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

Biomaterials. 2017 Feb:116:34-47. doi: 10.1016/j.biomaterials.2016.11.041. Epub 2016 Nov 25.

Abstract

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.

Keywords: Cancer; Collagen fiber; Multi-photon microscopy; Neuronal axon; Three-dimensional organization; Traumatic brain injury.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Axons / ultrastructure*
  • Elastic Tissue / ultrastructure*
  • Fibrillar Collagens / ultrastructure*
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Machine Learning*
  • Reproducibility of Results
  • Sensitivity and Specificity

Substances

  • Fibrillar Collagens