[Development of a Novel Male Reproductive Toxicity Assessment Method to Predict Male-mediated Effects on the Next Generation].

Less than 10% of the candidate drug compounds are associated with male reproductive toxicity. Genetic and/or epigenetic information on sperm may be crucial for fetal development. Therefore, developmental toxicity, such as paternally transmitted birth defects, is possible if genetic abnormalities in the male germ line persist and accumulate in the sperm during spermatogenesis. First, this study provides an overview of chemical and male reproductive toxicity, which may lead to developmental toxicity from the perspective of male reproduction. Second, we demonstrate methods for evaluating male reproductive toxicity to anticipate male-mediated developmental toxicity. We developed a novel staining technique for evaluating sperm quality, as well as a noninvasive imaging analysis of male reproductive toxicity. The former is a mammalian male germ cell-specific staining method using reactive blue 2 dye (RB2), as previously confirmed in human sperm, and a method for detecting the early-stage DNA fragmentation in a single nucleus from mouse spermatozoa using single-cell pulsed-field gel electrophoresis. The latter is a new, ready-to-use, and compact magnetic resonance imaging (MRI) platform utilizing a high-field permanent magnet to evaluate male reproductive toxicity. The histopathological analysis supported the suitability of the MRI platform. The present study, for the first time, revealed a rapid, noninvasive evaluation of male reproductive toxicity in vivo using compact MRI. These novel toxicity assessments can help predict male-mediated developmental toxicity, contributing to accelerated drug discovery and drug repositioning.

Rapid detection of guttae area using aniline blue staining in Fuchs endothelial corneal dystrophy mouse model.

Fuchs endothelial corneal dystrophy (FECD) is a leading cause of corneal endothelial degeneration resulting in impaired visual acuity. Excessive deposition of extracellular matrix (guttae) on Descemet's membrane (DM) is the hallmark of FECD. We sought to detect the guttae area rapidly using aniline blue (AB) staining in FECD mouse model. FECD mouse model was established via ultraviolet A (UVA) exposure. Masson's trichrome staining was utilized to stain the corneal sections. AB staining was utilized to stain both whole cornea tissues and stripped Descemet's membrane-endothelium complex (DMEC) flat mounts, while immunofluorescence staining of collagen I was employed to stain guttae areas. In Masson's trichrome staining, corneal collagen fibrils were stained blue with AB. The DMEC flat mounts were stained into relative dark blue areas and relative light blue areas using 2% AB staining. The areas of dark blue could almost overlap with collagen I-positive areas, and have an acellular centre and a moderately distinct boundary line with the surrounding corneal endothelial cells. In conclusion, AB staining is a rapid and effective method for the evaluation of the guttae areas in the FECD mouse model.

A ubiquitin-specific, proximity-based labeling approach for the identification of ubiquitin ligase substrates.

Over 600 E3 ligases in humans execute ubiquitination of specific target proteins in a spatiotemporal manner to elicit desired signaling effects. Here, we developed a ubiquitin-specific proximity-based labeling method to selectively biotinylate substrates of a given ubiquitin ligase. By fusing the biotin ligase BirA and an Avi-tag variant to the candidate E3 ligase and ubiquitin, respectively, we were able to specifically enrich bona fide substrates of a ligase using a one-step streptavidin pulldown under denaturing conditions. We applied our method, which we named Ub-POD, to the really interesting new gene (RING) E3 ligase RAD18 and identified proliferating cell nuclear antigen and several other critical players in the DNA damage repair pathway. Furthermore, we successfully applied Ub-POD to the RING ubiquitin ligase tumor necrosis factor receptor-associated factor 6 and a U-box-type E3 ubiquitin ligase carboxyl terminus of Hsc70-interacting protein. We anticipate that our method could be widely adapted to all classes of ubiquitin ligases to identify substrates.

The staining results of early gastric cancer by indigo carmine chromoendoscopy associated with histological structure: a retrospective study.

BACKGROUND: At present, conventional endoscopy and chromoendoscopy using indigo carmine (IC) is a very useful method to determine the demarcation line (DL) of early gastric cancer lesions, but it is not suitable for all lesions. AIMS: This study aimed to determine the applicable conditions for IC chromoendoscopy. METHODS: We retrospectively evaluated 187 lesions in 181 patients who had an endoscopic diagnosis of EGC and were treated with endoscopic submucosal dissection (ESD). According to the existence of the DL between the lesion mucosa and normal mucosa with IC chromoendoscopy, the lesions were divided into two groups: clear group and unclear group. Clinicopathological characteristics were evaluated in each group. From January 2022 to March 2023, the postoperative pathological sections of 19 lesions (81 slices) in the clear group and 19 lesions (80 slices) in unclear group were scanned with high definition, and the crypt structure between the two groups was evaluated. RESULTS: There was no significant difference in clinical factors between the clear group and unclear group. There were significant differences in crypt area, crypt length, and crypt opening diameter between the two groups. In the clear group, there were significant differences in crypt area, crypt length, and crypt opening diameter between the normal area and cancer area, but there was no significant difference in the unclear group. CONCLUSIONS: The margins of lesions with fused or absent crypt structures, a small crypt area, a short crypt length, and a short crypt opening diameter can be easily determined with IC chromoendoscopy.

Repurposing Proximity-Dependent Protein Labeling (BioID2) for Protein Interaction Mapping in E. coli.

Methods that identify protein-protein interactions are essential for understanding molecular mechanisms controlling biological systems. Proximity-dependent labeling has proven to be a valuable method for revealing protein-protein interaction networks in living cells. A mutant form of the biotin protein ligase enzyme from Aquifex aeolicus (BioID2) underpins this methodology by producing biotin that is attached to proteins that enter proximity to it. This labels proteins for capture, extraction, and identification. In this chapter, we present a toolkit for BioID2 specifically adapted for use in E. coli, exemplified by the chemotaxis protein CheA. We have created plasmids containing BioID2 as expression cassettes for proteins (e.g., CheA) fused to BioID2 at either the N or C terminus, optimized with an 8 × GGS linker. We provide a methodology for expression and verification of CheA-BioID2 fusion proteins in E. coli cells, the in vivo biotinylation of interactors by protein-BioID2 fusions, and extraction and analysis of interacting proteins that have been biotinylated.

Flagella and Cell Body Staining of Bacteria with Fluorescent Dyes.

Bacteria can propel themselves by rotating a flagellum or a flagellar bundle. To image this thin structure in motile bacteria, the flagella can be vitally stained with fluorophores. This chapter describes a flagellar staining protocol with the additional possibility of visualizing the cell body. It offers the opportunity to track conformational changes of flagella and simultaneously track the positions of the cell bodies. The additional use of a filter increases the number of motile cells and improves the signal-to-noise ratio of images. The flagellar staining requires a prior introduction of a surface-exposed cysteine, which is not covered in this chapter.

PathoDuet: Foundation models for pathological slide analysis of H&E and IHC stains.

Large amounts of digitized histopathological data display a promising future for developing pathological foundation models via self-supervised learning methods. Foundation models pretrained with these methods serve as a good basis for downstream tasks. However, the gap between natural and histopathological images hinders the direct application of existing methods. In this work, we present PathoDuet, a series of pretrained models on histopathological images, and a new self-supervised learning framework in histopathology. The framework is featured by a newly-introduced pretext token and later task raisers to explicitly utilize certain relations between images, like multiple magnifications and multiple stains. Based on this, two pretext tasks, cross-scale positioning and cross-stain transferring, are designed to pretrain the model on Hematoxylin and Eosin (H&E) images and transfer the model to immunohistochemistry (IHC) images, respectively. To validate the efficacy of our models, we evaluate the performance over a wide variety of downstream tasks, including patch-level colorectal cancer subtyping and whole slide image (WSI)-level classification in H&E field, together with expression level prediction of IHC marker, tumor identification and slide-level qualitative analysis in IHC field. The experimental results show the superiority of our models over most tasks and the efficacy of proposed pretext tasks. The codes and models are available at https://github.com/openmedlab/PathoDuet.

Conditions for late gadolinium enhancement MRI in myocardial infarction model rats that better reflect microscopic tissue staining.

Late gadolinium enhancement (LGE) is a widely used magnetic resonance imaging method for assessing cardiac disease. However, the relationship between different LGE signal thresholds and microscopic tissue staining images is unclear. In this study, we performed cardiovascular MRI on myocardial infarction (MI) model rats and evaluated the relationship between LGE with different signal thresholding methods and tissue staining images. We prepared 16 rats that underwent MRI 14-18 days following a surgery to create an MI model. We captured cine and LGE images of the cardiac short-axis and longitudinal two- and four-chamber views. The mean ± 2SD, ± 3SD, and ± 5SD of the pixel values in the non-infarcted area were defined as the LGE area. We compared areas of Sirius red staining, determined by the color tone, with their respective LGE areas at end-diastole and end-systole. We observed that the LGE area calculated as the mean ± 2SD of the non-infarcted area at end-diastole demonstrated a significant positive correlation with the area of Sirius red staining (Pearson's correlation coefficient in both: 0.81 [p < 0.01]). Therefore, the LGE area calculated as the mean ± 2SD of the non-infarcted area at end-diastole best reflected the MI area in tissue staining.

Assessing In Vivo Bacterial Extracellular Vesicle (BEV) Biodistribution Using Fluorescent Lipophilic Membrane Stains.

Bacterial extracellular vesicles (BEVs) are nano-size vesicles containing a cargo of bioactive molecules that can play key roles in microbe-microbe and microbe-host interactions. In tracking their biodistribution in vivo, BEVs can cross several physical host barriers including the intestinal epithelium, vascular endothelium, and blood-brain-barrier (BBB) to ultimately accumulate in tissues such as the liver, lungs, spleen, and the brain. This tissue-specific dissemination has been exploited for the delivery of biomolecules such as vaccines for mucosal delivery. Although numerous strategies for labeling and tracking BEVs have been described, most have constraints that impact on interpreting in vivo bioimaging patterns. Here, we describe a general method for labeling BEVs using lipophilic fluorescent membrane stains which can be adopted by non-expert users. We also describe how the procedure can be used to overcome potential limitations. Furthermore, we outline methods of quantitative ex vivo tissue imaging that can be used to evaluate BEV organ trafficking.

CX43 and oxidative stress are the targets of BCB staining to predict the developmental potential of buffalo oocytes.

This study used the brilliant cresyl blue (BCB) staining method to group buffalo oocytes (BCB+ and BCB-) and perform in vitro maturation, in vitro fertilization and embryo culture. At the same time, molecular biology techniques were used to detect gap junction protein expression and oxidative stress-related indicators to explore the molecular mechanism of BCB staining to predict oocyte developmental potential. The techniques of buffalo oocytes to analyse their developmental potential and used immunofluorescence staining to detect the expression level of CX43 protein, DCFH-DA probe staining to detect ROS levels and qPCR to detect the expression levels of the antioxidant-related genes SOD2 and GPX1. Our results showed that the in vitro maturation rate, embryo cleavage rate and blastocyst rate of buffalo oocytes in the BCB+ group were significantly higher than those in the BCB- group and the control group (p < .05). The expression level of CX43 protein in the BCB+ group was higher than that in the BCB- group both before and after maturation (p < .05). The intensity of ROS in the BCB+ group was significantly lower than that in the BCB- group (p < .05), and the expression levels of the antioxidant-related genes SOD2 and GPX1 in the BCB+ group were significantly higher than those in the BCB- group (p < .05). Brilliant cresyl blue staining could effectively predict the developmental potential of buffalo oocytes. The results of BCB staining were positively correlated with the expression of gap junction protein and antioxidant-related genes and negatively correlated with the reactive oxygen species level, suggesting that the mechanism of BCB staining in predicting the developmental potential of buffalo oocytes might be closely related to antioxidant activity.

Deep learning for rapid virtual H&E staining of label-free glioma tissue from hyperspectral images.

Hematoxylin and eosin (H&E) staining is a crucial technique for diagnosing glioma, allowing direct observation of tissue structures. However, the H&E staining workflow necessitates intricate processing, specialized laboratory infrastructures, and specialist pathologists, rendering it expensive, labor-intensive, and time-consuming. In view of these considerations, we combine the deep learning method and hyperspectral imaging technique, aiming at accurately and rapidly converting the hyperspectral images into virtual H&E staining images. The method overcomes the limitations of H&E staining by capturing tissue information at different wavelengths, providing comprehensive and detailed tissue composition information as the realistic H&E staining. In comparison with various generator structures, the Unet exhibits substantial overall advantages, as evidenced by a mean structure similarity index measure (SSIM) of 0.7731 and a peak signal-to-noise ratio (PSNR) of 23.3120, as well as the shortest training and inference time. A comprehensive software system for virtual H&E staining, which integrates CCD control, microscope control, and virtual H&E staining technology, is developed to facilitate fast intraoperative imaging, promote disease diagnosis, and accelerate the development of medical automation. The platform reconstructs large-scale virtual H&E staining images of gliomas at a high speed of 3.81 mm2/s. This innovative approach will pave the way for a novel, expedited route in histological staining.

CytoGAN: Unpaired staining transfer by structure preservation for cytopathology image analysis.

With the development of digital pathology, deep learning is increasingly being applied to endometrial cell morphology analysis for cancer screening. And cytology images with different staining may degrade the performance of these analysis algorithms. To address the impact of staining patterns, many strategies have been proposed and hematoxylin and eosin (H&E) images have been transferred to other staining styles. However, none of the existing methods are able to generate realistic cytological images with preserved cellular layout, and many important clinical structural information is lost. To address the above issues, we propose a different staining transformation model, CytoGAN, which can quickly and realistically generate images with different staining styles. It includes a novel structure preservation module that preserves the cell structure well, even if the resolution or cell size between the source and target domains do not match. Meanwhile, a stain adaptive module is designed to help the model generate realistic and high-quality endometrial cytology images. We compared our model with ten state-of-the-art stain transformation models and evaluated by two pathologists. Furthermore, in the downstream endometrial cancer classification task, our algorithm improves the robustness of the classification model on multimodal datasets, with more than 20 % improvement in accuracy. We found that generating specified specific stains from existing H&E images improves the diagnosis of endometrial cancer. Our code will be available on github.

Automatic segmentation and classification of Papanicolaou-stained cells and dataset for oral cancer detection.

BACKGROUND AND OBJECTIVE: Papanicolaou staining has been successfully used to assist early detection of cervix cancer for several decades. We postulate that this staining technique can also be used for assisting early detection of oral cancer, which is responsible for about 300,000 deaths every year. The rational for such claim includes two key observations: (i) nuclear atypia, i.e., changes in volume, shape, and staining properties of the cell nuclei can be linked to rapid cell proliferation and genetic instability; and (ii) Papanicolaou staining allows one to reliably segment cells' nuclei and cytoplasms. While Papanicolaou staining is an attractive tool due to its low cost, its interpretation requires a trained pathologist. Our goal is to automate the segmentation and classification of morphological features needed to evaluate the use of Papanicolaou staining for early detection of mouth cancer. METHODS: We built a convolutional neural network (CNN) for automatic segmentation and classification of cells in Papanicolaou-stained images. Our CNN was trained and evaluated on a new image dataset of cells from oral mucosa consisting of 1,563 Full HD images from 52 patients, annotated by specialists. The effectiveness of our model was evaluated against a group of experts. Its robustness was also demonstrated on five public datasets of cervical images captured with different microscopes and cameras, and having different resolutions, colors, background intensities, and noise levels. RESULTS: Our CNN model achieved expert-level performance in a comparison with a group of three human experts on a set of 400 Papanicolaou-stained images of the oral mucosa from 20 patients. The results of this experiment exhibited high Interclass Correlation Coefficient (ICC) values. Despite being trained on images from the oral mucosa, it produced high-quality segmentation and plausible classification for five public datasets of cervical cells. Our Papanicolaou-stained image dataset is the most diverse publicly available image dataset for the oral mucosa in terms of number of patients. CONCLUSION: Our solution provides the means for exploring the potential of Papanicolaou-staining as a powerful and inexpensive tool for early detection of oral cancer. We are currently using our system to detect suspicious cells and cell clusters in oral mucosa slide images. Our trained model, code, and dataset are available and can help practitioners and stimulate research in early oral cancer detection.

Proximity labeling expansion microscopy (PL-ExM) evaluates interactome labeling techniques.

Understanding protein-protein interactions (PPIs) through proximity labeling has revolutionized our comprehension of cellular mechanisms and pathology. Various proximity labeling techniques, such as HRP, APEX, BioID, TurboID, and µMap, have been widely used to biotinylate PPIs or organelles for proteomic profiling. However, the variability in labeling precision and efficiency of these techniques often results in limited reproducibility in proteomic detection. We address this persistent challenge by introducing proximity labeling expansion microscopy (PL-ExM), a super-resolution imaging technique that combines expansion microscopy with proximity labeling techniques. PL-ExM enabled up to 17 nm resolution with microscopes widely available, providing visual comparison of the labeling precision, efficiency, and false positives of different proximity labeling methods. Our mass spectrometry proteomic results confirmed that PL-ExM imaging is reliable in guiding the selection of proximity labeling techniques and interpreting the proteomic results with new spatial information.

Controlled labelling of tracer antibodies for time-resolved fluorescence-based immunoassays.

Tracer antibodies, which are labelled with fluorescent or other type of reporter molecules, are widely employed in diagnostic immunoassays. Time-resolved fluorescence immunoassay (TRFIA), recognized as one of the most sensitive immunoassay techniques, utilizes tracers labelled with lanthanide ion (Ln) chelates. The conventional approach for conjugating isothiocyanate (ITC) Ln-chelates to antibodies involves random chemical targeting of the primary amino group of Lys residues, requiring typically overnight exposure to an elevated pH of 9-9.3 and leading to heterogeneity. Moreover, efforts to enhance the sensitivity of the assays by introducing a higher number of Ln-chelates per tracer antibody are associated with an elevated risk of targeting critical amino acid residues in the binding site, compromising the binding properties of the antibody. Herein, we report a method to precisely label recombinant antibodies with a defined number of Ln-chelates in a well-controlled manner by employing the SpyTag/SpyCatcher protein ligation technology. We demonstrate the functionality of the method with a full-length recombinant antibody (IgG) as well as an antibody fragment by producing site-specifically labelled antibodies for TRFIA for cardiac troponin I (cTnI) detection with a significant improvement in assay sensitivity compared to that with conventionally labelled tracer antibodies. Overall, our data clearly illustrates the benefits of the site-specific labelling strategy for generating high-performing tracer antibodies for TRF immunoassays.

ß-Galactosidase- and Photo-Activatable Fluorescent Probes for Protein Labeling and Super-Resolution STED Microscopy in Living Cells.

We report on the synthesis of two fluorescent probes which can be activated by ß-Galactosidase (ß-Gal) enzymes and/or light. The probes contained 2-nitro-4-oxybenzyl and 3-nitro-4-oxybenzyl fragments, with ß-Gal residues linked to C-4. We performed the enzymatic and photoactivation of the probes in a cuvette and compared them, prior to the labeling of Vimentin-Halo fusion protein in live cells with overexpressed ß-galactosidase. The dye fluorescence afforded the observation of enzyme activity by means of confocal and super-resolution optical microscopy based on stimulated emission depletion (STED). The tracing of enzymatic activity with the retention of activated fluorescent products inside cells was combined with super-resolution imaging as a tool for use in biomedicine and life science.

Identification of Neighboring Proteins of Endosomal Regulators by Using TurboID-Based Proximity Labeling.

In the endomembrane system, multivesicular bodies (MVBs) play a crucial role in sorting ubiquitinated membrane proteins into intraluminal vesicles for degradation upon fusion with vacuoles or lysosomes. This process involves regulations by multiprotein complexes, including endosomal sorting complex required for transport (ESCRT) I-III, and accessory proteins. Although many organellar proteomes have been identified in plant cells, the information of specific proteomes associated with regulators engaged in MVB biogenesis remains limited. Here, using the ESCRT component FREE1 as an example, we describe a method to identify neighboring proteins of endosomal regulators by using an approach of TurboID-based proximity labeling.

Nuclear-Based Labeling of Cellular Immunotherapies: A Simple Protocol for Preclinical Use.

Labeling and tracking existing and emerging cell-based immunotherapies using nuclear imaging is widely used to guide the preclinical phases of development and testing of existing and new emerging off-the-shelf cell-based immunotherapies. In fact, advancing our knowledge about their mechanism of action and limitations could provide preclinical support and justification for moving towards clinical experimentation of newly generated products and expedite their approval by the Food and Drug Administration (FDA).Here we provide the reader with a ready to use protocol describing the labeling methodologies and practical procedures to render different candidate cell therapies in vivo traceable by nuclear-based imaging. The protocol includes sufficient practical details to aid researchers at all career stages and from different fields in familiarizing with the described concepts and incorporating them into their work.