Cell biologists have long sought the ability to observe intracellular structures in living cells without labels. This study presents procedures to adjust a commercially available apodized phase-contrast (APC) microscopy system for better visualizing the dynamic behaviors of various subcellular organelles in living cells. By harnessing the versatility of this technique to capture sequential images, we could observe morphological changes in cellular geometry after virus infection in real time without probes or invasive staining. The tune-up APC microscopy system is a highly efficient platform for simultaneously observing the dynamic behaviors of diverse subcellular structures with exceptional resolution.

Astrocytes are glycolytically active cells in the central nervous system playing a crucial role in various brain processes from homeostasis to neurotransmission. Astrocytes possess a complex branched morphology, frequently examined by fluorescent microscopy. However, staining and fixation may impact the properties of astrocytes, thereby affecting the accuracy of the experimental data of astrocytes dynamics and morphology. On the other hand, phase contrast microscopy can be used to study astrocytes morphology without affecting them, but the post-processing of the resulting low-contrast images is challenging. The main result of this work is a novel approach for recognition and morphological analysis of unstained astrocytes based on machine-learning recognition of microscopic images. We conducted a series of experiments involving the cultivation of isolated astrocytes from the rat brain cortex followed by microscopy. Using the proposed approach, we tracked the temporal evolution of the average total length of branches, branching, and area per astrocyte in our experiments. We believe that the proposed approach and the obtained experimental data will be of interest and benefit to the scientific communities in cell biology, biophysics, and machine learning.

Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope\'s sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.

Ribosome biogenesis is initiated in the nucleolus, a multiphase biomolecular condensate formed by liquid-liquid phase separation. The nucleolus is a powerful disease biomarker and stress biosensor whose morphology reflects function. Here we have used digital holographic microscopy (DHM), a label-free quantitative phase contrast microscopy technique, to detect nucleoli in adherent and suspension human cells. We trained convolutional neural networks to detect and quantify nucleoli automatically on DHM images. Holograms containing cell optical thickness information allowed us to define a novel index which we used to distinguish nucleoli whose material state had been modulated optogenetically by blue-light-induced protein aggregation. Nucleoli whose function had been impacted by drug treatment or depletion of ribosomal proteins could also be distinguished. We explored the potential of the technology to detect other natural and pathological condensates, such as those formed upon overexpression of a mutant form of huntingtin, ataxin-3, or TDP-43, and also other cell assemblies (lipid droplets). We conclude that DHM is a powerful tool for quantitatively characterizing nucleoli and other cell assemblies, including their material state, without any staining.

UNASSIGNED: Full-field optical coherence microscopy (FF-OCM) is a prevalent technique for backscattering and phase imaging with epi-detection. Traditional methods have two limitations: suboptimal utilization of functional information about the sample and complicated optical design with several moving parts for phase contrast.
UNASSIGNED: We report an OCM setup capable of generating dynamic intensity, phase, and pseudo-spectroscopic contrast with single-shot full-field video-rate imaging called bichromatic tetraphasic (BiTe) full-field OCM with no moving parts.
UNASSIGNED: BiTe OCM resourcefully uses the phase-shifting properties of anti-reflection (AR) coatings outside the rated bandwidths to create four unique phase shifts, which are detected with two emission filters for spectroscopic contrast.
UNASSIGNED: BiTe OCM overcomes the disadvantages of previous FF-OCM setup techniques by capturing both the intensity and phase profiles without any artifacts or speckle noise for imaging scattering samples in three-dimensional (3D). BiTe OCM also utilizes the raw data effectively to generate three complementary contrasts: intensity, phase, and color. We demonstrate BiTe OCM to observe cellular dynamics, image live, and moving micro-animals in 3D, capture the spectroscopic hemodynamics of scattering tissues along with dynamic intensity and phase profiles, and image the microstructure of fall foliage with two different colors.
UNASSIGNED: BiTe OCM can maximize the information efficiency of FF-OCM while maintaining overall simplicity in design for quantitative, dynamic, and spectroscopic characterization of biological samples.

Endothelial cell density (ECD) is a crucial parameter for the release of corneal grafts for transplantation. The Lions Eye Bank of Baden-Württemberg uses the \"Rhine-Tec Endothelial Analysis System\" for ECD quantification, which is based on a fixed counting frame method considering only a small sample of 15 to 40 endothelial cells. The measurement result therefore depends on the frame placement and manual correction of the cells counted within the frame. To increase the sample size and create higher objectivity, we developed a new method based on \"deep learning\" that automatically detects all visible endothelial cells in the image. This study aims to compare this new method with the conventional Rhine-Tec system. 9375 archived phase-contrast microscopic images of consecutive grafts from the Lions Eye Bank were evaluated with the deep learning method and compared with the corresponding archived analyses of the Rhine-Tec system. Means, Bland-Altman and correlation analyses were compared. Comparable results were obtained for both methods. The mean difference between the Rhine-Tec system and the deep learning method was only - 23 cells/mm2 (95% confidence interval - 29 to - 17). There was a statistically significant positive correlation between the two methods, with a correlation coefficient of 0.748. What was striking in the Bland-Altman analysis were clustered deviations in the cell density range between 2000 and 2500 cells/mm2 - with higher values in the Rhine-Tec system. The comparable results for cell density measurement values underline the validity of the deep learning-based method. The deviations around the formal threshold for graft release of 2000 cells/mm2 are most likely explained by the higher objectivity of the deep learning method and the fact that measurement frames and manual corrections were specifically selected to reach the formal threshold of 2000 cells/mm2 when the full area endothelial quality was good. This full area assessment of the graft endothelium cannot currently be replaced by deep learning methods and remains the most important basis for graft release for keratoplasty.
Die Endothelzelldichte ist ein objektiver Parameter für die Freigabe von Hornhauttransplantaten zur Operation. In der Lions Hornhautbank Baden-Württemberg wird für diese Quantifizierung das „Rhine-Tec Endothelial Analysis System“ verwendet, das auf der Methode des festen Zählrahmens basiert und nur eine kleine Stichprobe von 15 bis 40 Endothelzellen berücksichtigt. Das Messergebnis hängt daher von der Platzierung des Zählrahmens und der manuellen Nachkorrektur der im Zählrahmen gewerteten Zellen ab. Um den Stichprobenumfang zu erhöhen und eine höhere Objektivität zu schaffen, haben wir auf Grundlage von „Deep Learning“ eine neue Methode entwickelt, die alle sichtbaren Endothelzellen im Bild vollautomatisch erkennt. Ziel dieser Studie ist der Vergleich dieser neuen Methode mit dem konventionellen Rhine-Tec-System. 9375 archivierte phasenkontrastmikroskopische Bildaufnahmen von konsekutiven Transplantaten aus der Lions Hornhautbank wurden mit der Deep-Learning-Methode evaluiert und mit den korrespondierenden archivierten Analysen des Rhine-Tec-Systems verglichen. Zum Vergleich der Mittelwerte wurden Bland-Altman- und Korrelationsanalysen durchgeführt. Es ergaben sich vergleichbare Ergebnisse beider Methoden. Die mittlere Differenz zwischen Rhine-Tec-System und der Deep-Learning-Methode betrug lediglich − 23 Zellen/mm2 (95%-Konfidenzintervall: − 29 – − 17). Es zeigte sich eine statistisch signifikant positive Korrelation zwischen den beiden Methoden mit 0,748. Auffällig in der Bland-Altman-Analyse waren gehäufte Abweichungen im Zelldichtenbereich zwischen 2000 und 2500 Zellen/mm2 mit höheren Werten beim Rhine-Tec-System. Die vergleichbaren Ergebnisse bez. der Zelldichtenmesswerte unterstreichen die Wertigkeit des Deep-Learning-basierten Verfahrens. Die Abweichungen im Bereich der formalen Schwelle für eine Transplantatfreigabe von 2000 Zellen/mm2 sind sehr wahrscheinlich durch die höhere Objektivität der Deep-Learning-Methode erklärbar und der Tatsache geschuldet, dass Messrahmen und manuelle Nachkorrektur unter Berücksichtigung des Gesamtbildes aus der Endothelbewertung jeweils gezielt ausgewählt worden waren. Diese vollständige Sichtung des Transplantatendothels und Qualitätsbeurteilung kann aktuell noch nicht durch das Deep-Learning-System ersetzt werden und ist somit weiterhin die wichtigste Grundlage der Transplantatfreigabe zur Keratoplastik.

Although defocus can be used to generate partial phase contrast in transmission electron microscope images, cryo-electron microscopy (cryo-EM) can be further improved by the development of phase plates which increase contrast by applying a phase shift to the unscattered part of the electron beam. Many approaches have been investigated, including the ponderomotive interaction between light and electrons. We review the recent successes achieved with this method in high-resolution, single-particle cryo-EM. We also review the status of using pulsed or near-field enhanced laser light as alternatives, along with approaches that use scanning transmission electron microscopy (STEM) with a segmented detector rather than a phase plate.

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.

Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropriate demolition and removal operations at an early stage, a rapid method to measure atmospheric asbestos fibers is required. The current rapid measurement method is a combination of short-term atmospheric sampling and phase-contrast microscopy counting. However, visual counting takes a considerable amount of time and is not sufficiently fast. Using artificial intelligence (AI) to analyze microscope images to detect fibers may greatly reduce the time required for counting. Therefore, in this study, we investigated the use of AI image analysis for detecting fibers in phase-contrast microscope images. A series of simulated atmospheric samples prepared from standard samples of amosite and chrysotile were observed using a phase-contrast microscope. Images were captured, and training datasets were created from the counting results of expert analysts. We adopted 2 types of AI models-an instance segmentation model, namely the mask region-based convolutional neural network (Mask R-CNN), and a semantic segmentation model, namely the multi-level aggregation network (MA-Net)-that were trained to detect asbestos fibers. The accuracy of fiber detection achieved with the Mask R-CNN model was 57% for recall and 46% for precision, whereas the accuracy achieved with the MA-Net model was 95% for recall and 91% for precision. Therefore, satisfactory results were obtained with the MA-Net model. The time required for fiber detection was less than 1 s per image in both AI models, which was faster than the time required for counting by an expert analyst.

OBJECTIVE: 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.
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.
CONCLUSIONS: 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.