Setting up for a hemiclone assay

 Several of the assays we are conducting in the lab this summer are examining the genetic basis of female mating behaviours. There are many ways to do these assays, but (in my opinion) one of the best ways to measure ofstanding genetic is using hemiclonal analysis. Both Mireille and Arnold (above) have been learning the ropes of this technique, and this week set up the first (of many) assays.

The first step involves mating clone males (which carry a randomly-sampled haploid genome, and set of translocated autosomal chromosomes) with many wild-type virgin female collected from one of our outbred populations. These mated flies are placed into half-pint collectors outfiltted with 35mm (diameter) petrie dishes containing a grape-juice/agar media for no more than 18h.

During that time, eggs are laid on the surface (see above).

As fruit fly development can be strongly influenced by variation in larval density, it is essential that our assay vials are standardized. This is done by counting exact numbers of eggs (under the microscope, using a paintbrush)...

...which are then gently cut from the surface of the juice "cookie"....

...and transferred to vials containing ~10ml of our lab's standard banana Drosophila media!

Due to the nature of the cross between the clone males and the wild-type females, there is a 50% mortality (due to chromosomal imbalances). Thus, for every 100 eggs we transfer into the vial, only 50 will hatch into larvae. To further establish standard developmental conditions in the vials, we add an additional 50 eggs from one of our other populations that carry a recessive-brown-eyed marker, yielding a total of ~100 larvae per vial (which mimics the typical developmental conditions experienced in the our lab populations).

Now that our vials are set up, we'll incubate them, and then collect hemiclonal (red-eyed) females as they eclose starting 9d later. Check back for updates!

More great news!

I'm a little slow to post this, but last week was also Spring Convocation here at Laurier. It was great to see all the lab alumni (Ashley Guncay, Leah DeJong and Conor Delar) walk across the stage. After the ceremony, I had the honour of presenting Leah DeJong with the 2013-2014 Rick Playle Award for Best Thesis! Congratulations Leah!

Incidentally, Leah is the third student from the lab to win this award (following Erin Sonser in 2012-2013, and Jessie Young in 2011-2012)!

Bac(teria) to the Future

This week, the Long Lab was very busy examining the relationship between immunocompetence and parental characteristics (using fruit flies, as a model organism). This work, which began in the Fall with the work of Ashley Guncay (co-supervised with Dr. Joel Weadge) is being continued by two of our lab's summer students Katie Plagens & Thiropa Balasubramaniam (seen here with their flies). 

  So far this summer, Thiropa & Katie have been busy, first learning the ropes of fly pushing, then in running behavioural assays, and setting up immunocompetence assays. All of this has led up to the events of this week - a massive bacterial plating assay. It was hard work (but very rewarding), and would not have been possible without the concerted work of everyone in the lab (as well as many kind expert volunteers from the Weadge lab)!

Briefly, from each vial of flies (top), 5 (virgin) individuals were collected. They were then homogenized in LB media in order to release their bacteria into the solution. The next step is to create a serial dilution of the solution (transferring 10ul in succession into vials containing 90ul of LB media). These solutions are then plated onto petrie dishes (below) which are then checked on the following day for bacterial growth. The number of colonies on each plate (below) are counted, and from this, we can estimate the bacterial load of the original flies.

Ready for action!

How to create a multi-panelled line graph with error bars in ggplot2

#Below you will find some code that I cobbled together from various sources to create a multi-panelled line graph with error bars
 
#first I need to load the file and the ggplot2 package
library(ggplot2)
#FYI I am using ggplot v 0.8.9 on R 2.13.1 (I know, I should upgrade!)
BI393<-read.csv(file.choose())
#if you look at the file 

# https://www.dropbox.com/s/nlrem5i44lj9r71/393_FILE.csv?dl=0

# you will see the mean (and sd), and median scores on a standardized student survey for each of the last 3 years of my BI393 Biostatistics class (useful data for preparing a tenure application file). As the QUESTION column is a bit long & unweildy, I will force it to break every 70th character (helps with the formatting of the strip text in gglplot)
BI393$groupwrap = unlist(lapply(strwrap(BI393$QUESTION, width=70, simplify=FALSE), paste, collapse="\n"))
 
#now, to business: I want to plot the mean scores for each question by year
printing<- ggplot(BI393,aes(x=as.factor(YEAR),y=MEAN,colour=QUESTION))
#and add on some error bars
+geom_errorbar(aes(ymin=MEAN-SD,ymax=MEAN+SD),width=.1)
#connect the points with colour-coded lines
+geom_line(aes(group = QUESTION))
#and make the points clear to see
+geom_point(shape=21, fill="white")
#adjust the range of the y-axis, and customize the x-label
+ylim(1,7.5)+xlab("Academic Year")
#change the background colours
+theme_bw()
#wrap the scores by the questions
+facet_wrap(~ groupwrap,ncol=3)
#and get rid of a pesky legend (as the question is in the strip text)
+opts(legend.position="none")
#make an informative y-axis label
+ylab(expression(paste("BI393 Scale Grade (Mean" %+-% "1SD)")))
 
#and now print it off...
ggsave(printing, file="396.png", width=10, height=10)
dev.off()

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