3D organizational mapping of collagen fibers elucidates matrix remodeling in a hormone-sensitive 3D breast tissue model

Extracellular Hsp90 Binds to and Aligns Collagen-1 to Enhance Breast Cancer Cell Invasiveness

Cancer cell-secreted eHsp90 binds and activates proteins in the tumor microenvironment crucial in cancer invasion. Therefore, targeting eHsp90 could inhibit invasion, preventing metastasis-the leading cause of cancer-related mortality. Previous eHsp90 studies have solely focused on its role in cancer invasion through the 2D basement membrane (BM), a form of extracellular matrix (ECM) that lines the epithelial compartment. However, its role in cancer invasion through the 3D Interstitial Matrix (IM), an ECM beyond the BM, remains unexplored. Using a Collagen-1 binding assay and second harmonic generation (SHG) imaging, we demonstrate that eHsp90 directly binds and aligns Collagen-1 fibers, the primary component of IM. Furthermore, we show that eHsp90 enhances Collagen-1 invasion of breast cancer cells in the Transwell assay. Using Hsp90 conformation mutants and inhibitors, we established that the Hsp90 dimer binds to Collagen-1 via its N-domain. We also demonstrated that while Collagen-1 binding and alignment are not influenced by Hsp90's ATPase activity attributed to the N-domain, its open conformation is crucial for increasing Collagen-1 alignment and promoting breast cancer cell invasion. These findings unveil a novel role for eHsp90 in invasion through the IM and offer valuable mechanistic insights into potential therapeutic approaches for inhibiting Hsp90 to suppress invasion and metastasis.

Mapping port wine stain in vivo by optical coherence tomography angiography and multi-metric characterization

Port wine stain (PWS) is a congenital cutaneous capillary malformation composed of ecstatic vessels, while the microstructure of these vessels remains largely unknown. Optical coherence tomography angiography (OCTA) serves as a non-invasive, label-free and high-resolution tool to visualize the 3D tissue microvasculature. However, even as the 3D vessel images of PWS become readily accessible, quantitative analysis algorithms for their organization have mainly remained limited to analysis of 2D images. Especially, 3D orientations of vasculature in PWS have not yet been resolved at a voxel-wise basis. In this study, we employed the inverse signal-to-noise ratio (iSNR)-decorrelation (D) OCTA (ID-OCTA) to acquire 3D blood vessel images in vivo from PWS patients, and used the mean-subtraction method for de-shadowing to correct the tail artifacts. We developed algorithms which mapped blood vessels in spatial-angular hyperspace in a 3D context, and obtained orientation-derived metrics including directional variance and waviness for the characterization of vessel alignment and crimping level, respectively. Combining with thickness and local density measures, our method served as a multi-parametric analysis platform which covered a variety of morphological and organizational characteristics at a voxel-wise basis. We found that blood vessels were thicker, denser and less aligned in lesion skin in contrast to normal skin (symmetrical parts of skin lesions on the cheek), and complementary insights from these metrics led to a classification accuracy of ∼90% in identifying PWS. An improvement in sensitivity of 3D analysis was validated over 2D analysis. Our imaging and analysis system provides a clear picture of the microstructure of blood vessels within PWS tissues, which leads to a better understanding of this capillary malformation disease and facilitates improvements in diagnosis and treatment of PWS.

Label-free, multi-parametric assessments of cell metabolism and matrix remodeling within human and early-stage murine osteoarthritic articular cartilage

Osteoarthritis (OA) is characterized by the progressive deterioration of articular cartilage, involving complicated cell-matrix interactions. Systematic investigations of dynamic cellular and matrix changes during OA progression are lacking. In this study, we use label-free two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging to assess cellular and extracellular matrix features of murine articular cartilage during several time points at early stages of OA development following destabilization of medial meniscus surgery. We detect significant changes in the organization of collagen fibers and crosslink-associated fluorescence of the superficial zone as early as one week following surgery. Such changes become significant within the deeper transitional and radial zones at later time-points, highlighting the importance of high spatial resolution. Cellular metabolic changes exhibit a highly dynamic behavior, and indicate metabolic reprogramming from enhanced oxidative phosphorylation to enhanced glycolysis or fatty acid oxidation over the ten-week observation period. The optical metabolic and matrix changes detected within this mouse model are consistent with differences identified in excised human cartilage specimens from OA and healthy cartilage specimens. Thus, our studies reveal important cell-matrix interactions at the onset of OA that may enable improved understanding of OA development and identification of new potential treatment targets.

Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning

Significance

Rapid diagnosis and analysis of human keloid scar tissues in an automated manner are essential for understanding pathogenesis and formulating treatment solutions.

Aim

Our aim is to resolve the features of the extracellular matrix in human keloid scar tissues automatically for accurate diagnosis with the aid of machine learning.

Approach

Multiphoton microscopy was utilized to acquire images of collagen and elastin fibers. Morphological features, histogram, and gray-level co-occurrence matrix-based texture features were obtained to produce a total of 28 features. The minimum redundancy maximum relevancy feature selection approach was implemented to rank these features and establish feature subsets, each of which was employed to build a machine learning model through the tree-based pipeline optimization tool (TPOT).

Results

The feature importance ranking was obtained, and 28 feature subsets were acquired by incremental feature selection. The subset with the top 23 features was identified as the most accurate. Then stochastic gradient descent classifier optimized by the TPOT was generated with an accuracy of 96.15% in classifying normal, scar, and adjacent tissues. The area under curve of the classification results (scar versus normal and adjacent, normal versus scar and adjacent, and adjacent versus normal and scar) was 1.0, 1.0, and 0.99, respectively.

Conclusions

The proposed approach has great potential for future dermatological clinical diagnosis and analysis and holds promise for the development of computer-aided systems to assist dermatologists in diagnosis and treatment.

Mapping the 3D remodeling of the extracellular matrix in human hypertrophic scar by multi-parametric multiphoton imaging using endogenous contrast

The hypertrophic scar is an aberrant form of wound healing process, whose clinical efficacy is limited by a lack of understanding of its pathophysiology. Remodeling of collagen and elastin fibers in the extracellular matrix (ECM) is closely associated with scar progression. Herein, we perform label-free multiphoton microscopy (MPM) of both fiber components from human skin specimens and propose a multi-fiber metrics (MFM) analysis model for mapping the structural remodeling of the ECM in hypertrophic scars in a highly-sensitive, three-dimensional (3D) manner. We find that both fiber components become wavier and more disorganized in scar tissues, while content accumulation is observed from elastin fibers only. The 3D MFM analysis can effectively distinguish normal and scar tissues with better than 95% in accuracy and 0.999 in the area under the curve value of the receiver operating characteristic curve. Further, unique organizational features with orderly alignment of both fibers are observed in scar-normal adjacent regions, and an optimized combination of features from 3D MFM analysis enables successful identification of all the boundaries. This imaging and analysis system uncovers the 3D architecture of the ECM in hypertrophic scars and exhibits great translational potential for evaluating scars in vivo and identifying individualized treatment targets.

A systematic review and comparison of automated tools for quantification of fibrous networks

Fibrous networks are essential structural components of biological and engineered materials. Accordingly, many approaches have been developed to quantify their structural properties, which define their material properties. However, a comprehensive overview and comparison of methods is lacking. Therefore, we systematically searched for automated tools quantifying network characteristics in confocal, stimulated emission depletion (STED) or scanning electron microscopy (SEM) images and compared these tools by applying them to fibrin, a prototypical fibrous network in thrombi. Structural properties of fibrin such as fiber diameter and alignment are clinically relevant, since they influence the risk of thrombosis. Based on a systematic comparison of the automated tools with each other, manual measurements, and simulated networks, we provide guidance to choose appropriate tools for fibrous network quantification depending on imaging modality and structural parameter. These tools are often able to reliably measure relative changes in network characteristics, but absolute numbers should be interpreted with care. STATEMENT OF SIGNIFICANCE: Structural properties of fibrous networks define material properties of many biological and engineered materials. Many methods exist to automatically quantify structural properties, but an overview and comparison is lacking. In this work, we systematically searched for all publicly available automated analysis tools that can quantify structural properties of fibrous networks. Next, we compared them by applying them to microscopy images of fibrin networks. We also benchmarked the automated tools against manual measurements or synthetic images. As a result, we give advice on which automated analysis tools to use for specific structural properties. We anticipate that researchers from a large variety of fields, ranging from thrombosis and hemostasis to cancer research, and materials science, can benefit from our work.

A comprehensive review of computational and image analysis techniques for quantitative evaluation of striated muscle tissue architecture

Unbiased evaluation of morphology is crucial to understanding development, mechanics, and pathology of striated muscle tissues. Indeed, the ability of striated muscles to contract and the strength of their contraction is dependent on their tissue-, cellular-, and cytoskeletal-level organization. Accordingly, the study of striated muscles often requires imaging and assessing aspects of their architecture at multiple different spatial scales. While an expert may be able to qualitatively appraise tissues, it is imperative to have robust, repeatable tools to quantify striated myocyte morphology and behavior that can be used to compare across different labs and experiments. There has been a recent effort to define the criteria used by experts to evaluate striated myocyte architecture. In this review, we will describe metrics that have been developed to summarize distinct aspects of striated muscle architecture in multiple different tissues, imaged with various modalities. Additionally, we will provide an overview of metrics and image processing software that needs to be developed. Importantly to any lab working on striated muscle platforms, characterization of striated myocyte morphology using the image processing pipelines discussed in this review can be used to quantitatively evaluate striated muscle tissues and contribute to a robust understanding of the development and mechanics of striated muscles.

Highly accurate, automated quantification of 2D/3D orientation for cerebrovasculature using window optimizing method

SIGNIFICANCE

Deep-imaging of cerebral vessels and accurate organizational characterization are vital to understanding the relationship between tissue structure and function.

AIM

We aim at large-depth imaging of the mouse brain vessels based on aggregation-induced emission luminogens (AIEgens), and we create a new algorithm to characterize the spatial orientation adaptively with superior accuracy.

APPROACH

Assisted by AIEgens with near-infrared-II excitation, three-photon fluorescence (3PF) images of large-depth cerebral blood vessels are captured. A window optimizing (WO) method is developed for highly accurate, automated 2D/3D orientation determination. The application of this system is demonstrated by establishing the orientational architecture of mouse cerebrovasculature down to the millimeter-level depth.

RESULTS

The WO method is proved to have significantly higher accuracy in both 2D and 3D cases than the method with a fixed window size. Depth- and diameter-dependent orientation information is acquired based on in vivo 3PF imaging and the WO analysis of cerebral vessel images with a penetration depth of 800 μm in mice.

CONCLUSIONS

We built an imaging and analysis system for cerebrovasculature that is conducive to applications in neuroscience and clinical fields.

Identification of human ovarian cancer relying on collagen fiber coverage features by quantitative second harmonic generation imaging

Ovarian cancer has the highest mortality rate among all gynecological cancers, containing complicated heterogeneous histotypes, each with different treatment plans and prognoses. The lack of screening test makes new perspectives for the biomarker of ovarian cancer of great significance. As the main component of extracellular matrix, collagen fibers undergo dynamic remodeling caused by neoplastic activity. Second harmonic generation (SHG) enables label-free, non-destructive imaging of collagen fibers with submicron resolution and deep sectioning. In this study, we developed a new metric named local coverage to quantify morphologically localized distribution of collagen fibers and combined it with overall density to characterize 3D SHG images of collagen fibers from normal, benign and malignant human ovarian biopsies. An overall diagnosis accuracy of 96.3% in distinguishing these tissue types made local and overall density signatures a sensitive biomarker of tumor progression. Quantitative, multi-parametric SHG imaging might serve as a potential screening test tool for ovarian cancer.

Mapping physiological and pathological functions of cortical vasculature through aggregation-induced emission nanoprobes assisted quantitative, in vivo NIR-II imaging

Cerebrovascular disease includes all disorders that affect cerebrovascular and cerebral circulation. Unfortunately, there is currently a lack of a systematic method to image blood vessels directly and achieve accurate quantification. Herein, we build a non-invasive, quantitative imaging and characterization system applicable to mapping physiological and pathological functions of cortical vasculature. Assisted by aggregation-induced emission (AIE) luminogens with either excitation or emission at near-infrared-II (NIR-II) region, large-depth and/or high signal-to-background ratio images of cerebral blood vessels from mice and marmosets are captured, based on which we develop an optical metric of vessel thickness in an automated, pixel-wise manner and both two-dimensional (2D) and three-dimensional (3D) contexts. By monitoring time-dependent cerebrovascular images in marmosets, periodic changes in the diameter of vibrating cerebral blood vessels are found to be regulated mainly by heartbeat. In mice photothrombosis model, vessel alterations throughout the whole process of thrombotic stroke are found to be stage-dependent. From a large field of view, the distance-dependent vessel thickness variation before and right after stroke is obtained away from the thrombus site. Importantly, a buffer zone exists right surrounding the lesion, indicating the inhomogeneity of vascular morphological changes. Biologically excretable AIE nanoparticles are used for assessing physiological and pathological functions, offering great potential for clinical translation.

From 3D printing to 3D bioprinting: the material properties of polymeric material and its derived bioink for achieving tissue specific architectures

The application of 3D printing technologies fields for biological tissues, organs, and cells in the context of medical and biotechnology applications requires a significant amount of innovation in a narrow printability range. 3D bioprinting is one such way of addressing critical design challenges in tissue engineering. In a more general sense, 3D printing has become essential in customized implant designing, faithful reproduction of microenvironmental niches, sustainable development of implants, in the capacity to address issues of effective cellular integration, and long-term stability of the cellular constructs in tissue engineering. This review covers various aspects of 3D bioprinting, describes the current state-of-the-art solutions for all aforementioned critical issues, and includes various illustrative representations of technologies supporting the development of phases of 3D bioprinting. It also demonstrates several bio-inks and their properties crucial for being used for 3D printing applications. The review focus on bringing together different examples and current trends in tissue engineering applications, including bone, cartilage, muscles, neuron, skin, esophagus, trachea, tympanic membrane, cornea, blood vessel, immune system, and tumor models utilizing 3D printing technology and to provide an outlook of the future potentials and barriers.

Remodelling of the fibre-aggregate structure of collagen gels by cancer-associated fibroblasts: A time-resolved grey-tone image analysis based on stochastic modelling

Introduction

Solid tumors consist of tumor cells associated with stromal and immune cells, secreted factors and extracellular matrix (ECM), which together constitute the tumor microenvironment. Among stromal cells, activated fibroblasts, known as cancer-associated fibroblasts (CAFs) are of particular interest. CAFs secrete a plethora of ECM components including collagen and modulate the architecture of the ECM, thereby influencing cancer cell migration. The characterization of the collagen fibre network and its space and time-dependent microstructural modifications is key to investigating the interactions between cells and the ECM. Developing image analysis tools for that purpose is still a challenge because the structural complexity of the collagen network calls for specific statistical descriptors. Moreover, the low signal-to-noise ratio of imaging techniques available for time-resolved studies rules out standard methods based on image segmentation.

Methods

In this work, we develop a novel approach based on the stochastic modelling of the gel structure and on grey-tone image analysis. The method is then used to study the remodelling of a collagen matrix by migrating breast cancer-derived CAFs in a three-dimensional spheroid model of cellular invasion imaged by time-lapse confocal microscopy.

Results

The structure of the collagen at the scale of a few microns consists in regions with high fibre density separated by depleted regions, which can be thought of as aggregates and pores. The approach developped captures this two-scale structure with a clipped Gaussian field model to describe the aggregates-and-pores large-scale structure, and a homogeneous Boolean model to describe the small-scale fibre network within the aggregates. The model parameters are identified by fitting the grey-tone histograms and correlation functions of the images. The method applies to unprocessed grey-tone images, and it can therefore be used with low magnification, noisy time-lapse reflectance images. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.

Conclusion

We developed a novel and multidisciplinary image analysis approach to investigate the remodelling of fibrillar collagen in a 3D spheroid model of cellular invasion. The specificity of the method is that it applies to the unprocessed grey-tone images, and it can therefore be used with noisy time-lapse reflectance images of non-fluorescent collagen. When applied to the CAF spheroid time-resolved images, the method reveals different matrix densification mechanisms for the matrix in direct contact or far from the cells.

Matrix Composition Modulates Vitamin D3's Effects on 3D Collagen Fiber Organization by MCF10A Cells

Vitamin D3 (vitD3) has been implicated in various cellular functions affecting multiple tissue types. Epidemiological and laboratory studies suggest that vitD3 may be effective as a preventive or therapeutic option for breast cancer. However, randomized clinical trials have yet to confirm these suggestions. Breast neoplasias can arise from developmental alterations; based on this evidence, we seek to understand vitD3's role in normal breast development, particularly its role in epithelial morphogenetic processes such as ductal elongation, branching, and alveolar formation. These processes require extensive changes in the extracellular microenvironment, such as collagen fiber organization, and are largely influenced by hormones. Here, we build upon our past work to shed light on calcitriol's effects on collagen fiber organization by breast epithelial cells, and how such effects are modulated by extracellular matrix composition. We embedded MCF10A normal human breast epithelial cells in two different matrices-collagen type I and collagen type I + 10% Matrigel; treatment with calcitriol resulted in flatter epithelial structures. Next, using two-photon microscopy, we examined changes in collagen fiber organization and corresponding changes in epithelial structures. Applying a novel three-dimensional (3D) image analysis method, we show that increasing doses of calcitriol result in denser collagen fiber bundles in the localized area surrounding the epithelial structures, and that these bundles are aligned in a more parallel direction to epithelial structures when exposed to the highest vitD3 dose. Changed patterns in fiber organization may explain the flattening of epithelial structures; in turn, changes in biophysical forces in the matrix abutting these structures may be responsible for changes in the referred patterns. Addition of 10% Matrigel dampened the effects of calcitriol on both epithelial morphogenesis and fiber organization. Overall, we report novel functions of calcitriol in the breast epithelium and add to the growing body of evidence documenting how hormones affect biophysical processes.

Multiscale anisotropy analysis of second-harmonic generation collagen imaging of mouse skin

SIGNIFICANCE

Morphological collagen signatures are important for tissue function, particularly in the tumor microenvironment. A single algorithmic framework with quantitative, multiscale morphological collagen feature extraction may further the use of collagen signatures in understanding fundamental tumor progression.

AIM

A modification of the 2D wavelet transform modulus maxima (WTMM) anisotropy method was applied to both digitally simulated collagen fibers and second-harmonic-generation imaged collagen fibers of mouse skin to calculate a multiscale anisotropy factor to detect collagen fiber organization.

APPROACH

The modified 2D WTMM anisotropy method was initially validated on synthetic calibration images to establish the robustness and sensitivity of the multiscale fiber organization tool. Upon validation, the algorithm was applied to collagen fiber organization in normal wild-type skin, melanoma stimulated skin, and integrin α10KO skin.

RESULTS

Normal wild-type skin collagen fibers have an increased anisotropy factor at all sizes scales. Interestingly, the multiscale anisotropy differences highlight important dissimilarities between collagen fiber organization in normal wild-type skin, melanoma stimulated, and integrin α10KO skin. At small scales (∼2 to 3  μm), the integrin α10KO skin was vastly different than normal skin (p-value  ∼  10  -  8), whereas the melanoma stimulated skin was vastly different than normal at large scales (∼30 to 40  μm, p-value  ∼  10  -  15).

CONCLUSIONS

This objective computational collagen fiber organization algorithm is sensitive to collagen fiber organization across multiple scales for effective exploration of collagen morphological alterations associated with melanoma and the lack of α10 integrin binding.

Tissue Imaging and Quantification Relying on Endogenous Contrast

Cell-matrix interactions play an important role in regulating a variety of essential processes in multicellular organisms, and are closely associated with numerous diseases. Modified interactions have major effects upon key features of both cells and extracellular matrix (ECM), and a thorough understanding of changes in these features can lead to critically important insights of diseases as well as the identification of effective therapeutic targets. Here, we summarize recent advances in quantitative, optical imaging of cellular metabolism and ECM spatial organization using endogenous sources of contrast. Specifically, we focus on the two-photon excited fluorescence (TPEF) imaging of autofluorescent cellular coenzymes, NAD(P)H and FAD, for the extraction of metabolic information described by optical biomarkers including cellular redox state, NAD(P)H fluorescence lifetime, and mitochondrial clustering. We show representative applications in assessing adipose tissue function and detecting malignant lesions in human skin, and further demonstrate that a combination of these optical metrics can provide complementary insights into the underlying biological mechanisms. In addition, we review the development of quantitative analysis methods to extract spatial orientation and organization metrics of collagen fibers, a major ECM component, and demonstrate applications of these approaches in two and three dimensions in several diseases, including would healing, osteoarthritis and cancer, as well as assessments of matrix remodeling in hormone-regulated engineered breast tissues. Finally, we summarize this chapter and discuss important research directions that we expect will evolve in the near future.

Label-free identification of human coronary atherosclerotic plaque based on a three-dimensional quantitative assessment of multiphoton microscopy images

The rupture of coronary atherosclerotic plaque (CAP) and the resulting intracoronary thrombosis account for most acute coronary syndromes. Thus, the early identification and risk assessment of CAP is crucial for timely medical intervention. In this study, we propose a quantitative and label-free method for human CAP identification using multiphoton microscopy (MPM) and three-dimensional (3D) image analysis techniques. By detecting the intrinsic MPM signals, the microstructures of collagen and elastin fibers within normal and CAP-lesioned human coronary artery walls were imaged. Using a 3D gray level co-occurrence matrix method and 3D weighted vector summation algorithm, quantitative indicators that characterize the spatial texture and orientation features of the fibers were extracted. We demonstrate that these indicators show superior accuracy and repeatability over 2D texture features in CAP discrimination. Furthermore, by combining the 3D microstructural indicators, a support vector machine model that classifies CAP from the normal arterial wall with an accuracy of >97% was established. In conjunction with advances in multiphoton endoscopy, the proposed method shows great potential in providing a quantitative, label-free, and real-time tool for the early identification and risk assessment of CAP in the future.

Pixel-level angular quantification of capsular collagen in second harmonic generation microscopy images of encapsulated thyroid nodules

Polarization-resolved second harmonic generation microscopy is used to provide pixel-level angular distribution of collagen in thyroid nodule capsules. The pixel-level angular distribution is combined with textural analysis to quantify the collagen distribution in follicular adenoma (benign) and papillary thyroid carcinoma (malignant). Three second order nonlinear susceptibility tensor elements ratios, the collagen angular distribution and two parameters accounting for the collagen angular dispersion in different sized areas are extracted and corresponding images are computed in a pixel-by-pixel fashion. Subsequently, we show that texture analysis can be performed on these images to detect significant differences between the considered benign and malignant nodule capsules.

Factors associated with obesity alter matrix remodeling in breast cancer tissues

Obesity is associated with a higher risk of developing breast cancer and with worse disease outcomes for women of all ages. The composition, density, and organization of the breast tissue stroma are also known to play an important role in the development and progression of the disease. However, the connections between obesity and stromal remodeling are not well understood. We sought to characterize detailed organization features of the collagen matrix within healthy and cancerous breast tissues acquired from mice exposed to either a normal or high fat (obesity inducing) diet. We performed second-harmonic generation and spectral two-photon excited fluorescence imaging, and we extracted the level of collagen-associated fluorescence (CAF) along with metrics of collagen content, three-dimensional, and two-dimensional organization. There were significant differences in the CAF intensity and overall collagen organization between normal and tumor tissues; however, obesity-enhanced changes in these metrics, especially when three-dimensional organization metrics were considered. Thus, our studies indicate that obesity impacts significantly collagen organization and structure and the related pathways of communication may be important future therapeutic targets.

Morphological quantification of proliferation-to-invasion transition in tumor spheroids

BACKGROUND

Metastasis determines the lethality of cancer. In most clinical cases, patients are able to live with tumor proliferation before metastasis. Thus, the transition from tumor proliferation to metastasis/invasion is essential. However, the mechanism is still unclear and especially, the proliferation-to-metastasis/invasion transition point has not been well defined. Therefore, quantitative characterization of this transition is urgently needed.

METHODS

We have successfully developed a home-built living-cell incubation system combined with an inverted optical microscope, and a systematic, quantitative approach to describing the major characteristic morphological parameters for the identification of the critical transition points for tumor-cell spheroids in a collagen fiber scaffold.

RESULTS

The system focuses on in vitro tumor modeling, e.g. the development of tumor-cell spheroids in a collagen fiber scaffold and the monitoring of cell transition from proliferation to invasion. By applying this approach to multiple tumor spheroid models, such as U87 (glioma tumor), H1299 (lung cancer), and MDA-MB-231 (breast cancer) cells, we have obtained quantitative morphological references to evaluate the proliferation-to-invasion transition time, as well as differentiating the invasion potential of tumor cells upon environmental changes, i.e. drug application.

CONCLUSIONS

Our quantitative approach provides a feasible clarification for the proliferation-to-invasion transition of in vitro tumor models (spheroids). Moreover, the transition time is a useful reference for the invasive potential of tumor cells.

GENERAL SIGNIFICANCE

This quantitative approach is potentially applicable to primary tumor cells, and thus has potential applications in the fields of cancer metastasis investigations and clinical diagnostics.

Geometric characteristics of collagen have independent prognostic significance in breast ductal carcinoma in situ: an image analysis study

Collagen plays a key role in normal and malignant tissue homeostasis. While the prognostic significance of collagen fiber remodeling in invasive breast cancer has been studied, its role in ductal carcinoma in situ (DCIS) remains poorly defined. Using image analysis, we aimed to evaluate the prognostic significance of the geometric characteristics of collagen surrounding DCIS. A large well-characterized cohort of DCIS comprising pure DCIS (n = 610) and DCIS coexisting with invasive carcinoma (n = 180) were histochemically stained for collagen using picrosirius red. ImageJ software was used to assess collagen density, degree of collagen fiber dispersion and directionality in relation to DCIS ducts' boundary. We developed a collagen prognostic index and evaluated its prognostic significance. A poor index was observed in 24% of the pure DCIS and was associated with determinants of high-risk DCIS including higher nuclear grade, comedo type necrosis, hormonal receptor negativity, HER2 positivity and high proliferation index. High collagen prognostic index was associated with the collagen remodeling protein prolyl-4-hydroxlase alpha subunit 2 and the hypoxia-related protein hypoxia inducible factor 1α. DCIS coexisting with invasive breast cancer had a higher collagen prognostic index than pure DCIS ( p < 0.0001). High index was an independent poor prognostic factor for DCIS recurrence for all recurrences (HR = 2.3, p = 0.005) and just invasive recurrences (HR = 3.4, p = 0.003). Interaction between collagen prognostic index and radiotherapy showed that the index was associated with poor outcome even with adjuvant radiotherapy ( p = 0.0001). Collagen reorganization around DCIS is associated with poor outcome and provides a potential predictor for disease progression and resistance to radiotherapy. Mechanistic studies are warranted to decipher the underlying mechanisms.

Vitamin D3 constrains estrogen's effects and influences mammary epithelial organization in 3D cultures

Vitamin D3 (vitD3) and its active metabolite, calcitriol (1,25-(OH)₂D₃), affect multiple tissue types by interacting with the vitamin D receptor (VDR). Although vitD3 deficiency has been correlated with increased incidence of breast cancer and less favorable outcomes, randomized clinical trials have yet to provide conclusive evidence on the efficacy of vitD3 in preventing or treating breast cancer. Additionally, experimental studies are needed to assess the biological plausibility of these outcomes. The mammary gland of VDR KO mice shows a florid phenotype revealing alterations of developmental processes that are largely regulated by mammotropic hormones. However, most research conducted on vitD3's effects used 2D cell cultures and supra-physiological doses of vitD3, conditions that spare the microenvironment in which morphogenesis takes place. We investigated the role of vitD3 in mammary epithelial morphogenesis using two 3D culture models. VitD3 interfered with estrogen's actions on T47D human breast cancer cells in 3D differently at different doses, and recapitulated what is observed in vivo. Also, vitD3 can act autonomously and affected the organization of estrogen-insensitive MCF10A cells in 3D collagen matrix by influencing collagen fiber organization. Thus, vitD3 modulates mammary tissue organization independent of its effects on cell proliferation.

A novel xenograft mouse model for testing approaches targeting human kappa light-chain diseases

Patients with immunoglobulin (Ig) light-chain (LC) diseases such as LC light-chain amyloidosis die with organ failure and need new therapies. We sought a model to test anti-LC siRNA delivery to human plasma cells, requiring circulating LC, in vivo indicators of tumor presence, and capacity for multiple injections of delivery vehicle. The JJN-3 human myeloma reporter cell line expressing firefly luciferase (FFL) implanted intraperitoneally (IP) in the NOD scid γ (NSG) mouse has a 90% prompt tumor-take, rapid LC production, and in vivo indicators of tumor measurable on day 5 post-implant (κ LC, bioluminescent signal, and soluble B-cell maturation antigen [sBCMA]) with median day 5 serum levels of κ LC of 1482 ng/mL (range, 255-4831) and robust correlations with all in vivo indicators. In preliminary attempts to deliver siRNA against κ LC constant region mRNA, we identified the 306-O18B3 lipidoid nanoparticle (LNP) as promising, safe and efficient in vitro. In vivo in the JJN-3 NSG IP model, after daily IP 306-O18B3:siRNA injections on days 5-10, a reduction in κ LC was observed on day 8 between control and test groups that continued through day 12 at sacrifice. This model is potentially useful as a platform for refining anti-LC therapies.