Autocrine IL-6 drives cell and extracellular matrix anisotropy in scar fibroblasts.

Fibrosis is associated with dramatic changes in extracellular matrix (ECM) architecture of unknown etiology. Here we exploit keloid scars as a paradigm to understand fibrotic ECM organization. We reveal that keloid patient fibroblasts uniquely produce a globally aligned ECM network in 2-D culture as observed in scar tissue. ECM anisotropy develops after rapid initiation of a fibroblast supracellular actin network, suggesting that cell alignment initiates ECM patterning. Keloid fibroblasts produce elevated levels of IL-6, and autocrine IL-6 production is both necessary and sufficient to induce cell and ECM alignment, as evidenced by ligand stimulation of normal dermal fibroblasts and treatment of keloid fibroblasts with the function blocking IL-6 receptor monoclonal antibody, tocilizumab. Downstream of IL-6, supracellular organization of keloid fibroblasts is controlled by activation of cell-cell adhesion. Adhesion formation inhibits contact-induced cellular overlap leading to nematic organization of cells and an alignment of focal adhesions. Keloid fibroblasts placed on isotropic ECM align the pre-existing matrix, suggesting that focal adhesion alignment leads to active anisotropic remodeling. These results show that IL-6-induced fibroblast cooperativity can control the development of a nematic ECM, highlighting both IL-6 signaling and cell-cell adhesions as potential therapeutic targets to inhibit this common feature of fibrosis.

A workflow for rapid unbiased quantification of fibrillar feature alignment in biological images.

Measuring the organisation of the cellular cytoskeleton and the surrounding extracellular matrix (ECM) is currently of wide interest as changes in both local and global alignment can highlight alterations in cellular functions and material properties of the extracellular environment. Different approaches have been developed to quantify these structures, typically based on fibre segmentation or on matrix representation and transformation of the image, each with its own advantages and disadvantages. Here we present AFT-Alignment by Fourier Transform, a workflow to quantify the alignment of fibrillar features in microscopy images exploiting 2D Fast Fourier Transforms (FFT). Using pre-existing datasets of cell and ECM images, we demonstrate our approach and compare and contrast this workflow with two other well-known ImageJ algorithms to quantify image feature alignment. These comparisons reveal that AFT has a number of advantages due to its grid-based FFT approach. 1) Flexibility in defining the window and neighbourhood sizes allows for performing a parameter search to determine an optimal length scale to carry out alignment metrics. This approach can thus easily accommodate different image resolutions and biological systems. 2) The length scale of decay in alignment can be extracted by comparing neighbourhood sizes, revealing the overall distance that features remain anisotropic. 3) The approach is ambivalent to the signal source, thus making it applicable for a wide range of imaging modalities and is dependent on fewer input parameters than segmentation methods. 4) Finally, compared to segmentation methods, this algorithm is computationally inexpensive, as high-resolution images can be evaluated in less than a second on a standard desktop computer. This makes it feasible to screen numerous experimental perturbations or examine large images over long length scales. Implementation is made available in both MATLAB and Python for wider accessibility, with example datasets for single images and batch processing. Additionally, we include an approach to automatically search parameters for optimum window and neighbourhood sizes, as well as to measure the decay in alignment over progressively increasing length scales.