Datahandling OptoMotorics

on Monday, September 17th, 2018 12:31 | by

Table with the generated fly test data:

Test Description Average right Average left Differece general
Ex4 normal 4.55382 4.624879 0.071059 4.5893495
Ex4 repositioned 4.562121 4.579682 0.017561 4.5709015
Ex5 normal 4.273299 4.434383 0.161084 4.353841
Ex5 repositioned 4.562121 4.579682 0.1610834 4.5709015
Ex6 normal 3.535669 3.587334 0.051665 3.5615015
Ex7 no arena 3.349097 3.337588 -0.0115097 3.343343
Ex8 bad fly 3.42903 3.394603 -0.034427 3.411816
Ex9 no arena 4.391139 4.363279 -0.0278599 4.377209
Ex9 normal 4.404213 4.456195 0.05198185 4.430204
Ex10 no arena 4.259827 4.195971 -0.0638559 4.227899
Ex10 normal 4.243121 4.360124 0.1170035 4.301622

 

Ex8:

 

Ex9:

 

Ex10:

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Protocerebral bridge in FoxP mutated flies

on Friday, September 14th, 2018 1:22 | by

The first 3 images show the protocerebral bridge in FoxP mutated flies. In the second line the first picture shows the protocerebral bridge in a FoxP isoB-Gal4 fly. The last image is taken and modifies from the Virtual Fly Brain

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Larvae FoxPB-Gal4 with ELAV or REPO

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Larvae ELAV (20x and 40x)

Adults (20x and 40x)

Larvae REPO (20x and 40x)

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17d

on Monday, September 10th, 2018 1:03 | by

Been working on 17d for the past month.

The experiments in the flight simulator. Self-learning performance indices in a two-minute test with the heat switched off after 4 and 8 minutes of training, indicated impairment of 17d-TNT flies.

The flies also showed clear impairments in their flight performance. To quantify this I assessed both possible alterations in their motor coordination (using climbing assay) as well as flight performance. The climbing assay relies on walking rather than flying. Both experiments show reduced ability of motor coordination and flight performance.

 

 

To confirm the specificity of the 17d-Gal4 fly I used the trans-tango flies.

Neuron, 96 (2017) 783-799. doi:10.1016/j.neuron.2017.10.011

 

The trans-tango is notorious for having a low expression in adult flies, which was also observed by me. The image is taking without any GFP-antibody.

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First Dataset From OptMoto Experiment

Text data file with all the rawdata generated by my new programme: Rawdata OptMoto

R programme which generates the plot:

library(dygraphs)

rawdata <- read.csv(“KHZtext.txt”, header = FALSE, sep = ” “)
flydata <- rawdata[c(3, 1)]
#dygraph(flydata) %>% dyRangeSelector()

arenadata <- rawdata[c(3, 2)]

rawdata <- rawdata[c(3, 1, 2)]

dygraph(rawdata, main = “test graph”) %>%
dyRangeSelector()
# dySeries(“V1”, name = “fly”) %>%
# dySeries(“V2”, name = “arena”) %>%
# dyAxis(“y”, label = “Voltage (V)”) %>%
#

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First try for GABA antibody + FoxP-all_isoforms-Gal4

on Sunday, September 2nd, 2018 12:41 | by

UNIVERSITY PART

First try for GABA antibody on FoxPisoB-Gal4 adult brain. The reaction is not optimal, there is a lot of background and the signal is too strong. We will have to try again changing conditions in order to be sure of any colocalization.

We also have found some FoxP-all_Isoforms-Gal4 homozygous, we thus wanted to see if there was any macro problem in the overall structure of the brain, but the structure looks fairly normal

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Larvae FoxPisoB-Gal4

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UNIVERSITY PART

Larvae FoxPisoB-Gal4 + Bruch Pilot (20x and 40x)

 

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Searching interesting lines

on Friday, August 31st, 2018 4:23 | by

For finding if there are phenotype correlations in the different setups one needs a full matrix, which is not my case since I tested to some extent different lines in different setups. To make it more graphically, the observed results are shown in the above-left table. Colours just means values obtained (colorbars are unfortunately missing but it´s not so important) and each of the rows is a setup and each of the columns a fly line tested.

For testing correlations among setups, and similarly for doing a PCA one needs a full matrix, and not a sparse matrix as I have. What are the options? a few…

There is LSEOF (Empirical Orthogonal Function Analysis), RSEOF which is like LSEOF but recursive and normally achieves better results. There is another algorithm that is called DINEOF which consists of an additional step, interpolation, before doing LSEOF. The latter has shown to yield the best results. That is why I opted for this for filling my matrix for further analysis.

The results are shown in the figure below

Here the correlation of the mean results for the lines tested in the experiments. From the correlation numbers I would not say that there is any clear correlation of any experiment to the other ( Adj R² = 0.1392 for Joystick and Y-mazes; Adj R² = 0.06809 for Joystick and yellow T-maze)

Even if there is no clear correlation among setups that the PCA could use, I decided to do a PCA to see what comes out of it. In the table below one can see the PCA loadings from the 4 different setup. We see that the PC1 uses a positive correlation of Joystick,Ymazes and yellow T-maze. PC2 a negative correlation of red T-maze and Joystick and positive Y-mazes and yellow T-mazes. After all there is not much to say about this, I think.

This scree plot without any clear elbow gives me the impression that there is not much information here.

This is how the table of “discrete phenotypes” looks like. The first column is positive scores and the second is negative scores. That means that we are looking for lines that have 4´s. This means that it has 4 negative/positive scores (Joystick, Ymazes, Tmaze red and yellow). In this case TH-G1 and TH-D1 have 4 positive scores (HL9 and TH-C1 have 3). TH-D’ have 4 negative scores (MB025B has 3). Unfortunately not all lines were tested in all setups so we might miss some interesting lines.

This is an histogram of the amount of zeros, ones,… that are in the above table.

The histogram below is the same as above  but by generating surrogate data. I just sampled data from each experiment and checked what this  imaginary lines might do. The histogram is quite similar to the one above. I think that having the same histogram shape only shows that there is not correlation of effects among setups, which was already shown above.

This is another way of looking for the interesting lines. I decided to withen the data so that all have mean zero and equal deviation so that all experiments have same weight. Then I did a mean for all experiments for each line. The extreme values show that they had a very strong overall phenotype. Here one can confirm what was seen with the “discrete phenotypes”. TH-D’ shows a negative score. TH-D1 a positive one, as well as MB060B and HL9.

Here we plot in different colours the effect from each of the setups tested. Here are also lines like TH-D’ and TH-D1 outstanding. I think it is important to see that the longer the bars the most interesting, but also the more mixed contribution of each experiment the more stronger the statistics might be.

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Western Blot FoxP-IsoB

on Friday, August 24th, 2018 2:19 | by

KLINIKUM PART

I did the WB again in order to have a better and clearer image, additionally I made a coomassie staining and a silver staining in order to see if there is any contamination in the purified FoxP-IsoB protein

I also made a WB on Drosophila brains using the mouse serum. The signal is not clean but we can see a band of the appropriate size (60 kDa)

 

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