Abstract
Integrative analytical approaches are needed to study and understand T cell motility as it is a highly coordinated and complex process. Several computational algorithms and tools are available to track motile cells in time-lapse microscopy images. In contrast, there has only been limited effort towards the development of tools that take advantage of multi-channel microscopy data and facilitate integrative analysis of cell-motility. We have implemented algorithms for detecting, tracking, and analyzing cell motility from multi-channel time-lapse microscopy data. We have integrated these into a MATLAB-based toolset we call TIAM (Tool for Integrative Analysis of Motility). The cells are detected by a hybrid approach involving edge detection and Hough transforms from transmitted light images. Cells are tracked using a modified nearest-neighbor association followed by an optimization routine to join shorter segments. Cell positions are used to perform local segmentation for extracting features from transmitted light, reflection and fluorescence channels and associating them with cells and cell-tracks to facilitate integrative analysis. We found that TIAM accurately captures the motility behavior of T cells and performed better than DYNAMIK, Icy, Imaris, and Volocity in detecting and tracking motile T cells. Extraction of cell-associated features from reflection and fluorescence channels was also accurate with less than 10% median error in measurements. Finally, we obtained novel insights into T cell motility that were critically dependent on the unique capabilities of TIAM. We found that 1) the CD45RO subset of human CD8 T cells moved faster and exhibited an increased propensity to attach to the substratum during CCL21-driven chemokinesis when compared to the CD45RA subset; and 2) attachment area and arrest coefficient during antigen-induced motility of the CD45A subset is correlated with surface density of integrin LFA1 at the contact.
Highlights
We have observed that a sizeable fraction of CD45RO+ve human CD8 T cells have higher motility on CCL21- and ICAM1-coated glass compared to CD45RA+ve cells (Fig. 5b)
The CD45RO subset consists of central memory and effector memory cells (Willinger et al, 2005)
It is likely that central memory cells are the most responsive to CCL21 among all the subsets of CD8 T cells in our experiments
Summary
In a common framework referred to as ‘tracking by detection’, cell detection is performed in each frame independently, and the detection results are joined together between frames via cell tracking algorithms. A popular basis for tracking known as the ‘nearest neighbor’ associates a detected cell in a given frame with the nearest detected cell in an adjacent frame. Modelbased methods have been developed for cell tracking (Dufour et al, 2011; Maska et al, 2014; Padfield et al, 2011). These methods comprise model-based representations of cells that evolve between subsequent frames to perform cell tracking
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