Sprod for de-noising spatially resolved transcriptomics data based on position and image information.

Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.

Deep Learning-Based H-Score Quantification of Immunohistochemistry-Stained Images.

Immunohistochemistry (IHC) is a well-established and commonly used staining method for clinical diagnosis and biomedical research. In most IHC images, the target protein is conjugated with a specific antibody and stained using diaminobenzidine (DAB), resulting in a brown coloration, whereas hematoxylin serves as a blue counterstain for cell nuclei. The protein expression level is quantified through the H-score, calculated from DAB staining intensity within the target cell region. Traditionally, this process requires evaluation by 2 expert pathologists, which is both time consuming and subjective. To enhance the efficiency and accuracy of this process, we have developed an automatic algorithm for quantifying the H-score of IHC images. To characterize protein expression in specific cell regions, a deep learning model for region recognition was trained based on hematoxylin staining only, achieving pixel accuracy for each class ranging from 0.92 to 0.99. Within the desired area, the algorithm categorizes DAB intensity of each pixel as negative, weak, moderate, or strong staining and calculates the final H-score based on the percentage of each intensity category. Overall, this algorithm takes an IHC image as input and directly outputs the H-score within a few seconds, significantly enhancing the speed of IHC image analysis. This automated tool provides H-score quantification with precision and consistency comparable to experienced pathologists but at a significantly reduced cost during IHC diagnostic workups. It holds significant potential to advance biomedical research reliant on IHC staining for protein expression quantification.

Enhanced Pathology Image Quality with Restore-Generative Adversarial Network.

Whole slide imaging is becoming a routine procedure in clinical diagnosis. Advanced image analysis techniques have been developed to assist pathologists in disease diagnosis, staging, subtype classification, and risk stratification. Recently, deep learning algorithms have achieved state-of-the-art performances in various imaging analysis tasks, including tumor region segmentation, nuclei detection, and disease classification. However, widespread clinical use of these algorithms is hampered by their performances often degrading due to image quality issues commonly seen in real-world pathology imaging data such as low resolution, blurring regions, and staining variation. Restore-Generative Adversarial Network (GAN), a deep learning model, was developed to improve the imaging qualities by restoring blurred regions, enhancing low resolution, and normalizing staining colors. The results demonstrate that Restore-GAN can significantly improve image quality, which leads to improved model robustness and performance for existing deep learning algorithms in pathology image analysis. Restore-GAN has the potential to be used to facilitate the applications of deep learning models in digital pathology analyses.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Spatial molecular profiling: platforms, applications and analysis tools.

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

Deep Learning of Rhabdomyosarcoma Pathology Images for Classification and Survival Outcome Prediction.

Rhabdomyosarcoma (RMS), the most common malignant soft tissue tumor in children, has several histologic subtypes that influence treatment and predict patient outcomes. Assistance with histologic classification for pathologists as well as discovery of optimized predictive biomarkers is needed. A convolutional neural network for RMS histology subtype classification was developed using digitized pathology images from 80 patients collected at time of diagnosis. A subsequent embryonal rhabdomyosarcoma (eRMS) prognostic model was also developed in a cohort of 60 eRMS patients. The RMS classification model reached a performance of an area under the receiver operating curve of 0.94 for alveolar rhabdomyosarcoma and an area under the receiver operating curve of 0.92 for eRMS at slide level in the test data set (n = 192). The eRMS prognosis model separated the patients into predicted high- and low-risk groups with significantly different event-free survival outcome (likelihood ratio test; P = 0.02) in the test data set (n = 136). The predicted risk group is significantly associated with patient event-free survival outcome after adjusting for patient age and sex (predicted high- versus low-risk group hazard ratio, 4.64; 95% CI, 1.05-20.57; P = 0.04). This is the first comprehensive study to develop computational algorithms for subtype classification and prognosis prediction for RMS histopathology images. Such models can aid pathology evaluation and provide additional parameters for risk stratification.

Deep learning in digital pathology for personalized treatment plans of cancer patients.

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

Mice With Increased Numbers of Polyploid Hepatocytes Maintain Regenerative Capacity But Develop Fewer Hepatocellular Carcinomas Following Chronic Liver Injury.

BACKGROUND & AIMS: Thirty to 90% of hepatocytes contain whole-genome duplications, but little is known about the fates or functions of these polyploid cells or how they affect development of liver disease. We investigated the effects of continuous proliferative pressure, observed in chronically damaged liver tissues, on polyploid cells. METHODS: We studied Rosa-rtTa mice (controls) and Rosa-rtTa;TRE-short hairpin RNA mice, which have reversible knockdown of anillin, actin binding protein (ANLN). Transient administration of doxycycline increases the frequency and degree of hepatocyte polyploidy without permanently altering levels of ANLN. Mice were then given diethylnitrosamine and carbon tetrachloride (CCl4) to induce mutations, chronic liver damage, and carcinogenesis. We performed partial hepatectomies to test liver regeneration and then RNA-sequencing to identify changes in gene expression. Lineage tracing was used to rule out repopulation from non-hepatocyte sources. We imaged dividing hepatocytes to estimate the frequency of mitotic errors during regeneration. We also performed whole-exome sequencing of 54 liver nodules from patients with cirrhosis to quantify aneuploidy, a possible outcome of polyploid cell divisions. RESULTS: Liver tissues from control mice given CCl4 had significant increases in ploidy compared with livers from uninjured mice. Mice with knockdown of ANLN had hepatocyte ploidy above physiologic levels and developed significantly fewer liver tumors after administration of diethylnitrosamine and CCl4 compared with control mice. Increased hepatocyte polyploidy was not associated with altered regenerative capacity or tissue fitness, changes in gene expression, or more mitotic errors. Based on lineage-tracing experiments, non-hepatocytes did not contribute to liver regeneration in mice with increased polyploidy. Despite an equivalent rate of mitosis in hepatocytes of differing ploidies, we found no lagging chromosomes or micronuclei in mitotic polyploid cells. In nodules of human cirrhotic liver tissue, there was no evidence of chromosome-level copy number variations. CONCLUSIONS: Mice with increased polyploid hepatocytes develop fewer liver tumors following chronic liver damage. Remarkably, polyploid hepatocytes maintain the ability to regenerate liver tissues during chronic damage without generating mitotic errors, and aneuploidy is not commonly observed in cirrhotic livers. Strategies to increase numbers of polypoid hepatocytes might be effective in preventing liver cancer.

Recent advances in understanding the roles of sialyltransferases in tumor angiogenesis and metastasis.

Abnormal glycosylation is a common characteristic of cancer cells and there is a lot of evidence that glycans can regulate the biological behavior of tumor cells. Sialylation modification, a form of glycosylation modification, plays an important role in cell recognition, cell adhesion and cell signal transduction. Abnormal sialylation on the surface of tumor cells is related to tumor migration and invasion, with abnormal expression of sialyltransferases being one of the main causes of abnormal sialylation. Recent studies provide a better understanding of the importance of the sialyltransferases, and how they influences cancer cell angiogenesis, adhesion and Epithelial-Mesenchymal Transition (EMT). The present review will provide a direction for future studies in determining the roles of sialyltransferases in cancer metastasis, and abnormal sialyltransferases are likely to be potential biomarkers for cancer.

Pathology Image Analysis Using Segmentation Deep Learning Algorithms.

With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges. Recently, deep learning algorithms have shown great promise in pathology image analysis, such as in tumor region identification, metastasis detection, and patient prognosis. Many machine learning algorithms, including convolutional neural networks, have been proposed to automatically segment pathology images. Among these algorithms, segmentation deep learning algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. Thus, deep learning-based pathology image segmentation has become an important tool in WSI analysis. In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning into pathology image analysis and to provide some potential ways of further improving segmentation performance. Although there have been previous reviews on using machine learning methods in digital pathology image analysis, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.