About the Project

• Background
• What do you want to do?
• Goals
• Instructions
• Important variables
• Where to find things
• Metadata

Background

Insulin is a hormone associated with processes like blood glucose regulation, and it also acts in the brain. For my honours project, I explored the effect of insulin on neurons in a brain region called the dorsomedial hypothalamus (DMH). DMH neurons stimulate appetite and they also have insulin receptors, but almost nothing is known about the effect of insulin on neuronal activity in the DMH.

I used whole-cell patch clamp electrophysiology to record from the living DMH neurons of young, male and female Sprague-Dawley rats. I recorded excitatory evoked currents (a measure of synaptic transmission) and action potentials (a measure of excitability) before and after exposing neurons to 500 nM of insulin.

What do you want to do?

I want to read the thesis!

Here is a link to the Thesis.pdf file. You may find it easier to download and read the PDF outside of GitHub. The download button is on the top right of the screen.

I want to explore the code!

Scroll further to find instructions, metadata, and additional information.

Goals

I developed this code to achieve the following goals:

• Analyze changes in current amplitudes (evoked excitatory post-synaptic currents; eEPSCs) over time
• Analyze changes in action potential parameters before and after insulin exposure
• Group and summarize the raw data into publication-ready plots
• Perform appropriate statistical tests on the raw data
• Compile my thesis in a print-ready format with embedded code and figures

Instructions

You could clone the repository using git clone:

git clone https://github.com/christelinda-laureijs/honours-thesis.git

Or, if you don’t have GitHub set up, you could also download the repository as a zip file.

Either way, be sure to click on the honours-thesis.Rproj file. This will open RStudio and set the working directory to the project folder on your computer.

IMPORTANT: If you don’t open RStudio through the .Rproj file, R will not know where to find any of the scripts, and you will see error messages about missing files.

Read through the comments in the file (Thesis.Rmd or Scripts-from-Thesis-Rmd.R) and run each section in groups (press Ctrl+Enter to run chunks at a time). To minimize error messages, go from the top to the bottom and don’t skip lines.

The file Scripts/Scripts-from-Thesis-Rmd.R contains just the R code contained within the Thesis/Thesis.Rmd file. This is useful if you want to quickly run the same script without needing to scroll past the thesis text. It is also useful for debugging; just make sure that you make changes within the main Thesis.Rmdfile, not the script.

When adding new recordings, use the P1-Analysis-Script.Rmd in the Scripts folder. Be sure to read the instructions contained within the comments there.

If you just want to quickly view the plots, make sure that you have run the Thesis.Rmd file at least up to the “Begin statistical analysis” code. This will ensure that the data are processed and ready for plotting. Then, you can open and run the Figures/Quick-Plot-Viewer.Rmd file.

Important variables

There are two variables near the top of the scripts that you may want to change.

save_choice: Change this to “no” if you do not want to save the plots each time the file is run. This is useful for debugging or when repeatedly running the script, since it will significantly reduce the run time.

my_colours: Use these to define the colours globally. The main project palette is:

To generate the thesis as a PDF file, go to the Thesis/ folder, open the Thesis.Rmd file and click the Knit button. If you just want to run the script and get statistical output and plots, you could click on the Run button.

Note: The thesis is best viewed as a PDF. LaTeX features (like figure cross-referencing) will not work in the other outputs and you will receive an error message.

Where to find things

Data/ - Contains raw data including metadata (eEPSC-metadata.csv), which can also be found below in the metadata table.

• Output-Data-from-R contains the processed and cleaned data as R object files (.RDS). These are required to run the plots document. These are generated using the scripts within Thesis.Rmd.
• Raw-CSVs contains the CSV files from copying and pasting from Clampfit on the lab computer. This will mainly contain action potential parameters such as threshold and half-width.

Figures/ - Folder containing all figures. Figures generated from the Thesis.Rmd document will automatically go into folders with names starting with Output-. Also contains the folders Gap-Free-Plots/, Pruned Summary Plots/, and Representative-Traces/.

Scripts/ - Folder containing R scripts.

• Functions.r contains custom functions that I wrote to create the summary plots. I put this as an external .R file instead of keeping it within Thesis.Rmd so that I could use it elsewhere.

• Scripts-from-Thesis-Rmd.R contains just the R code contained within code chunks of the Thesis.Rmd file. This is useful if you want to quickly run the same script without needing to scroll past the thesis text. It is also useful for debugging; just make sure that you make changes within the main Thesis.Rmdfile, not the script.

Use the following code to extract just the scripts from the thesis:

knitr::purl(
  input = here::here("Thesis/Thesis.Rmd"),
  output = here::here("Scripts/Scripts-from-Thesis-Rmd.R"),
  documentation = 0
)

Templates/ - Contains my custom style templates:

• MtA-Thesis-Preamble.tex for PDF
• my-CSS-theme.css for HTML
• thesis-citations.bib for all in-text citations.
• packages.bib for all R packages used in my scripts

The MtA-Thesis-Preamble.tex requires additional LaTeX packages, and it may take some time to run the first time as it installs them. This also requires the font EB Garamond, which is a sub-folder in Templates/.

Thesis/ - Contains the Thesis.Rmd file used to generate my thesis, as well as its PDF output.

R-Work.Rproj - The project file, which sets the working directory for scripts and ensures that the relative paths work properly. Please click on this to open up R, otherwise the file paths will not work.

Metadata

These are all variables included the Raw-eEPSC-Data.csv file. Values marked with a * will be automatically generated during the R script.

Variable	Type	Description
Letter	character	Letter ID of the recording for easy cross-referencing to my lab book
Synapses	character	Synapses being observed
Sex	character	Biological sex of the animal
Treatment	character	Treatment applied, see README file
Time	numeric	Time in minutes
ID	character	ID from Clampex; not used in this file
P1	numeric	The first evoked excitatory post-synaptic current (eEPSC) amplitude
P2	numeric	The second eEPSC amplitude
X	numeric	Location of the cell in $\mu$M lateral to the top of the third ventricle
Y	numeric	Location of the cell in $\mu$M ventral to the top of the third ventricle
Age	integer	Age of the animal in days
Animal	numeric	Unique ID of the animal; could be adapted to include both n and N on graphs
Category	factor: 3 levels	Experiment category; see below in README for further explanations
PPR	numeric	*The paired pulse ratio is the ratio of P2/P1
Interval	factor: 6 levels	*Divides time into six 5-min intervals for MANOVAR
baseline_range	logic	*Returns TRUE if Time <= 5 min. Required for the normalization function later
baseline_mean	numeric	*Mean current amplitude within the baseline period of each recording
P1_transformed	numeric	*P1 normalized as a percentage relative to the baseline
P2_transformed	numeric	*P2 normalized as a percentage relative to the baseline
Animal_factor	factor: many levels	*Animal column coded as a factor

Additional notes about some variables:

Category: I assigned each experiment type with its own number ID and used this convention for all scripts.

1. HFS in control conditions
2. Adding insulin in control conditions
3. HFS in the presence of insulin

HFS: High frequency stimulation was 100 Hz applied for 4 seconds, which was repeated again after a 20-second interval.

Treatment:

• None indicates no additional drugs or modifications.
• HNMPA is a tyrosine kinase inhibitor that blocks insulin receptors.
• PPP is an insulin-like growth factor 1 receptor blocker.
• AM251 blocks CB1 cannabinoid receptors.
• Fasting marks experiments in which I removed food from the rats 24 hours before euthanization.

P1 and P2: Although the raw values were in pA, I normalized these values relative to the baseline current for each cell. The resulting percent data has _transformed in the variable name.

Interval: I named each interval ‘t_to_’ to avoid using numbers and special characters in variable names.