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Reports Automated feature detection and imaging for highresolution screening of zebrafish embryos Ravindra Peravali1,*, Jochen Gehrig1,*, Stefan Giselbrecht2, Dominic S. Lütjohann1,3, Yavor Hadzhiev4, Ferenc Müller4, and Urban Liebel1 1 Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany, 2 Institute for Biological Interfaces, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany, 3Institute of Organic Chemistry, Karlsruhe Institute of Technology, Karlsruhe, Germany, and 4School of Clinical and Experimental Medicine, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK BioTechniques 50:319-324 (May 2011) doi 10.2144/000113669 Keywords: automated microscopy; zebrafish screening; high content screening Supplementary material for this article is available at www.BioTechniques.com/article/113669. *R.P. and J.G. contributed equally to this work.
The development of automated microscopy platforms has enabled large-scale observation of biological processes, thereby complementing genome scale biochemical techniques. However, commercially available systems are restricted either by fixed-field-of-views, leading to potential omission of features of interest, or by low-resolution data of whole objects lacking cellular detail. This limits the efficiency of high-content screening assays, especially when large complex objects are used as in whole-organism screening. Here we demonstrate a toolset for automated intelligent high-content screening of whole zebrafish embryos at cellular resolution on a standard wide-field screening microscope. Using custom-developed algorithms, predefined regions of interest—such as the brain—are automatically detected. The regions of interest are subsequently imaged automatically at high magnification, enabling rapid capture of cellular resolution data. We utilize this approach for acquiring 3-D datasets of embryonic brains of transgenic zebrafish. Moreover, we report the development of a mold design for accurate orientation of zebrafish embryos for dorsal imaging, thereby facilitating standardized imaging of internal organs and cellular structures. The toolset is flexible and can be readily applied for the imaging of different specimens in various applications. A main challenge of the postgenomic era is to understand the complex cellular networks underlying development, growth, and survival. Imaging techniques provide a means for observing biological processes and the phenotypic consequences of network perturbations in the context of a living cell or organism. However, microscopic approaches often lack the throughput of biochemical methods; this has motivated the development of robotic microscopy platforms allowing for automated observation of biological processes on a large scale (1–3). The advent of automated imaging technologies has generated a growing interest in exploiting the complexity of the living organism in high-content screening (HCS) experiments. The zebrafish embryo is an ideal model system for whole-organism screening approaches due to its various experimental advantages such as its high fecundity and feasibility of live imaging (4–6). Consequently, several successful zebrafish HCS Vol. 50 | No. 5 | 2011
assays have been demonstrated including drug screening approaches (7), toxicological studies (8), behavioral screens (9) and the large scale analysis of gene regulatory elements (10). A major drawback of current automated imaging systems is that they are limited by fixed scan-field settings, resulting in the potential omission of important features of interest, especially at higher magnifications where the field of view (FOV) is reduced proportionally (11). This has important limitations particularly when screening complex objects, such as whole embryos. It is technically unfeasible to position large numbers of specimens such that the region of interest (ROI) matches the fixed FOV of high–magnification objectives. As a consequence, a large FOV is classically chosen to ensure capture of ROIs (7–10). This results in the acquisition of low-resolution data, which impedes detailed visualization of morphology and biological processes at the cellular level. Alternatively, multiple scan-fields
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can be acquired and subsequently assembled (“stitching”) (12–13); however, this results in significantly increased imaging times and excessive and redundant data volumes when carried out in large-scale screening experiments. Therefore, there is an increasing demand for tools allowing automatic detection and imaging of features of interest at high resolution. Such systems would overcome current limitations of HCS platforms. Although significant advances have been made in developing methods that automatically detect features of interest in microscopic images of zebrafish embryos (8,10,14), there is a lack of tools combining automatic detection and subsequent imaging of ROIs at high resolution. Recently, a software for automated detection and imaging of in vitro cellular phenotypes was reported (15). Furthermore, a custom-developed imaging platform has been demonstrated to address limitations in wholeorganism screening (16). However, these www.BioTechniques.com
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Figure 1. A pipeline for automated intelligent high-content screening. (A) Overview of workflow for automated intelligent HCS screening on the Olympus Scan^R screening microscope. The flowchart illustrates the steps carried out to obtain high-resolution widefield screening data (for further details, see “Materials and methods” and “Results and discussion” in the main text, as well as Supplementary Figure S1). The last box depicts optional image restoration techniques to improve experimental data. (B–E) Illustrative example for acquired HCS data; bright-field views are shown. (B) Overview of prescreen data from a single 96-well plate acquired using a 2.5× objective. (C) Single embryo from prescreen data. The bounding box (blue rectangle) indicates the image region matching the chosen template. Red asterisk indicates the pixel coordinates chosen as the center of the field of view for the subsequent high-resolution imaging (see panels D and E). (D) Extended-focus image of 60 z-slices (10-µm slice distance) acquired with a 4× objective. (E) Extendedfocus image of 150 z-slices (4-µm slice distance) acquired with a 10× objective. Extended-focus images were generated using Adobe Photoshop CS4. Scale bars: 320 µm (C), 200 µm (D), and 80 µm (E).
current solutions are either restricted to cellbased assays and require a demanding setup, or allow only limited access to custom-built hardware, which restricts wider use. Thus, the development of external tools that do not require modification of commercially available HCS systems would enable nonexpert users to perform complex HCS experiments. To this end, we developed a simple protocol and software tools (freely available at www.itg. kit.edu/liebel-lab-resources.php) that allow automatic (or manual) ROI detection from low-resolution prescreen data and subsequent automated cellular-resolution imaging of zebrafish embryos.
Materials and methods
Generation of reporter constructs The -2.4shh:cfpABC vector was constructed by replacing the gfp-SV40polyA fragment from -2.4shh:gfpABC (17) with the ecfpsv40polyA fragment from pCS2:cfp (P. Blader and U. Strähle, unpublished data) using XhoI/NotI restriction sites. The -2.4shh:dsRed-T4ABC was constructed by cloning the PCR amplified dsRed-T4 (18) fragment into -2.4shh:gfpABC using XhoI/ SnaBI, replacing the gfp fragment. For the amplification of the dsRed-T4 fragment following oligonucleotides were used: 5′-TAAActcgagccATGGCCTCCTCCGAGGACGTC-3′ (forward primer), 5′-TAAAtacgtaCTACAGGAACAGGTGGTGGCGG-3′ (reverse primer). Lowercase letters indicate the XhoI or SnaBI site, respectively. Vol. 50 | No. 5 | 2011
Fish keeping and embryo handling Adult zebrafish of the WIK/AB wild-type strain and the ETvmat2:gfp transgenic line (19) were maintained according to Reference 20. Eggs were collected from pairwise and batch crossings. The developmental stage of embryos was determined as previously described (21). Embryos were raised, dechorionated, and anesthetized according to Reference 10. Embryos were arrayed in 96-well U-bottom plates (Cat. no. 143761; Nunc, Roskilde, Denmark) or in agarose molds facilitating ventral orientation. Microinjection Plasmids for microinjection were diluted with nuclease-free water to a final concentration of 10 ng/µL and 0.1% phenol red (Sigma-Aldrich, Taufkirchen, Germany). Injection solutions were micro-injected manually through the chorion into the cytoplasm of one cell–stage embryos. For double-injection experiments, two independent injection solutions were injected successively with a time difference of 5–10 min. Preparation of array of 96 agarose molds An array of 96 molds, matching the well positions of a microtiter plate, was milled into a polymethylmethacrylate dish with a graver (Datron AG, Mühltal, Germany). Dimensions of a single mold were 800-µm depth, 60° side wall inclination, and 5-mm length. The array was replicated by casting a degassed 10:1 mixture of silicone elastomer base component (Sylgard 184, Dow Corning, Wiesbaden, Germany). Entrapped
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air bubbles were removed by evacuation within a desiccator. The silicone was cured at room temperature for 48 h and demolded. The silicone replica was used as a template to generate an array of 96 molds in agarose. A thin layer of hot 1% agarose was poured into the lid of a 96-well plate (Cat. no. 3598; Corning, Amsterdam, The Netherlands). The silicone replica was positioned such that the molds matched the well positions indicated on the lid. The foil of a MicroClear plate (Cat. no. 655096; Greiner, Frickenhausen, Germany) was removed to obtain a bottomless plate. After solidification of the agarose, the silicone replica was removed and the well plate was pressed into the agarose to minimize water movements and stabilize the position of embryos. Image acquisition Embryos were imaged on a standard Scan^R high-content screening microscope (Olympus, Hamburg, Germany) (22) as described in Reference 10. Low-resolution prescreen data was acquired using a single z-slice per embryo and channel using a 2.5× (N.A. = 0.08) objective. Embryos were imaged with a 4× (N.A. = 0.13) (60 z slices, Δz = 10 µm) or with a 10× objective (N.A. = 0.3) [150 z-slices, Δz = 4 µm (lateral) and 120 z-slices, Δz = 4µm (dorsal)]. The central plane of the z-stack was set using the interpolate focus function. Higher-resolution imaging was carried out by executing individual XML configuration files (see below) using the built-in batch job function of the Scan^R software. Generation of template XML file To generate an XML template configuration file, a single well was imaged with modified parameters: selection of higher magnification objective, adjustment of exposure times, adjustment of number of z-slices, and setting of the central plane of the z-stack. All other parameters were identical to the prescreen acquisition. The template XML file was extracted from the generated experimental folder. Region of interest coordinate extraction Algorithms were developed in Matlab R2010b with the image processing toolbox (MathWorks, Ismaning, Germany). An algorithm based on template matching was developed for automatic head detection of bright-field images. The template images were created by manually cropping the head regions from randomly chosen low-resolution images of two laterally oriented embryos and one dorsally oriented embryo. The algorithm was based on computing a normalized crosscorrelation measure, which is mathematically defined as (23) www.BioTechniques.com
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γ (u , v ) =
∑[ f (x , y) − f x, y
u ,v
][ t( x − u , y − v) − t ] 0.5
2 2 ∑[ f (x , y)− fu ,v ] ∑[t (x − u , y − v)− t ] x, y x, y
,
[Eq. 1] where f is the source image, t is the template image, t is the mean of the template image, and f u,v is the mean of the source image in the region under the template. The pixel location in the source image yielding the largest normalized cross-correlation measure was considered the best match between the template and the source image, and was chosen as the center of the FOV for subsequent high-resolution imaging. For automated GFP filtering, a bounding box—which is a rectangle covering the best match—was computed. This bounding box was used to crop the corresponding fluorescent-channel image. The cropped fluorescent image was intensity-thresholded to retrieve the pixels showing the fluorescent signal, and was median-filtered to remove extraneous noise. Morphological operators were applied to the filtered image to extract fluorescent-positive clusters by connecting pixels lying in close proximity to each other (connected component labeling) (24). If fluorescent-positive clusters were detected, the corresponding ROI was transformed into microscope stage coordinates. The highresolution XML template file was modified to include these stage coordinates. XML modification tools were developed using several standalone C# software modules under the .NET Framework of the Windows operating system (Microsoft, Redmond, WA, USA). One XML file was generated for each well. Alternatively, a manual ROI selection
was carried out by clicking on the feature of interest in the prescreen data. Extended focus, deconvolution and 3-D visualization Extended focus images of z-stacks were generated using Adobe Photoshop CS4 (Mountain View, CA, USA). Fluorescence z-stacks were deconvolved with Huygens Professional deconvolution software (SVI, Hilversum, The Netherlands) using a theoretical point spread function based on microscope parameters. Batch deconvolution was carried out on a workstation with 24 cores and 64 GB RAM. 3-D visualization was carried out using V3D (25). Colocalization analysis Detection of colocalization was carried out using the Colocalization Threshold and Colocalization Test plug-ins based on Reference 26 included in the Fiji ImageJ distribution (http://pacific.mpi-cbg.de/ wiki/index.php/Fiji).
Results and discussion
To implement an automated ROI detection and imaging functionality on the Scan^R widefield HCS system, we exploited the fact that all imaging parameters are set by loading an XML configuration file. We designed a method to generate a single XML file containing unique scan field coordinates for each well, which are then executed using the built-in batch job function of the Scan^R software. The developed HCS protocol consists of a sequence of experimental steps (Figure 1A and Supplementary Protocol). Initially, zebrafish embryos were prepared according to the protocol described in Reference 10
and arrayed in 96-well plates. Thereafter, low-resolution prescreen image data of whole embryos was acquired with one z-slice per well using a 2.5× objective. Furthermore, a template XML configuration file was generated by simply imaging a single embryo with required high-resolution settings (e.g., magnification factor). To automatically detect embryo heads, an algorithm based on template matching was developed in Matlab (Supplementary Figure S1) that robustly detected the head region of randomly laterally oriented embryos from the prescreen data (94.1%, n = 849). The pixel coordinates of detected ROIs were extracted and transformed into microscope stage coordinates. One XML file per well was automatically generated by modifying the previously created template XML file with the respective ROI stage coordinates. Then, the resulting single XML files, providing well-towell coordinates, were batch-loaded, leading to the automatic acquisition of embryonic heads in each well at a higher magnification, resulting in a significantly increased level of detail (Figure 1, B–E). A separate “click on ROI tool” was developed for users who want to identify different regions of interests manually. The remaining experimental steps were identical. All algorithms were implemented as a graphical user interface to facilitate ease of use (Figure 2) (freely available at www.itg. kit.edu/liebel-lab-resources.php). One of the key advantages of the zebrafish embryo is the ease of live imaging in combination with the plethora of available transgenic lines in which different cell and tissue types are labeled (27–29). Thus, besides morphology-based features, the actual ROI is often highlighted by the presence of fluorescently labeled structures. To demonstrate the usefulness of the developed HCS protocol in
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Figure 2. Implementation of algorithms as a graphical user interface. (A) Screenshot of the graphical user interface for the execution of automatic and manual ROI detection, coordinate transformation, and XML batch job file generation. Red numbers depict (1) dropdown menu to specify the orientation of zebrafish embryos in the acquired prescreen data, (2) entry fields to set an optional offset for the x-y coordinates used in the high resolution screening step, (3) buttons to choose the automatic detection algorithm (bright-field head detection with or without GFP filtering), and (4) button to launch the manual ROI selection tool as described in panel B. (B) Screenshot of the manual click tool. Users are prompted with a sequence of images from prescreen data. ROIs to be imaged in high resolution can be manually chosen by clicking on the feature of interest (indicated by the red cross-hair).
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Figure 3. Automated dorsal imaging of zebrafish embryonic brains. (A) Laser scanning image of milled keel-shaped cavities for facilitating orientation of zebrafish embryos (depth, 800 µm; side wall inclination, 60°; and length, 5 mm) and serving as template to generate the silicone tool shown in panel B. (B) Photograph of polydimethylsiloxane replica of the array of cavities used to generate an array of 96 agarose molds. (C) Schematic depiction of a single well with ventrally oriented embryo within an agarose mold. The bottomless well plate, agarose molds containing a ventrally oriented embryo, and the imaging direction are indicated. Mold dimensions and agarose thickness are as in panel A. Drawing is not to scale. (D–F) Dorsal views of a single embryo lying within the agarose mold. (D) Prescreen data acquired with 2.5× objective. Rectangle and asterisk are as in Figure 1C. (E) Extended-focus image of 120 z-slices (4-µm slice distance) of the same embryo, bright-field view. (F) Extended-focus images of 120 z-slices (4-µm slice distance) in the GFP channel previously deconvolved using Huygens Professional software (see also Supplementary Movie 1). Scale bars: 320 µm (D) and 80 µm (E and F).
fluorescence imaging, model experiments were carried out using GFP-positive and -negative embryos derived from crossings of adults of unknown genotype of the ETvmat2:gfp transgenic line in which monoaminergic neurons are labeled by GFP expression (19). This enhancer trap line provided an illustrative model system for scoring the potentially complex spatial reporter gene expression patterns within the embryonic brain, often observed in transgenic zebrafish embryos. Embryos were arrayed and prescreen data was acquired in the bright-field and fluorescence channel. Embryonic heads were detected and the presence of GFP-positive clusters within the corresponding region of the fluorescence image was analyzed (see “Materials and methods”). Embryos without GFP signal within the head region were automatically filtered out by the algorithm (93.4%, n = 703). It therefore allowed the acquisition of high-resolution lateral views of embryonic heads for GFP-positive embryos only, thereby avoiding the unnecessary screening of wells of no interest (Supplementary Figure S2). Consequently, the combination of morphological feature detection and fluorescence analysis provided drastically reduced total imaging times and data volumes. Due to the thickness and bilateral body plan of the zebrafish embryo, visualization of many organs is hindered by lateral orientation. Therefore, a simple keel-shaped mold geometry was designed that allows tilt-free ventral orientation of 2–3 day–old embryos, facilitating Vol. 50 | No. 5 | 2011
the imaging of dorsal views when using an inverted microscope (Figure 3, A–C). An array of 96 molds was milled into a polymethylmethacrylate dish and replicated by casting a silicone elastomer (Figure 3, A and B). This silicone replica was used again as a template to generate an array of 96 molds into a thin layer of agarose poured into the lid of a 96-well plate. To minimize water movements and thus stabilize embryos, a bottomless 96-well plate was pressed onto the array of molds (Figure 3C). The design of the agarose plate prevents its usage in drug or toxicological screens, as liquid can diffuse through the agarose. However, it is highly beneficial for imaging the development and function of internal organs or other applications requiring dorsal views, especially when combined with the automatic detection of the organ of interest. To demonstrate this, it was applied during acquisition of dorsal views of embryonic brains. Therefore, a different template was implemented in the algorithm. This modified algorithm detected the position of GFP-positive embryonic brains with an accuracy of 98.3% (n = 366) (Figure 3D). In a model experiment, 48 hpf embryos of the ETvmat2:gfp transgenic line were ventrally arrayed and prescreen data of dorsal views were acquired. Subsequently, for GFP-positive embryos, 120 z-slices with a distance of 4 µm were acquired using a 10× objective, giving rise to data sets allowing a much more detailed visualization of the embryonic brain compared with lateral views (Figure 3, E–F; see also Supplementary Figure S2).
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The advantage of the chosen widefield approach is that imaging times per embryo are short; they are solely dependent on the choice of exposure times and number of z-dimension slices per channel and are not limited by the scan speed of detectors such as those used in confocal microcopy. For example, the acquisition of a 120–z-slice two-channel data set of one embryo takes ~1 min. However, the widefield setup also often necessitates the use of image restoration techniques that improve image quality and axial resolution to fully exploit the information content of spatial data. To this end, GFP z-stacks were batch-deconvolved with Huygens Professional software using a theoretical point spread function. This results in high-resolution 3-D data sets visualizing the spatial organization of GFP-positive cell clusters within the embryonic brain (Figure 3F and Supplementary Movie S1). Thus, the protocol enables automatic, highthroughput acquisition of high-resolution z-volumes and, in combination with deconvolution, gives rise to cellular-resolution 3-D data sets with sufficient detail to analyze the spatial distribution of fluorescence signal within the complexity of a living organism. A standard application in fluorescence imaging is the analysis of cellular structures labeled by different fluorescence reporters. To test the applicability of the protocol in multicolor imaging, a model experiment was carried out for detecting colocalization of fluorescent signals within 3-D data sets. Zebrafish embryos were successively injected www.BioTechniques.com
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with two reporter constructs (-2.4shh:cfpABC and -2.4shh:dsRed-T4ABC) containing the same cell type specific regulatory elements of the sonic hedgehog gene, which drive expression in cells of the embryonic midline and ventral brain. Due to differential distribution of the plasmids, the embryos displayed mosaic and partially overlapping fluorescence signals within distinct cell clusters in the brain. z-stacks of dorsal views of embryonic brains were acquired and restored by deconvolution. The partial overlap of CFP and DsRed-T4 expression within the distinct cells could be detected by quantitative colocalization analysis (26) (Supplementary Figure S3). Therefore, the developed toolset in combination with deconvolution allowed the acquisition of data of sufficient detail to analyze complex spatial reporter gene expression patterns within the living embryo. Here, we demonstrate a protocol for intelligent HCS experiments that enables the performance of complex screening assays on a widefield screening microscope. It enables automatic detection and imaging of features of interest on a standard commercially available HCS system, therefore overcoming a major limitation in current screening assays. The toolset increases the content of acquired data by automatically imaging ROIs and facilitating the use of higher-magnification objectives, which leads to a significantly increased level
of detail. Moreover, it increases throughput and reduces data volumes of experiments by omitting the acquisition of nonrelevant data. The throughput is further enhanced by the utilization of widefield microscopy, allowing the rapid acquisition of high-resolution data. We show the utility of the protocol using the zebrafish embryo, which, due to its various experimental advantages, has manifested as a key vertebrate model organism used in whole-organism screening. In addition, we focused on whole-organism screening since the automated detection and imaging of ROIs—which are characterized by morphological or fluorescently labeled structures— will provide a particular advantage to these approaches. Although we placed a strong emphasis on zebrafish, the software tools are not limited to HCS assays using this model. The developed automated feature extraction is flexible, since it solely depends on the chosen template used to match the ROI. Users can thereby apply this protocol to various features of interest by replacing the template image used in the algorithm. Moreover, smallscale screening experiments using any kind of specimen can be readily carried out using the provided manual ROI selection tool. Furthermore, our mold design for dorsal imaging eliminates a common limitation in zebrafish screening approaches. However, it is not suitable for chemical screening due to
diffusion of compounds through agarose. This would require investigating alternative materials (e.g., elastomers) or custom plate designs that avoid cross-contamination between adjacent wells. Ultimately, we believe that the developed tools will facilitate the automated and rapid observation of biological processes and cellular phenotypes within various specimens. We propose the demonstrated approach as a model for developing similar tools for other commercially available microscopy platforms.
Acknowledgments
We thank Sebastian Hötzel for fish care, Jörg Bohn for technical assistance, Bettina Ryll (Uppsala University, Sweden) for sharing plasmids, and Wolfgang Driever (University of Freiburg, Germany) for the ETvmat2:gfp transgenic line. This work was supported by the FP7-Cooperation-Health funded project DOPAMINET (grant no. 223744). We thank Uwe Strähle, Stefan Bräse and Joachim Wittbrodt for general support. J.G. carried out biological experiments; R.P. designed and created image analysis and automation tools; J.G. and R.P. carried out the imaging; D.S.L. generated XML file modification software tools; S.G., J.G., and R.P. designed and constructed the embryo orientation tool; Y.H. generated constructs; J.G. and R.P.
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analyzed data; R.P., J.G., and F.M. designed experiments; R.P., J.G., and U.L. designed the study; J.G., R.P., S.G., F.M., and U.L. wrote the manuscript. All authors read and approved the manuscript.
Competing interests
The authors declare no competing interests.
References
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1. Pepperkok, R. and J. Ellenberg. 2006. Highthroughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7:690-696. 2. Conrad, C. and D.W. Gerlich. 2010. Automated microscopy for high-content RNAi screening. J. Cell Biol. 188:453-461. 3. Zanella, F., J.B. Lorens, and W. Link. 2010. High content screening: seeing is believing. Trends Biotechnol. 28:237-245. 4. Zon, L.I. and R.T. Peterson. 2005. In vivo drug discovery in the zebrafish. Nat. Rev. Drug Discov. 4:35-44. 5. Lieschke, G.J. and P.D. Currie. 2007. Animal models of human disease: zebrafish swim into view. Nat. Rev. Genet. 8:353-367. 6. Yang, L., N.Y. Ho, R. Alshut, J. Legradi, C. Weiss, M. Reischl, R. Mikut, U. Liebel, et al. 2009. Zebrafish embryos as models for embryotoxic and teratological effects of chemicals. Reprod. Toxicol. 28:245-253. 7. Tran, T.C., B. Sneed, J. Haider, D. Blavo, A. White, T. Aiyejorun, T.C. Baranowski, A.L. Rubinstein, et al. 2007. Automated, quantitative screening assay for antiangiogenic compounds using transgenic zebrafish. Cancer Res. 67:11386-11392. 8. Burns, C.G., D.J. Milan, E.J. Grande, W. Rottbauer, C.A. MacRae, and M.C. Fishman. 2005. High-throughput assay for small molecules that modulate zebrafish embryonic heart rate. Nat. Chem. Biol. 1:263-264. 9. Rihel, J., D.A. Prober, A. Arvanites, K. Lam, S. Zimmerman, S. Jang, S.J. Haggarty, D. Kokel, et al. 2010. Zebrafish behavioral profiling links drugs to biological targets and rest/wake regulation. Science 327:348-351. 10. Gehrig, J., M. Reischl, E. Kalmar, M. Ferg, Y. Hadzhiev, A. Zaucker, C. Song, S. Schindler, et al. 2009. Automated high-throughput mapping of promoter-enhancer interactions in zebrafish embryos. Nat. Methods 6:911-916. 11. Gough, A.H. and P.A. Johnston. 2007. Requirements, features, and performance of high content screening platforms. Methods Mol. Biol. 356:41-61. 12. Emmenlauer, M., O. Ronneberger, A. Ponti, P. Schwarb, A. Griffa, A. Filippi, R. Nitschke, W. Driever, and H. Burkhardt. 2009. XuvTools: free, fast and reliable stitching of large 3D datasets. J. Microsc. 233:42-60. 13. Preibisch, S., S. Saalfeld, and P. Tomancak. 2009. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25:1463-1465. 14. Vogt, A., A. Cholewinski, X. Shen, S.G. Nelson, J.S. Lazo, M. Tsang, and N.A. Hukriede. 2009. Automated image-based phenotypic analysis in zebrafish embryos. Dev. Dyn. 238:656-663. 15. Conrad, C., A. Wunsche, T.H. Tan, J. Bulkescher, F. Sieckmann, F. Verissimo, A. Edelstein, T. Walter, et al. 2011. Micropilot: automation of fluorescence microscopy-based imaging for systems biology. Nat. Methods 8:246-249.
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16. Pardo-Martin, C., T.Y. Chang, B.K. Koo, C.L. Gilleland, S.C. Wasserman, and M.F. Yanik. 2010. High-throughput in vivo vertebrate screening. Nat. Methods 7:634-636. 17. Ertzer, R., F. Muller, Y. Hadzhiev, S. Rathnam, N. Fischer, S. Rastegar, and U. Strahle. 2007. Cooperation of sonic hedgehog enhancers in midline expression. Dev. Biol. 301:578-589. 18. Bevis, B.J. and B.S. Glick. 2002. Rapidly maturing variants of the Discosoma red fluorescent protein (DsRed). Nat. Biotechnol. 20:83-87. 19. Wen, L., W. Wei, W. Gu, P. Huang, X. Ren, Z. Zhang, Z. Zhu, S. Lin, and B. Zhang. 2008. Visualization of monoaminergic neurons and neurotoxicity of MPTP in live transgenic zebrafish. Dev. Biol. 314:84-92. 20. Westerfield, M. 2000. The zebrafish book. A guide for the laboratory use of zebrafish (Danio rerio), 4th edition. University of Oregon Press, Eugene, OR. 21. Kimmel, C.B., W.W. Ballard, S.R. Kimmel, B. Ullmann, and T.F. Schilling. 1995. Stages of embryonic development of the zebrafish. Dev. Dyn. 203:253-310. 22. Liebel, U., V. Starkuviene, H. Erfle, J.C. Simpson, A. Poustka, S. Wiemann, and R. Pepperkok. 2003. A microscope-based screening platform for large-scale functional protein analysis in intact cells. FEBS Lett. 554:394-398. 23. Haralick, R.M. and L.G. Shapiro. 1992. Computer and Robot Vision, p. 316-317. Vol. 2. AddisonWesley, Reading, MA. 24. Gonzalez, R.C. and R.E. Woods. 2008. Digital Image Processing. 3rd ed. Prentice Hall, Upper Saddle River, NJ. 25. Peng, H., Z. Ruan, F. Long, J.H. Simpson, and E.W. Myers. 2010. V3D enables real-time 3D visualization and quantitative analysis of largescale biological image data sets. Nat. Biotechnol. 28:348-353. 26. Costes, S.V., D. Daelemans, E.H. Cho, Z. Dobbin, G. Pavlakis, and S. Lockett. 2004. Automatic and quantitative measurement of protein-protein colocalization in live cells. Biophys. J. 86:3993-4003. 27. Choo, B.G., I. Kondrichin, S. Parinov, A. Emelyanov, W. Go, W.C. Toh, and V. Korzh. 2006. Zebrafish transgenic Enhancer TRAP line database (ZETRAP). BMC Dev. Biol. 6:5. 28. Kawakami, K., G. Abe, T. Asada, K. Asakawa, R. Fukuda, A. Ito, P. Lal, N. Mouri, et al. 2010. zTrap: zebrafish gene trap and enhancer trap database. BMC Dev. Biol. 10:105. 29. Sprague, J., L. Bayraktaroglu, D. Clements, T. Conlin, D. Fashena, K. Frazer, M. Haendel, D.G. Howe, et al. 2006. The Zebrafish Information Network: the zebrafish model organism database. Nucleic Acids Res. 34:D581-D585. 30. Muller, F., B. Chang, S. Albert, N. Fischer, L. Tora, and U. Strahle. 1999. Intronic enhancers control expression of zebrafish sonic hedgehog in floor plate and notochord. Development 126:2103-2116. Received 7 Feb 2011; accepted 25 March 2011. Address correspondence to Ravindra Peravali, Jochen Gehrig, or Urban Liebel, Institute of Toxicology and Genetics, Karlsruhe Institute of Technology, Campus North, D-76344 EggensteinLeopoldshafen, Germany. e-mail:
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