BIO407 Group1 Image processing of live cells, treated with DFX
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2025
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| author | Vanhimbeeck, Lars Gysin, Alexander Snyder, Sara |
| author_facet | Vanhimbeeck, Lars Gysin, Alexander Snyder, Sara |
| contents | <div>## Study:</div> <div>This data is part of the practical course BIO407 2025 at the University of Zurich, titled 'Advanced Microscopy: From preparation to data and visualization'.</div> <div>The effects of Deferasirox (DFX) on mitochondria morphology were examined using different microscopy techniques.</div> <div>The data is originally from the paper:</div> <div>Gottwald EM, Schuh CD, Drücker P, Haenni D, Pearson A, Ghazi S, Bugarski M, Polesel M, Duss M, Landau EM, Kaech A, Ziegler U, Lundby AKM, Lundby C, Dittrich PS, Hall AM. The iron chelator Deferasirox causes severe mitochondrial swelling without depolarization due to a specific effect on inner membrane permeability. Sci Rep. 2020 Jan 31;10(1):1577. doi: 10.1038/s41598-020-58386-9. PMID: 32005861; PMCID: PMC6994599.</div> <div> </div> <div>## Study Component:</div> <div>Job: Image Processing</div> <div>Task: 4) Timelapse of mitochondria in deferasirox (DFX) treated cells</div> <div>Timelapse data was analyzed: Mitochondria were segmented, their morphology and signal intensity were measured over time, and plotted.</div> <div> </div> <div>## Biosample:</div> <div>Opossum kidney (OK) cells (kind gift from the group of Prof O. Devuyst (Physiology, University of Zurich))</div> <div> </div> <div>## Specimen:</div> <div>DFX treatment: 200uM</div> <div>Dyes:</div> <div>-Mitochondria-GFP BacMam 2.0</div> <div>-TMRM (mitochondrial membrane potential dependent dye)</div> <div> </div> <div>## Image Acquisition:</div> <div>Images were acquired using a Leica SP8 inverse STED 3x.</div> <div>Acquired channels:</div> <div>Channel 1: Mitochondria-GFP, 488nm excitation, 493nm-548nm emission</div> <div>Channel 2: TMRM, 553nm excitation, 564nm-650nm emission</div> <div>Objective: HC PL APO CS2 100x/1.40 OIL</div> <div> </div> <div>## Image Data:</div> <div>Raw timelapse data:</div> <div>151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lif</div> <div> </div> <div>Timelapse data separated into the two color channels:</div> <div>- Timelapse data of Mito_GFP channel</div> <div>151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_channel1.tif</div> <div>- Timelapse data of TMRM channel</div> <div>151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_channel2.tif</div> <div> </div> <div>Segmentation masks of mitochondria:</div> <div>- Segmentation mask of GFP channel</div> <div>C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_mask.tif</div> <div>- Segmentation mask of TMRM channel</div> <div>C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_mask.tif</div> <div> </div> <div>Ilastik project files used for mitochondria segmentation:</div> <div>-Segmentation according to GFP signal:</div> <div>ImageProcessing_Task3_Group1_PixelClassification_Mito_GFP.ilp</div> <div>-Segmentation according to TMRM signal:</div> <div>ImageProcessing_Task3_Group1_PixelClassification_TMRM.ilp</div> <div> </div> <div>FIJI macro, documenting the analysis steps:</div> <div>Macro_Image_processing_task3.ijm</div> <div> </div> <div>Measurements of the Segmentation of the GFP channel:</div> <div>- Excel sheet containing all measurements:</div> <div>All_Measurements_Mito_GFP.csv</div> <div>- Excel sheet containing the summary of the measurements (mean GFP signal & Mean Circularity)</div> <div>Summary_Measurements_Mito_GFP.csv</div> <div> </div> <div>Measurements of the Segmentation of the TMRM channel:</div> <div>- Excel sheet containing all measurements:</div> <div>All_Measurements_TMRM.csv</div> <div>- Excel sheet containing the summary of the measurements (mean TMRM signal & Mean Circularity)</div> <div>Summary_Measurements_TMRM.csv</div> <div> </div> <div>RStudio Files for making the plots:</div> <div>Plotting_Measurements_GFP_signal.R</div> <div>Plotting_Measurements_TMRM_signal.R</div> <div> </div> <div>Plots of mean mitochondria circularity over time:</div> <div>- Conducted on the GFP channel</div> <div>Plot of mean mitochondria circularity over time_GFP.png</div> <div>- Conducted on the TMRM channel</div> <div>Plot of mean mitochondria circularity over time_TMRM.png</div> <div> </div> <div>Plot of mean GFP signal in mitochondria over time:</div> <div>Plot of mean GFP intensity over time.png</div> <div> </div> <div>Plot of mean TMRM signal in mitochondria over time:</div> <div>Plot of mean TMRM intensity over time.png</div> <div> </div> <div>## Image Correlations</div> <div>- C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_mask.tif contains the segmentation masks for the timelapse in 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_channel1.tif</div> <div>- C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_mask.tif contains the segmentation masks for the timelapse in 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_channel2.tif</div> <div> </div> <div> </div> <div>## Image Analysis</div> <div>We used Ilastik (v1.4.0) to train a pixel classifier to segment mitochondria. We did a training for the GFP and the TMRM channel.</div> <div>The segmentation masks were analyzed in FIJI and the 'Analyze Particles' command was used to measure the circularity and mean signal intensity of all segmented mitochondria. </div> <div>The mean measurements were then plotted for each timepoint.</div> <div>All Analysis steps were conducted for the GFP and the TMRM channel.</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14978702 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | BIO407 Group1 Image processing of live cells, treated with DFX Vanhimbeeck, Lars Gysin, Alexander Snyder, Sara <div>## Study:</div> <div>This data is part of the practical course BIO407 2025 at the University of Zurich, titled 'Advanced Microscopy: From preparation to data and visualization'.</div> <div>The effects of Deferasirox (DFX) on mitochondria morphology were examined using different microscopy techniques.</div> <div>The data is originally from the paper:</div> <div>Gottwald EM, Schuh CD, Drücker P, Haenni D, Pearson A, Ghazi S, Bugarski M, Polesel M, Duss M, Landau EM, Kaech A, Ziegler U, Lundby AKM, Lundby C, Dittrich PS, Hall AM. The iron chelator Deferasirox causes severe mitochondrial swelling without depolarization due to a specific effect on inner membrane permeability. Sci Rep. 2020 Jan 31;10(1):1577. doi: 10.1038/s41598-020-58386-9. PMID: 32005861; PMCID: PMC6994599.</div> <div> </div> <div>## Study Component:</div> <div>Job: Image Processing</div> <div>Task: 4) Timelapse of mitochondria in deferasirox (DFX) treated cells</div> <div>Timelapse data was analyzed: Mitochondria were segmented, their morphology and signal intensity were measured over time, and plotted.</div> <div> </div> <div>## Biosample:</div> <div>Opossum kidney (OK) cells (kind gift from the group of Prof O. Devuyst (Physiology, University of Zurich))</div> <div> </div> <div>## Specimen:</div> <div>DFX treatment: 200uM</div> <div>Dyes:</div> <div>-Mitochondria-GFP BacMam 2.0</div> <div>-TMRM (mitochondrial membrane potential dependent dye)</div> <div> </div> <div>## Image Acquisition:</div> <div>Images were acquired using a Leica SP8 inverse STED 3x.</div> <div>Acquired channels:</div> <div>Channel 1: Mitochondria-GFP, 488nm excitation, 493nm-548nm emission</div> <div>Channel 2: TMRM, 553nm excitation, 564nm-650nm emission</div> <div>Objective: HC PL APO CS2 100x/1.40 OIL</div> <div> </div> <div>## Image Data:</div> <div>Raw timelapse data:</div> <div>151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression.lif</div> <div> </div> <div>Timelapse data separated into the two color channels:</div> <div>- Timelapse data of Mito_GFP channel</div> <div>151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_channel1.tif</div> <div>- Timelapse data of TMRM channel</div> <div>151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_channel2.tif</div> <div> </div> <div>Segmentation masks of mitochondria:</div> <div>- Segmentation mask of GFP channel</div> <div>C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_mask.tif</div> <div>- Segmentation mask of TMRM channel</div> <div>C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_mask.tif</div> <div> </div> <div>Ilastik project files used for mitochondria segmentation:</div> <div>-Segmentation according to GFP signal:</div> <div>ImageProcessing_Task3_Group1_PixelClassification_Mito_GFP.ilp</div> <div>-Segmentation according to TMRM signal:</div> <div>ImageProcessing_Task3_Group1_PixelClassification_TMRM.ilp</div> <div> </div> <div>FIJI macro, documenting the analysis steps:</div> <div>Macro_Image_processing_task3.ijm</div> <div> </div> <div>Measurements of the Segmentation of the GFP channel:</div> <div>- Excel sheet containing all measurements:</div> <div>All_Measurements_Mito_GFP.csv</div> <div>- Excel sheet containing the summary of the measurements (mean GFP signal & Mean Circularity)</div> <div>Summary_Measurements_Mito_GFP.csv</div> <div> </div> <div>Measurements of the Segmentation of the TMRM channel:</div> <div>- Excel sheet containing all measurements:</div> <div>All_Measurements_TMRM.csv</div> <div>- Excel sheet containing the summary of the measurements (mean TMRM signal & Mean Circularity)</div> <div>Summary_Measurements_TMRM.csv</div> <div> </div> <div>RStudio Files for making the plots:</div> <div>Plotting_Measurements_GFP_signal.R</div> <div>Plotting_Measurements_TMRM_signal.R</div> <div> </div> <div>Plots of mean mitochondria circularity over time:</div> <div>- Conducted on the GFP channel</div> <div>Plot of mean mitochondria circularity over time_GFP.png</div> <div>- Conducted on the TMRM channel</div> <div>Plot of mean mitochondria circularity over time_TMRM.png</div> <div> </div> <div>Plot of mean GFP signal in mitochondria over time:</div> <div>Plot of mean GFP intensity over time.png</div> <div> </div> <div>Plot of mean TMRM signal in mitochondria over time:</div> <div>Plot of mean TMRM intensity over time.png</div> <div> </div> <div>## Image Correlations</div> <div>- C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_mask.tif contains the segmentation masks for the timelapse in 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_Mito_GFP_channel1.tif</div> <div>- C1-151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_mask.tif contains the segmentation masks for the timelapse in 151106_slide1_bacmam20_PPC_7473_Esther_DH_Progression_TMRM_channel2.tif</div> <div> </div> <div> </div> <div>## Image Analysis</div> <div>We used Ilastik (v1.4.0) to train a pixel classifier to segment mitochondria. We did a training for the GFP and the TMRM channel.</div> <div>The segmentation masks were analyzed in FIJI and the 'Analyze Particles' command was used to measure the circularity and mean signal intensity of all segmented mitochondria. </div> <div>The mean measurements were then plotted for each timepoint.</div> <div>All Analysis steps were conducted for the GFP and the TMRM channel.</div> |
| title | BIO407 Group1 Image processing of live cells, treated with DFX |
| url | https://doi.org/10.5281/zenodo.14978702 |