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.

SummaryStromal collagen alignment has been shown to have clinical significance in a variety of cancers and in other diseases accompanied by fibrosis. While much of the biological and clinical importance of collagen changes has been demonstrated using second harmonic generation (SHG) imaging in experimental settings, implementation into routine clinical pathology practice is currently prohibitive. To translate the assessment of collagen organization into routine pathology workflow, a surrogate visualization method needs to be examined. The objective of the present study was to quantitatively compare collagen metrics generated from SHG microscopy and commonly available picrosirius red stain with standard polarization microscopy (PSR-POL). Each technique was quantitatively compared with established image segmentation and fiber tracking algorithms using human pancreatic cancer as a model, which is characterized by a pronounced stroma with reorganized collagen fibers. Importantly, PSR-POL produced similar quantitative trends for most collagen metrics in benign and cancerous tissues as measured by SHG. We found it notable that PSR-POL detects higher fiber counts, alignment, length, straightness, and width compared with SHG imaging but still correlates well with SHG results. PSR-POL may provide sufficient and additional information in a conventional clinical pathology laboratory for certain types of collagen quantification. (J Histochem Cytochem 64:519-529, 2016)

Background The traditional pathologic grading for human renal cell carcinoma (RCC) has low concordance between biopsy and surgical specimen. There is a need to investigate adjunctive pathology technique that does not rely on the nuclear morphology that defines the traditional grading. Changes in collagen organization in the extracellular matrix have been linked to prognosis or grade in breast, ovarian, and pancreatic cancers, but collagen organization has never been correlated with RCC grade. In this study, we used Second Harmonic Generation (SHG) based imaging to quantify possible differences in collagen organization between high and low grades of human RCC. Methods A tissue microarray (TMA) was constructed from RCC tumor specimens. Each TMA core represents an individual patient. A 5 μm section from the TMA tissue was stained with standard hematoxylin and eosin (H&E). Bright field images of the H&E stained TMA were used to annotate representative RCC regions. In this study, 70 grade 1 cores and 51 grade 4 cores were imaged on a custom-built forward SHG microscope, and images were analyzed using established software tools to automatically extract and quantify collagen fibers for alignment and density assessment. A linear mixed-effects model with random intercepts to account for the within-patient correlation was created to compare grade 1 vs. grade 4 measurements and the statistical tests were two-sided. Results Both collagen density and alignment differed significantly between RCC grade 1 and RCC grade 4. Specifically, collagen fiber density was greater in grade 4 than in grade 1 RCC ( p < 0.001). Collagen fibers were also more aligned in grade 4 compared to grade 1 ( p < 0.001). Conclusions Collagen density and alignment were shown to be significantly higher in RCC grade 4 vs. grade 1. This technique of biopsy sampling by SHG could complement classical tumor grading approaches. Furthermore it might allow biopsies to be more clinically relevant by informing diagnostics. Future studies are required to investigate the functional role of collagen organization in RCC.

Mouse urinary behavior is quantifiable and is used to pinpoint mechanisms of voiding dysfunction and evaluate potential human therapies. Approaches to evaluate mouse urinary function vary widely among laboratories, however, complicating cross-study comparisons. Here, we describe development and multi-institutional validation of a new tool for objective, consistent, and rapid analysis of mouse void spot assay (VSA) data. Void Whizzard is a freely available software plugin for FIJI (a distribution of ImageJ) that facilitates VSA image batch processing and data extraction. We describe its features, demonstrate them by evaluating how specific VSA method parameters influence voiding behavior, and establish Void Whizzard as an expedited method for VSA analysis. This study includes control and obese diabetic mice as models of urinary dysfunction to increase rigor and ensure relevance across distinct voiding patterns. In particular, we show that Void Whizzard is an effective tool for quantifying nonconcentric overlapping void spots, which commonly confound analyses. We also show that mouse genetics are consistently more influential than assay design parameters when it comes to VSA outcomes. None of the following procedural modifications to reduce overlapping spots masked these genetic-related differences: reduction of VSA testing duration, water access during the assay period, placement of a wire mesh cage bottom on top of or elevated over the filter paper, treatment of mesh with a hydrophobic spray, and size of wire mesh opening. The Void Whizzard software and rigorous validation of VSA methodological parameters described here advance the goal of standardizing mouse urinary phenotyping for comprehensive urinary phenome analyses.