Group Work 2: Analysing Oscillations in Time-Series Images

Group Work 2: Analysing Oscillations in Time-Series Images#

In this exercise, you will build a small analysis workflow in a Jupyter Notebook to investigate whether the expression of a gene oscillates over time. The notebook is based on the example in Solution Notebook, and it uses time-resolved microscopy images together with cell and nucleus label masks.

The images we will use for this exercise can be downloaded from the Group Work 2 Dataset.

To go through this exercise, create a new folder named bobiac_group_work. In the instructions below, we assume this folder is on your Desktop, but you can choose a different location if you prefer.

Step 1#

Create a new Jupyter Notebook with uv’s juv tool

  1. Open a new terminal or PowerShell and navigate to the bobiac_group_work folder.

  2. Inside the bobiac_group_work folder, create a new folder named group_work_2 and navigate into it.

  3. Inside group_work_2, create a new Jupyter Notebook named oscillations.ipynb using juv.

  4. Run the notebook in your browser with juv.

  5. Close the notebook tab in your browser and stop the notebook from the terminal (cmd + c or ctrl + c) before continuing to Step 2.

Solution

  1. Open the terminal or PowerShell.

  2. cd .../Desktop/bobiac_group_work

  3. mkdir group_work_2

  4. cd .../Desktop/bobiac_group_work/group_work_2

  5. uvx juv init oscillations.ipynb

  6. uvx juv run oscillations.ipynb

  7. Close the notebook tab in your browser and stop the notebook from the terminal (cmd + c or ctrl + c)

Step 2#

Add dependencies to the notebook with juv

  1. In your notebook created in Step 1, add the dependencies you need for the analysis.

  2. Run the notebook with juv.

  3. Import the libraries in the first code cell.

  4. Run the cell to confirm that the imports work without errors.

The analysis in the example notebook uses the following packages:

  • matplotlib

  • numpy

  • pandas

  • seaborn

  • scikit-image

  • scipy

  • tifffile

  • imagecodecs

  • bobiac_tools

Note

If you prefer to work in Google Colab instead of locally, copy the .ipynb file to your Google Drive and open it from the Colab website. In that case, you will need to add a !pip install <package_name> cell for each dependency before importing the libraries.

Solution

  1. cd .../Desktop/bobiac_group_work/group_work_2

  2. Add the dependencies:

    uvx juv add oscillations.ipynb matplotlib numpy pandas seaborn scikit-image scipy tifffile imagecodecs
uvx juv add oscillations.ipynb "bobiac_tools @ git+https://github.com/bobiac/bobiac-tools.git"

  3. uvx juv run oscillations.ipynb

  4. In the first code cell, add imports such as:

    from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import skimage
import tifffile
from bobiac_tools import overlay_labels

  5. Run the cell to confirm that the libraries import without errors.

Step 3#

Load and inspect the time-series images

In this step, you will load the image files and the segmentation masks and inspect the data visually.

  1. Create a new code cell in the notebook.

  2. Download the dataset folder that contains the time-lapse file F01_1615_tcyx.tif.

  3. For the F01_1615_tcyx.tif file, display the first eight time points of the last channel in the as a grid of images.

  4. In the same dataset folder, there is a F01_1615_tcyx_cell_labels.tif file (labeled mask of the cells) and a F01_1615_tcyx_nuclei_labels.tif file (labeled mask of the nuclei). Visualize these masks as an overlay

The example notebook uses the data in the course repository under the images/ and masks/ folders for the time-series example.

Solution

See Solution Notebook for a worked example.

Step 4#

Measure nuclear intensity for the first time point

In this step, you will analyse the first image and quantify the intensity of each nucleus.

  1. Load the first time point of the last channel in the F01_1615_tcyx.tif file.

  2. Remove objects touching the border of the image.

  3. Measure the intensity of each nucleus with skimage.measure.regionprops_table.

  4. Subtract the background intensity from the measurements and inspect the resulting values.

Tip

You can use the same workflow as in the earlier measurement notebooks: load the image, apply a mask, extract region measurements, and store the results in a pandas.DataFrame.

Solution

See Solution Notebook for a worked example.

Step 5#

Perform quality control and analyse all time points

Now you will turn the workflow into a batch analysis over all 16 images.

  1. Check the measurements for artifacts, for example by plotting the distribution of background-corrected intensities.

  2. Overlay the measured values on the image and inspect whether any nuclei look suspicious.

  3. Loop through all time points, measure the intensity of each nucleus, and add a time column.

  4. Combine all measurements into a single DataFrame.

Solution

See Solution Notebook for a worked example.

Step 6#

Visualise the oscillations and estimate the frequency

In this final step, you will answer the main question: what is the frequency of the oscillations?

  1. Use sns.lineplot to visualise the intensity of each nucleus over time.

  2. Add a second plot showing the average signal across cells.

  3. Estimate the oscillation frequency from the pattern you observe.

  4. Write a short conclusion in a markdown cell.

Solution

See Solution Notebook for a worked example.

Bonus#

Try a nuclear/cytoplasmic ratio

If you want an extra challenge, replace the nuclear intensity measurement with a ratio of nuclear intensity divided by cytoplasmic intensity. This can sometimes make the oscillation easier to interpret.