Automated quantification of three-dimensional organization of fiber-like structures in biological tissues.

Fiber-like structures are prevalent in biological tissues, yet quantitative approaches to assess their three-dimensional (3D) organization are lacking. We develop 3D directional variance, as a quantitative biomarker of truly 3D fibrillar organization by extending the directional statistics formalism developed for describing circular data distributions (i.e. when 0° and 360° are equivalent) to axial ones (i.e. when 0° and 180° are equivalent). Significant advantages of this analysis include its time efficiency, sensitivity and ability to provide quantitative readouts of organization over different size scales of a given data set. We establish a broad range of applications for this method by characterizing collagen fibers, neuronal axons and fibroblasts in the context of cancer diagnostics, traumatic brain injury and cell-matrix interactions in developing engineered tissues. This method opens possibilities for unraveling in a sensitive, and quantitative manner the organization of essential fiber-like structures in tissues and ultimately its impact on tissue function.