Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer

A mouse model of TB-associated lung fibrosis reveals persistent inflammatory macrophage populations during treatment

Post-TB lung disease (PTLD) causes a significant burden of global disease. Fibrosis is a central component of many clinical features of PTLD. To date, we have a limited understanding of the mechanisms of TB-associated fibrosis and how these mechanisms are similar to or dissimilar from other fibrotic lung pathologies. We have adapted a mouse model of TB infection to facilitate the mechanistic study of TB-associated lung fibrosis. We find that the morphologies of fibrosis that develop in the mouse model are similar to the morphologies of fibrosis observed in human tissue samples. Using Second Harmonic Generation (SHG) microscopy, we are able to quantify a major component of fibrosis, fibrillar collagen, over time and with treatment. Inflammatory macrophage subpopulations persist during treatment; matrix remodeling enzymes and inflammatory gene signatures remain elevated. Our mouse model suggests that there is a therapeutic window during which adjunctive therapies could change matrix remodeling or inflammatory drivers of tissue pathology to improve functional outcomes after treatment for TB infection.

Emulation and evaluation of tumor cell combined chemotherapy in isotropic/anisotropic collagen fiber microenvironments

The rapid emergence of anisotropic collagen fibers in the tissue microenvironment is a critical transition point in late-stage breast cancer. Specifically, the fiber orientation facilitates the likelihood of high-speed tumor cell invasion and metastasis, which pose lethal threats to patients. Thus, based on this transition point, one key issue is how to determine and evaluate efficient combination chemotherapy treatments in late-stage cancer. In this study, we designed a collagen microarray chip containing 241 high-throughput microchambers with embedded metastatic breast cancer cell MDA-MB-231-RFP. By utilizing collagen's unique structure and hydromechanical properties, the chip constructed three-dimensional isotropic and anisotropic collagen fiber structures to emulate the tumor cell microenvironment at early and late stages. We injected different chemotherapeutic drugs into its four channels and obtained composite biochemical concentration profiles. Our results demonstrate that anisotropic collagen fibers promote cell proliferation and migration more than isotropic collagen fibers, suggesting that the geometric arrangement of fibers plays an important role in regulating cell behavior. Moreover, the presence of anisotropic collagen fibers may be a potential factor leading to the poor efficacy of combined chemotherapy in late-stage breast cancer. We investigated the efficacy of various chemotherapy drugs using cell proliferation inhibitors paclitaxel and gemcitabine and tumor cell migration inhibitors 7rh and PP2. To ensure the validity of our findings, we followed a systematic approach that involved testing the inhibitory effects of these drugs. According to our results, the drug combinations' effectiveness could be ordered as follows: paclitaxel + gemcitabine > gemcitabine + 7rh > PP2 + paclitaxel > 7rh + PP2. This study shows that the biomimetic chip system not only facilitates the creation of a realistic in vitro model for examining the cell migration mechanism in late-stage breast cancer but also has the potential to function as an effective tool for future chemotherapy assessment and personalized medicine.

Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues

Significance

Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues.

Aim

To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures.

Approach

We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment.

Results

Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment.

Conclusions

We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.

Quantifying in vivo collagen reorganization during immunotherapy in murine melanoma with second harmonic generation imaging

Significance

Increased collagen linearization and deposition during tumorigenesis can impede immune cell infiltration and lead to tumor metastasis. Although melanoma is well studied in immunotherapy research, studies that quantify collagen changes during melanoma progression and treatment are lacking.

Aim

We aim to image in vivo collagen in preclinical melanoma models during immunotherapy and quantify the collagen phenotype in treated and control mice.

Approach

Second-harmonic generation imaging of collagen was performed in mouse melanoma tumors in vivo over a treatment time course. Animals were treated with a curative radiation and immunotherapy combination. Collagen morphology was quantified over time at an image and single-fiber level using CurveAlign and CT-FIRE software.

Results

In immunotherapy-treated mice, collagen was reorganized toward a healthy phenotype, including shorter, wider, curlier collagen fibers, with modestly higher collagen density. Temporally, collagen fiber straightness and length changed late in treatment (days 9 and 12), while width and density changed early (day 6) compared with control mice. Single-fiber collagen features calculated in CT-FIRE were the most sensitive to the changes among treatment groups compared with bulk collagen features.

Conclusions

Quantitative second-harmonic generation imaging can provide insight into collagen dynamics in vivo during immunotherapy, with key implications in improving immunotherapy response in melanoma and other cancers.

Characterisation and correction of polarisation effects in fluorescently labelled fibres

Many biological structures take the form of fibres and filaments, and quantitative analysis of fibre organisation is important for understanding their functions in both normal physiological conditions and disease. In order to visualise these structures, fibres can be fluorescently labelled and imaged, with specialised image analysis methods available for quantifying the degree and strength of fibre alignment. Here we show that fluorescently labelled fibres can display polarised emission, with the strength of this effect varying depending on structure and fluorophore identity. This can bias automated analysis of fibre alignment and mask the true underlying structural organisation. We present a method for quantifying and correcting these polarisation effects without requiring polarisation-resolved microscopy and demonstrate its efficacy when applied to images of fluorescently labelled collagen gels, allowing for more reliable characterisation of fibre microarchitecture.

Overcoming neutrophil-induced immunosuppression in postoperative cancer therapy: Combined sialic acid-modified liposomes with scaffold-based vaccines

Immunotherapy is a promising approach for preventing postoperative tumor recurrence and metastasis. However, inflammatory neutrophils, recruited to the postoperative tumor site, have been shown to exacerbate tumor regeneration and limit the efficacy of cancer vaccines. Consequently, addressing postoperative immunosuppression caused by neutrophils is crucial for improving treatment outcomes. This study presents a combined chemoimmunotherapeutic strategy that employs a biocompatible macroporous scaffold-based cancer vaccine (S-CV) and a sialic acid (SA)-modified, doxorubicin (DOX)-loaded liposomal platform (DOX@SAL). The S-CV contains whole tumor lysates as antigens and imiquimod (R837, Toll-like receptor 7 activator)-loaded PLGA nanoparticles as immune adjuvants for cancer, which enhance dendritic cell activation and cytotoxic T cell proliferation upon localized implantation. When administered intravenously, DOX@SAL specifically targets and delivers drugs to activated neutrophils in vivo, mitigating neutrophil infiltration and suppressing postoperative inflammatory responses. In vivo and vitro experiments have demonstrated that S-CV plus DOX@SAL, a combined chemo-immunotherapeutic strategy, has a remarkable potential to inhibit postoperative local tumor recurrence and distant tumor progression, with minimal systemic toxicity, providing a new concept for postoperative treatment of tumors.

Biopathologic Characterization and Grade Assessment of Breast Cancer With 3-D Multiparametric Ultrasound Combining Shear Wave Elastography and Backscatter Tensor Imaging

OBJECTIVE

Despite recent improvements in medical imaging, the final diagnosis and biopathologic characterization of breast cancers currently still requires biopsies. Ultrasound is commonly used for clinical examination of breast masses. B-Mode and shear wave elastography (SWE) are already widely used to detect suspicious masses and differentiate benign lesions from cancers. But additional ultrasound modalities such as backscatter tensor imaging (BTI) could provide relevant biomarkers related to tissue organization. Here we describe a 3-D multiparametric ultrasound approach applied to breast carcinomas in the aims of (i) validating the ability of BTI to reveal the underlying organization of collagen fibers and (ii) assessing the complementarity of SWE and BTI to reveal biopathologic features of diagnostic interest.

METHODS

Three-dimensional SWE and BTI were performed ex vivo on 64 human breast carcinoma samples using a linear ultrasound probe moved by a set of motors. Here we describe a 3-D multiparametric representation of the breast masses and quantitative measurements combining B-mode, SWE and BTI.

RESULTS

Our results reveal for the first time that BTI can capture the orientation of the collagen fibers around tumors. BTI was found to be a relevant marker for assessing cancer stages, revealing a more tangent tissue orientation for in situ carcinomas than for invasive cancers. In invasive cases, the combination of BTI and SWE parameters allowed for classification of invasive tumors with respect to their grade with an accuracy of 95.7%.

CONCLUSION

Our results highlight the potential of 3-D multiparametric ultrasound imaging for biopathologic characterization of breast tumors.

Collagen modifications predictive of lymph node metastasis in dogs with carcinoma in mixed tumours

Introduction

Mixed tumours in the canine mammary gland are the most common histological type in routine diagnosis. In general, these neoplasms have a favourable prognosis that does not evolve into metastatic disease. However, some cases develop into lymph node metastases and are associated with worse patient survival rates.

Methods

Here is a retrospective study of 46 samples of primary mixed tumours of the canine mammary gland: 15 cases of benign mixed tumours (BMT), 16 cases of carcinoma in mixed tumours without lymph node metastasis (CMT), and 15 cases of carcinomas in mixed tumours with lymph node metastasis (CMTM). In addition, we selected 23 cases of normal mammary glands (NMT) for comparison. The samples were collected from biopsies performed during nodulectomy, simple mastectomy, regional mastectomy, or unilateral/bilateral radical mastectomy. We used multiphoton microscopy, second harmonic generation, and two-photon excited fluorescence, to evaluate the characteristics of collagen fibres and cellular components in biopsies stained with haematoxylin and eosin. We performed Ki67, ER, PR, and HER-2 immunostaining to define the immunophenotype and COX-2. We showed that carcinomas that evolved into metastatic disease (CMTM) present shorter and wavier collagen fibres as compared to CMT.

Results and discussion

When compared to NMT and BMT the carcinomas present a smaller area of fibre coverage, a larger area of cellular coverage, and a larger number of individual fibres. Furthermore, we observed a correlation between the strong expression of COX-2 and a high rate of cell proliferation in carcinomas with a smaller area covered by cell fibres and a larger number of individual fibres. These findings highlight the fundamental role of collagen during tumour progression, especially in invasion and metastatic dissemination.

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

Polarized hyperspectral microscopic imaging system for enhancing the visualization of collagen fibers and head and neck squamous cell carcinoma

Significance

Polarized hyperspectral microscopes with the capability of full Stokes vector imaging have potential for many biological and medical applications.

Aim

The aim of this study is to investigate polarized hyperspectral imaging (PHSI) for improving the visualization of collagen fibers, which is an important biomarker related to tumor development, and improving the differentiation of normal and tumor cells on pathologic slides.

Approach

We customized a polarized hyperspectral microscopic imaging system comprising an upright microscope with a motorized stage, two linear polarizers, two liquid crystal variable retarders (LCVRs), and a compact SnapScan hyperspectral camera. The polarizers and LCVRs worked in tandem with the hyperspectral camera to acquire polarized hyperspectral images, which were further used to calculate four Stokes vectors: S 0 , S 1 , S 2 , and S 3 . Synthetic RGB images of the Stokes vectors were generated for the visualization of cellular components in PHSI images. Regions of interest of collagen, normal cells, and tumor cells in the synthetic RGB images were selected, and spectral signatures of the selected components were extracted for comparison. Specifically, we qualitatively and quantitatively investigated the enhanced visualization and spectral characteristics of dense fibers and sparse fibers in normal stroma tissue, fibers accumulated within tumors, and fibers accumulated around tumors.

Results

By employing our customized polarized hyperspectral microscope, we extract the spectral signatures of Stokes vector parameters of collagen as well as of tumor and normal cells. The measurement of Stokes vector parameters increased the image contrast of collagen fibers and cells in the slides.

Conclusions

With the spatial and spectral information from the Stokes vector data cubes (S 0 , S 1 , S 2 , and S 3 ), our PHSI microscope system enhances the visualization of tumor cells and tumor microenvironment components, thus being beneficial for pathology and oncology.

Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation

The role of fibrillar collagen in the tissue microenvironment is critical in disease contexts ranging from cancers to chronic inflammations, as evidenced by many studies. Quantifying fibrillar collagen organization has become a powerful approach for characterizing the topology of collagen fibers and studying the role of collagen fibers in disease progression. We present a deep learning-based pipeline to quantify collagen fibers' topological properties in microscopy-based collagen images from pathological tissue samples. Our method leverages deep neural networks to extract collagen fiber centerlines and deep generative models to create synthetic training data, addressing the current shortage of large-scale annotations. As a part of this effort, we have created and annotated a collagen fiber centerline dataset, with the hope of facilitating further research in this field. Quantitative measurements such as fiber orientation, alignment, density, and length can be derived based on the centerline extraction results. Our pipeline comprises three stages. Initially, a variational autoencoder is trained to generate synthetic centerlines possessing controllable topological properties. Subsequently, a conditional generative adversarial network synthesizes realistic collagen fiber images from the synthetic centerlines, yielding a synthetic training set of image-centerline pairs. Finally, we train a collagen fiber centerline extraction network using both the original and synthetic data. Evaluation using collagen fiber images from pancreas, liver, and breast cancer samples collected via second-harmonic generation microscopy demonstrates our pipeline's superiority over several popular fiber centerline extraction tools. Incorporating synthetic data into training further enhances the network's generalizability. Our code is available at https://github.com/uw-loci/collagen-fiber-metrics.

Fiber alignment in 3D collagen networks as a biophysical marker for cell contractility

Cells cultured in 3D fibrous biopolymer matrices exert traction forces on their environment that induce deformations and remodeling of the fiber network. By measuring these deformations, the traction forces can be reconstructed if the mechanical properties of the matrix and the force-free matrix configuration are known. These requirements limit the applicability of traction force reconstruction in practice. In this study, we test whether force-induced matrix remodeling can instead be used as a proxy for cellular traction forces. We measure the traction forces of hepatic stellate cells and different glioblastoma cell lines and quantify matrix remodeling by measuring the fiber orientation and fiber density around these cells. In agreement with simulated fiber networks, we demonstrate that changes in local fiber orientation and density are directly related to cell forces. By resolving Rho-kinase (ROCK) inhibitor-induced changes of traction forces, fiber alignment, and fiber density in hepatic stellate cells, we show that the method is suitable for drug screening assays. We conclude that differences in local fiber orientation and density, which are easily measurable, can be used as a qualitative proxy for changes in traction forces. The method is available as an open-source Python package with a graphical user interface.

Quantifying in vivo collagen reorganization during immunotherapy in murine melanoma with second harmonic generation imaging

Significance

Increased collagen linearization and deposition during tumorigenesis can impede immune cell infiltration and lead to tumor metastasis. Although melanoma is well studied in immunotherapy research, studies that quantify collagen changes during melanoma progression and treatment are lacking.

Aim

Image in vivo collagen in preclinical melanoma models during immunotherapy and quantify the collagen phenotype in treated and control mice.

Approach

Second harmonic generation imaging of collagen was performed in mouse melanoma tumors in vivo over a treatment time-course. Animals were treated with a curative radiation and immunotherapy combination. Collagen morphology was quantified over time at an image and single fiber level using CurveAlign and CT-FIRE software.

Results

In immunotherapy-treated mice, collagen reorganized toward a healthy phenotype, including shorter, wider, curlier collagen fibers, with modestly higher collagen density. Temporally, collagen fiber straightness and length changed late in treatment (Day 9 and 12) while width and density changed early (Day 6) compared to control mice. Single fiber level collagen analysis was most sensitive to the changes between treatment groups compared to image level analysis.

Conclusions

Quantitative second harmonic generation imaging can provide insight into collagen dynamics in vivo during immunotherapy, with key implications in improving immunotherapy response in melanoma and other cancers.

Evaluation of a polarization-enhanced laparoscopy prototype for improved intra-operative visualization of peritoneal metastases

Despite careful staging, the accuracy for preoperative detection of small distant metastases remains poor, creating a clinical need for enhanced operative staging to detect occult peritoneal metastases. This study evaluates a polarization-enhanced laparoscopy (PEL) prototype and assesses its potential for label-free contrast enhancement of peritoneal metastases. This is a first-in-human feasibility study, including 10 adult patients who underwent standard staging laparoscopy (SSL) for gastrointestinal malignancy along with PEL. Image frames of all detectable peritoneal lesions underwent analysis. Using Monte Carlo simulations, contrast enhancement based on the color dependence of PEL (mPEL) was assessed. The prototype performed safely, yet with limitations in illumination, fogging of the distal window, and image co-registration. Sixty-five lesions (56 presumed benign and 9 presumed malignant) from 3 patients represented the study sample. While most lesions were visible under human examination of both SSL and PEL videos, more lesions were apparent using SSL. However, this was likely due to reduced illumination under PEL. When controlling for such effects through direct comparisons of integrated (WLL) vs differential (PEL) polarization laparoscopy images, we found that PEL imaging yielded an over twofold Weber contrast enhancement over WLL. Further, enhancements in the discrimination between malignant and benign lesions were achieved by exploiting the PEL color contrast to enhance sensitivity to tissue scattering, influenced primarily by collagen. In conclusion, PEL appears safe and easy to integrate into the operating room. When controlling for the degree of illumination, image analysis suggested a potential for mPEL to provide improved visualization of metastases.

Cancer-Associated Fibroblast Heterogeneity and Its Influence on the Extracellular Matrix and the Tumor Microenvironment

The tumor microenvironment comprises multiple cell types, like cancer cells, endothelial cells, fibroblasts, and immune cells. In recent years, there have been massive research efforts focusing not only on cancer cells, but also on other cell types of the tumor microenvironment, thereby aiming to expand and determine novel treatment options. Fibroblasts represent a heterogenous cell family consisting of numerous subtypes, which can alter immune cell fractions, facilitate or inhibit tumor growth, build pre-metastatic niches, or stabilize vessels. These effects can be achieved through cell-cell interactions, which form the extracellular matrix, or via the secretion of cytokines or chemokines. The pro- or antitumorigenic fibroblast phenotypes show variability not only among different cancer entities, but also among intraindividual sites, including primary tumors or metastatic lesions. Commonly prescribed for arterial hypertension, the inhibitors of the renin-angiotensin system have recently been described as having an inhibitory effect on fibroblasts. This inhibition leads to modified immune cell fractions and increased tissue stiffness, thereby contributing to overcoming therapy resistance and ultimately inhibiting tumor growth. However, it is important to note that the inhibition of fibroblasts can also have the opposite effect, potentially resulting in increased tumor growth. We aim to summarize the latest state of research regarding fibroblast heterogeneity and its intricate impact on the tumor microenvironment and extracellular matrix. Specifically, we focus on highlighting recent advancements in the comprehension of intraindividual heterogeneity and therapy options within this context.

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies

Tumors, their microenvironment, and the mechanisms by which collagen morphology changes throughout cancer progression have recently been a topic of interest. Second harmonic generation (SHG) and polarization second harmonic (P-SHG) microscopy are label-free, hallmark methods that can highlight this alteration in the extracellular matrix (ECM). This article uses automated sample scanning SHG and P-SHG microscopy to investigate ECM deposition associated with tumors residing in the mammary gland. We show two different analysis approaches using the acquired images to distinguish collagen fibrillar orientation changes in the ECM. Lastly, we apply a supervised deep-learning model to classify naïve and tumor-bearing mammary gland SHG images. We benchmark the trained model using transfer learning with the well-known MobileNetV2 architecture. By fine-tuning the different parameters of these models, we show a trained deep-learning model that suits such a small dataset with 73% accuracy.

Quantitative Image Analysis of Fibrillar Collagens Reveals Novel Diagnostic and Prognostic Biomarkers and Histotype-dependent Aberrant Mechanobiology in Lung Cancer

Fibrillar collagens are the most abundant extracellular matrix components in non-small cell lung cancer (NSCLC). Yet, the potential of collagen fiber descriptors as a source of clinically-relevant biomarkers in NSCLC is mainly unknown. Likewise, our understanding of the aberrant collagen organization and associated tumor-promoting effects needs to be better defined. To address these limitations, we identified a digital pathology approach that can be easily implemented in pathology units based on the Curvelet Transform filtering and single Fiber Reconstruction (CT-FIRE) software analysis of picrosirius (PSR) stains of fibrillar collagens imaged with polarized light (PL). CT-FIRE settings were pre-optimized to assess a panel of collagen fiber descriptors in PSR-PL images of tissue microarrays from surgical NSCLC patients (106 adenocarcinomas (ADC), 89 squamous cell carcinomas (SCC)). Using this approach, we identified straightness as the single high-accuracy diagnostic collagen fiber descriptor (average area under the curve AUC = 0.92) and fiber density as the single descriptor consistently associated with poor prognosis in both ADC and SCC independently of the gold standard based on tumor size, lymph node involvement and metastasis (TNM) staging (Hazard ratio HR = 2.69 (1.55-4.66), p < 0.001). Moreover, we found that collagen fibers were markedly straighter, longer, and more aligned in tumors compared to paired samples from uninvolved pulmonary tissue, particularly in ADC, which is indicative of increased tumor stiffening. Consistently, we observed an increase in a panel of stiffness-associated processes in the high collagen fiber density patient group selectively in ADC, including venous/lymphatic invasion, fibroblast activation (alpha-smooth muscle actin (α-SMA)), and immune evasion (programmed death-ligand 1 (PD-L1)). Likewise, transcriptional correlation analysis supported the potential involvement of the major Yes-associated protein 1 (YAP)/TAZ mechanobiology pathway in ADC. Our results provide a proof-of-principle to use CT-FIRE analysis of PSR-PL images to assess new collagen fiber-based diagnostic and prognostic biomarkers in pathology units, which may improve the clinical management of surgical NSCLC patients. Our findings also unveil an aberrant stiff microenvironment in lung ADC that may foster immune evasion and dissemination, encouraging future work to identify therapeutic opportunities.

Persistent homology analysis distinguishes pathological bone microstructure in non-linear microscopy images

We present a topological method for the detection and quantification of bone microstructure from non-linear microscopy images. Specifically, we analyse second harmonic generation (SHG) and two photon excited autofluorescence (TPaF) images of bone tissue which capture the distribution of matrix (fibrillar collagen) structure and autofluorescent molecules, respectively. Using persistent homology statistics with a signed Euclidean distance transform filtration on binary patches of images, we are able to quantify the number, size, distribution, and crowding of holes within and across samples imaged at the microscale. We apply our methodology to a previously characterized murine model of skeletal pathology whereby vascular endothelial growth factor expression was deleted in osteocalcin-expressing cells (OcnVEGFKO) presenting increased cortical porosity, compared to wild type (WT) littermate controls. We show significant differences in topological statistics between the OcnVEGFKO and WT groups and, when classifying the males, or females respectively, into OcnVEGFKO or WT groups, we obtain high prediction accuracies of 98.7% (74.2%) and 77.8% (65.8%) respectively for SHG (TPaF) images. The persistence statistics that we use are fully interpretable, can highlight regions of abnormality within an image and identify features at different spatial scales.

Unusual Suspects: Bone and Cartilage ECM Proteins as Carcinoma Facilitators

The extracellular matrix (ECM) is the complex three-dimensional network of fibrous proteins and proteoglycans that constitutes an essential part of every tissue to provide support for normal tissue homeostasis. Tissue specificity of the ECM in its topology and structure supports unique biochemical and mechanical properties of each organ. Cancers, like normal tissues, require the ECM to maintain multiple processes governing tumor development, progression and spread. A large body of experimental and clinical evidence has now accumulated to demonstrate essential roles of numerous ECM components in all cancer types. Latest findings also suggest that multiple tumor types express, and use to their advantage, atypical ECM components that are not found in the cancer tissue of origin. However, the understanding of cancer-specific expression patterns of these ECM proteins and their exact roles in selected tumor types is still sketchy. In this review, we summarize the latest data on the aberrant expression of bone and cartilage ECM proteins in epithelial cancers and their specific functions in the pathogenesis of carcinomas and discuss future directions in exploring the utility of this selective group of ECM components as future drug targets.

Prognostic risk analysis related to radioresistance genes in colorectal cancer

Background

Radiotherapy (RT) is one of the most important treatments for patients with colorectal cancer (CRC). Radioresistance is the crucial cause of poor therapeutic outcomes in colorectal cancer. However, the underlying mechanism of radioresistance in colorectal cancer is still poorly defined. Herein we established a radioresistant colorectal cancer cell line and performed transcriptomics analyses to search for the underlying genes that contribute to radioresistance and investigate its association with the prognosis of CRC patients.

Methods

The radioresistant cell line was developed from the parental HCT116 cell by a stepwise increased dose of irradiation. Differential gene analysis was performed using cellular transcriptome data to identify genes associated with radioresistance, from which extracellular matrix (ECM) and cell adhesion-related genes were screened. Survival data from a CRC cohort in the TCGA database were used for further model gene screening and validation. The correlation between the risk score model and tumor microenvironment, clinical phenotype, drug treatment sensitivity, and tumor mutation status were also investigated.

Results

A total of 493 different expression genes were identified from the radioresistant and wild-type cell line, of which 94 genes were associated with ECM and cell adhesion-related genes. The five model genes TNFRSF13C, CD36, ANGPTL4, LAMB3, and SERPINA1 were identified for CRC radioresistance via screening using the best model. A ROC curve indicated that the AUC of the resulting prognostic model (based on the 5-gene risk score and other clinical parameters, including age, sex, and tumor stages) was 0.79, 0.77, and 0.78 at 1, 2, and 3 years, respectively. The calibration curve showed high agreement between the risk score prediction and actual survival probability. The immune microenvironment, drug treatment sensitivity, and tumor mutation status significantly differed between the high- and low-risk groups.

Conclusions

The risk score model built with five radioresistance genes in this study, including TNFRSF13C, CD36, ANGPTL4, LAMB3, and SERPINA1, showed favorable performance in prognosis prediction after radiotherapy for CRC.

Biomedical Applications of Collagen

Extracellular matrix proteins (ECMs) provide structural support and dynamic signaling cues that regulate cell behavior and tissue morphogenesis [...].

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Polarized hyperspectral microscopic imaging for collagen visualization on pathologic slides of head and neck squamous cell carcinoma

We developed a polarized hyperspectral microscope to collect four types of Stokes vector data cubes (S0, S1, S2, and S3) of the pathologic slides with head and neck squamous cell carcinoma (HNSCC). Our system consists of an optical light microscope with a movable stage, two polarizers, two liquid crystal variable retarders (LCVRs), and a SnapScan hyperspectral camera. The polarizers and LCVRs work in tandem with the hyperspectral camera to acquire polarized hyperspectral images. Synthetic pseudo-RGB images are generated from the four Stokes vector data cubes based on a transformation function similar to the spectral response of human eye for the visualization of hyperspectral images. Collagen is the most abundant extracellular matrix (ECM) protein in the human body. A major focus of studying the ECM in tumor microenvironment is the role of collagen in both normal and abnormal function. Collagen tends to accumulate in and around tumors during cancer development and growth. In this study, we acquired images from normal regions containing normal cells and collagen fibers and from tumor regions containing cancerous squamous cells and collagen fibers on HNSCC pathologic slides. The preliminary results demonstrated that our customized polarized hyperspectral microscope is able to improve the visualization of collagen on HNSCC pathologic slides under different situations, including thick fibers of normal stroma, thin fibers of normal stroma, fibers of normal muscle cells, fibers accumulated in tumors, fibers accumulated around tumors. Our preliminary results also demonstrated that the customized polarized hyperspectral microscope is capable of extracting the spectral signatures of collagen based on Stokes vector parameters and can have various applications in pathology and oncology.

Evaluation of miRNA Expression in 3D In Vitro Scaffold-Based Cancer Models

Accumulating experimental evidence suggests that 3D in vitro cancer models strengthen our understanding of vital processes in the tumor microenvironment (TME) and accelerate the drug discovery pipeline. Previous studies examining the effects of specific miRNAs on cancer cells in vitro have involved ectopic expression of miRNA mimics in 2D in vitro culture. Assessment of cell viability and gene expression ensures that upregulation of the chosen miRNA and repression of its target genes have been achieved. However, this 2D culture is overly simplified and lacks the complex cell to extracellular matrix (ECM) interactions observed in the native TME, yielding results often not reproduced when progressed to in vivo studies. Hence, this chapter describes a novel method of overexpressing the miRNA mimic in cells cultured on 3D collagen-based scaffolds adapted from tissue engineering techniques. Cell growth on scaffolds is sequentially monitored via a DNA quantification assay, and overexpression of the miRNA mimic and repression of its target gene is assessed via reverse transcription quantitative PCR (RT-qPCR).

Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity

Biomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.

A pan-cancer analysis of matrisome proteins reveals CTHRC1 and a related network as major ECM regulators across cancers

The extracellular matrix in the tumour microenvironment can regulate cancer cell growth and progression. A pan-cancer analysis of TCGA data from 30 cancer types, identified the top 5% of matrisome genes with amplifications or deletions in their copy number, that affect their expression and cancer survival. A similar analysis of matrisome genes in individual cancers identified CTHRC1 to be significantly altered. CTHRC1, a regulator of collagen synthesis, was identified as the most prominently upregulated matrisome gene of interest across cancers. Differential gene expression analysis identified 19 genes whose expression is increased with CTHRC1. STRING analysis of these genes classified them as ‘extracellular’, involved most prominently in ECM organization and cell adhesion. KEGG analysis showed their involvement in ECM-receptor and growth factor signalling. Cytohubba analysis of these genes revealed 13 hub genes, of which MMP13, POSTN, SFRP4, ADAMTS16 and FNDC1 were significantly altered in their expression with CTHRC1 and seen to affect survival across cancers. This could in part be mediated by their overlapping roles in regulating ECM (collagen or fibronectin) expression and organisation. In breast cancer tumour samples CTHRC1 protein levels are significantly upregulated with POSTN and MMP13, further supporting the need to evaluate their crosstalk in cancers.

Proof of Concept of 3-D Backscatter Tensor Imaging Tomography for Non-invasive Assessment of Human Breast Cancer Collagen Organization

Tumor growth, similarly to several other pathologies, tends to change the structural orientation of soft tissue fibers, which can become relevant markers for diagnosis. Current diagnosis protocols may require a biopsy for histological analysis, which is an invasive, painful and stressful procedure with a minimum turnaround time of 2 d. Otherwise, diagnosis may involve the use of complex methods with limited availability such as diffusion tensor imaging (magnetic resonance diffusion tensor imaging), which is not widely used in medical practice. Conversely, advanced methodologies in ultrasound imaging such as backscatter tensor imaging (BTI) might become a routine procedure in clinical practice at a limited cost. This method evaluates the local organization of soft tissues based on the spatial coherence of their backscattered ultrasonic echoes. Previous work has proven that BTI applied with matrix probes enables measurement of the orientation of soft tissue fibers, especially in the myocardium. The aims of the study described here were (i) to present for the first time a methodology for performing BTI in a volume on ex vivo human breast tumors using a linear probe and (ii) to display a first proof of concept of the link between BTI measurements and the orientation of collagen fibers.

Biomimetic hydrogel supports initiation and growth of patient-derived breast tumor organoids

Patient-derived tumor organoids (PDOs) are a highly promising preclinical model that recapitulates the histology, gene expression, and drug response of the donor patient tumor. Currently, PDO culture relies on basement-membrane extract (BME), which suffers from batch-to-batch variability, the presence of xenogeneic compounds and residual growth factors, and poor control of mechanical properties. Additionally, for the development of new organoid lines from patient-derived xenografts, contamination of murine host cells poses a problem. We propose a nanofibrillar hydrogel (EKGel) for the initiation and growth of breast cancer PDOs. PDOs grown in EKGel have histopathologic features, gene expression, and drug response that are similar to those of their parental tumors and PDOs in BME. In addition, EKGel offers reduced batch-to-batch variability, a range of mechanical properties, and suppressed contamination from murine cells. These results show that EKGel is an improved alternative to BME matrices for the initiation, growth, and maintenance of breast cancer PDOs.

Patient-derived tumour organoids are important preclinical models but suffer from variability from the use of basement-membrane extract and cell contamination. Here, the authors report on the development of mimetic nanofibrilar hydrogel which supports tumour organoid growth with reduced batch variability and cell contamination.

Multiphoton Microscopy for Identifying Collagen Signatures Associated with Biochemical Recurrence in Prostate Cancer Patients

Prostate cancer is a heterogeneous disease that remains dormant for long periods or acts aggressively with poor clinical outcomes. Identifying aggressive prostate tumor behavior using current glandular-focused histopathological criteria is challenging. Recent evidence has implicated the stroma in modulating prostate tumor behavior and in predicting post-surgical outcomes. However, the emergence of stromal signatures has been limited, due in part to the lack of adoption of imaging modalities for stromal-specific profiling. Herein, label-free multiphoton microscopy (MPM), with its ability to image tissue with stromal-specific contrast, is used to identify prostate stromal features associated with aggressive tumor behavior and clinical outcome. MPM was performed on unstained prostatectomy specimens from 59 patients and on biopsy specimens from 17 patients with known post-surgery recurrence status. MPM-identified collagen content, organization, and morphological tumor signatures were extracted for each patient and screened for association with recurrent disease. Compared to tumors from patients whose disease did not recur, tumors from patients with recurrent disease exhibited higher MPM-identified collagen amount and collagen fiber intensity signal and width. Our study shows an association between MPM-identified stromal collagen features of prostate tumors and post-surgical disease recurrence, suggesting their potential for prostate cancer risk assessment.

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.

Gene Expression Analysis Provides New Insights into the Mechanism of Intramuscular Fat Formation in Japanese Black Cattle

Japanese Black cattle (Japanese Wagyu) have a unique phenotype in which ectopic intramuscular fat accumulates in skeletal muscle, producing finely marbled beef. However, the mechanism of intramuscular fat formation in Japanese Black cattle remains unclear. To investigate the key genes involved in intramuscular fat accumulation, we comprehensively analyzed mRNA levels in subcutaneous and intramuscular fat tissues using RNA sequence (RNA-seq) analysis, which detected 27,606 genes. We identified eight key genes, namely carboxypeptidase E, tenascin C, transgelin, collagen type IV alpha 5 (COL4A5), cysteine and glycine-rich protein 2, PDZ, and LIM domain 3, phosphatase 1 regulatory inhibitor subunit 14A, and regulator of calcineurin 2. These genes were highly and specifically expressed in intramuscular fat tissue. Immunohistochemical analysis revealed a collagen network, including COL4A5, in the basement membrane around the intramuscular fat tissue. Moreover, pathway analysis revealed that, in intramuscular fat tissue, differentially expressed genes are related to cell adhesion, proliferation, and cancer pathways. Furthermore, pathway analysis showed that the transforming growth factor-β (TGF-β) and small GTPases regulators RASGRP3, ARHGEF26, ARHGAP10, ARHGAP24, and DLC were upregulated in intramuscular fat. Our study suggests that these genes are involved in intramuscular fat formation in Japanese Black cattle.

Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance

Assessment of intratumoral heterogeneity and tumor-host interaction within the tumor microenvironment is becoming increasingly important for innovative cancer therapy decisions because of the unique information it can generate about the state of the disease. However, its assessment and quantification are limited by ambiguous definitions of the tumor-host interface and by human cognitive capacity in current pathology practice. Advances in machine learning and artificial intelligence have opened the field of digital pathology to novel tissue image analytics and feature extraction for generation of high-capacity computational disease management models. A particular benefit is expected from machine-learning applications that can perform extraction and quantification of subvisual features of both intratumoral heterogeneity and tumor microenvironment aspects. These methods generate information about cancer cell subpopulation heterogeneity, potential tumor-host interactions, and tissue microarchitecture, derived from morphologically resolved content using both explicit and implicit features. Several studies have achieved promising diagnostic, prognostic, and predictive artificial intelligence models that often outperform current clinical and pathology criteria. However, further effort is needed for clinical adoption of such methods through development of standardizable high-capacity workflows and proper validation studies.

Postnatal structural development of mammalian Basilar Membrane provides anatomical basis for the maturation of tonotopic maps and frequency tuning

The basilar membrane (BM) of the mammalian cochlea constitutes a spiraling acellular ribbon that is intimately attached to the organ of Corti. Its graded stiffness, increasing from apex to the base of the cochlea provides the mechanical basis for sound frequency analysis. Despite its central role in auditory signal transduction, virtually nothing is known about the BM's structural development. Using polarized light microscopy, the present study characterized the architectural transformations of freshly dissected BM at time points during postnatal development and maturation. The results indicate that the BM structural elements increase progressively in size, becoming radially aligned and more tightly packed with maturation and reach the adult structural signature by postnatal day 20 (P20). The findings provide insight into structural details and developmental changes of the mammalian BM, suggesting that BM is a dynamic structure that changes throughout the life of an animal.