuration to the least measure. The corrected inline holograms are then reconstructed by the angular spectrum method to extract the 2D wrapping phase-contrast images. The 2D wrapping phase-contrast images are then unwrapped using the graph cuts algorithm to extract the continuous 2D phase-contrast images. The continuous 2D phase-contrast images are reconstructed at different projections by the multiplicative technique to extract the 3D shape of the normal and the cancerous RBCs. Experimental results show that any deformation in the shape of the normal and the cancerous RBCs can be seen clearly at any rotational angle in 3D. This method, which is based on the degree of deformation from the best fitting, can be used as an alternative method of counting method for discrimination between normal and cancerous cells and hence diagnoses the disease easily.

Microvascular imaging of the unstained human superior colliculus using synchrotron-radiation phase-contrast microtomography.

Characterizing the microvasculature of the human brain is critical to advance understanding of brain vascular function. Most methods rely on tissue staining and microscopy in two-dimensions, which pose several challenges to visualize the three-dimensional structure of microvessels. In this study, we used an edge-based segmentation method to extract the 3D vasculature from synchrotron radiation phase-contrast microtomography (PC-µCT) of two unstained, paraffin-embedded midbrain region of the human brain stem. Vascular structures identified in PC-µCT were validated with histology of the same specimen. Using the Deriche-Canny edge detector that was sensitive to the boundary between tissue and vascular space, we could segment the vessels independent of signal variations in PC-µCT images. From the segmented volumetric vasculature, we calculated vessel diameter, vessel length and volume fraction of the vasculature in the superior colliculi. From high resolution images, we found the most frequent vessel diameter to be between 8.6-10.2 µm. Our findings are consistent with the known anatomy showing two types of vessels with distinctive morphology: peripheral collicular vessels and central collicular vessels. The proposed method opens up new possibilities for vascular research of the central nervous system using synchrotron radiation PC-µCT of unstained human tissue.

Evaluation of imaging setups for quantitative phase contrast nanoCT of mineralized biomaterials.

X-ray nano-tomography with phase contrast (nanoCT) using synchrotron radiation is a powerful tool to non-destructively investigate 3D material properties at the nanoscale. In large bone lesions, such as severe bone fractures, bone cancer or other diseases, bone grafts substituting the lost bone might be necessary. Such grafts can be of biological origin or be composed of a synthetic bone substitute. The long-term functioning of artificial bone substitutes depends on many factors. Synchrotron nanoCT imaging has great potential to contribute to further the understanding of integration of implants into bone tissue by imaging the spatial interaction between bone tissue and implant, and by accessing the interface between implant material and bone tissue. With this aim, a methodology for evaluating the image quality is presented for in-line phase contrast nanoCT images of bone scaffold samples. A PMMA-embedded tricalcium phosphate scaffold was used with both a closed and an open porosity structure and bone ingrowths as a representative system of three known materials. Parameters such as spatial resolution and signal-to-noise ratio were extracted and used to explore and quantitatively compare the effects of implementation choices in the imaging setup, such as camera technology and imaging energy, on the resulting image quality. Increasing the X-ray energy from 17.5 keV to 29.6 keV leads to a notable improvement in image quality regardless of the camera technology used, with the two tested camera setups performing at a comparable level when the recorded intensity was kept constant.

In Vitro Preparation of Actively Maturing Bovine Articular Cartilage Explants for X-Ray Phase Contrast Imaging.

Understanding the mechanisms that underpin post-natal maturation of articular cartilage is of crucial importance for designing the next generation of tissue engineering strategies and potentially repairing diseased or damaged cartilage. In general, postnatal maturation of the articular cartilage, which is a wholesale change in collagen structure and function of the tissue to accommodate growth of the organism, occurs over a timescale ranging from months to years. Conversely dissolution of the structural organization of the cartilage that also occurs over long timescales is the hallmark of tissue degeneration. Our ability to study these biological processes in detail have been enhanced by the findings that growth factors can induce precocious in vitro maturation of immature articular cartilage. The developmental and disease related changes that occur in the joint involve bone and cartilage and an ability to co-image these tissues would significantly increase our understanding of their intertwined roles. The simultaneous visualization of soft tissue, cartilage and bone changes is nowadays a challenge to overcome for conventional preclinical imaging modalities used for the joint disease follow-up. Three-dimensional X-ray Phase-Contrast Imaging methods (PCI) have been under perpetual developments for 20 years due to high performance for imaging low density objects and their ability to provide additional information compared to conventional X-ray imaging. In this protocol we detail the procedure used in our experiments from biopsy of the cartilage, generation of in vitro matured cartilage to data analysis of image collected using X-ray phase contrast imaging.

Live-dead assay on unlabeled cells using phase imaging with computational specificity.

Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, the step of exogenous staining makes these methods undesirable for rapid, nondestructive, and long-term investigation. Here, we present an instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity. This concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by label-free quantitative phase imaging. Demonstrated on different live cell cultures, the proposed method reports approximately 95% accuracy in identifying live and dead cells. The evolution of the cell dry mass and nucleus area for the labeled and unlabeled populations reveal that the chemical reagents decrease viability. The nondestructive approach presented here may find a broad range of applications, from monitoring the production of biopharmaceuticals to assessing the effectiveness of cancer treatments.

Volumetric High-Resolution X-Ray Phase-Contrast Virtual Histology of Breast Specimens With a Compact Laboratory System.

The assessment of margin involvement is a fundamental task in breast conserving surgery to prevent recurrences and reoperations. It is usually performed through histology, which makes the process time consuming and can prevent the complete volumetric analysis of large specimens. X-ray phase contrast tomography combines high resolution, sufficient penetration depth and high soft tissue contrast, and can therefore provide a potential solution to this problem. In this work, we used a high-resolution implementation of the edge illumination X-ray phase contrast tomography based on "pixel-skipping" X-ray masks and sample dithering, to provide high definition virtual slices of breast specimens. The scanner was originally designed for intra-operative applications in which short scanning times were prioritised over spatial resolution; however, thanks to the versatility of edge illumination, high-resolution capabilities can be obtained with the same system simply by swapping x-ray masks without this imposing a reduction in the available field of view. This makes possible an improved visibility of fine tissue strands, enabling a direct comparison of selected CT slices with histology, and providing a tool to identify suspect features in large specimens before slicing. Combined with our previous results on fast specimen scanning, this works paves the way for the design of a multi-resolution EI scanner providing intra-operative capabilities as well as serving as a digital pathology system.

Mitotic Activation Around Wound Edges and Epithelialization Repair in UVB-Induced Capsular Cataracts.

Purpose: Ultraviolet B (UVB) has been well documented to induce capsular cataracts; however, the mechanism of the lens epithelial cell-mediated repair process after UVB irradiation is not fully understood. The purpose of this study was to better understand lens epithelial cell repair after UVB-induced epithelium damage. Method: C57BL/6J mice were irradiated by various doses of UVB. Lens morphology and lens capsule opacity were monitored by slit lamp, darkfield microscopy, and phase-contrast microscopy. Lens epithelial cell mitotic activation and cell apoptosis were measured by immunohistochemistry. Lens epithelial ultrastructure was analyzed by transmission electron microscopy. Results: UVB irradiation above a dose of 2.87 kJ/m2 triggered lens epithelial cell apoptosis and subcapsular cataract formation, with a ring-shaped structure composed of multilayered epithelial cell clusters manifesting a dense ring-shaped capsular cataract. The epithelial cells immediately outside the edge of the ring-shaped aggregates transitioned to mitotically active cells and performed wound healing through the epithelialization process. However, repairs ceased when lens epithelial cells made direct contact, and scar-like tissue in the center of the anterior capsule remained even by 6 months after UVB irradiation. Conclusions: Our present study demonstrates that normally quiescent lens epithelial cells can be reactivated for epithelialization repair in response to UV-induced damage.

Rapid, label-free classification of tumor-reactive T cell killing with quantitative phase microscopy and machine learning.

Quantitative phase microscopy (QPM) enables studies of living biological systems without exogenous labels. To increase the utility of QPM, machine-learning methods have been adapted to extract additional information from the quantitative phase data. Previous QPM approaches focused on fluid flow systems or time-lapse images that provide high throughput data for cells at single time points, or of time-lapse images that require delayed post-experiment analyses, respectively. To date, QPM studies have not imaged specific cells over time with rapid, concurrent analyses during image acquisition. In order to study biological phenomena or cellular interactions over time, efficient time-dependent methods that automatically and rapidly identify events of interest are desirable. Here, we present an approach that combines QPM and machine learning to identify tumor-reactive T cell killing of adherent cancer cells rapidly, which could be used for identifying and isolating novel T cells and/or their T cell receptors for studies in cancer immunotherapy. We demonstrate the utility of this method by machine learning model training and validation studies using one melanoma-cognate T cell receptor model system, followed by high classification accuracy in identifying T cell killing in an additional, independent melanoma-cognate T cell receptor model system. This general approach could be useful for studying additional biological systems under label-free conditions over extended periods of examination.

Machine Learning Assisted Classification of Cell Lines and Cell States on Quantitative Phase Images.

In this report, we present implementation and validation of machine-learning classifiers for distinguishing between cell types (HeLa, A549, 3T3 cell lines) and states (live, necrosis, apoptosis) based on the analysis of optical parameters derived from cell phase images. Validation of the developed classifier shows the accuracy for distinguishing between the three cell types of about 93% and between different cell states of the same cell line of about 89%. In the field test of the developed algorithm, we demonstrate successful evaluation of the temporal dynamics of relative amounts of live, apoptotic and necrotic cells after photodynamic treatment at different doses.

A morphological classification of the fat particles found in the urinary sediment of patients with Fabry disease.

OBJECTIVES: The search in the urinary sediment (U-sed) of fat particles with peculiar morphology is a simple and inexpensive tool for the diagnosis of Fabry disease (FD) nephropathy. In this study we investigated the morphology of a high number of such fat particles with the aim to obtain a morphological classification to be used for their identification. METHODS: Study of the morphology of fat particles in the U-sed of a cohort of FD patients using: bright field plus phase contrast microscopy (BF + PC), polarized light microscopy (POL), and transmission electron microscopy (TEM). Comparison of these results with those obtained for the fat particles seen in the U-sed of a control group (CG) of patients with non-FD glomerulopathies. RESULTS: FD: 18 U-sed from six patients (three samples/patient) were prospectively investigated and 506 fat particles identified. With BF + PC, these were classified in eight morphological categories (seven of which were confirmed by TEM), and with POL in 10 others. CG: eight U-sed from eight patients were investigated and 281 fat particles identified. These fell into four BF + PC morphological categories and into eight POL categories. While some categories were significantly more frequent in FD others were more frequent in the CG. CONCLUSIONS: Our study demonstrates that 1. The morphology of fat particles found in the U-sed of FD patients is much wider and complex than that described so far 2. Several significant differences exist in the morphology of such fat particles between FD and CG patients.

Thyroid Stem Cells But Not Differentiated Thyrocytes Are Sensitive to Slightly Increased Concentrations of Heavy Metals.

Thyroid cancer incidence is markedly increased in volcanic areas where residents are biocontaminated by chronic lifelong exposure to slightly increased metals in the environment. Metals can influence the biology of living cells by a variety of mechanisms, depending not only on the dose and length of exposure but also on the type and stage of differentiation of target cells. We explored the effect of five heavy metals (Cu, Hg, Pd, W and Zn) at nanomolar concentrations (the biocontamination level in residents of the volcanic area in Sicily where thyroid cancer is increased) on stimulating the proliferation of undifferentiated (thyrospheres) and differentiated human thyroid cells. Thyrosphere proliferation was significantly increased after exposure to each individual metal and a greater stimulating effect was observed when a mixture of the examined metals was used. No effect was seen in differentiated thyrocytes. For all metals, the dose-response curve followed a biphasic pattern that is typical of hormesis. Thyrosphere growth concerned the size rather than number, except with the metal mixture. An altered morphology was also observed in metal-treated thyrospheres. Metal-induced proliferation was due to activation of the ERK1/2 pathway, as confirmed by growth inhibition when ERK1/2 signaling was blocked. These studies show that stem/precursor thyroid cells are sensitive to small increases in environmental metal concentrations that are harmless for differentiated thyrocytes.

Micro- and nano-tomography analysis of mouse soleus muscle using radiation.

In this study, we analyze radiation images of muscle structure of mice soleus muscles using radiation source-based microtomography and nanotomography. Soleus muscle samples were collected for analysis from 8-week-old male Institute of Cancer Research mice. First, phase-contrast X-ray microtomography was employed in these experiments. Then to obtain images with excellent contrast, imaging was performed using monochromatic light with excellent transmission power. To analyze additional muscle structures in higher magnification images than these images, nanotomography was performed, which facilitated obtaining high-magnification and high-resolution images. Muscle tissue microstructures were confirmed through three-dimensional images obtained from phase-contrast X-ray microtomography. Thus, the muscle tissue's overall shape at microscopic level can be captured. Additionally, a single muscle fiber was examined using hard X-ray nano-imaging, through which we could observe the alignment of countless myofibrils, that is, actin and myosin filaments in the muscle fibers. Thus, the methodology adopted here proved to be advantageous in analyzing the muscle tissue's overall structure with microtomography and in observing the myofibrils in detail using nanotomography.