Multiscale photocatalytic proximity labeling reveals cell surface neighbors on and between cells.

Proximity labeling proteomics (PLP) strategies are powerful approaches to yield snapshots of protein neighborhoods. Here, we describe a multiscale PLP method with adjustable resolution that uses a commercially available photocatalyst, Eosin Y, which upon visible light illumination activates different photo-probes with a range of labeling radii. We applied this platform to profile neighborhoods of the oncogenic epidermal growth factor receptor and orthogonally validated more than 20 neighbors using immunoassays and AlphaFold-Multimer prediction. We further profiled the protein neighborhoods of cell-cell synapses induced by bispecific T cell engagers and chimeric antigen receptor T cells. This integrated multiscale PLP platform maps local and distal protein networks on and between cell surfaces, which will aid in the systematic construction of the cell surface interactome, revealing horizontal signaling partners and reveal new immunotherapeutic opportunities.

A simple and economical method for unbiasedly quantifying the alveolar size of entire mouse lung tissue utilizing Hematoxylin and Eosin Staining.

Alveolar, the smallest structural and functional units within the respiratory system, play a crucial role in maintaining lung function. Alveolar damage is a typical pathological hallmark of respiratory diseases. Nevertheless, there is currently no simple, rapid, economical, and unbiased method for quantifying alveolar size for entire lung tissue. Here, firstly, we conducted lung sample slicing based on the size, shape, and distribution of airway branches of different lobes. Next, we performed HE staining on different slices. Then, we provided an unbiased quantification of alveolar size using free software ImageJ. Through this protocol, we demonstrated that C57Bl/6 mice exhibit varying alveolar sizes among different lobes. Collectively, we provided a simple and unbiased method for a more comprehensive quantification of alveolar size in mice, which holds promise for a broader range of respiratory research using mouse models.

Effective removal of acetamiprid and eosin Y by adsorption on pristine and modified MIL-101(Fe).

In this work, the efficacy of two metal-organic frameworks (MIL-101(Fe) and NH2-MIL-101(Fe)) in eliminating acetamiprid (ATP) insecticide and eosin Y (EY) dye from aqueous solution is tested. An analysis was conducted on the developed nanocomposite's optical, morphological, and structural characteristics. The adsorption isotherm, kinetics, thermodynamics, reusability, and mechanisms for ATP and EY dye removal were assessed. NH2-MIL-101(Fe) adsorbed 76% and 90% of ATP pesticide and EY dye, respectively after 10 to 15 min in optimum conditions. For both adsorbents, with regard to explaining the isotherm data, the Langmuir model offered the most accurate description. Moreover, the adsorption of ATP and EY dye is described by the pseudo-second-order kinetic model. The maximum adsorption capacities of ATP and EY dye on MIL-101(Fe) were 57.6 and 48.9 mg/g compared to 70.5 and 97.8 mg/g using NH2-MIL-101(Fe). The greatest amount of ATP and EY dye clearance was obtained at a neutral medium for both adsorbents. The results of this investigation demonstrate the effectiveness of MIL-101(Fe) and NH2-MIL-101(Fe) as effective substances in the adsorption process for removing pesticides and dyes from aqueous solution.

Polarization enhancement mechanism from tissue staining in multispectral Mueller matrix microscopy.

Mueller matrix microscopy can provide comprehensive polarization-related optical and structural information of biomedical samples label-freely. Thus, it is regarded as an emerging powerful tool for pathological diagnosis. However, the staining dyes have different optical properties and staining mechanisms, which can put influence on Mueller matrix microscopic measurement. In this Letter, we quantitatively analyze the polarization enhancement mechanism from hematoxylin and eosin (H&E) staining in multispectral Mueller matrix microscopy. We examine the influence of hematoxylin and eosin dyes on Mueller matrix-derived polarization characteristics of fibrous tissue structures. Combined with Monte Carlo simulations, we explain how the dyes enhance diattenuation and linear retardance as the illumination wavelength changed. In addition, it is demonstrated that by choosing an appropriate incident wavelength, more visual Mueller matrix polarimetric information can be observed of the H&E stained tissue sample. The findings can lay the foundation for the future Mueller matrix-assisted digital pathology.

Comparison of staining quality between rapid and routine hematoxylin and eosin staining of frozen breast tissue sections: an observational study.

OBJECTIVE: To compare the staining quality between rapid hematoxylin and eosin (H&E) staining and routine H&E staining of frozen breast tissue sections. METHODS: In this cross-sectional observational study, 120 frozen breast tissue sections were randomly assigned to rapid or routine H&E staining (n = 60 per group). Rapid H&E staining used a 7:1 mixture of modified Gill's hematoxylin and alcohol-soluble 1% eosin Y. The staining quality of each section was evaluated and scored. A score of >7 was considered excellent, a score of 6 to 7 good, and a score of ≤5 poor. RESULTS: The staining time for rapid staining was approximately 3 minutes, whereas that of routine staining was approximately 12 minutes. There were no significant differences in the staining quality scores or proportions of sections in each grade between the two staining methods. The proportions of sections that were classified as excellent or good were 96.7% and 98.3% for rapid and routine staining, respectively. CONCLUSIONS: In frozen breast tissue sections, rapid H&E staining may provide staining quality that is comparable to that of routine staining, while markedly reducing the staining time.

Identification of the role of DAB2 and CXCL8 in uterine spiral artery remodeling in early-onset preeclampsia.

Aberrant remodeling of uterine spiral arteries (SPA) is strongly associated with the pathogenesis of early-onset preeclampsia (EOPE). However, the complexities of SPA transformation remain inadequately understood. We conducted a single-cell RNA sequencing analysis of whole placental tissues derived from patients with EOPE and their corresponding controls, identified DAB2 as a key gene of interest and explored the mechanism underlying the communication between Extravillous trophoblast cells (EVTs) and decidual vascular smooth muscle cells (dVSMC) through cell models and a placenta-decidua coculture (PDC) model in vitro. DAB2 enhanced the motility and viability of HTR-8/SVneo cells. After exposure to conditioned medium (CM) from HTR-8/SVneoshNC cells, hVSMCs exhibited a rounded morphology, indicative of dedifferentiation, while CM-HTR-8/SVneoshDAB2 cells displayed a spindle-like morphology. Furthermore, the PDC model demonstrated that CM-HTR-8/SVneoshDAB2 was less conducive to vascular remodeling. Further in-depth mechanistic investigations revealed that C-X-C motif chemokine ligand 8 (CXCL8, also known as IL8) is a pivotal regulator governing the dedifferentiation of dVSMC. DAB2 expression in EVTs is critical for orchestrating the phenotypic transition and motility of dVSMC. These processes may be intricately linked to the CXCL8/PI3K/AKT pathway, underscoring its central role in intricate SPA remodeling.

Teacher-student collaborated multiple instance learning for pan-cancer PDL1 expression prediction from histopathology slides.

Programmed cell death ligand 1 (PDL1), as an important biomarker, is quantified by immunohistochemistry (IHC) with few established histopathological patterns. Deep learning aids in histopathological assessment, yet heterogeneity and lacking spatially resolved annotations challenge precise analysis. Here, we present a weakly supervised learning approach using bulk RNA sequencing for PDL1 expression prediction from hematoxylin and eosin (H&E) slides. Our method extends the multiple instance learning paradigm with the teacher-student framework, which assigns dynamic pseudo-labels for intra-slide heterogeneity and retrieves unlabeled instances using temporal ensemble model distillation. The approach, evaluated on 12,299 slides across 20 solid tumor types, achieves a weighted average area under the curve of 0.83 on fresh-frozen and 0.74 on formalin-fixed specimens for 9 tumors with PDL1 as an established biomarker. Our method predicts PDL1 expression patterns, validated by IHC on 20 slides, offering insights into histologies relevant to PDL1. This demonstrates the potential of deep learning in identifying diverse histological patterns for molecular changes from H&E images.

Transcutaneous carbon dioxide suppresses skeletal muscle atrophy in a mouse model of oral squamous cell carcinoma.

Cancer cachexia causes skeletal muscle atrophy, impacting the treatment and prognosis of patients with advanced cancer, but no treatment has yet been established to control cancer cachexia. We demonstrated that transcutaneous application of carbon dioxide (CO2) could improve local blood flow and reduce skeletal muscle atrophy in a fracture model. However, the effects of transcutaneous application of CO2 in cancer-bearing conditions are not yet known. In this study, we calculated fat-free body mass (FFM), defined as the skeletal muscle mass, and evaluated the expression of muscle atrophy markers and uncoupling protein markers as well as the cross-sectional area (CSA) to investigate whether transcutaneous application of CO2 to skeletal muscle could suppress skeletal muscle atrophy in cancer-bearing mice. Human oral squamous cell carcinoma was transplanted subcutaneously into the upper dorsal region of nude mice, and 1 week later, CO2 gas was applied to the legs twice a week for 4 weeks and FFM was calculated by bioimpedance spectroscopy. After the experiment concluded, the quadriceps were extracted, and muscle atrophy markers (muscle atrophy F-box protein (MAFbx), muscle RING-finger protein 1 (MuRF-1)) and uncoupling protein markers (uncoupling protein 2 (UCP2) and uncoupling protein 3 (UCP3)) were evaluated by real-time polymerase chain reaction and immunohistochemical staining, and CSA by hematoxylin and eosin staining. The CO2-treated group exhibited significant mRNA and protein expression inhibition of the four markers. Furthermore, immunohistochemical staining showed decreased MAFbx, MuRF-1, UCP2, and UCP3 in the CO2-treated group. In fact, the CSA in hematoxylin and eosin staining and the FFM revealed significant suppression of skeletal muscle atrophy in the CO2-treated group. We suggest that transcutaneous application of CO2 to skeletal muscle suppresses skeletal muscle atrophy in a mouse model of oral squamous cell carcinoma.

Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address this issue, we introduce a comprehensive histopathology image dataset named PathoLogy Images of Scanners and Mobile phones (PLISM). The dataset consisted of 46 human tissue types stained using 13 hematoxylin and eosin conditions and captured using 13 imaging devices. Precisely aligned image patches from different domains allowed for an accurate evaluation of color and texture properties in each domain. Variation in PLISM was assessed and found to be significantly diverse across various domains, particularly between whole-slide images and smartphones. Furthermore, we assessed the improvement in domain shift using a convolutional neural network pre-trained on PLISM. PLISM is a valuable resource that facilitates the precise evaluation of domain shifts in digital pathology and makes significant contributions towards the development of robust machine learning models that can effectively address challenges of domain shift in histological image analysis.

Morphological Evaluation and Immunohistochemical Analysis of the Reparative Potential of the Buccal Fat Pad.

Background and Objectives: There are many surgical techniques for oroantral communication treatment, one of which is the buccal fat pad. Of particular interest is the high reparative potential of the buccal fat pad, which may be contributed to by the presence of mesenchymal stem cells. The purpose of this work is to evaluate the reparative potential of BFP cells using morphological and immunohistochemical examination. Materials and Methods: 30 BFP samples were provided by the Clinic of Maxillofacial and Plastic Surgery of the Russian University of Medicine (Moscow, Russia) from 28 patients. Morphological examination of 30 BFP samples was performed at the Institute of Clinical Morphology and Digital Pathology of Sechenov University. Hematoxylin-eosin, Masson trichrome staining and immunohistochemical examination were performed to detect MSCs using primary antibodies CD133, CD44 and CD10. Results: During staining with hematoxylin-eosin and Masson's trichrome, we detected adipocytes of white adipose tissue united into lobules separated by connective tissue layers, a large number of vessels of different calibers, as well as the general capsule of BFP. The thin connective tissue layers contained neurovascular bundles. Statistical processing of the results of the IHC examination of the samples using the Mann-Whitney criterion revealed that the total number of samples in which the expression of CD44, CD10 and CD133 antigens was confirmed was statistically significantly higher than the number of samples where the expression was not detected (p < 0.05). Conclusions: During the morphological study of the BFP samples, we revealed statistically significant signs of MSCs presence (p < 0.05), including in the brown fat tissue, which proves the high reparative potential of this type of tissue and can make the BFP a choice option among other autogenous donor materials when eliminating OAC and other surgical interventions in the maxillofacial region.

A new Eosin Y-based 'turnon' fluorescent sensor for ratiometric sensing of toxic mercury ion (Hg2+) offering unaided eye detection and its antibacterial activity.

A unique fluorescent molecule (ND-S) was obtained from Eosin Y in two simple yet high yielding steps (1). ND-S has special metal ion sensing ability, such that it can selectively detect toxic Hg2+ present in very low concentration in aqueous solutions in the presence of other competing metal ions. The host-guest complexation is ratiometric and is associated with significant increase in fluorescence during the process. Isothermal titration calorimetry (ITC) experiments provided thermodynamic parameters related to interaction between ND-S and Hg2+. Using inductively coupled plasma mass spectrometry (ICP-MS), the Hg2+(aq) removal efficiency of ND-S was estimated to be 99.88%. Appreciable limit of detection (LOD = 7.4 nM) was observed. Other competing ions did not interfere with the sensing of Hg2+ by ND-S. The effects of external stimuli (temperature and pH) were studied. Besides, the complex (ND-M), formed by 1:1 coordination of ND-S and Hg2+ was found to be effective against the survival of Gram-positive bacteria (S. aureus and B. subtilis) with a high selectivity index. Moreover, bacterial cell death mechanism was studied systematically. Overall, we have shown the transformation of a toxic species (Hg2+), extracted from polluted water by a biocompatible sensor (ND-S), into an effective and potent antibacterial agent (ND-M).

DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology.

Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.

Nextflow pipeline for Visium and H&E data from patient-derived xenograft samples.

We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole-slide image (WSI), optimized for patient-derived xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genome input and stand-alone image analysis. We show the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of H&E imaging features reveal similar patterns arising from the two data modalities.

The Effects of Different Storage Conditions on Leukocytes in Human Breast Milk.

Objectives: Breast milk is the best baby food because it contains various nutrients and important factors for the baby's immune system, including leukocytes. This study aimed to determine the effects on morphology, number of cells and breast milk leukocytes count of various ways of storing breast milk based on different temperatures and storage durations. Methods: This study was conducted at the Biochemistry Laboratory, Integrated Laboratory and the Histology Laboratory, Universitas Indonesia, Jakarta, Indonesia from September 2022 to February 2023. Transitional breast milk samples from 7 breastfeeding mothers were utilised in the study. A total of 50 mL was divided into 4 tubes of 12.5 mL each and treated based on temperature, storage time and method of thawing frozen breast milk based on the Centers for Disease Control and Prevention's (CDC) recommendations for breast milk storage. The breast milk cells were isolated to calculate the cell number and leukocyte population. Subsequently, the breast milk cells were stained with haematoxylin and eosin to analyse the number and morphology of leukocytes. Results: The findings showed a significant decrease in the breast milk's total number and population and changes in the morphology of breast milk leukocytes after storage. Conclusion: This study indicates that CDC storage recommendations do not affect the quantity of the CD45+ leukocyte population; however, there is a decrease in the total number of leukocytes and alterations in their microscopic morphology. Thus, additional research is recommended to determine whether these modifications influence the function of the breast milk cells.

Direct three-dimensional segmentation of prostate glands with nnU-Net.

Significance: In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. Aim: To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). Approach: For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. Results: nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Conclusions: Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

TR4 worsen urosepsis by regulating GSDMD.

BACKGROUND: Urosepsis is a life-threatening organ disease in which pathogenic microorganisms in the urine enter the blood through the vessels, causing an imbalance in the immune response to infection. The aim of this study was to elucidate the role of testicular orphan receptor 4 (TR4) in urosepsis. METHODS: The role of TR4 in the progression and prognosis of urosepsis was confirmed by analyzing data from online databases and clinical human samples. To mimic urosepsis, we injected E. coli bacteria into the renal pelvis of mice to create a urosepsis model. Hematoxylin and eosin staining was used to observe histopathological changes in urosepsis. The effects of the upregulation or downregulation of TR4 on macrophage pyroptosis were verified in vitro. Chromatin immunoprecipitation assay was used to verify the effect of TR4 on Gasdermin D (GSDMD) transcription. RESULTS: TR4 was more highly expressed in the nonsurviving group than in the surviving group. Furthermore, overexpressing TR4 promoted inflammatory cytokine expression, and knocking down TR4 attenuated inflammatory cytokine expression. Mechanistically, TR4 promoted pyroptosis by regulating the expression of GSDMD in urosepsis. Furthermore, we also found that TR4 knockdown protected mice from urosepsis induced by the E. coli. CONCLUSIONS: TR4 functions as a key regulator of urosepsis by mediating pyroptosis, which regulates GSDMD expression. Targeting TR4 may be a potential strategy for urosepsis treatment.

Virtual formalin-fixed and paraffin-embedded staining of fresh brain tissue via stimulated Raman CycleGAN model.

Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid-/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors.

Pregnancy-Specific Beta-1-Glycoprotein 1 Increases HTR-8/SVneo Cell Migration through the Orai1/Akt Signaling Pathway.

The impaired invasion ability of trophoblast cells is related to the occurrence of preeclampsia (PE). We previously found that pregnancy-specific beta-1-glycoprotein 1 (PSG1) levels were decreased in the serum of individuals with early-onset preeclampsia (EOPE). This study investigated the effect of PSG1 on Orai1-mediated store-operated calcium entry (SOCE) and the Akt signaling pathway in human trophoblast cell migration. An enzyme-linked immunosorbent assay (ELISA) was used to determine the level of PSG1 in the serum of pregnant women with EOPE. The effects of PSG1 on trophoblast proliferation and migration were examined using cell counting kit-8 (CCK8) and wound healing experiments, respectively. The expression levels of Orai1, Akt, and phosphorylated Akt (p-Akt) were determined through Western blotting. The results confirmed that the serum PSG1 levels were lower in EOPE women than in healthy pregnant women. The PSG1 treatment upregulated the protein expression of Orai1 and p-Akt. The selective inhibitor of Orai1 (MRS1845) weakened the migration-promoting effect mediated by PSG1 via suppressing the Akt signaling pathway. Our findings revealed one of the mechanisms possibly involved in EOPE pathophysiology, which was that downregulated PSG1 may reduce the Orai1/Akt signaling pathway, thereby inhibiting trophoblast migration. PSG1 may serve as a potential target for the treatment and diagnosis of EOPE.

CellViT: Vision Transformers for precise cell segmentation and classification.

Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in combination with large scale pre-training in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vision Transformer called CellViT. CellViT is trained and evaluated on the PanNuke dataset, which is one of the most challenging nuclei instance segmentation datasets, consisting of nearly 200,000 annotated nuclei into 5 clinically important classes in 19 tissue types. We demonstrate the superiority of large-scale in-domain and out-of-domain pre-trained Vision Transformers by leveraging the recently published Segment Anything Model and a ViT-encoder pre-trained on 104 million histological image patches - achieving state-of-the-art nuclei detection and instance segmentation performance on the PanNuke dataset with a mean panoptic quality of 0.50 and an F1-detection score of 0.83. The code is publicly available at https://github.com/TIO-IKIM/CellViT.