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.

Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD.

Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure. In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results. In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels.

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.

Exploring feasibility of citric acid infused lignocellulosic waste derived from chestnut and water melon peels for phytofiltration of Eosin yellow dye from water.

The adsorption efficiency of cheap, ecofriendly, and easily available agro-waste, Trapa natans (Chestnut) and Citrullus lanatus (Watermelon) peels, has been investigated in their native forms (TNAT and CLAN) as well as citric acid impregnated forms (C-TNAT and C-CLAN), respectively, for the detoxification of toxic, deleterious, and carcinogenic Eosin yellow dye (EYD) from wastewater streams. Different operational parameters were optimized for the investigation of isothermal, kinetic and the thermodynamic models. R2 for sportive decontamination of Eosin by citric acid treated adsorbents were close to one, supporting the applicability of Langmuir, Temkin, and pseudo-second-order in this investigation. Maximum sorption capabilities were 222 and 667 mg/g for chemically treated bio-waste C-TNAT and C-CLAN, respectively, reflecting their efficient and promising performance, while Gibbs free energy revealed exothermic and spontaneous adsorption behavior. The kinetic statics for qe (cal) are quite close to qe (exp), indicating the viability and fitness of pseudo-second-order mechanisms. The present study suggests that both citric acid fabricated bio-waste C-TNAT and C-CLAN can be substantially employed to decontaminate persistent organic pollutants, like: Eosin yellow dye from wastewater using green approach to resolve socio-economic problems of developing countries.

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.

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.

Prediction of Epidermal Growth Factor Receptor Mutation Subtypes in Non-Small Cell Lung Cancer From Hematoxylin and Eosin-Stained Slides Using Deep Learning.

Accurate assessment of epidermal growth factor receptor (EGFR) mutation status and subtype is critical for the treatment of non-small cell lung cancer patients. Conventional molecular testing methods for detecting EGFR mutations have limitations. In this study, an artificial intelligence-powered deep learning framework was developed for the weakly supervised prediction of EGFR mutations in non-small cell lung cancer from hematoxylin and eosin-stained histopathology whole-slide images. The study cohort was partitioned into training and validation subsets. Foreground regions containing tumor tissue were extracted from whole-slide images. A convolutional neural network employing a contrastive learning paradigm was implemented to extract patch-level morphologic features. These features were aggregated using a vision transformer-based model to predict EGFR mutation status and classify patient cases. The established prediction model was validated on unseen data sets. In internal validation with a cohort from the University of Science and Technology of China (n = 172), the model achieved patient-level areas under the receiver-operating characteristic curve (AUCs) of 0.927 and 0.907, sensitivities of 81.6% and 83.3%, and specificities of 93.0% and 92.3%, for surgical resection and biopsy specimens, respectively, in EGFR mutation subtype prediction. External validation with cohorts from the Second Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of Wannan Medical College (n = 193) yielded patient-level AUCs of 0.849 and 0.867, sensitivities of 79.2% and 80.7%, and specificities of 91.7% and 90.7% for surgical and biopsy specimens, respectively. Further validation with The Cancer Genome Atlas data set (n = 81) showed an AUC of 0.861, a sensitivity of 84.6%, and a specificity of 90.5%. Deep learning solutions demonstrate potential advantages for automated, noninvasive, fast, cost-effective, and accurate inference of EGFR alterations from histomorphology. Integration of such artificial intelligence frameworks into routine digital pathology workflows could augment existing molecular testing pipelines.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.