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Some compounds without apparent chelation sites have been shown to chelate cupric ions using the hematoxylin assay. Since these compounds also have reduction potential (direct antioxidant effect), the aim of this study was to determine the possible interference of reducing agents with the hematoxylin assay. Four different known reducing agents (hydroxylamine, vitamin C, trolox - a water-soluble form of vitamin E and reduced glutathione /GSH/) were selected for the study together with oxidized glutathione (GSSG) for comparison. All tested compounds behaved as cupric chelators in the spectrophotometric mildly competitive hematoxylin assay. In-depth analysis however showed that only GSH and GSSG were able to form complexes with both cupric and cuprous ions and only GSSG partly retained copper in its complexes in the more competitive bathocuproine assay. Further experiments showed that with the exception of GSSG, all other compounds reduce Cu2+ ions. Conclusion: Compounds reducing copper such as antioxidants can give false positive results in the hematoxylin-screening assay. GSSG is a stronger Cu chelator than GSH and does not reduce Cu, in contrast to the latter and thus may be a protective element after oxidation of GSH.
We previously established mouse models of biliary tract cancer (BTC) based on the injection of cells with biliary epithelial stem cell properties derived from KRAS(G12V)-expressing organoids into syngeneic mice. The resulting mouse tumors appeared to recapitulate the pathological features of human BTC. Here we analyzed images of hematoxylin and eosin (H&E) staining for both the mouse tumor tissue and human cholangiocarcinoma tissue by pixel-level clustering with machine learning. A pixel-clustering model that was established via training with mouse images revealed homologies of tissue structure between the mouse and human tumors, suggesting similarities in tumor characteristics independent of animal species. Analysis of the human cholangiocarcinoma tissue samples with the model also revealed that the entropy distribution of cancer regions was higher than that of noncancer regions, with the entropy of pixels thus allowing discrimination between these two types of regions. Histograms of entropy tended to be broader for noncancer regions of late-stage human cholangiocarcinoma. These analyses indicate that our mouse BTC models are appropriate for investigation of BTC carcinogenesis and may support the development of new therapeutic strategies. In addition, our pixel-level clustering model is highly versatile and may contribute to the development of a new BTC diagnostic tool.
A whole-slide imaging (WSI) system is a digital color imaging system used in digital pathology with the potential to substitute the conventional light microscope. A WSI system digitalizes a glass slide by converting the optical image to digital data with a scanner and then converting the digital data back to the optical image with a display. During the digital-to-optical or optical-to-digital conversion, a color space is required to define the mapping between the digital domain and the optical domain so that the numerical data of each color pixel can be interpreted meaningfully. Unfortunately, many current WSI products do not specify the designated color space clearly, which leaves the user using the universally default color space, sRGB. sRGB is a legacy color space that has a limited color gamut, which is known to be unable to reproduce all color shades present in histology slides. In this work, experiments were conducted to quantitatively investigate the limitation of the sRGB color space used in WSI systems. Eight hematoxylin and eosin (H and E)-stained tissue samples, including human bladder, brain, breast, colon, kidney, liver, lung, and uterus, were measured with a multispectral imaging system to obtain the true colors at the pixel level. The measured color truth of each pixel was converted into the standard CIELAB color space to test whether it was within the color gamut of the sRGB color space. Experiment results show that all the eight images have a portion of pixels outside the sRGB color gamut. In the worst-case scenario, the bladder sample, about 35% of the image exceeded the sRGB color gamut. The results suggest that the sRGB color space is inadequate for WSI scanners to encode H and E-stained whole-slide images, and an sRGB display may have insufficient color gamut for displaying H and E-stained histology images.
Acquiring information on the precise distribution of a tumor is essential to evaluate intratumoral heterogeneity. Conventional hematoxylin and eosin staining, which has been used by pathologists for more than 100 years, is the gold standard of tumor diagnosis. However, it is difficult to stain entire tumor tissues with hematoxylin and eosin and then acquire the three-dimensional distribution of cells in solid tumors due to difficulties in the staining and rinsing. In this paper, we propose a modified hematoxylin and eosin staining method, in which delipidation and ultrasound waves were applied to enhance tissue permeability and accelerate dye diffusion. This improved hematoxylin and eosin staining method is termed iHE (intact tissue hematoxylin and eosin staining). We applied the iHE method to stain intact organs of mice, which were then sectioned and imaged sequentially. The results showed that the whole tissue was stained homogeneously. Combined with micro-optical sectioning tomography (MOST), the iHE method can be used for 3D volume imaging and to evaluate the intratumoral heterogeneity of the entire tumor tissue spatially. Therefore, this method may help to accurately diagnose the invasion stage of tumors and guide clinical treatments.
Multiplexed Imaging technologies are powerful techniques that enable ultrahigh-plex spatial phenotyping of whole tissue sections at single cell spatial resolution. Co-Detection by Indexing (CODEX) multiplexing can detect up to 100 proteins using cyclic detection of DNA conjugated antibodies applied to tissue sections. However, it is necessary to correlate multiplexed fluorescent (mIF) spatial images with Hematoxylin and Eosin (H&E) stained sections post analysis. To effectively correlate mIF spatial images with H&E morphology, an (H&E) staining protocol was developed that is directly applied to the CODEX Fusion flow-cell slide after analysis allowing for direct H&E correlation and annotation with mIF images.
Histopathologically stained archived tissue slides are stored in hospital archives for years to decades. They are the largest available source of biological materials and are a potentially useful resource that can be used for retrospective epidemiological studies. DNA recovered from the slides can be used for several downstream molecular processes including polymerase chain reaction, single nucleotide polymorphism analysis, and whole genome sequencing. The DNA from these slides can be utilized to compare gene signatures of normal and diseased tissues. However, extraction of high-quality DNA from archived stained hematoxylin and eosin (H&E) slides remains challenging.
Deep learning applications are emerging as promising new tools that can support the diagnosis and classification of different cancer types. While such solutions hold great potential for hematological malignancies, there have been limited studies describing the use of such applications in this field. The rapid diagnosis of double/triple-hit lymphomas (DHLs/THLs) involving MYC, BCL2 and/or BCL6 rearrangements is obligatory for optimal patient care. Here, we present a novel deep learning tool for diagnosing DHLs/THLs directly from scanned images of biopsy slides. A total of 57 biopsies, including 32 in a training set (including five DH lymphoma cases) and 25 in a validation set (including 10 DH/TH cases), were included. The DHL-classifier demonstrated a sensitivity of 100%, a specificity of 87% and an AUC of 0.95, with only two false positive cases, compared to FISH. The DHL-classifier showed a 92% predictive value as a screening tool for performing conventional FISH analysis, over-performing currently used criteria. The work presented here provides the proof of concept for the potential use of an AI tool for the identification of DH/TH events. However, more extensive follow-up studies are required to assess the robustness of this tool and achieve high performances in a diverse population.
Hematoxylin (HT) as a natural phenolic dye compound is generally used together with eosin (E) dye as H&E in the histological staining of tissues. Here, we report for the first time the polymeric particle preparation from HT as poly(Hematoxylin) ((p(HT)) microgels via microemulsion method in a one-step using a benign crosslinker, glycerol diglycidyl ether (GDE). P(HT) microgels are about 10 µm and spherical in shape with a zeta potential value of -34.6 ± 2.8 mV and an isoelectric point (IEP) of pH 1.79. Interestingly, fluorescence properties of HT molecules were retained upon microgel formation, e.g., the fluorescence emission intensity of p(HT) at 343 nm was about 2.8 times less than that of the HT molecule at λex: 300 nm. P(HT) microgels are hydrolytically degradable and can be controlled by using an amount of crosslinker, GDE, e.g., about 40%, 20%, and 10% of p(HT) microgels was degraded in 15 days in aqueous environments for the microgels prepared at 100, 200, and 300% mole ratios of GDE to HT, respectively. Interestingly, HT molecules at 1000 mg/mL showed 22.7 + 0.4% cell viability whereas the p(HT) microgels exhibited a cell viability of 94.3 + 7.2% against fibroblast cells. Furthermore, even at 2000 mg/mL concentrations of HT and p(HT), the inhibition% of α-glucosidase enzyme were measured as 93.2 ± 0.3 and 81.3 ± 6.3%, respectively at a 0.03 unit/mL enzyme concentration, establishing some potential application of p(HT) microgels for neurogenerative diseases. Moreover, p(HT) microgels showed two times higher MBC values than HT molecules, e.g., 5.0 versus 2.5 mg/mL MIC values against Gram-negative E. coli and Gram-positive S. aureus, respectively.
Cell segmentation is a key step for a wide variety of biological investigations, especially in the context of muscle science. Currently, automated methods still struggle to perform skeletal muscle fiber quantification on Hematoxylin-Eosin (HE) stained histopathological whole slide images due to low contrast. On the other hand, the Deep Learning algorithm Cellpose offers new perspectives considering its increasing adoption for segmentation of a wide range of cells. Combining two open-source tools, Cellpose and QuPath, we developed MyoSOTHES, an automated Myofibers Segmentation wOrkflow Tuned for HE Staining. MyoSOTHES enables solving segmentation inconsistencies encountered by default Cellpose model in presence of large range size cells and provides information related to muscle Feret's diameter distribution and Centrally Nucleated Fibers, thus depicting muscle health and treatment effects. MyoSOTHES achieves high quality segmentation compared to baseline workflow with a detection F1-score increasing from 0.801 to 0.919 and a Root Mean Square Error (RMSE) on diameter improved by 31%. MyoSOTHES was validated on an animal study featuring gene transfer in [Formula: see text]-Sarcoglycanopathy, for which dose-response effect is visible and conclusions drawn are consistent with those previously published. MyoSOTHES thus paves the way for wide quantification of HE stained muscle sections and retrospective analysis of HE labeled slices used in laboratories for decades.
Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature-for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers-specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.
We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&E staining on Ki67-immunostained/H&E-decolored tissue samples. Finally, we tested our digital framework on a case series of oral squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA (tissue microarray) block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides and generated a "false color map" (FCM) based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features on H&E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears to be a promising tool to quickly forecast the tumor's proliferation fraction directly on routinely H&E-stained digital sections.
We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin-stained whole slide images (WSIs), to distinguish between stage II and stage IV colon cancers. Our discovery cohort comprised 100 stage II and stage IV colon cancer cases sourced from the University Hospitals Cleveland Medical Center (UHCMC). We performed initial (independent) model validation on 51 (143) stage II and 79 (54) stage IV colon cancer cases from UHCMC (The Cancer Genome Atlas's Colon Adenocarcinoma, TCGA-COAD, cohort). Our approach comprised the following steps: (1) a fully convolutional deep neural network with VGG-18 architecture was trained to locate cancer on WSIs; (2) another deep-learning model based on Mask-RCNN with Resnet-50 architecture was used to segment all nuclei from within the identified cancer region; (3) a total of 26 641 quantitative morphometric features pertaining to nuclear shape, size, and texture were extracted from within and outside tumor nuclei; (4) a random forest classifier was trained to distinguish between stage II and stage IV colon cancers using the five most discriminatory features selected by the Wilcoxon rank-sum test. Our trained classifier using these top five features yielded an AUC of 0.81 and 0.78, respectively, on the held-out cases in the UHCMC and TCGA validation sets. For 197 TCGA-COAD cases, the Cox proportional hazards model yielded a hazard ratio of 2.20 (95% CI 1.24-3.88) with a concordance index of 0.71, using only the top five features for risk stratification of overall survival. The Kaplan-Meier estimate also showed statistically significant separation between the low-risk and high-risk patients, with a log-rank P value of 0.0097. Finally, unsupervised clustering of the top five features revealed that stage IV colon cancers with peritoneal spread were morphologically more similar to stage II colon cancers with no long-term metastases than to stage IV colon cancers with hematogenous spread. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
The prognostic significance of tumor-infiltrating lymphocytes has been determined in cancers of the lung, colon and breast, though there is no standardized method for using this prognostic indicator for lung cancer. We applied a modified version of the method proposed by the International Immuno-Oncology Biomarkers Working Group to primary lung adenocarcinoma, which uses histologic findings of hematoxylin and eosin sections. The study included a total cohort of 146 lung adenocarcinoma patients who underwent lobectomy with lymph node dissection at two hospitals between 2008 and 2012. The full-face sections of hematoxylin and eosin-stained slides were reviewed, and we evaluated the level of tumor-infiltrating lymphocytes as a percentage of the area occupied out of the total intra-tumoral stromal area. Histopathologic factors include histologic grade, necrosis, extracellular mucin, lymphovascular invasion, lymph node metastasis, level of tumor infiltrating lymphocytes, tertiary lymphoid structures around the tumor, and the presence of a germinal center in tertiary lymphoid structures. The high level of tumor-infiltrating lymphocytes was found to be significantly correlated with the histologic grade (p = 0.023), necrosis (p = 0.042), abundance of tertiary lymphoid structures(p<0.001) and presence of a germinal center in tertiary lymphoid structures (p = 0.004). A high level of tumor-infiltrating lymphocytes was associated with better progression-free survival (p = 0.011) as well as overall survival (p = 0.049). On multivariable analysis, high tumor-infiltrating lymphocyte levels were a good independent prognostic factor for progression-free survival (Hazard ratio: 0.389, 95% confidence interval: 0.161-0.941, p = 0.036). Histologic evaluation of tumor-infiltrating lymphocytes level in lung adenocarcinoma with H&E sections therefore has prognostic value in routine surgical pathology.
The diagnosis of solid tumors of epithelial origin (carcinomas) represents a major part of the workload in clinical histopathology. Carcinomas consist of malignant epithelial cells arranged in more or less cohesive clusters of variable size and shape, together with stromal cells, extracellular matrix, and blood vessels. Distinguishing stroma from epithelium is a critical component of artificial intelligence (AI) methods developed to detect and analyze carcinomas. In this paper, we propose a novel automated workflow that enables large-scale guidance of AI methods to identify the epithelial component. The workflow is based on re-staining existing hematoxylin and eosin (H&E) formalin-fixed paraffin-embedded sections by immunohistochemistry for cytokeratins, cytoskeletal components specific to epithelial cells. Compared to existing methods, clinically available H&E sections are reused and no additional material, such as consecutive slides, is needed. We developed a simple and reliable method for automatic alignment to generate masks denoting cytokeratin-rich regions, using cell nuclei positions that are visible in both the original and the re-stained slide. The registration method has been compared to state-of-the-art methods for alignment of consecutive slides and shows that, despite being simpler, it provides similar accuracy and is more robust. We also demonstrate how the automatically generated masks can be used to train modern AI image segmentation based on U-Net, resulting in reliable detection of epithelial regions in previously unseen H&E slides. Through training on real-world material available in clinical laboratories, this approach therefore has widespread applications toward achieving AI-assisted tumor assessment directly from scanned H&E sections. In addition, the re-staining method will facilitate additional automated quantitative studies of tumor cell and stromal cell phenotypes.
The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.
Histological analysis is a fundamental and principal method used in biological research and even for disease diagnosis. The result shows the status of cells and tissues in organs and enables us to infer the condition of the whole body. The tissue staining method known as hematoxylin and eosin staining (HE) is one of the most general methods of investigating the status of cells and tissues. Hematoxylin stains the nucleus violet and eosin stains cytosol pink. HE staining shows the unique morphologies of tissues and cells. However, after being stained with HE, tissues are very difficult to use in another histological analysis because hematoxylin is hard to remove from the sections due to its stain stability. Therefore, serial sections of the tissue are used to obtain more information through another staining, including immunohistochemistry. The adjacent tissue section is not the same as the HE-stained section, however, so the results from the adjacent sections can cause confusion or ambiguity. The present study showed that our decolorization solution can decolor the hematoxylin or iron hematoxylin stain from stained structures, including the nucleus, and the decolored section could be stained again in another staining, including immunohistochemistry. This decolorization method is very valuable, in that it can determine the accurate distribution of substances and features in cells and tissues, and thus it can improve the robustness of the resulting data.
The cytotoxicity in freshwater fishes due to different industrial dyes in industrial effluents is a major worldwide issue. Hematoxylin dye has a wide range of uses in textile industries and laboratories. This study was aimed to evaluate the toxic effects of hematoxylin's sublethal effect in vitro in Cirrhinus mrigala. The fish was exposed to different grading concentrations of dye in the aquarium. Fish were sacrificed and dissected to remove the kidney after exposure to hematoxylin dye for specific time intervals. Nephrotoxicity and cytotoxicity induced by this dye were detected through histopathology by using the paraffin wax method. Immediate mortality of fish was noticed against the exposure to 0.08 g/L (LC50) concentration of dye, but at 0.008 mg/L and 0.018 mg/L, it showed tremendous tissue damage in the kidneys, significant reduction in fish growth. This dye induced many alterations in the kidney such as tubular degeneration, vacuolation, shrinkage of a glomerulus, reduced lumen, congestion in the kidney, glomerulonephritis, absence of Bowmen space, necrosis of the hematopoietic interstitial tissues, clogging of tubules, necrosis in the glomerulus and increased space between glomerulus and bowmen's capsule. Although this dye has a wide range of biological and industrial applications, a minute amount of hematoxylin released in effluents is quite toxic to aquatic fauna.
Immunohistochemical loss of CDX2 has been proposed as a biomarker of dismal survival in colorectal carcinoma (CRC), especially in UICC Stage II/III. However, it remains unclear, how CDX2 expression is related to central hematoxylin-eosin (HE)-based morphologic parameters defined by 2019 WHO classification and how its prognostic relevance is compared to these parameters.
Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II-III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge. In this study, we aimed to identify tissue-derived image features using a machine learning approach to predict OS in a mixed histology and grade cohort of lower grade glioma patients. To achieve this aim, we used H and E stained slides from the public LGG cohort of The Cancer Genome Atlas (TCGA) to create a machine learned dictionary of "image-derived visual words" associated with OS. We then evaluated the combined efficacy of using these visual words in predicting short versus long OS by training a generalized machine learning model. Finally, we mapped these predictive visual words back to molecular signaling cascades to infer potential drivers of the machine learned survival-associated phenotypes.
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