Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species.

Detecting skeletal or bone-related deformities in model and aquaculture fish is vital for numerous biomedical studies. In biomedical research, model fish with bone-related disorders are potential indicators of various chemically induced toxins in their environment or poor dietary conditions. In aquaculture, skeletal deformities are affecting fish health, and economic losses are incurred by fish farmers. This survey paper focuses on showcasing the cutting-edge image analysis tools and techniques based on artificial intelligence that are currently applied in the analysis of bone-related deformities in aquaculture and model fish. These methods and tools play a significant role in improving research by automating various aspects of the analysis. This paper also sheds light on some of the hurdles faced when dealing with high-content bioimages and explores potential solutions to overcome these challenges.

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy.

As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. Virtual microscopy provides fresh opportunities for investigating user behavior during the histology learning process, through digitized histological slides. This study establishes how students' perceptions and user behavior data can be processed and analyzed using machine learning algorithms. These also provide predictive data called learning analytics that enable predicting students' performance and behavior favorable for academic success. This information can be interpreted and used for validating instructional designs. Data on the perceptions, performances, and user behavior of 552 students enrolled in a histology course were collected from the virtual microscope, Cytomine®. These data were analyzed using an ensemble of machine learning algorithms, the extra-tree regression method, and predictive statistics. The predictive algorithms identified the most pertinent histological slides and descriptive tags, alongside 10 types of student behavior conducive to academic success. We used these data to validate our instructional design, and align the educational purpose, learning outcomes, and evaluation methods of digitized histological slides on Cytomine®. This model also predicts students' examination scores, with an error margin of <0.5 out of 20 points. The results empirically demonstrate the value of a digital learning environment for both students and teachers of histology.

Bioimage analysis workflows: community resources to navigate through a complex ecosystem.

Workflows are the keystone of bioimage analysis, and the NEUBIAS (Network of European BioImage AnalystS) community is trying to gather the actors of this field and organize the information around them.  One of its most recent outputs is the opening of the F1000Research NEUBIAS gateway, whose main objective is to offer a channel of publication for bioimage analysis workflows and associated resources. In this paper we want to express some personal opinions and recommendations related to finding, handling and developing bioimage analysis workflows.  The emergence of "big data" in bioimaging and resource-intensive analysis algorithms make local data storage and computing solutions a limiting factor. At the same time, the need for data sharing with collaborators and a general shift towards remote work, have created new challenges and avenues for the execution and sharing of bioimage analysis workflows. These challenges are to reproducibly run workflows in remote environments, in particular when their components come from different software packages, but also to document them and link their parameters and results by following the FAIR principles (Findable, Accessible, Interoperable, Reusable) to foster open and reproducible science. In this opinion paper, we focus on giving some directions to the reader to tackle these challenges and navigate through this complex ecosystem, in order to find and use workflows, and to compare workflows addressing the same problem. We also discuss tools to run workflows in the cloud and on High Performance Computing resources, and suggest ways to make these workflows FAIR.

Multi-Task Pre-Training of Deep Neural Networks for Digital Pathology.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

BIAFLOWS: A Collaborative Framework to Reproducibly Deploy and Benchmark Bioimage Analysis Workflows.

Image analysis is key to extracting quantitative information from scientific microscopy images, but the methods involved are now often so refined that they can no longer be unambiguously described by written protocols. We introduce BIAFLOWS, an open-source web tool enabling to reproducibly deploy and benchmark bioimage analysis workflows coming from any software ecosystem. A curated instance of BIAFLOWS populated with 34 image analysis workflows and 15 microscopy image datasets recapitulating common bioimage analysis problems is available online. The workflows can be launched and assessed remotely by comparing their performance visually and according to standard benchmark metrics. We illustrated these features by comparing seven nuclei segmentation workflows, including deep-learning methods. BIAFLOWS enables to benchmark and share bioimage analysis workflows, hence safeguarding research results and promoting high-quality standards in image analysis. The platform is thoroughly documented and ready to gather annotated microscopy datasets and workflows contributed by the bioimaging community.

Open Practices and Resources for Collaborative Digital Pathology.

In this paper, we describe open practices and open resources in the field of digital pathology with a specific focus on approaches that ease collaboration in research and education settings. Our review includes open access journals and open peer review, open-source software (libraries, desktop tools, and web applications), and open access collections. We illustrate applications and discuss current limitations and perspectives.

Ozone-primed neutrophils promote early steps of tumour cell metastasis to lungs by enhancing their NET production.

BACKGROUND: Air pollution, including particulates and gazes such as ozone (O3), is detrimental for patient's health and has repeatedly been correlated to increased morbidity and mortality in industrialised countries. Although studies have described a link between ambient particulate matter and increased lung cancer morbidity, no direct relation has yet been established between O3 exposure and metastatic dissemination to lungs. OBJECTIVES: To outline the mechanisms through which pulmonary O3 exposure modulates metastasis kinetics in an experimental mouse model of O3 exposure. METHODS: Metastatic responses to pulmonary O3 exposure were assessed using a reliable experimental mouse model of concomitant pulmonary O3 exposure and tumour cell injection. Roles of neutrophils in O3-induced lung metastasis were highlighted using blocking anti-Ly6G antibodies; moreover, the implication of neutrophil extracellular traps (NETs) in metastatic processes was evaluated using MRP8cre-Pad4lox/lox mice or by treating mice with DNase I. RESULTS: Pulmonary O3 exposure strongly facilitates the establishment of lung metastasis by (1) Inducing a pulmonary injury and neutrophilic inflammation, (2) Influencing very early steps of metastasis, (3) Priming neutrophils' phenotype to release NETs that favour tumour cell colonisation in lungs. The ability of O3-primed neutrophils to enhance lung colonisation by tumour cells was proven after their adoptive transfer in Balb/c mice unexposed to O3. CONCLUSIONS: Pulmonary neutrophils induced by O3 promote metastatic dissemination to lungs by producing NETs. These findings open new perspectives to improve treatment and prevention strategies in patients affected by metastatic diseases.

Cytomine: Toward an Open and Collaborative Software Platform for Digital Pathology Bridged to Molecular Investigations.

PURPOSE: Digital histology is being increasingly used in research and clinical applications. In parallel, new tissue imaging methods (e.g., imaging mass spectrometry) are currently regarded as very promising approaches for better molecular diagnosis in pathology. However, these new data sources are still often underexploited because of the lack of collaborative software to share and correlate information for multimodal analysis. EXPERIMENTAL DESIGN: The open science paradigm is followed to develop new features in the web-based Cytomine software to support next-generation digital pathology bridged to molecular investigations. RESULTS: New open-source developments allow to explore whole-slide classical histology with Matrix Assisted Laser Desorption Ionisation (MALDI) imaging and to support preprocessing for biomarker discovery using laser microdissection-based microproteomics. CONCLUSIONS AND CLINICAL RELEVANCE: The updated version of Cytomine is the first open and web-based tool to enable sharing data from classical histology, molecular imaging, and cell counting for proteomics preprocessing. It holds good promise to fulfill imminent needs in molecular histopathology.

[A Massive Open Oneline Course (MOOC) on pratical histology: A goal, a tool, a large public ! Return on a first experience]. / Un « Massive Open Online Course ¼ (MOOC) sur des travaux pratiques en histologie : un objectif, un outil, un public varié ! Retour sur une première expérience.

A goal ! The MOOC entitled "Introduction to Histology, A Human Tissue Exploration" correspond to our vision of the practice of General Histology, which is based on the ability to diagnose 5 families of biological tissues. Ultimately, participants must be able to recognize the different types of cells and all the surrounding elements in order to understand how they organize themselves to form tissues with specific functions. A tool ! This know-how is based on reasoning from observations of microscopic structures. Learners are therefore invited to manipulate a virtual microscope to explore biological samples on histological slides digitized. Annotations, comments, drawings or photos are associated with landmarks that enrich the study of these histological sections. A target audience ! Two educational paths allow deepening the subject in a different way and thus matching the goals or motivations of each one. After a first year of experience, usage statistics and surveys of our learners show that the MOOC Histo has allowed each of them to find an interest and federate a community of motivated learners.

Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach.

The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research.

Human Wharton's Jelly-Derived Stem Cells Display a Distinct Immunomodulatory and Proregenerative Transcriptional Signature Compared to Bone Marrow-Derived Stem Cells.

Mesenchymal stromal cells (MSCs) are multipotent stem cells with immunosuppressive and trophic support functions. While MSCs from different sources frequently display a similar appearance in culture, they often show differences in their surface marker and gene expression profiles. Although bone marrow is considered the "gold standard" tissue to isolate classical MSCs (BM-MSC), MSC-like cells are currently also derived from more easily accessible extra-embryonic tissues such as the umbilical cord. In this study, we defined the best way to isolate MSCs from the Wharton's jelly of the human umbilical cord (WJ-MSC) and assessed the mesenchymal and immunological phenotype of BM-MSC and WJ-MSC. Moreover, the gene expression profile of established WJ-MSC cultures was compared to two different bone marrow-derived stem cell populations (BM-MSC and multipotent adult progenitor cells or MAPC®). We observed that explant culturing of Wharton's jelly matrix is superior to collagenase tissue digestion for obtaining mesenchymal-like cells, with explant isolated cells displaying increased expansion potential. While being phenotypically similar to adult MSCs, WJ-MSC show a different gene expression profile. Gene ontology analysis revealed that genes associated with cell adhesion, proliferation, and immune system functioning are enriched in WJ-MSC. In vivo transplantation confirms their immune modulatory effect on T cells, similar to BM-MSC and MAPC. Furthermore, WJ-MSC intrinsically overexpress genes involved in neurotrophic support and their secretome induces neuronal maturation of SH-SY5Y neuroblastoma cells to a greater extent than BM-MSC. This signature makes WJ-MSC an attractive candidate for cell-based therapy in neurodegenerative and immune-mediated central nervous system disorders such as multiple sclerosis, Parkinson's disease, or amyotrophic lateral sclerosis.

The Need for Careful Data Collection for Pattern Recognition in Digital Pathology.

Effective pattern recognition requires carefully designed ground-truth datasets. In this technical note, we first summarize potential data collection issues in digital pathology and then propose guidelines to build more realistic ground-truth datasets and to control their quality. We hope our comments will foster the effective application of pattern recognition approaches in digital pathology.

Inflammation-Generated Extracellular Matrix Fragments Drive Lung Metastasis.

Mechanisms explaining the propensity of a primary tumor to metastasize to a specific site still need to be unveiled, and clinical studies support a link between chronic inflammation and cancer dissemination to specific tissues. Using different mouse models, we demonstrate the role of inflammation-generated extracellular matrix fragments ac-PGP (N-acetyl-proline-glycine-proline) on tumor cells dissemination to lung parenchyma. In mice exposed to cigarette smoke or lipopolysaccharide, lung neutrophilic inflammation produces increased levels of MMP-9 (matrix metalloproteinase 9) that contributes to collagen breakdown and allows the release of ac-PGP tripeptides. By silencing CXCR2 gene expression in tumor cells, we show that these generated ac-PGP tripeptides exert a chemotactic activity on tumor cells in vivo by binding CXCR2.

Collaborative analysis of multi-gigapixel imaging data using Cytomine.

MOTIVATION: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. RESULTS: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications. AVAILABILITY AND IMPLEMENTATION: Cytomine (http://www.cytomine.be/) is freely available under an open-source license from http://github.com/cytomine/ A documentation wiki (http://doc.cytomine.be) and a demo server (http://demo.cytomine.be) are also available. CONTACT: info@cytomine.be SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluation of BRCA1-related molecular features and microRNAs as prognostic factors for triple negative breast cancers.

BACKGROUND: The BRCA1 gene plays a key role in triple negative breast cancers (TNBCs), in which its expression can be lost by multiple mechanisms: germinal mutation followed by deletion of the second allele; negative regulation by promoter methylation; or miRNA-mediated silencing. This study aimed to establish a correlation among the BRCA1-related molecular parameters, tumor characteristics and clinical follow-up of patients to find new prognostic factors. METHODS: BRCA1 protein and mRNA expression was quantified in situ in the TNBCs of 69 patients. BRCA1 promoter methylation status was checked, as well as cytokeratin 5/6 expression. Maintenance of expressed BRCA1 protein interaction with BARD1 was quantified, as a marker of BRCA1 functionality, and the tumor expression profiles of 27 microRNAs were determined. RESULTS: miR-548c-5p was emphasized as a new independent prognostic factor in TNBC. A combination of the tumoral expression of miR-548c and three other known prognostic parameters (tumor size, lymph node invasion and CK 5/6 expression status) allowed for relapse prediction by logistic regression with an area under the curve (AUC) = 0.96. BRCA1 mRNA and protein in situ expression, as well as the amount of BRCA1 ligated to BARD1 in the tumor, lacked any associations with patient outcomes, likely due to high intratumoral heterogeneity, and thus could not be used for clinical purposes. CONCLUSIONS: In situ BRCA1-related expression parameters could be used for clinical purposes at the time of diagnosis. In contrast, miR-548c-5p showed a promising potential as a prognostic factor in TNBC.

The timing of surgery after neoadjuvant radiotherapy influences tumor dissemination in a preclinical model.

Neoadjuvant radiotherapy (neoRT) used in cancer treatments aims at improving local tumor control and patient overall survival. The neoRT schedule and the timing of the surgical treatment (ST) are empirically based and influenced by the clinician's experience. The current study examines how the sequencing of neoRT and ST affects metastatic dissemination. In a breast carcinoma model, tumors were exposed to different neoRT schedules (2x5Gy or 5x2Gy) followed by surgery at day 4 or 11 post-RT. The impact on the tumor microenvironment and lung metastases was evaluated through immunohistochemical and flow cytometry analyses. After 2x5Gy, early ST (at day 4 post-RT) led to increased size and number of lung metastases as compared to ST performed at day 11. Inversely, after 5x2Gy neoRT, early ST protected the mice against lung metastases. This intriguing relationship between tumor aggressiveness and ST timing could not be explained by differences in classical parameters studied such as hypoxia, vessel density and matrix remodeling. The study of tumor-related inflammation and immunity reveals an increased circulating NK cell percentage following neoRT as compared to non irradiated mice. Then, radiation treatment and surgery were applied to tumor-bearing NOD/SCID mice. In the absence of NK cells, neoRT appears to increase lung metastatic dissemination as compared to non irradiated tumor-bearing mice. Altogether our data demonstrate that the neoRT schedule and the ST timing affect metastasis formation in a pre-clinical model and points out the potential role of NK cells. These findings highlight the importance to cautiously tailor the optimal window for ST following RT.

Zebrafish Bone and General Physiology Are Differently Affected by Hormones or Changes in Gravity.

Teleost fish such as zebrafish (Danio rerio) are increasingly used for physiological, genetic and developmental studies. Our understanding of the physiological consequences of altered gravity in an entire organism is still incomplete. We used altered gravity and drug treatment experiments to evaluate their effects specifically on bone formation and more generally on whole genome gene expression. By combining morphometric tools with an objective scoring system for the state of development for each element in the head skeleton and specific gene expression analysis, we confirmed and characterized in detail the decrease or increase of bone formation caused by a 5 day treatment (from 5dpf to 10 dpf) of, respectively parathyroid hormone (PTH) or vitamin D3 (VitD3). Microarray transcriptome analysis after 24 hours treatment reveals a general effect on physiology upon VitD3 treatment, while PTH causes more specifically developmental effects. Hypergravity (3g from 5dpf to 9 dpf) exposure results in a significantly larger head and a significant increase in bone formation for a subset of the cranial bones. Gene expression analysis after 24 hrs at 3g revealed differential expression of genes involved in the development and function of the skeletal, muscular, nervous, endocrine and cardiovascular systems. Finally, we propose a novel type of experimental approach, the "Reduced Gravity Paradigm", by keeping the developing larvae at 3g hypergravity for the first 5 days before returning them to 1g for one additional day. 5 days exposure to 3g during these early stages also caused increased bone formation, while gene expression analysis revealed a central network of regulatory genes (hes5, sox10, lgals3bp, egr1, edn1, fos, fosb, klf2, gadd45ba and socs3a) whose expression was consistently affected by the transition from hyper- to normal gravity.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge.

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Activation of the calcium-sensing receptor before renal ischemia/reperfusion exacerbates kidney injury.

Activation of the calcium-sensing receptor (CaSR) by ischemia/reperfusion (I/R) favours apoptosis in cardiomyocytes, hepatocytes and neurons. Its role in renal I/R is unknown. We investigated the impact of pharmacological preactivation of the CaSR on kidney structure and function in a murine model of bilateral renal 30-min ischemia and 48-hour reperfusion, and in a 6-year cohort of kidney transplant recipients (KTR). C57BL/6J mice were administered daily with CaSR agonist, R-568, or with vehicle for 48 hours. Evaluation of serum urea and creatinine levels, renal histology and urine metabolome by nuclear magnetic resonance showed that R-568 was not nephrotoxic per se. Following I/R, serum urea and creatinine levels increased higher in R-568-treated animals than in controls. Jablonski's score was significantly greater in R-568-treated kidneys, which showed a higher rate of cell proliferation and apoptosis in comparison to controls. Next, we retrospectively identified 36 patients (10.7% of our cohort) who were treated by CaSR agonist, cinacalcet, at the time of kidney transplantation (KTx). After matching these to 61 KTR upon type of donor, cold ischemic time, residual diuresis, and donor age, we observed that delayed graft function, i.e. need for dialysis in the first week after KTx, occurred in 42 and 23% of cinacalcet-treated and control groups, respectively (p≤0.05). These data suggest that pharmacological preactivation of the CaSR before renal I/R exacerbates kidney injury.

Phenotype classification of zebrafish embryos by supervised learning.

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.