A High-Throughput Automated Technique for Counting Females of Heterodera glycines using a Fluorescence-Based Imaging System

The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybean. Methods to phenotype soybean varieties for resistance to SCN are currently very laborious and time consuming. Streamlining a portion of this phenotyping process could increase productivity and accuracy. Here we report an automated method to count SCN females using a fluorescence-based imaging system that is well suited to high-throughput SCN phenotyping methods used in greenhouse screening. For optimal automated imaging, females were washed from roots at 30 days post-inoculation into small Petri dishes. Using a Kodak Image Station 4000MM Pro, the Petri dishes were scanned using excitation and emission wavelengths of 470 nm and 535 nm, respectively. Fluorescent images were captured and analyzed with Carestream Molecular Imaging Software for automated counting. We demonstrate that the automated fluorescent-based imaging system is just as accurate (r(2) ≥ 0.95) and more efficient (>50% faster) than manual counting under a microscope. This method can greatly improve the consistency and turnaround of data while reducing the time and labor commitment associated with SCN female counting.     

Automated Analyses of Innate Olfactory Behaviors in Rodents

Olfaction based behavioral experiments are important for the investigation of sensory coding, perception, decision making and memory formation. The predominant experimental paradigms employ forced choice operant assays, which require associative learning and reinforced training. Animal performance in these assays not only reflects odor perception but also the confidence in decision making and memory. In this study, we describe a versatile and automated setup, “Poking-Registered Olfactory Behavior Evaluation System” (PROBES), which can be adapted to perform multiple olfactory assays. In addition to forced choice assays, we employ this system to examine animal’s innate ability for odor detection, discrimination and preference without elaborate training procedures. These assays provide quantitative measurements of odor discrimination and robust readouts of odor preference. Using PROBES, we find odor detection thresholds are at lower concentrations in naïve animals than those determined by forced choice assays. PROBES-based automated assays provide an efficient way of analyzing innate odor-triggered behaviors.     

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions.Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal''s overt movement is minimized by requiring the animal to ''fixate'' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.     

A System for Distinguishing Octoploid Strawberry Cultivars Using High-Throughput SNP Genotyping

Cultivated strawberry (Fragaria × ananassa) is an important commercial berry crop grown throughout the world. Improved strawberry cultivars are developed to meet the needs of consumers and breeders. Strawberries are usually propagated through runners, which sometimes lead to mislabeling or misinterpretation of cultivars. However, perfect identification of strawberry cultivars is essential for germplasm maintenance and for breeding programs. Molecular marker technology has been widely used to distinguish cultivars of other crops, but marker development in octoploid strawberries is complicated. Therefore, SNP marker with high-density and even distribution in the genome has been used currently as efficient DNA markers. In this report, previously published high-quality poly high resolution (PHR) SNPs from the 90 K Axiom® SNP array were utilized to develop a Fluidigm 24 SNPs genotyping system. Hundred nine (109) octoploid strawberry cultivars were screened using this 24 SNPs chip set. In addition, 24 SNPs were mapped to six chromosomes of diploid strawberry (Fragaria vesca). Our developed SNPs fluidigm genotyping is automatable, easy and reliable for processing and interpretation of data. Thus, this high-throughput SNP genotyping system will be a useful tool for distinguishing strawberry cultivars and find out parent-offspring relationship.     

A High-Throughput Fluorescence-Based Assay System for Appetite-Regulating Gene and Drug Screening

The increasing number of people suffering from metabolic syndrome and obesity is becoming a serious problem not only in developed countries, but also in developing countries. However, there are few agents currently approved for the treatment of obesity. Those that are available are mainly appetite suppressants and gastrointestinal fat blockers. We have developed a simple and rapid method for the measurement of the feeding volume of Danio rerio (zebrafish). This assay can be used to screen appetite suppressants and enhancers. In this study, zebrafish were fed viable paramecia that were fluorescently-labeled, and feeding volume was measured using a 96-well microplate reader. Gene expression analysis of brain-derived neurotrophic factor (bdnf), knockdown of appetite-regulating genes (neuropeptide Y, preproinsulin, melanocortin 4 receptor, agouti related protein, and cannabinoid receptor 1), and the administration of clinical appetite suppressants (fluoxetine, sibutramine, mazindol, phentermine, and rimonabant) revealed the similarity among mechanisms regulating appetite in zebrafish and mammals. In combination with behavioral analysis, we were able to evaluate adverse effects on locomotor activities from gene knockdown and chemical treatments. In conclusion, we have developed an assay that uses zebrafish, which can be applied to high-throughput screening and target gene discovery for appetite suppressants and enhancers.     

High-Throughput In Vivo Genotoxicity Testing: An Automated Readout System for the Somatic Mutation and Recombination Test (SMART)

Genotoxicity testing is an important component of toxicity assessment. As illustrated by the European registration, evaluation, authorization, and restriction of chemicals (REACH) directive, it concerns all the chemicals used in industry. The commonly used in vivo mammalian tests appear to be ill adapted to tackle the large compound sets involved, due to throughput, cost, and ethical issues. The somatic mutation and recombination test (SMART) represents a more scalable alternative, since it uses Drosophila, which develops faster and requires less infrastructure. Despite these advantages, the manual scoring of the hairs on Drosophila wings required for the SMART limits its usage. To overcome this limitation, we have developed an automated SMART readout. It consists of automated imaging, followed by an image analysis pipeline that measures individual wing genotoxicity scores. Finally, we have developed a wing score-based dose-dependency approach that can provide genotoxicity profiles. We have validated our method using 6 compounds, obtaining profiles almost identical to those obtained from manual measures, even for low-genotoxicity compounds such as urethane. The automated SMART, with its faster and more reliable readout, fulfills the need for a high-throughput in vivo test. The flexible imaging strategy we describe and the analysis tools we provide should facilitate the optimization and dissemination of our methods.     

A High-Throughput Cidality Screen for Mycobacterium Tuberculosis

Exposure to Mycobacterium tuberculosis (Mtb) aerosols is a major threat to tuberculosis (TB) researchers, even in bio-safety level-3 (BSL-3) facilities. Automation and high-throughput screens (HTS) in BSL3 facilities are essential for minimizing manual aerosol-generating interventions and facilitating TB research. In the present study, we report the development and validation of a high-throughput, 24-well ‘spot-assay’ for selecting bactericidal compounds against Mtb. The bactericidal screen concept was first validated in the fast-growing surrogate Mycobacterium smegmatis (Msm) and subsequently confirmed in Mtb using the following reference anti-tubercular drugs: rifampicin, isoniazid, ofloxacin and ethambutol (RIOE, acting on different targets). The potential use of the spot-assay to select bactericidal compounds from a large library was confirmed by screening on Mtb, with parallel plating by the conventional gold standard method (correlation, r² = 0.808). An automated spot-assay further enabled an MBC90 determination on resistant and sensitive Mtb clinical isolates. The implementation of the spot-assay in kinetic screens to enumerate residual Mtb after either genetic silencing (anti-sense RNA, AS-RNA) or chemical inhibition corroborated its ability to detect cidality. This relatively simple, economical and quantitative HTS considerably minimized the bio-hazard risk and enabled the selection of novel vulnerable Mtb targets and mycobactericidal compounds. Thus, spot-assays have great potential to impact the TB drug discovery process.     

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.     

A High-Throughput Kinetic Assay for RNA-Cleaving Deoxyribozymes

Determining kinetic constants is important in the field of RNA-cleaving deoxyribozymes (DNAzymes). Using todays conventional gel assays for DNAzyme assays is time-consuming and laborious. There have been previous attempts at producing new and improved assays; however these have drawbacks such as incompatibility with structured DNAzymes, enzyme or substrate modifications and increased cost. Here we present a new method for determining single-turnover kinetics of RNA-cleaving DNAzymes in real-time and in a high-throughput fashion. The assay is based on an intercalating fluorescent dye, PicoGreen, with high specificity for double-stranded DNA and heteroduplex DNA-RNA in this case formed between the DNAzyme and the target RNA. The fluorescence decreases as substrate is converted to product and is released from the enzyme. Using a Flexstation II multimode plate reader with built in liquid handling we could automate parts of the assay. This assay gives the possibility to determine single-turnover kinetics for up to 48 DNAzymes simultaneously. As the fluorescent probe is extrinsic there is no need for enzyme or substrate modifications, making this method less costly compared to other methods. The main novelty of this assay is the possibility of using full-length mRNA as the DNAzyme target.     

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays.     

A Loop Material Flow System Design for Automated Guided Vehicles

We develop an exact integer programming formulation to design a loop material flow system for unit-load automated guided vehicles. The model simultaneously determines both the design of the unidirectional loop flow pattern and the location of the pickup and delivery stations. The objective is to minimize the total loaded-vehicle trip distances. To solve the problem, we concentrate on developing a better formulation for the LP subproblem, preprocessing the problem, identifying the appropriate set of LP/IP routines, analyzing the mathematical properties of the problem, and developing an intelligent branch and bound solution procedure.     

A System for Automated Bacterial (genome) Integrated Annotation--SABIA

A web-based software suite, SABIA (System for Automated Bacterial Integrated Annotation), is described that provides a comprehensive computational support for the assembly and annotation of whole bacterial genomes from the data derived from sequencing projects. AVAILABILITY: Both SABIA and supplementary materials are available at http://www.sabia.lncc.br     

Automated High-Throughput Quantification of Mitotic Spindle Positioning from DIC Movies of Caenorhabditis Embryos

The mitotic spindle is a microtubule-based structure that elongates to accurately segregate chromosomes during anaphase. Its position within the cell also dictates the future cell cleavage plan, thereby determining daughter cell orientation within a tissue or cell fate adoption for polarized cells. Therefore, the mitotic spindle ensures at the same time proper cell division and developmental precision. Consequently, spindle dynamics is the matter of intensive research. Among the different cellular models that have been explored, the one-cell stage C. elegans embryo has been an essential and powerful system to dissect the molecular and biophysical basis of spindle elongation and positioning. Indeed, in this large and transparent cell, spindle poles (or centrosomes) can be easily detected from simple DIC microscopy by human eyes.To perform quantitative and high-throughput analysis of spindle motion, we developed a computer program ACT for Automated-Centrosome-Tracking from DIC movies of C. elegans embryos. We therefore offer an alternative to the image acquisition and processing of transgenic lines expressing fluorescent spindle markers. Consequently, experiments on large sets of cells can be performed with a simple setup using inexpensive microscopes. Moreover, analysis of any mutant or wild-type backgrounds is accessible because laborious rounds of crosses with transgenic lines become unnecessary. Last, our program allows spindle detection in other nematode species, offering the same quality of DIC images but for which techniques of transgenesis are not accessible. Thus, our program also opens the way towards a quantitative evolutionary approach of spindle dynamics.Overall, our computer program is a unique macro for the image- and movie-processing platform ImageJ. It is user-friendly and freely available under an open-source licence. ACT allows batch-wise analysis of large sets of mitosis events. Within 2 minutes, a single movie is processed and the accuracy of the automated tracking matches the precision of the human eye.     

A Self-Adapting System for the Automated Detection of Inter-Ictal Epileptiform Discharges

Purpose

Scalp EEG remains the standard clinical procedure for the diagnosis of epilepsy. Manual detection of inter-ictal epileptiform discharges (IEDs) is slow and cumbersome, and few automated methods are used to assist in practice. This is mostly due to low sensitivities, high false positive rates, or a lack of trust in the automated method. In this study we aim to find a solution that will make computer assisted detection more efficient than conventional methods, while preserving the detection certainty of a manual search.

Methods

Our solution consists of two phases. First, a detection phase finds all events similar to epileptiform activity by using a large database of template waveforms. Individual template detections are combined to form “IED nominations”, each with a corresponding certainty value based on the reliability of their contributing templates. The second phase uses the ten nominations with highest certainty and presents them to the reviewer one by one for confirmation. Confirmations are used to update certainty values of the remaining nominations, and another iteration is performed where ten nominations with the highest certainty are presented. This continues until the reviewer is satisfied with what has been seen. Reviewer feedback is also used to update template accuracies globally and improve future detections.

Key Findings

Using the described method and fifteen evaluation EEGs (241 IEDs), one third of all inter-ictal events were shown after one iteration, half after two iterations, and 74%, 90%, and 95% after 5, 10 and 15 iterations respectively. Reviewing fifteen iterations for the 20–30 min recordings 1took approximately 5 min.

Significance

The proposed method shows a practical approach for combining automated detection with visual searching for inter-ictal epileptiform activity. Further evaluation is needed to verify its clinical feasibility and measure the added value it presents.     

A High-Throughput Approach for Identification of Novel General Anesthetics

Anesthetic development has been a largely empirical process. Recently, we described a GABAergic mimetic model system for anesthetic binding, based on apoferritin and an environment-sensitive fluorescent probe. Here, a competition assay based on 1-aminoanthracene and apoferritin has been taken to a high throughput screening level, and validated using the LOPAC¹²⁸⁰ library of drug-like compounds. A raw hit rate of ∼15% was reduced through the use of computational filters to yield an overall hit rate of ∼1%. These hits were validated using isothermal titration calorimetry. The success of this initial screen and computational triage provides feasibility to undergo a large scale campaign to discover novel general anesthetics.     

Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded

ObjectiveComparison of a fully-automated segmentation method that uses compartmental volume information to a semi-automatic user-guided and FDA-approved segmentation technique.MethodsNineteen patients with a recently diagnosed and histologically confirmed glioblastoma (GBM) were included and MR images were acquired with a 1.5 T MR scanner. Manual segmentation for volumetric analyses was performed using the open source software 3D Slicer version 4.2.2.3 (www.slicer.org). Semi-automatic segmentation was done by four independent neurosurgeons and neuroradiologists using the computer-assisted segmentation tool SmartBrush® (referred to as SB), a semi-automatic user-guided and FDA-approved tumor-outlining program that uses contour expansion. Fully automatic segmentations were performed with the Brain Tumor Image Analysis (BraTumIA, referred to as BT) software. We compared manual (ground truth, referred to as GT), computer-assisted (SB) and fully-automated (BT) segmentations with regard to: (1) products of two maximum diameters for 2D measurements, (2) the Dice coefficient, (3) the positive predictive value, (4) the sensitivity and (5) the volume error.ResultsSegmentations by the four expert raters resulted in a mean Dice coefficient between 0.72 and 0.77 using SB. BT achieved a mean Dice coefficient of 0.68. Significant differences were found for intermodal (BT vs. SB) and for intramodal (four SB expert raters) performances. The BT and SB segmentations of the contrast-enhancing volumes achieved a high correlation with the GT. Pearson correlation was 0.8 for BT; however, there were a few discrepancies between raters (BT and SB 1 only). Additional non-enhancing tumor tissue extending the SB volumes was found with BT in 16/19 cases. The clinically motivated sum of products of diameters measure (SPD) revealed neither significant intermodal nor intramodal variations. The analysis time for the four expert raters was faster (1 minute and 47 seconds to 3 minutes and 39 seconds) than with BT (5 minutes).ConclusionBT and SB provide comparable segmentation results in a clinical setting. SB provided similar SPD measures to BT and GT, but differed in the volume analysis in one of the four clinical raters. A major strength of BT may its independence from human interactions, it can thus be employed to handle large datasets and to associate tumor volumes with clinical and/or molecular datasets ("-omics") as well as for clinical analyses of brain tumor compartment volumes as baseline outcome parameters. Due to its multi-compartment segmentation it may provide information about GBM subcompartment compositions that may be subjected to clinical studies to investigate the delineation of the target volumes for adjuvant therapies in the future.     

Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.Plant bioinformatics faces the challenge of integrating information from the related “omics” fields to elucidate the functional relationship between genotype and observed phenotype (Edwards and Batley, 2004), known as the genotype-phenotype map (Houle et al., 2010). One of the main obstacles is our currently limited ability of systemic depiction and quantification of plant phenotypes, representing the so-called phenotyping bottleneck phenomenon (Furbank and Tester, 2011). To get a comprehensive genotype-phenotype map, more accurate and precise phenotyping strategies are required to empower high-resolution linkage mapping and genome-wide association studies in order to uncover underlying genetic variants associated with complex phenotypic traits, which aim to improve the efficiency, effectiveness, and economy of cultivars in plant breeding (Cobb et al., 2013). In the era of phenomics, automatic high-throughput phenotyping in a noninvasive manner is applied to identify and quantify plant phenotypic traits. Plants are bred in fully automated greenhouses under predefined environmental conditions with controlled temperature, watering, and humidity. To meet the demand of data access, exchange, and sharing, several phenomics-related projects in the context of several consortia have been launched, such as the International Plant Phenotyping Network (http://www.plantphenomics.com/), the European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/), and the German Plant Phenotyping Network (http://www.dppn.de/).Thanks to the development of new imaging and transport systems, various automated or semiautomated high-throughput plant phenotyping systems are being developed and used to examine plant function and performance under controlled conditions. PHENOPSIS (Granier et al., 2006) is one of the pioneering platforms that was developed to dissect genotype-environment effects on plant growth in Arabidopsis (Arabidopsis thaliana). GROWSCREEN (Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Nagel et al., 2012) was designed for rapid optical phenotyping of different plant species with respect to different biological aspects. Other systems in the context of high-throughput phenotyping include Phenodyn/Phenoarch (Sadok et al., 2007), TraitMill (Reuzeau et al., 2005; Reuzeau, 2007), Phenoscope (Tisné et al., 2013), RootReader3D (Clark et al., 2011), GROW Map (http://www.fz-juelich.de/ibg/ibg-2/EN/methods_jppc/methods_node.html), and LemnaTec Scanalyzer 3D. These developments enable the phenotyping of specific organs (e.g. leaf, root, and shoot) or of whole plants. Some of them are even used for three-dimensional plant analysis (Clark et al., 2011). Consequently, several specific software applications (a comprehensive list can be found at http://www.phenomics.cn/links.php), such as HYPOTrace (Wang et al., 2009), HTPheno (Hartmann et al., 2011), LAMINA (Bylesjö et al., 2008), PhenoPhyte (Green et al., 2012), Rosette Tracker (De Vylder et al., 2012), LeafAnalyser (Weight et al., 2008), RootNav (Pound et al., 2013), SmartGrain (Tanabata et al., 2012), and LemnaGrid, were designed to extract a wide range of measurements, such as height/length, width, shape, projected area, digital volume, compactness, relative growth rate, and colorimetric analysis.The huge amount of generated image data from various phenotyping systems requires appropriate data management as well as an appropriate analytical framework for data interpretation (Fiorani and Schurr, 2013). However, most of the developed image-analysis tools are designed for a specific task, for specific plant species, or are not freely available to the research community. They lack flexibility in terms of needed adaptations to meet new analysis requirements. For example, it would be desirable that a system could handle imaging data from different sources (either from fully automated high-throughput phenotyping systems or from setups where images are acquired manually), different imaging modalities (fluorescence, near-infrared, and thermal imaging), and/or different species (wheat [Triticum aestivum], barley [Hordeum vulgare], maize [Zea mays], and Arabidopsis).In this work, we present Integrated Analysis Platform (IAP), a scalable open-source framework, for high-throughput plant phenotyping data processing. IAP handles different image sources and helps to organize phenotypic data by retaining the metadata from the input in the result data set. In order to measure phenotypic traits in new or modified setups, users can easily create new analysis pipelines or modify the predefined ones. IAP provides various user-friendly interfaces at different system levels to meet the demands of users (e.g. software developers, bioinformaticians, and biologists) with different experiences in software programming.