Depression is a pressing global issue with a prevalence of 3.8 % worldwide, leading to severe social and economic consequences. Due to the complexity of the disease, research into prevention, diagnosis, and treatment options faces significant challenges. Understanding the intricate pathways by which depression develops and progresses is critical to expanding our knowledge base and developing effective intervention strategies. One area of interest is the possible role of hormonal contraception as a risk factor. While some studies suggest a link between hormonal contraceptives and depression, others find no association, leading to controversy in the medical literature.
Our project takes a biochemical approach, focusing on the effects of steroid hormones on neuronal cells in vitro to address potential biases in previous research. By treating neural progenitor cells with various hormonal contraceptives and analyzing resultant protein changes using quantitative proteomics, we aim to uncover insights into the pharmacological effects of steroids on the neuronal proteome.
We hope to provide a significant contribution to clarifying the possible influence of steroids on the development of depression. This knowledge is of utmost importance due to the widespread and long-term use of oral contraceptives by healthy people and could lead to better risk-benefit assessments.
The following descriptions are only intended to give an overall impression of what this code is used for. For further information, please refer to the published article.
- 2D cell culture
- Cell line: ReNcell VM
- Treatments: 100 ng/mL of ethinyl estradiol, levonorgestrel, their combination, and S-23 in DMSO
- Controls: 100 ppm DMSO & pure cell culture medium
- Treatment duration: 14 days
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Aliquot 1 – label-based quantification (LBQ):
- lysis
- reduction
- alkylation
- digestion with Trypsin/LysC
- labeling with isobaric mass tags: TMTpro 16plex
- pooling
- desalting and pre-fractionation at high pH
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Aliquot 2 – label-free quantification (LFQ):
- lysis
- reduction
- alkylation
- digestion with Trypsin
-
LBQ:
- Institute of Biochemistry at the German Sport University Cologne, Cologne, Germany
- Q Exactive
- Top-15 DDA
- Full scan: Res: 70,000 FWHM (at m/z 200); AGC: 3e6, IT: 50 ms; m/z 375–1500
- MS/MS: Res: 35,000 FWHM (at m/z 200); AGC: 2e5; IT: 250 ms; Isolation width: m/z 0.7
- 0 % to 40 % ACN in 180 min
-
LFQ:
- Leibnitz-Institut für Analytische Wissenschaften—ISAS—e.V., Dortmund, Germany
- Q Exactive HF
- Top-15 DDA
- Full scan: Res: 60,000 FWHM (at m/z 200); AGC: 3e6, IT: 120 ms; m/z 300–1500
- MS/MS: Res: 15,000 FWHM (at m/z 200); AGC: 5e4; IT: 50 ms; Isolation width: m/z 1.6
- 0 % to 30 % ACN in 120 min
- Database search:
- Proteome Discoverer 3.0 with Sequest HT
- UniProtKB/Swiss-Prot human reference proteome (Tax. ID: 9606)
- Statistical analysis:
MSstatsandMSststsTMTclusterProfilerandDOSE
To execute the pipeline yourself, follow these steps:
Setup: Ensure you have installed JupyterLab with an R kernel. For simplicity and to avoid compatibility issues, you can use my Docker container.
Prepare the Code: Download or fork this repository. If you want, rename the folder (e.g., to thilmany-etal) and move it into the notebooks/ directory of your JupyterLab Docker environment.
Run the Pipeline: Start the Docker container and access JupyterLab. Navigate to notebooks/thilmany-etal/, open the Jupyter Notebook (Analysis.ipynb), and select "Restart Kernel and Run All Cells..." from the "Run" menu.
Depending on Docker's resource allocation and your network connection, the process may take a while to finish, so do yourself a favor and have a cup of coffee. ☕️