Motility of cells is a highly complex, dynamic and coordinated mechano-chemical process that

is influenced by hundreds of proteins (Lauffenburger and Horwitz, 1996, Parent and Weiner, 2013 and Ridley et al., 2003). Study of T cell motility, along with that of other leukocytes, presents additional challenges when compared to the motility of cells of mesenchymal and epithelial origin. Leukocytes can move at speeds upwards of 10 μm/min and exhibit multiple modes of motility with remarkable flexibility to shift from one mode to the other (Friedl and Weigelin, 2008, Jacobelli et find more al., 2009, Lammermann and Sixt, 2009 and Sixt, 2011). Leukocytes can also move with or without attachment to the substratum. Further, there is mTOR inhibitor appreciable heterogeneity in the motility of leukocytes within a population. Thus, the study of leukocyte motility necessitates integrative

experimental and analytical approaches to develop coherent understanding of the process (Zhang et al., 2013). Multi-channel or multi-mode microscopy offers a powerful platform to collect data and enable integrative analysis (Welch et al., 2011). An example of integrative analysis is relating polarization of a molecule of interest to thymocyte motility (Melichar et al., 2011 and Pham et al., 2013). In order to conduct integrative analysis, one needs to be able to track cells and integrate information from multiple image series. Packages such as Volocity (from PerkinElmer), CellProfiler (Carpenter et al., 2006) and TACTICS (Pham et al., 2013) have the basic framework for tracking cells and associating information from additional Nintedanib (BIBF 1120) image series to the tracks. Interference reflection microscopy (IRM) provides information on adhesion and spreading on the substratum due to interference between light reflected from the cover-glass

and the apposing cell membrane (Limozin and Sengupta, 2009). As T cells can move with or without attachment to the substratum and change contact area continuously, it is beneficial to include IRM along with fluorescence and transmitted light modes of microscopy. However, IRM is extremely sensitive to focus and planarity drifts as a result of which the IRM image series typically have spatiotemporally varying background and foreground intensity values. This presents a challenge to the aforementioned tools for integrative analysis as they rely on global thresholding for segmenting cells and generally report intensity values of additional channels upon global segmentation in the primary channel. It is desirable to treat individual image channels separately and also perform local segmentation. In order to be able to accurately integrate IRM data, along with fluorescence and transmitted light data in 2D image series, we have developed a MATLAB-based toolset that we call ‘Tool for Integrative Analysis of Motility’ (TIAM).