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.

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.

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.

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.

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.

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 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.

Evaluation of CellSolutions BestPrep® automated thin-layer liquid-based cytology Papanicolaou slide preparation and BestCyte® cell sorter imaging system

OBJECTIVE

A double-blind study was conducted to compare the performance of the new BestPrep® (CellSolutions) liquid-based thin-layer Papanicolaou (Pap) test with ThinPrep® (Hologic).

STUDY DESIGN

Samples from the study patients (n = 105) were collected twice in the same encounter with the ThinPrep sample always taken first and the BestPrep sample collected second. Slides were prepared according to both manufacturers' protocols and evaluated using manual microscopic review and the BestCyte® cell sorter imaging system (CellSolutions). Diagnostic truth for each case was determined by independent manual review of both slides by multiple pathologists and histology when available. The presence of atypical squamous cells of undetermined significance was the threshold for positive for sensitivity and specificity calculations.

RESULTS

BestPrep and ThinPrep, by manual review, had sensitivities for high-grade squamous intraepithelial lesion (HSIL) cases of 100 and 95.6%, respectively. Using the BestCyte cell sorter, both had 100% sensitivity. For the same HSIL cases, the digene HC2 high-risk human papillomavirus DNA test had sensitivities of 100% (BestPrep) and 95.6% (ThinPrep). Specificities were 71.4% (BestPrep) and 54.8% (ThinPrep).

CONCLUSIONS

BestPrep was equivalent to ThinPrep for manual review even though BestPrep was always the second sample collected. The BestCyte cell sorter provides a practical alternative to manual review for both BestPrep and ThinPrep slides.

Identification of protein biomarkers associated with cardiac ischemia by a proteomic approach

Angina is chest pain induced by ischemia of the heart muscle, generally due to obstruction or spasm of the coronary arteries. People that suffer from average to severe cases of angina have an increased percentage of death before the age of 55, usually around 60%. Therefore, prevention of major complications, optimizing diagnosis, prognosis and therapeutics are of primary importance. The main objective of this study was to uncover biomarkers by comparing serum protein profiles of patients suffering from stable or unstable angina and controls. We identified by non-targeted proteomic approach and confirmed by the means of independent techniques, the differential expression of several proteins indicating significantly increased vascular inflammation response, disturbance in the lipid metabolism and in atherogenic plaques stability.

Automated processing of zebrafish imaging data: a survey

Due to the relative transparency of its embryos and larvae, the zebrafish is an ideal model organism for bioimaging approaches in vertebrates. Novel microscope technologies allow the imaging of developmental processes in unprecedented detail, and they enable the use of complex image-based read-outs for high-throughput/high-content screening. Such applications can easily generate Terabytes of image data, the handling and analysis of which becomes a major bottleneck in extracting the targeted information. Here, we describe the current state of the art in computational image analysis in the zebrafish system. We discuss the challenges encountered when handling high-content image data, especially with regard to data quality, annotation, and storage. We survey methods for preprocessing image data for further analysis, and describe selected examples of automated image analysis, including the tracking of cells during embryogenesis, heartbeat detection, identification of dead embryos, recognition of tissues and anatomical landmarks, and quantification of behavioral patterns of adult fish. We review recent examples for applications using such methods, such as the comprehensive analysis of cell lineages during early development, the generation of a three-dimensional brain atlas of zebrafish larvae, and high-throughput drug screens based on movement patterns. Finally, we identify future challenges for the zebrafish image analysis community, notably those concerning the compatibility of algorithms and data formats for the assembly of modular analysis pipelines.

High-density lipoprotein proteome dynamics in human endotoxemia

Background

A large variety of proteins involved in inflammation, coagulation, lipid-oxidation and lipid metabolism have been associated with high-density lipoprotein (HDL) and it is anticipated that changes in the HDL proteome have implications for the multiple functions of HDL. Here, SELDI-TOF mass spectrometry (MS) was used to study the dynamic changes of HDL protein composition in a human experimental low-dose endotoxemia model. Ten healthy men with low HDL cholesterol (0.7+/-0.1 mmol/L) and 10 men with high HDL cholesterol levels (1.9+/-0.4 mmol/L) were challenged with endotoxin (LPS) intravenously (1 ng/kg bodyweight). We previously showed that subjects with low HDL cholesterol are more susceptible to an inflammatory challenge. The current study tested the hypothesis that this discrepancy may be related to differences in the HDL proteome.

Results

Plasma drawn at 7 time-points over a 24 hour time period after LPS challenge was used for direct capture of HDL using antibodies against apolipoprotein A-I followed by subsequent SELDI-TOF MS profiling. Upon LPS administration, profound changes in 21 markers (adjusted p-value < 0.05) were observed in the proteome in both study groups. These changes were observed 1 hour after LPS infusion and sustained up to 24 hours, but unexpectedly were not different between the 2 study groups. Hierarchical clustering of the protein spectra at all time points of all individuals revealed 3 distinct clusters, which were largely independent of baseline HDL cholesterol levels but correlated with paraoxonase 1 activity. The acute phase protein serum amyloid A-1/2 (SAA-1/2) was clearly upregulated after LPS infusion in both groups and comprised both native and N-terminal truncated variants that were identified by two-dimensional gel electrophoresis and mass spectrometry. Individuals of one of the clusters were distinguished by a lower SAA-1/2 response after LPS challenge and a delayed time-response of the truncated variants.

Conclusions

This study shows that the semi-quantitative differences in the HDL proteome as assessed by SELDI-TOF MS cannot explain why subjects with low HDL cholesterol are more susceptible to a challenge with LPS than those with high HDL cholesterol. Instead the results indicate that hierarchical clustering could be useful to predict HDL functionality in acute phase responses towards LPS.

Discovery and biochemical characterisation of four novel biomarkers for osteoarthritis

OBJECTIVE

Knee osteoarthritis (OA) is a heterogeneous, complex joint pathology of unknown aetiology. Biomarkers have been widely used to investigate OA but currently available biomarkers lack specificity and sensitivity. Therefore, novel biomarkers are needed to better understand the pathophysiological processes of OA initiation and progression.

METHODS

Surface enhanced laser desorption/ionisation-time of flight-mass spectrometry proteomic technique was used to analyse protein expression levels in 284 serum samples from patients with knee OA classified according to Kellgren and Lawrence (K&L) score (0-4). OA serum samples were also compared to serum samples provided by healthy individuals (negative control subjects; NC; n=36) and rheumatoid arthritis (RA) patients (n=25). Proteins that gave similar signal in all K&L groups of OA patients were ignored, whereas proteins with increased or decreased levels of expression were selected for further studies.

RESULTS

Two proteins were found to be expressed at higher levels in sera of OA patients at all four K&L scores compared to NC and RA, and were identified as V65 vitronectin fragment and C3fpeptide. Of the two remaining proteins, one showed increased expression (unknown protein at m/z of 3762) and the other (identified as connective tissue-activating peptide III protein) was decreased in K&L scores >2 subsets compared to NC, RA and K&L scores 0 or 1 subsets.

CONCLUSION

The authors detected four unexpected biomarkers (V65 vitronectin fragment, C3f peptide, CTAP-III and m/z 3762 protein) that could be relevant in the pathophysiological process of OA as having significant correlation with parameters reflecting local inflammation and bone remodelling, as well as decrease in cartilage turnover.

Oligodendrocyte development and myelinogenesis are not impaired by high concentrations of phenylalanine or its metabolites

Phenylketonuria (PKU) is a metabolic genetic disease characterized by deficient phenylalanine hydroxylase (PAH) enzymatic activity. Brain hypomyelination has been reported in untreated patients, but its mechanism remains unclear. We therefore investigated the influence of phenylalanine (Phe), phenylpyruvate (PP), and phenylacetate (PA) on oligodendrocytes. We first showed in a mouse model of PKU that the number of oligodendrocytes is not different in corpus callosum sections from adult mutants or from control brains. Then, using enriched oligodendroglial cultures, we detected no cytotoxic effect of high concentrations of Phe, PP, or PA. Finally, we analyzed the impact of Phe, PP, and PA on the myelination process in myelinating cocultures using both an in vitro index of myelination, based on activation of the myelin basic protein (MBP) promoter, and the direct quantification of myelin sheaths by both optical measurement and a bioinformatics method. None of these parameters was affected by the increased levels of Phe or its derivatives. Taken together, our data demonstrate that high levels of Phe, such as in PKU, are unlikely to directly induce brain hypomyelination, suggesting involvement of alternative mechanisms in this myelination defect.

P2X1 ion channels promote neutrophil chemotaxis through Rho kinase activation

ATP, released at the leading edge of migrating neutrophils, amplifies chemotactic signals. The aim of our study was to investigate whether neutrophils express ATP-gated P2X(1) ion channels and whether these channels could play a role in chemotaxis. Whole-cell patch clamp experiments showed rapidly desensitizing currents in both human and mouse neutrophils stimulated with P2X(1) agonists, alphabeta-methylene ATP (alphabetaMeATP) and betagammaMeATP. These currents were strongly impaired or absent in neutrophils from P2X(1)(-/-) mice. In Boyden chamber assays, alphabetaMeATP provoked chemokinesis and enhanced formylated peptide- and IL-8-induced chemotaxis of human neutrophils. This agonist similarly increased W-peptide-induced chemotaxis of wild-type mouse neutrophils, whereas it had no effect on P2X(1)(-/-) neutrophils. In human as in mouse neutrophils, alphabetaMeATP selectively activated the small RhoGTPase RhoA that caused reversible myosin L chain phosphorylation. Moreover, the alphabetaMeATP-elicited neutrophil movements were prevented by the two Rho kinase inhibitors, Y27632 and H1152. In a gradient of W-peptide, P2X(1)(-/-) neutrophils migrated with reduced speed and displayed impaired trailing edge retraction. Finally, neutrophil recruitment in mouse peritoneum upon Escherichia coli injection was enhanced in wild-type mice treated with alphabetaMeATP, whereas it was significantly impaired in the P2X(1)(-/-) mice. Thus, activation of P2X(1) ion channels by ATP promotes neutrophil chemotaxis, a process involving Rho kinase-dependent actomyosin-mediated contraction at the cell rear. These ion channels may therefore play a significant role in host defense and inflammation.

[Proteomics studies on arthritis by SELDI-TOF-MS: identification of the S100 proteins family as proteins of interest]

Clinical proteomics is a technical approach studying the entire proteome expressed by cells, tissues or organs. It describes the dynamics of cell regulation by detecting molecular events related to diseases development. Proteomic techniques focus mainly on identification of new biomarkers or new therapeutic targets. It is a multidisciplinary approach using medical, biological, bioanalytical and bioinformatics knowledges. A strong collaboration between these fields allowed SELDI-TOF-MS proteomics studies to be performed at the CHU and the University of Liège, in GIGA-Research facilities. The aim of these studies was driven along three main axes of research related to the identification of biomarkers specific to a studied pathology, to a common biological pathway and, finally, to a treatment response. This work was presented in the setting of the "Synthèse CHU 2009" meeting.

Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study

OBJECTIVES

Infliximab is the first anti-TNFalpha accepted by the Food and Drug Administration for use in inflammatory bowel disease treatment. Few clinical, biological and genetic factors tend to predict response in Crohn's disease (CD) patient subcategories, none widely predicting response to infliximab.

DESIGN AND METHODS

Twenty CD patients showing clinical response or non response to infliximab were used for serum proteomic profiling on Surface Enhanced Lazer Desorption Ionisation-Time of Flight-Mass Spectrometry (SELDI-TOF-MS), each before and after treatment. Univariate and multivariate data analysis were performed for prediction and characterization of response to infliximab.

RESULTS

We obtained a model of classification predicting response to treatment and selected relevant potential biomarkers, among which platelet aggregation factor 4 (PF4). We quantified PF4, sCD40L and IL-6 by ELISA for correlation studies.

CONCLUSIONS

This first proteomic pilot study on response to infliximab in CD suggests association between platelet metabolism and response to infliximab and requires validation studies on a larger cohort of patients.

Random subwindows and extremely randomized trees for image classification in cell biology

Background

With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks.

Results

We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose.

Conclusion

Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems.

PREDetector: a new tool to identify regulatory elements in bacterial genomes

In the post-genomic area, the prediction of transcription factor regulons by position weight matrix-based programmes is a powerful approach to decipher biological pathways and to modelize regulatory networks in bacteria. The main difficulty once a regulon prediction is available is to estimate its reliability prior to start expensive experimental validations and therefore trying to find a way how to identify true positive hits from an endless list of potential target genes of a regulatory protein. Here we introduce PREDetector (Prokaryotic Regulatory Elements Detector), a tool developed for predicting regulons of DNA-binding proteins in bacterial genomes that, beside the automatic prediction, scoring and positioning of potential binding sites and their respective target genes in annotated bacterial genomes, it also provides an easy way to estimate the thresholds where to find reliable possible new target genes. PREDetector can be downloaded freely at http://www.montefiore.ulg.ac.be/~hiard/PreDetector/PreDetector.php.

[New approaches to the treatment of the HIV-infected patient]

HIV infection remains a major problem of public health in Belgium as well as globally. The number of new diagnosies of HIV infection in Belgium remains between two and three daily. Given the dramatic effect of antiretroviral therapy on the mortality due to HIV infection, the number of patients is constantly increasing. The different problems related to HIV care are also changing. Aging of the patients and chronic exposure to antiretroviral medications have induced new complications. We will present in this brief article several new experimental and clinical approaches in which our centre has participated during the last two years.