quantifying-fibrillar-collagen-organization-with-curvelet-transform

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Fibrillar collagens are prominent extracellular matrix (ECM) components, and their topology changes have been shown to be associated with the progression ...

University of Wisconsin-Madison

Components of the extracellular matrix (ECM) have recently been shown to influence stem cell specification. However, it has been challenging to assess the spatial and temporal dynamics of stem cell-ECM interactions because most methodologies utilized to date require sample destruction or fixation. We examined the efficacy of utilizing the endogenous optical signals of two important ECM proteins, elastin (Eln), through autofluorescence, and type I collagen (ColI), through second harmonic generation (SHG), during mouse embryonic stem cell differentiation. After finding favorable overlap between antibody labeling and the endogenous fluorescent signal of Eln, we used this endogenous signal to map temporal changes in Eln and ColI during murine embryoid body differentiation and found that Eln increases until day 9 and then decreases slightly by day 12, while Col1 steadily increases over the 12-day period. Furthermore, we combined endogenous fluorescence imaging and SHG with antibody labeling of cardiomyocytes to examine the spatial relationship between Eln and ColI accumulation and cardiomyocyte differentiation. Eln was ubiquitously present, with enrichment in regions with cardiomyocyte differentiation, while there was an inverse correlation between ColI and cardiomyocyte differentiation. This work provides an important first step for utilizing endogenous optical signals, which can be visualized in living cells, to understand the relationship between the ECM and cardiomyocyte development and sets the stage for future studies of stem cell-ECM interactions and dynamics relevant to stem cells as well as other cell and tissue types.

Endogenous Optical Signals Reveal Changes of Elastin and Collagen Organization During Differentiation of Mouse Embryonic Stem Cells.

During the progression of pulmonary hypertension (PH), proximal pulmonary arteries (PAs) increase in both thickness and stiffness. Collagen, a component of the extracellular matrix, is mainly responsible for these changes via increased collagen fiber amount (or content) and crosslinking. We sought to differentiate the effects of collagen content and cross-linking on mouse PA mechanical changes using a constitutive model with parameters derived from experiments in which collagen content and cross-linking were decoupled during hypoxic pulmonary hypertension (HPH). We employed an eight-chain orthotropic element model to characterize collagen's mechanical behavior and an isotropic neo-Hookean form to represent elastin. Our results showed a strong correlation between the material parameter related to collagen content and measured collagen content (R(2)=0.82, P<0.0001) and a moderate correlation between the material parameter related to collagen crosslinking and measured crosslinking (R(2)=0.24, P=0.06). There was no significant change in either the material parameter related to elastin or the measured elastin content from histology. The model-predicted pressure at which collagen begins to engage was ∼25mmHg, which is consistent with experimental observations. We conclude that this model may allow us to predict changes in the arterial extracellular matrix from measured mechanical behavior in PH patients, which may provide insight into prognoses and the effects of therapy.

Validation of an arterial constitutive model accounting for collagen content and crosslinking.

Stromal 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.

Comparison of Picrosirius Red Staining With Second Harmonic Generation Imaging for the Quantification of Clinically Relevant Collagen Fiber Features in Histopathology Samples.

The development and progression of estrogen receptor alpha positive (ERα+) breast cancer has been linked epidemiologically to prolactin. However, activation of the canonical mediator of prolactin, STAT5, is associated with more differentiated cancers and better prognoses. We have reported that density/stiffness of the extracellular matrix potently modulates the repertoire of prolactin signals in human ERα + breast cancer cells in vitro: stiff matrices shift the balance from the Janus kinase (JAK)2/STAT5 cascade toward pro-tumor progressive extracellular regulated kinase (ERK)1/2 signals, driving invasion. However, the consequences for behavior of ERα + cancers in vivo are not known.

Elevated collagen-I augments tumor progressive signals, intravasation and metastasis of prolactin-induced estrogen receptor alpha positive mammary tumor cells.

 Collagen fibers surrounding breast ducts may influence breast cancer progression. Syndecan-1 interacts with constituents in the extracellular matrix, including collagen fibers, and may contribute to cancer cell migration. Thus, the orientation of collagen fibers surrounding ductal carcinoma  (DCIS) lesions and stromal syndecan-1 expression may predict recurrence. We evaluated collagen fiber alignment and syndecan-1 expression in 227 women diagnosed with DCIS in 1995 to 2006 followed through 2014 (median, 14.5 years; range, 0.7-17.6). Stromal collagen alignment was evaluated from diagnostic tissue slides using second harmonic generation microscopy and fiber analysis software. Univariate analysis was conducted using χ tests and ANOVA. The association between collagen alignment -scores, syndecan-1 staining intensity, and time to recurrence was evaluated using HRs and 95% confidence intervals (CIs). Greater fiber angles surrounding DCIS lesions, but not syndecan-1 staining intensity, were related to positive HER2 ( = 0.002) status, comedo necrosis ( = 0.03), and negative estrogen receptor ( = 0.002) and progesterone receptor ( = 0.02) status. Fiber angle distributions surrounding lesions included more angles closer to 90 degrees than normal ducts ( = 0.06). Collagen alignment -scores for DCIS lesions were positively related to recurrence (HR = 1.25; 95% CI, 0.84-1.87 for an interquartile range increase in average fiber angles). Although collagen alignment and stromal syndecan-1 expression did not predict recurrence, collagen fibers perpendicular to the duct perimeter were more frequent in DCIS lesions with features typical of poor prognosis. Follow-up studies are warranted to examine whether additional features of the collagen matrix may more strongly predict patient outcomes. .

Collagen Alignment as a Predictor of Recurrence after Ductal Carcinoma .

Clinical outcomes after nerve injury and repair remain suboptimal. Patients may be plagued by poor functional recovery and painful neuroma at the repair site, characterized by disorganized collagen and sprouting axons. Collagen deposition during wound healing can be intrinsically imaged using second harmonic generation (SHG) microscopy. The purpose of this study was to develop a protocol for SHG imaging of nerves and to assess whether collagen alignment can be quantified after nerve repair.

Quantification of Collagen Organization after Nerve Repair.

Vocal fold collagen composition is an important determinant of material properties and mucosal wave propagation. Collagen alignment and straightness are quantitatively characterized by second harmonic generation (SHG) imaging. We examined leporine, canined and porcine vocal folds showing collagen composition variation that is species, location, and strain specific.

Quantitative second harmonic generation imaging of leporine, canine, and porcine vocal fold collagen.

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.

Collagen organization of renal cell carcinoma differs between low and high grade tumors.

Tissue biopsy evaluation in the clinic is in need of quantitative disease markers for diagnosis and, most importantly, prognosis. Among the new technologies, quantitative phase imaging (QPI) has demonstrated promise for histopathology because it reveals intrinsic tissue nanoarchitecture through the refractive index. However, a vast majority of past QPI investigations have relied on imaging unstained tissues, which disrupts the established specimen processing. Here we present color spatial light interference microscopy (cSLIM) as a new whole-slide imaging modality that performs interferometric imaging on stained tissue, with a color detector array. As a result, cSLIM yields in a single scan both the intrinsic tissue phase map and the standard color bright-field image, familiar to the pathologist. Our results on 196 breast cancer patients indicate that cSLIM can provide stain-independent prognostic information from the alignment of collagen fibers in the tumor microenvironment. The effects of staining on the tissue phase maps were corrected by a mathematical normalization. These characteristics are likely to reduce barriers to clinical translation for the new cSLIM technology.

Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM).

The use of second-harmonic generation (SHG) microscopy in biomedical research is rapidly increasing. This is due in large part to the wide spread interest of using this imaging technique to examine the role of fibrillar collagen organization in diseases such as cancer. The co-examination of SHG images and traditional bright-field (BF) images of hematoxylin and eosin (H&E) stained tissue as a gold standard clinical validation is usually required. However, image registration of these two modalities has been mostly done by manually selecting corresponding landmarks which is labor intensive and error prone. We designed, implemented, and validated the first image intensity-based registration method capable of automatically aligning SHG images and BF images. In our algorithmic approach, a feature extractor is used to pre-process the BF image to block the content features not visible in SHG images and the output image is then aligned with the SHG image by maximizing the common image features. An alignment matrix maximizing the image mutual information is found by evolutionary optimization and the optimization is facilitated using a hierarchical multiresolution framework. The automatic registration results were compared to traditional manual registration to assess the performance of the algorithm. The proposed algorithm has been successfully used in several biomedical studies such as pancreatic and kidney cancer studies and shown great efficacy.

Intensity-based registration of bright-field and second-harmonic generation images of histopathology tissue sections.

Quantification of fibrillar collagen organization has given new insight into the possible role of collagen topology in many diseases and has also identified candidate image-based bio-markers in breast cancer and pancreatic cancer. We have been developing collagen quantification tools based on the curvelet transform (CT) algorithm and have demonstrated this to be a powerful multiscale image representation method due to its unique features in collagen image denoising and fiber edge enhancement. In this paper, we present our CT-based collagen quantification software platform with a focus on new features and also giving a detailed description of curvelet-based fiber representation. These new features include C++-based code optimization for fast individual fiber tracking, Java-based synthetic fiber generator module for method validation, automatic tumor boundary generation for fiber relative quantification, parallel computing for large-scale batch mode processing, region-of-interest analysis for user-specified quantification, and pre- and post-processing modules for individual fiber visualization. We present a validation of the tracking of individual fibers and fiber orientations by using synthesized fibers generated by the synthetic fiber generator. In addition, we provide a comparison of the fiber orientation calculation on pancreatic tissue images between our tool and three other quantitative approaches. Lastly, we demonstrate the use of our software tool for the automatic tumor boundary creation and the relative alignment quantification of collagen fibers in human breast cancer pathology images, as well as the alignment quantification of  mouse xenograft breast cancer images.

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

The importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.

Yuming Liu

Laboratory for Optical and Computational Instrumentation,
Center for Quantitative Cell Imaging,
Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging

University of Wisconsin-Madison

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Yuming Liu

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Laboratory for Optical and Computational Instrumentation,Center for Quantitative Cell Imaging,Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging

University of Wisconsin-Madison

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Laboratory for Optical and Computational Instrumentation,
Center for Quantitative Cell Imaging,
Laboratory for Optical and Computational Instrumentation, Center for Quantitative Cell Imaging