Parallel content-based sub-image retrieval using hierarchical searching

Comparative transcriptome analysis identifies crucial candidate genes and pathways in the hypothalamic-pituitary-gonadal axis during external genitalia development of male geese

Background

All birds reproduce via internal fertilization, but only ~3% of male birds possess the external genitalia that allows for intromission. Waterfowl (e.g., duck and goose) are representatives of them, and the external genitalia development of male geese is directly related to mating ability. Notably, some male geese show abnormal external genitalia development during ontogenesis. However, until now little is known about the molecular mechanisms of the external genitalia development in goose. In the present study, comparative transcriptomic analyses were performed on the hypothalamus, pituitary gland, testis, and external genitalia isolated from the 245-day-old male Tianfu meat geese showing normal (NEGG, n = 3) and abnormal (AEGG, n = 3) external genitals in order to provide a better understanding of the mechanisms controlling the development of the external genitalia in aquatic bird species.

Results

There were 107, 284, 2192, and 1005 differentially expressed genes (DEGs) identified in the hypothalamus, pituitary gland, testis and external genitalia between NEGG and AEGG. Functional enrichment analysis indicated that the DEGs identified in the hypothalamus were mainly enriched in the ECM-receptor interaction pathway. The ECM-receptor interaction, focal adhesion, and neuroactive ligand-receptor interaction pathways were significantly enriched by the DEGs in the pituitary gland. In the testis, the DEGs were enriched in the neuroactive ligand-receptor interaction, cell cycle, oocyte meiosis, and purine metabolism. In the external genitalia, the DEGs were enriched in the metabolic, neuroactive ligand-receptor interaction, and WNT signaling pathways. Furthermore, through integrated analysis of protein-protein interaction (PPI) network and co-expression network, fifteen genes involved in the neuroactive ligand-receptor interaction and WNT signaling pathways were identified, including KNG1, LPAR2, LPAR3, NPY, PLCB1, AVPR1B, GHSR, GRM3, HTR5A, FSHB, FSHR, WNT11, WNT5A, WIF1, and WNT7B, which could play crucial roles in the development of goose external genitalia.

Conclusions

This study is the first systematically comparing the hypothalamus, pituitary gland, testis, and external genitalia transcriptomes of male geese exhibiting normal and abnormal external genitals. Both bioinformatic analysis and validation experiments indicated that the neuroactive ligand-receptor interaction pathway could regulate the WNT signaling pathway through PLCB1 to control male goose external genitalia development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08374-2.

RNA-combine: a toolkit for comprehensive analyses on transcriptome data from different sequencing platforms

Background

Understanding the transcriptome has become an essential step towards the full interpretation of the biological function of a cell, a tissue or even an organ. Many tools are available for either processing, analysing transcriptome data, or visualizing analysis results. However, most existing tools are limited to data from a single sequencing platform and only several of them could handle more than one analysis module, which are far from enough to meet the requirements of users, especially those without advanced programming skills. Hence, we still lack an open-source toolkit that enables both bioinformatician and non-bioinformatician users to process and analyze the large transcriptome data from different sequencing platforms and visualize the results.

Results

We present a Linux-based toolkit, RNA-combine, to automatically perform the quality assessment, downstream analysis of the transcriptome data generated from different sequencing platforms, including bulk RNA-seq (Illumina platform), single cell RNA-seq (10x Genomics) and Iso-Seq (PacBio) and visualization of the results. Besides, this toolkit is implemented with at least 10 analysis modules more than other toolkits examined in this study. Source codes of RNA-combine are available on GitHub: https://github.com/dongxuemin666/RNA-combine.

Conclusion

Our results suggest that RNA-combine is a reliable tool for transcriptome data processing and result interpretation for both bioinformaticians and non-bioinformaticians.

An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities

BACKGROUND

Population-based state cancer registries are an authoritative source for cancer statistics in the United States. They routinely collect a variety of data, including patient demographics, primary tumor site, stage at diagnosis, first course of treatment, and survival, on every cancer case that is reported across all U.S. states and territories. The goal of our project is to enrich NCI's Surveillance, Epidemiology, and End Results (SEER) registry data with high-quality population-based biospecimen data in the form of digital pathology, machine-learning-based classifications, and quantitative histopathology imaging feature sets (referred to here as Pathomics features).

MATERIALS AND METHODS

As part of the project, the underlying informatics infrastructure was designed, tested, and implemented through close collaboration with several participating SEER registries to ensure consistency with registry processes, computational scalability, and ability to support creation of population cohorts that span multiple sites. Utilizing computational imaging algorithms and methods to both generate indices and search for matches makes it possible to reduce inter- and intra-observer inconsistencies and to improve the objectivity with which large image repositories are interrogated.

RESULTS

Our team has created and continues to expand a well-curated repository of high-quality digitized pathology images corresponding to subjects whose data are routinely collected by the collaborating registries. Our team has systematically deployed and tested key, visual analytic methods to facilitate automated creation of population cohorts for epidemiological studies and tools to support visualization of feature clusters and evaluation of whole-slide images. As part of these efforts, we are developing and optimizing advanced search and matching algorithms to facilitate automated, content-based retrieval of digitized specimens based on their underlying image features and staining characteristics.

CONCLUSION

To meet the challenges of this project, we established the analytic pipelines, methods, and workflows to support the expansion and management of a growing repository of high-quality digitized pathology and information-rich, population cohorts containing objective imaging and clinical attributes to facilitate studies that seek to discriminate among different subtypes of disease, stratify patient populations, and perform comparisons of tumor characteristics within and across patient cohorts. We have also successfully developed a suite of tools based on a deep-learning method to perform quantitative characterizations of tumor regions, assess infiltrating lymphocyte distributions, and generate objective nuclear feature measurements. As part of these efforts, our team has implemented reliable methods that enable investigators to systematically search through large repositories to automatically retrieve digitized pathology specimens and correlated clinical data based on their computational signatures.

Machine Learning Methods for Histopathological Image Analysis: A Review

Histopathological images (HIs) are the gold standard for evaluating some types of tumors for cancer diagnosis. The analysis of such images is time and resource-consuming and very challenging even for experienced pathologists, resulting in inter-observer and intra-observer disagreements. One of the ways of accelerating such an analysis is to use computer-aided diagnosis (CAD) systems. This paper presents a review on machine learning methods for histopathological image analysis, including shallow and deep learning methods. We also cover the most common tasks in HI analysis, such as segmentation and feature extraction. Besides, we present a list of publicly available and private datasets that have been used in HI research.

Unbiased RNA-Seq-driven identification and validation of reference genes for quantitative RT-PCR analyses of pooled cancer exosomes

Background

Exosomes are extracellular vesicles (EVs) derived from endocytic compartments of eukaryotic cells which contain various biomolecules like mRNAs or miRNAs. Exosomes influence the biologic behaviour and progression of malignancies and are promising candidates as non-invasive diagnostic biomarkers or as targets for therapeutic interventions. Usually, quantitative real-time polymerase chain reaction (qRT-PCR) is used to assess gene expression in cancer exosomes, however, the ideal reference genes for normalization yet remain to be identified.

Results

In this study, we performed an unbiased analysis of high-throughput mRNA and miRNA-sequencing data from exosomes of patients with various cancer types and identify candidate reference genes and miRNAs in cancer exosomes. The expression stability of these candidate reference genes was evaluated by the coefficient of variation “CV” and the average expression stability value “M”. We subsequently validated these candidate reference genes in exosomes from an independent cohort of ovarian cancer patients and healthy control individuals by qRT-PCR.

Conclusions

Our study identifies OAZ1 and hsa-miR-6835-3p as the most reliable individual reference genes for mRNA and miRNA quantification, respectively. For superior accuracy, we recommend the use of a combination of reference genes - OAZ1/SERF2/MPP1 for mRNA and hsa-miR-6835-3p/hsa-miR-4468-3p for miRNA analyses.

Yottixel - An Image Search Engine for Large Archives of Histopathology Whole Slide Images

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. Image retrieval can provide pathologists with unprecedented access to the evidence embodied in already diagnosed and treated cases from the past. This paper proposes a search engine specialized for digital pathology, called Yottixel, a portmanteau for "one yotta pixel," alluding to the big-data nature of histopathology images. The most impressive characteristic of Yottixel is its ability to represent whole slide images (WSIs) in a compact manner. Yottixel can perform millions of searches in real-time with a high search accuracy and low storage profile. Yottixel uses an intelligent indexing algorithm capable of representing WSIs with a mosaic of patches which are then converted into barcodes, called "Bunch of Barcodes" (BoB), the most prominent performance enabler of Yottixel. The performance of the prototype platform is qualitatively tested using 300 WSIs from the University of Pittsburgh Medical Center (UPMC) and 2,020 WSIs from The Cancer Genome Atlas Program (TCGA) provided by the National Cancer Institute. Both datasets amount to more than 4,000,000 patches of 1000 × 1000 pixels. We report three sets of experiments that show that Yottixel can accurately retrieve organs and malignancies, and its semantic ordering shows good agreement with the subjective evaluation of human observers.

Comparative functional genomics analysis of cytochrome P450 gene superfamily in wheat and maize

BACKGROUND

The cytochrome P450s (CYP450s) as the largest enzyme family of plant metabolism participate in various physiological processes, whereas no study has demonstrated interest in comprehensive comparison of the genes in wheat and maize. Genome-wide survey, characterization and comparison of wheat and maize CYP450 gene superfamily are useful for genetic manipulation of the Gramineae crops.

RESULTS

In total, 1285 and 263 full-length CYP450s were identified in wheat and maize, respectively. According to standard nomenclature, wheat CYP450s (TaCYP450s) were categorized into 45 families, while maize CYP450s (ZmCYP450s) into 43 families. A comprehensive analysis of wheat and maize CYP450s, involved in functional domains, conserved motifs, phylogeny, gene structures, chromosome locations and duplicated events was performed. The result showed that each family/subfamily in both species exhibited characteristic features, suggesting their phylogenetic relationship and the potential divergence in their functions. Functional divergence analysis at the amino acid level of representative clans CYP51, CYP74 and CYP97 in wheat, maize and rice identified some critical amino acid sites that are responsible for functional divergence of a gene family. Expression profiles of Ta-, ZmCYP450s were investigated using RNA-seq data, which contribute to infer the potential functions of the genes during development and stress responses. We found in both species CYP450s had preferential expression in specific tissues, and many tissue-specific genes were identified. Under water-deficit condition, 82 and 39 significantly differentially expressed CYP450s were respectively detected in wheat and maize. These genes may have some roles in protecting plants against drought damage. Thereinto, fourteen CYP450s were selected to validate their expression level through qRT-PCR. To further elucidating molecular mechanisms of CYP450 action, gene co-expression network was constructed. In total, 477 TaCYP450s were distributed in 22 co-expression modules, and some co-expressed genes that likely take part in the same biochemical pathway were identified. For instance, the expression of TaCYP74A98_4D was highly correlated with TaLOX9, TaLOX36, TaLOX39, TaLOX44 and TaOPR8, and all of them may be involved in jasmonate (JA) biosynthesis. TaCYP73A201_3A showed coexpression with TaPAL1.25, TaCCoAOMT1.2, TaCOMT.1, TaCCR1.6 and TaLAC5, which probably act in the wheat stem and/or root lignin synthesis pathway.

CONCLUSION

Our study first established systematic information about evolutionary relationship, expression pattern and function characterization of CYP450s in wheat and maize.

Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks

Automatic skeletal muscle image segmentation (MIS) is crucial in the diagnosis of muscle-related diseases. However, accurate methods often suffer from expensive computations, which are not scalable to large-scale, whole-slide muscle images. In this paper, we present a fast and accurate method to enable the more clinically meaningful whole-slide MIS. Leveraging on recently popular convolutional neural network (CNN), we train our network in an end-to-end manner so as to directly perform pixelwise classification. Our deep network is comprised of the encoder and decoder modules. The encoder module captures rich and hierarchical representations through a series of convolutional and max-pooling layers. Then, the multiple decoders utilize multilevel representations to perform multiscale predictions. The multiscale predictions are then combined together to generate a more robust dense segmentation as the network output. The decoder modules have independent loss function, which are jointly trained with a weighted loss function to address fine-grained pixelwise prediction. We also propose a two-stage transfer learning strategy to effectively train such deep network. Sufficient experiments on a challenging muscle image dataset demonstrate the significantly improved efficiency and accuracy of our method compared with recent state of the arts.

High throughput automatic muscle image segmentation using parallel framework

BACKGROUND

Fast and accurate automatic segmentation of skeletal muscle cell image is crucial for the diagnosis of muscle related diseases, which extremely reduces the labor-intensive manual annotation. Recently, several methods have been presented for automatic muscle cell segmentation. However, most methods exhibit high model complexity and time cost, and they are not adaptive to large-scale images such as whole-slide scanned specimens.

METHODS

In this paper, we propose a novel distributed computing approach, which adopts both data and model parallel, for fast muscle cell segmentation. With a master-worker parallelism manner, the image data in the master is distributed onto multiple workers based on the Spark cloud computing platform. On each worker node, we first detect cell contours using a structured random forest (SRF) contour detector with fast parallel prediction and generate region candidates using a superpixel technique. Next, we propose a novel hierarchical tree based region selection algorithm for cell segmentation based on the conditional random field (CRF) algorithm. We divide the region selection algorithm into multiple sub-problems, which can be further parallelized using multi-core programming.

RESULTS

We test the performance of the proposed method on a large-scale haematoxylin and eosin (H &E) stained skeletal muscle image dataset. Compared with the standalone implementation, the proposed method achieves more than 10 times speed improvement on very large-scale muscle images containing hundreds to thousands of cells. Meanwhile, our proposed method produces high-quality segmentation results compared with several state-of-the-art methods.

CONCLUSIONS

This paper presents a parallel muscle image segmentation method with both data and model parallelism on multiple machines. The parallel strategy exhibits high compatibility to our muscle segmentation framework. The proposed method achieves high-throughput effective cell segmentation on large-scale muscle images.

Scalable Database Indexing and Fast Image Retrieval Based on Deep Learning and Hierarchically Nested Structure Applied to Remote Sensing and Plant Biology

Digitalisation has opened a wealth of new data opportunities by revolutionizing how images are captured. Although the cost of data generation is no longer a major concern, the data management and processing have become a bottleneck. Any successful visual trait system requires automated data structuring and a data retrieval model to manage, search, and retrieve unstructured and complex image data. This paper investigates a highly scalable and computationally efficient image retrieval system for real-time content-based searching through large-scale image repositories in the domain of remote sensing and plant biology. Images are processed independently without considering any relevant context between sub-sets of images. We utilize a deep Convolutional Neural Network (CNN) model as a feature extractor to derive deep feature representations from the imaging data. In addition, we propose an effective scheme to optimize data structure that can facilitate faster querying at search time based on the hierarchically nested structure and recursive similarity measurements. A thorough series of tests were carried out for plant identification and high-resolution remote sensing data to evaluate the accuracy and the computational efficiency of the proposed approach against other content-based image retrieval (CBIR) techniques, such as the bag of visual words (BOVW) and multiple feature fusion techniques. The results demonstrate that the proposed scheme is effective and considerably faster than conventional indexing structures.

Clinical Applications of Whole-slide Imaging in Anatomic Pathology

The development of whole-slide imaging has paved the way for digitizing of glass slides that are the basis for surgical pathology. This transformative technology has changed the landscape in research applications and education but despite its tremendous potential, its adoption for clinical use has been slow. We review the various niche applications that initiated awareness of this technology, provide examples of clinical use cases, and discuss the requirements and challenges for full adoption in clinical diagnosis. The opportunities for applications of image analysis tools in a workflow will be changed by integration of whole-slide imaging into routine diagnosis.

AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle

Idiopathic inflammatory myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross-section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) histogram-based feature extraction and quantification; (3) content-based image retrieval to search and retrieve similar cases in the database for comparative study; and (4) majority voting-based classification to provide decision support for computer-aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer-aided diagnosis of IIM.

Dictionary-enhanced imaging cytometry

State-of-the-art high-throughput microscopes are now capable of recording image data at a phenomenal rate, imaging entire microscope slides in minutes. In this paper we investigate how a large image set can be used to perform automated cell classification and denoising. To this end, we acquire an image library consisting of over one quarter-million white blood cell (WBC) nuclei together with CD15/CD16 protein expression for each cell. We show that the WBC nucleus images alone can be used to replicate CD expression-based gating, even in the presence of significant imaging noise. We also demonstrate that accurate estimates of white blood cell images can be recovered from extremely noisy images by comparing with a reference dictionary. This has implications for dose-limited imaging when samples belong to a highly restricted class such as a well-studied cell type. Furthermore, large image libraries may endow microscopes with capabilities beyond their hardware specifications in terms of sensitivity and resolution. We call for researchers to crowd source large image libraries of common cell lines to explore this possibility.

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to interobserver variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literature. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast, fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

BACKGROUND

We describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands.

RESULTS

The proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system.

CONCLUSIONS

Our work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.