Fluorescence microscopy

Simple workflow description for ImageJ, step-by-step description for delineating focal adhesions, count and characterize their positions.  

Measurement of dynamics is not involved.

This macro is meant to segment the cells of a multicellular tissue. It is written for images showing highly contrasted and uniformly stained cell membranes. The geometry of the cells and their organization is automatically extracted and exported to an ImageJ results table. This includes: Cell area, major, minor fitted ellipse radii + major axis orientation and number of neighbors of the cells. Manual correction of the automatic segmentation is supported (merge split cells, split merged cells).

Sample image data is available in the documentation page. 

<p>Particle detection is based on "Analyze Particles" in ImageJ. It probably could also be used in spot detection, not limited to centromere. &gt;This macro is described in Bodor et al. (2012). The macro recognizes centromere or kinetochore foci in Delta Vision or TIFF images and determines their centroid position. Fluorescent intensities are then measured for each centromere by placing a small box around the centroid position of the centromere. The peak intensity value within the box is corrected for local background by subtraction of the minimum pixel value. This process results in an accurate measurement of large numbers of centromere or kinetochore-specific signals. Following papers uses CRaQ (picked up, maybe more): - [Fachinetti et al. (2017)](https://www.cell.com/developmental-cell/pdf/S1534-5807(16)30909-1.pdf), Developmental Cell 40, 104–113, - [Guo et al. (2017)](https://www.nature.com/articles/ncomms15775) Nature Communications volume 8, Article number: 15775 (2017) doi:10.1038/ncomms15775 - [Lgosdon et. al. (2015)](http://jcb.rupress.org/content/208/5/521) J Cell Biol Mar 2015, 208 (5) 521-531; DOI: 10.1083/jcb.201412011 - [Bodor et al. (2014)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4091408/), eLife. 2014; 3: e02137</p>