FiloQuant, an ImageJ tool to rapidly quantify filopodia length and density. (A) Workflow depicting FiloQuant analysis of filopodia density and length. Representative images obtained at the different stages of analysis are displayed. In brief, the original image (input) undergoes two parallel processing steps. In the top panel, the cell edge is defined and detected by intensity based thresholding and by ”erasing” the filopodia (edge detection). In the bottom panel, the image is enhanced to optimize detection of faint filopodia without introducing noise (filopodia detection). The resulting images are subtracted to isolate edge filopodia (filopodia extraction), and filopodia number and length are automatically analyzed using the Skeletonize3D and AnalyzeSkeleton algorithms. Detected filopodia are highlighted in magenta in the final image. Filopodia density can be also quantified by determining the ratio of filopodia number to edge length (extracted from the edge detection image). The original image shows MCF10A ductal carcinoma in situ (DCIS.COM) cells invading collectively through a fibrillar collagen gel (circular invasion assay), stained for actin and imaged using a spinning disk confocal (SDC) microscope (100× objective, CMOS camera). Bars: (main) 20 µm; (inset) 5 µm. (B) Images illustrate how two of the settings (CLAHE and Convolve) available in FiloQuant can help to improve the detection of faint filopodia. Bars: (main) 20 µm; (inset) 5 µm. (C) Image illustrating that FiloQuant detects and quantifies only the filopodia present at the cell edge (inset 1) and not the filopodia present at cell-cell junctions (inset 2) or ventral (inset 3) and dorsal filopodia. The image shows a DCIS.COM cell plated on fibronectin for 2 h, stained for actin, and imaged using an SIM. Bars: (main) 20 µm; (inset) 2 µm.