An intuitive user interface has been built onto TIAM to guide through the steps for choosing parameters to perform detection and subsequent analysis of motility characteristics. An additional user interface for dynamic visualization of selected tracks is also provided. As our main interest lies in T cell biology, we have validated the implemented algorithms on chemokine-induced and antigen-induced motility of human CD8 T cells and obtained novel insights that were critically dependent on the unique capabilities of TIAM. The overall approach for integrative analysis of motility
by TIAM is summarized in Fig. 1. Detection, tracking, feature extraction, and track editing algorithms were implemented in MATLAB (from MathWorks). The user interface to facilitate user-inputs Ponatinib in vitro was implemented in Java. A second user interface for dynamic visualization of individual or pairs of tracks was implemented in MATLAB. The TIAM software project has been deposited in GitHub for free
access to the source code (https://github.com/willieneis/TIAM). Talazoparib manufacturer A detailed user guide, demo and the URL link for benchmark datasets are provided in the Github repository. Additional description of algorithms can be found in the Supplementary methods section. TIAM is equipped to detect and track cells in transmitted light image series, such as those acquired by bright-field, differential interference contrast (DIC), or phase-contrast microscopy. We chose this approach for multiple reasons: a) Cell boundaries can be difficult to discern from fluorescence information when cells are in a crowded environment; the inherent nature of transmitted light imaging ensures that cell boundaries provide some contrast even in a crowded environment. b) Using transmitted light imaging for tracking of cells frees up a fluorescence channel for acquiring additional information about cells’ behavior. c) Using
transmitted light microscopy instead of fluorescence microscopy allows for long-term live-cell imaging as phototoxicity is minimized. ifoxetine TIAM’s cell detection strategy involves finding cell-shaped patterns in the set of edges detected in an image. A Canny edge filter (Canny, 1986) is used to produce a binary image depicting all edges in a given video frame, and a circular Hough transform (CHT) (Duda and Hart, 1972) operates on this binary image to detect individual cells (Fig. 2a–d). This two-step strategy has been applied previously to detect nuclei in zebra fish embryos (Melani et al., 2007). The Hough transform is a robust method for detecting parameterized curves in images, where the task of detecting complex patterns of pixels (a costly global search problem) is transformed into the task of constructing peaks in a parameter space.