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Integrative analysis of T cell motility from multi-channel microscopy data using TIAM
Viveka Mayya1, Willie Neiswanger2, Ricardo Medina2
1Skirball Institute of Biomolecular Medicine, NYU Medical Center, 540 First Avenue, New York, NY 10016, USA.
Journal of Immunological Methods
|December 3, 2014
Summary
A new MATLAB tool, TIAM, enhances T cell motility analysis using multi-channel microscopy. It accurately tracks cells and reveals insights into T cell subsets and LFA1 interactions during immune responses.
Area of Science:
- Immunology
- Cell Biology
- Bioinformatics
Background:
- T cell motility is crucial for immune responses but complex to analyze.
- Existing tools lack integrative analysis of multi-channel microscopy data for cell motility.
Purpose of the Study:
- To develop a tool for integrative analysis of T cell motility using multi-channel time-lapse microscopy.
- To improve the accuracy and scope of T cell tracking and feature extraction.
Main Methods:
- Implemented algorithms for cell detection, tracking, and feature extraction from multi-channel images.
- Integrated algorithms into a MATLAB-based toolset named TIAM (Tool for Integrative Analysis of Motility).
- Utilized edge detection, Hough transforms, and nearest-neighbor association for cell analysis.
Main Results:
- TIAM demonstrated superior accuracy in detecting and tracking motile T cells compared to existing software (DYNAMIK, Icy, Imaris, Volocity).
- Feature extraction from reflection and fluorescence channels showed less than 10% median error.
- Identified distinct motility behaviors between CD45RO and CD45RA human CD8 T cell subsets.
Conclusions:
- TIAM provides novel insights into T cell motility, including subset-specific differences and LFA1's role in antigen-induced motility.
- The tool facilitates a more comprehensive understanding of T cell migration dynamics.
- TIAM's capabilities are essential for uncovering complex T cell behaviors.
