PDAC CellTracksColab
The CellTracksColab notebooks have been adapted to analyze tracking data featured in the manuscript "Quantitative analysis of pancreatic cancer cell attachment to endothelial cells." This suite of notebooks is part of the CellTracksColab project, designed to facilitate comprehensive analyses of cell tracking data. The project's resources are accessible via the GitHub repository provided below.
The CellTracksColab project repository: CellMigrationLab/CellTracksColab.
This notebook was used for loading and compiling the tracking data. It also includes functionalities for calculating additional tracking metrics, such as the Forward Motion Index (FMI). The notebook is designed to offer a comprehensive overview of the dataset.
Important Consideration: Due to the substantial size of the dataset discussed in the paper, reanalysis using the free version of Google Colab may be constrained by memory and processing limitations.
This notebook is designed to process data frames generated by the General Notebook within the CellTracksColab framework. It focuses on analyzing cell tracking data by identifying and counting the number of tracks that exhibit arrest behavior at each time point throughout the observation period. Key functionalities of this notebook include:
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Loading CellTracksColab Dataframes: It starts by importing the comprehensive data frames prepared by the General Notebook, ensuring a seamless transition from data compilation to detailed analysis.
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Counting Arrested Tracks: The notebook identifies tracks where cells have ceased to move, categorizing them as 'arrested.' This allows for a quantitative analysis of cell arrest dynamics over time.
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Computing Attachment Rates: Beyond counting arrested tracks, it calculates the cell attachment rate over the observation period. This metric is crucial for understanding how PDAC cells interact with endothelial cells over time.
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Generating Attachment Plots: Visual representations of attachment rates and arrested cell counts are produced, offering intuitive insights into the temporal dynamics of cell behavior.
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This notebook leverages a modified version of Ripley's K function—adapted here as Ripley's L function—to assess the spatial distribution and clustering patterns within fields of view (FOV). Its key features include:
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Loading CellTracksColab Dataframes: Initiates the analysis by importing the detailed data frames compiled by the General Notebook.
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Identifying Arrested Cell Locations: Determines the precise locations of arrested cells within each FOV, setting the stage for subsequent spatial analysis.
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Computing Ripley's L Function for Each FOV: Applies Ripley's L function to quantify the degree of spatial clustering of arrested cells, providing insights into their distribution patterns.
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Performing Monte Carlo Simulations: Conducts Monte Carlo simulations to establish baseline expectations for spatial distributions, which are crucial for assessing the significance of observed clustering patterns.
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Comparing Ripley's L Values Across Conditions and Cell Types: Facilitates the comparison of spatial clustering metrics across different experimental conditions and cell types, allowing for a nuanced interpretation of cell behavior.
This notebook focuses on the analysis of cell tracks exhibiting distinct landing patterns. Through a detailed examination of track dynamics, this tool provides deep insights into the mechanisms of cell landing, arrest, and interaction with the endothelium. Here's an overview of its functionalities:
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Track Filtering Based on Instantaneous Speed: The initial step involves segregating tracks that demonstrate a clear landing pattern by analyzing their instantaneous speed. This process ensures that only tracks relevant to the landing behavior are included for detailed analysis.
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Measurement of Track Parameters: Once filtered, the notebook facilitates measuring a range of track parameters.
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Proximity Analysis to Endothelial Features: A unique feature of this notebook is its ability to measure the shortest distance of each track from previously segmented features, including endothelial cell nuclei and cell junctions. This analysis is pivotal in understanding the spatial relationships and interactions between circulating cells and the endothelium.
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Visualization of Track Parameters: To aid in the interpretation and presentation of findings, the notebook includes functionality for plotting the computed parameters of the tracks. These visualizations facilitate a clear and intuitive understanding of the data, highlighting key trends and patterns in cell behavior.
For detailed instructions on using these notebooks, including required libraries, data formats, and analysis workflows, please look at the documentation within each notebook.
Guillaume Jacquemet wrote notebooks. Further contribution are welcome.