The overall goal of this procedure is to perform a phenotypic screen to identify immune modulatory activity of compounds using an in vivo zebra fish inflammation assay. This is accomplished by first distributing zebra fish larva to a 384. Well plate next compounds to be screened are added to the larvae to allow tissue penetration.
Then copper sulfate solution is added to inflict tissue specific damage. Finally, the compounds and copper sulfate are replaced by fresh medium. Ultimately, results can be obtained that show the extent of tissue specific leukocyte influx over time through analysis of real time in vivo microscopy data.
The main advantage of this technique is that it allows for simultaneous induction of inflammation in hundreds of individuals and thereby enables experimental and analytical automation. Demonstrating the procedure will be Christina Whitman, a PhD student from my laboratory For the chin assay. Set up group matings between homozygous double transgenic and wild type AB fish collect embryos by natural spawning and raise 50 to 70 per plate in E three medium at 29 degrees Celsius to three days post fertilization.
Check all larva under a fluorescent stereoscope for fluorescent reporter expression, spontaneous inflammation, and appropriate age-related development. After preparing screening medium and adding 20 microliters to the wells of a 384, well plate transfer single anesthetized larva in 74 microliters of medium to each well. Using a pipette tip that has been cut to a two millimeter bore size with a flexible micro pipette tip.
Orient the larva in a lateral position within the well Conduct all following liquid handling steps with a robot liquid handling workstation to ensure simultaneous treatment of all larva for drug treatment. Pipette up and down five times to mix compounds in a drug stock plate. Add 16 microliters of the 7.5 x drug stock plate to the screening plate and mix five times at 10 microliters per second.
Cover the screening plate with aluminum foil and incubate it for one hour at 29 degrees Celsius. To induce chemical wounding. Add 10 microliters of 120 micromolar copper sulfate working solution to each.
Well accept the negative controls mix four times and incubate again for one hour at 29 degrees Celsius. Wash the embryos by removing and exchanging 80 microliters of medium from each well twice beginning 90 minutes. After the initial copper treatment, acquire images on an inverted automated microscope using a Forex objective.
Set the initial Z level so that neuro mass from the right and left posterior lateral line are visible and image each well once per hour. Using the Brightfield side three and GFP channels in four focal planes. The custom lab view software creates extended focus images from four focal planes for each of the channels.
The images are then merged to create a final RGB overlay image. A pattern recognition tool identifies neuromas within the RGB overlay images and creates an empirically defined area of interest around the neuromas red fluorescent leukocytes surrounding the injured neuromas are scored, resulting in a primary readout of percent area occupied by leukocytes or pale. On average, 95%of larva are detected properly and can subsequently be subjected to further data processing.
The success of individual experiments is assessed using inflammation maps or eye maps. The pale data are color coded with bright green, representing a high initial inflammatory index, and black indicates no inflammation. This quick overview allows for rapid identification of failed experiments, which can then be excluded from further data analysis.
iMaps at time 0.0 and time 0.5 clearly show whether an experiment should be included in further data processing. Copper controls show up in varying shades of green, whereas DMSO control wells appear in dark green or black at time 0.5. Inflammation is almost resolved in copper controls now indicated in dark shades of green or black to process the controls.
The 32 control replicates are averaged and standard deviation is calculated.Only. Data points within two standard deviations are included. The average value of the negative control DMSO is set to zero and the highest average PAOL for the copper control is set to one each compound's PAOL is linearly, interpolated, or extrapolated to the respective controls on the experimental plate.
The normalized raw data from replicate experiments are averaged producing a final readout, the inflammatory index due to inflammation resolution. Over time, a monotonic exponential nonlinear regression fitting is performed towards the initial inflammation. By using this formula where a zero is the measure for the initial response at time equals zero and a one is related to the slope of the magnitude over time to generate the feature space, a non-linear regression is applied and a cluster analysis divides a feature space into characteristic regions, thus allowing automated identification of interesting candidates from different immune modulatory categories.
All parameters are displayed in the 2D plot based on the parameters ACE of zero and ace of one. The data handling routines create webpages that provide a quick overview of the results, as well as a detailed view and requested by the user. Soft links to other relevant heterogeneous, biological, and chemical databases are integrated as well to provide a valuable resource for comparative and novel studies by these routines.
Proper data standards and meta information are set for long-term storage of this data in a pilot screen, an analysis was performed in a library of 640 FDA approved known bioactive compounds for effect on initiation progression or resolution of a granulocytic inflammatory response. Based on the observed effects, four types of immune modulatory phenotypes were classified that may be indicative of different modes of action, anti-inflammatory, anti resolution, pro-inflammatory, and pro resolution. 45 out of 640 compounds exerted significant anti-inflammatory effects by reducing the initial inflammatory index to 50%or less.
Within this category, six compounds were found that belonged to the pharmacological class of bonafide anti-inflammatory drugs. Confirming the validity of this approach, After watching this video, you should have a good understanding of how phenotypic in vivo screening for immune modulatory compound activity can be largely automated, allowing for both consistent data acquisition and analysis in a whole animal context.
