time-series

The goal of this workflow is to track cells captured in a time-lapse movie of a syncytial blastoderm stage Drosophila embryo and quantify their movement.

This example shows an example of object tracking. This pipeline analyzes a time-lapse experiment to identify the cells and track them from frame to frame, which is challenging since the cells are also moving. In addition, this pipeline also extracts metadata from the filename and uses groups the images by metadata in order to independently process several sequences of images and output the measurements of each.

Sample images

A portion of a time lapse movie of a syncytial blastoderm stage Drosophila embryo with a GFP-histone gene which renders chromatin fluorescent in live embryos. The movie shows nuclear divisions 10 through 13.

Victoria Foe made this movie on a Bio-Rad Radiance 2000 laser scanning confocal microscope using a 40X 1.3NA oil objective. The frames are 7 seconds apart and plays at 30 frames per second

GFP-histone transformed files provided by Rob Saint

V.Foe and G.Odell, . 26 July 2001

If your images are corrupted by a strong dominant Gaussian noise you can try this simple filter. It is based on thresholding in the DCT domain and is usually vastly superior to typical Gaussian filtering in term of detail preservation / noise reduction trade-off. The filter unfortunately introduces some block like artifacts that can be mitigated by averaging out overlaping shifted windows (as implemented in the Matlab version) and performing maximum intensity projection after the filtering: As such the filter is way more adapted to process 3D stacks that you plan to maximum intensity project than to process single z slice images.

This macro can stitch a (Z,T,C) data set with virtually no limit on the number of Z slices and time frames. The input to the macro is a folder with the raw tiff images (one image per file) as typically exported by motorized microscopes. These files must all be stores in the same folder and the file naming should ideally comply to OME-TIFF. The macro is however quite flexible: Only --X, --Y and --Z fields with user defined number of digits are compulsory. --T, --C and --L fields with user defined number of digits are necessary for multiple time frames / channels data sets. A compatible data set is provided as a .zip archive. Before processing it unzip it to a given location. The stitching is performed in a reference Z slice (and in a specific reference time frame and channel). The same displacements are applied to all the Z slices, time frames and channels. Before starting the batch processing a montage with the original images of the selected Z slice / time frame / channel is displayed together with the stitched image in this stack. If you are not satisfied with the result you can select another reference. The stitching is then performed time frame by time frame and slice by slice and the stitched images are exported to a single user defined output folder. The macro can also process a data set with multiple channels, the stitching is then computed once on a reference channel and then applied to the other channels.

This macro builds a stitched image from a muti-position 3D + time hyperstack. The XY positions of the montage should be coded as channels in the input hyperstack. Channel ordering can be configured in the dialog box to adapt to Column/Row and Meander/Comb configurations: The images should appear in this order when browsing the hyperstack with the channel slider. Fine stitching is supported (requires sufficient overlap between the views). The XY displacements of each field of view for stitching are computed for a single reference (Z,T) slice (user configurable) and applied to all slices (Z and T).

Tracking of focal adhesions includes a number of challenges:

1. Detection of focal adhesion regions in areas of highly variable background
2. Separation of "clumped" adhesions in different objects.
3. Dynamics: Focal adhesions dynamically, grow, shrink, change their shape, they can fuse with neighboring adhesions or one adhesion can be split into multiple children.

Würflinger et al (2011) describe how to detect focal adhesion objects and how to track them over time. Interestingly, tracking results are fed back to segmentation to improve separation of clumped adhesions.

The authors implemented the workflow in Matlab, but do not provide a ready-to-use script.