Risk factors for pancreatic ductal adenocarcinoma (PDAC) progression after surgery are unclear, and additional prognostic factors are needed to inform treatment regimens and therapeutic targets. PDAC is characterized by advanced sclerosis of the extracellular matrix, and interactions between cancer cells, fibrillar collagen, and other stromal components play an integral role in progression. Changes in stromal collagen alignment have been shown to modulate cancer cell behavior and have important clinical value in other cancer types, but little is known about its role in PDAC and prognostic value. We hypothesized that the alignment of collagen is associated with PDAC patient survival. To address this, pathology-confirmed tissues from 114 PDAC patients that underwent curative-intent surgery were retrospectively imaged with Second Harmonic Generation (SHG) microscopy, quantified with fiber segmentation algorithms, and correlated to patient survival. The same tissue regions were analyzed for epithelial-to-mesenchymal (EMT), α-SMA, and syndecan-1 using complimentary immunohistostaining and visualization techniques. Significant inter-tumoral variation in collagen alignment was found, and notably high collagen alignment was observed in 12% of the patient cohort. Stratification of patients according to collagen alignment revealed that high alignment is an independent negative factor following PDAC resection (p = 0.0153, multivariate). We also found that epithelial expression of EMT and the stromal expression of α-SMA and syndecan-1 were positively correlated with collagen alignment. In summary, stromal collagen alignment may provide additional, clinically-relevant information about PDAC tumors and underscores the importance of stroma-cancer interactions.

Recent evidence has implicated collagen, particularly fibrillar collagen, in a number of diseases ranging from osteogenesis imperfecta and asthma to breast and ovarian cancer. A key property of collagen that has been correlated with disease has been the alignment of collagen fibers. Collagen can be visualized using a variety of imaging techniques including second-harmonic generation (SHG) microscopy, polarized light microscopy, and staining with dyes or antibodies. However, there exists a great need to easily and robustly quantify images from these modalities for individual fibers in specified regions of interest and with respect to relevant boundaries. Most currently available computational tools rely on calculation of pixel-wise orientation or global window-wise orientation that do not directly calculate or give visible fiber-wise information and do not provide relative orientation against boundaries. We describe and detail how to use a freely available, open-source MATLAB software framework that includes two separate but linked packages "CurveAlign" and "CT-FIRE" that can address this need by either directly extracting individual fibers using an improved fiber tracking algorithm or directly finding optimal representation of fiber edges using the curvelet transform. This curvelet-based framework allows the user to measure fiber alignment on a global, region of interest, and fiber basis. Additionally, users can measure fiber angle relative to manually or automatically segmented boundaries. This tool does not require prior experience of programming or image processing and can handle multiple files, enabling efficient quantification of collagen organization from biological datasets.

The low cost and simplicity of picrosirius red (PSR) staining have driven its popularity for collagen detection in tissue sections. We extended the versatility of this method by using fluorescent imaging to detect the PSR signal and applying automated quantification tools. We also developed the first PSR protocol that is fully compatible with multiplex immunostaining, making it possible to test whether collagen structure differs across immunohistochemically labeled regions of the tissue landscape. We compared our imaging method with two gold standards in collagen imaging, linear polarized light microscopy and second harmonic generation imaging, and found that it is at least as sensitive and robust to changes in sample orientation. As proof of principle, we used a genetic approach to overexpress beta catenin in a patchy subset of mouse prostate epithelial cells distinguished only by immunolabeling. We showed that collagen fiber length is significantly greater near beta catenin overexpressing cells than near control cells. Our fluorescent PSR imaging method is sensitive, reproducible, and offers a new way to guide region of interest selection for quantifying collagen in tissue sections.