Rapid three-dimensional quantification of voxel-wise collagen fiber orientation

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained 'de-aberration' networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

The importance of 3D fibre architecture in cancer and implications for biomaterial model design

The need for improved prediction of clinical response is driving the development of cancer models with enhanced physiological relevance. A new concept of 'precision biomaterials' is emerging, encompassing patient-mimetic biomaterial models that seek to accurately detect, treat and model cancer by faithfully recapitulating key microenvironmental characteristics. Despite recent advances allowing tissue-mimetic stiffness and molecular composition to be replicated in vitro, approaches for reproducing the 3D fibre architectures found in tumour extracellular matrix (ECM) remain relatively unexplored. Although the precise influences of patient-specific fibre architecture are unclear, we summarize the known roles of tumour fibre architecture, underlining their implications in cell-matrix interactions and ultimately clinical outcome. We then explore the challenges in reproducing tissue-specific 3D fibre architecture(s) in vitro, highlighting relevant biomaterial fabrication techniques and their benefits and limitations. Finally, we discuss imaging and image analysis techniques (focussing on collagen I-optimized approaches) that could hold the key to mapping tumour-specific ECM into high-fidelity biomaterial models. We anticipate that an interdisciplinary approach, combining materials science, cancer research and image analysis, will elucidate the role of 3D fibre architecture in tumour development, leading to the next generation of patient-mimetic models for mechanistic studies and drug discovery.

Uniaxial mechanical stretch properties correlated with three-dimensional microstructure of human dermal skin

The intact and healthy skin forms a barrier to the outside world and protects the body from mechanical impact. The skin is a complex structure with unique mechano-elastic properties. To better direct the design of biomimetic materials and induce skin regeneration in wounds with optimal outcome, more insight is required in how the mechano-elastic properties emerge from the skin's main constituents, collagen and elastin fibers. Here, we employed two-photon excited autofluorescence and second harmonic generation microscopy to characterize collagen and elastin fibers in 3D in 24 human dermis skin samples. Through uniaxial stretching experiments, we derive uni-directional mechanical properties from resultant stress-strain curves, including the initial Young's modulus, elastic Young's modulus, maximal stress, and maximal and mid-strain values. The stress-strain curves show a large variation, with an average Young's modules in the toe and linear regions of 0.1 MPa and 21 MPa. We performed a comprehensive analysis of the correlation between the key mechanical properties with age and with microstructural parameters, e.g., fiber density, thickness, and orientation. Age was found to correlate negatively with Young's modulus and collagen density. Moreover, real-time monitoring during uniaxial stretching allowed us to observe changes in collagen and elastin alignment. Elastin fibers aligned significantly in both the heel and linear regions, and the collagen bundles engaged and oriented mainly in the linear region. This research advances our understanding of skin biomechanics and yields input for future first principles full modeling of skin tissue.

Fourier transform-based method for quantifying the three-dimensional orientation distribution of fibrous units

Several materials and tissues are characterized by a microstructure composed of fibrous units embedded in a ground matrix. In this paper, a novel three-dimensional (3D) Fourier transform-based method for quantifying the distribution of fiber orientations is presented. The method allows for an accurate identification of individual fiber families, their in-plane and out-of-plane dispersion, and showed fast computation times. We validated the method using artificially generated 3D images, in terms of fiber dispersion by considering the error between the standard deviation of the reconstructed and the prescribed distributions of the artificial fibers. In addition, we considered the measured mean orientation angles of the fibers and validated the robustness using a measure of fiber density. Finally, the method is employed to reconstruct a full 3D view of the distribution of collagen fiber orientations based on in vitro second harmonic generation microscopy of collagen fibers in human and mouse skin. The dispersion parameters of the reconstructed fiber network can be used to inform mechanical models of soft fiber-reinforced materials and biological tissues that account for non-symmetrical fiber dispersion.

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.

Visualizing dynamic three-dimensional changes of human reticular dermal collagen under mechanical strain

In clinical practice, plastic surgeons are often faced with large skin defects that are difficult to close primarily. Management of large skin wounds e.g. burns or traumatic lacerations requires knowledge of skin biomechanic properties. Research into skin microstructural adaptation to mechanical deformation has only been performed using static regimes due to technical limitations. Here, we combine uniaxial stretch tests with fast second harmonic generation imaging and we apply this for the first time to investigate dynamic collagen rearrangement in reticular human dermis.Ex vivohuman skin from the abdomen and upper thigh was simultaneously uniaxially stretched while either periodically visualizing 3D reorganization, or visualizing 2D changes in real time. We determined collagen alignment via orientation indices and found pronounced variability across samples. Comparing mean orientation indices at the different stages of the stress strain curves (toe, heel, linear) showed a significant increase in collagen alignment during the linear part of the mechanical response. We conclude that fast SHG imaging during uni-axial extension is a promising research tool for future studies on skin biomechanic properties.

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.

Three-dimensional analysis of load-dependent changes in the orientation of dermal collagen fibers in human skin: A pilot study

The availability of quantitative structural data on the orientation of collagen fibers is of crucial importance for understanding the behavior of connective tissues. These fibers can be visualized using a variety of imaging techniques, including second harmonic generation (SHG) microscopy. However, characterization of the collagen network requires the accurate extraction of parameters from imaging data. To this end, several automated processes have been developed to identify the preferred orientation of collagen fibers. Common methods include fast Fourier transforms and curvelet transforms, but these tools are mostly used to infer a single preferred orientation. The purpose of this pilot study was to develop an easy procedure for comprehensively comparing multiple human skin samples with the goal of analyzing load-dependent changes via SHG microscopy. We created a 3D model based upon 2D image stacks that provide fiber orientation data perpendicular and parallel to the plane of the epidermis. The SHG images were analyzed by CurveAlign to obtain angle histogram plots containing information about the multiple fiber orientations in each single image. Subsequently, contour plots of the angle histogram intensities were created to provide a useful visual plotting method to clearly show the anomalies in the angle histograms in all samples. Our results provided additional details on how the collagen network carries a load. In fact, analysis of SHG images indicated that increased stretch was accompanied by an increase in the alignment of fibers in the loading direction. Moreover, these images demonstrated that more than one type of preferred orientation is present. In particular, the 3D network of fibers appears to have two preferred orientations in the planes both perpendicular and parallel to the plane of the epidermis.

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.

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.

Multiscale Computational Model Predicts Mouse Skin Kinematics Under Tensile Loading

Skin is a complex tissue whose biomechanical properties are generally understood in terms of an incompressible material whose microstructure undergoes affine deformations. A growing number of experiments, however, have demonstrated that skin has a high Poisson's ratio, substantially decreases in volume during uniaxial tensile loading, and demonstrates collagen fiber kinematics that are not affine with local deformation. In order to better understand the mechanical basis for these properties, we constructed multiscale mechanical models (MSM) of mouse skin based on microstructural multiphoton microscopy imaging of the dermal microstructure acquired during mechanical testing. Three models that spanned the cases of highly aligned, moderately aligned, and nearly random fiber networks were examined and compared to the data acquired from uniaxially stretched skin. Our results demonstrate that MSMs consisting of networks of matched fiber organization can predict the biomechanical behavior of mouse skin, including the large decrease in tissue volume and nonaffine fiber kinematics observed under uniaxial tension.

Polarization enhanced laparoscope for improved visualization of tissue structural changes associated with peritoneal cancer metastasis

A polarization enhanced laparoscopy (PEL) imaging system was developed to examine the feasibility of utilizing PEL to augment conventional white light laparoscopy (WLL) in the visualization of peritoneal cancer metastases. The system includes a modified tip to illuminate tissue with linearly polarized light and elements in the detection path enabling recording of corresponding images linearly co- and cross-polarized relative to the incident light. WLL and PEL images from optical tissue phantoms with features of distinct scattering cross-section confirm the enhanced sensitivity of PEL to such characteristics. Additional comparisons based on images acquired from collagen gels with different levels of fiber alignment highlight another source of PEL contrast. Finally, PEL and WLL images of ex vivo human tissue illustrate the potential of PEL to improve visualization of cancerous tissue surrounded by healthy peritoneum. Given the simplicity of the approach and its potential for seamless integration with current clinical practice, our results provide motivation for clinical translation.

A Nonparametric Approach for Estimating Three-Dimensional Fiber Orientation Distribution Functions (ODFs) in Fibrous Materials

Many biological tissues contain an underlying fibrous microstructure that is optimized to suit a physiological function. The fiber architecture dictates physical characteristics such as stiffness, diffusivity, and electrical conduction. Abnormal deviations of fiber architecture are often associated with disease. Thus, it is useful to characterize fiber network organization from image data in order to better understand pathological mechanisms. We devised a method to quantify distributions of fiber orientations based on the Fourier transform and the Qball algorithm from diffusion MRI. The Fourier transform was used to decompose images into directional components, while the Qball algorithm efficiently converted the directional data from the frequency domain to the orientation domain. The representation in the orientation domain does not require any particular functional representation, and thus the method is nonparametric. The algorithm was verified to demonstrate its reliability and used on datasets from microscopy to show its applicability. This method increases the ability to extract information of microstructural fiber organization from experimental data that will enhance our understanding of structure-function relationships and enable accurate representation of material anisotropy in biological tissues.

Identification of endoplasmic reticulum formation mechanism by multi-parametric, quantitative super-resolution imaging

The endoplasmic reticulum (ER) is a highly dynamic membrane-bound organelle in eukaryotic cells which spreads throughout the whole cell and contacts and interacts with almost all organelles, yet quantitative approaches to assess ER reorganization are lacking. Herein we propose a multi-parametric, quantitative method combining pixel-wise orientation and waviness features and apply it to the time-dependent images of co-labeled ER and microtubule (MT) from U2OS cells acquired from two-dimensional structured illumination microscopy (2D SIM). Analysis results demonstrate that these morphological features are sensitive to ER reshaping and a combined use of them is a potential biomarker for ER formation. A new, to the best of our knowledge, mechanism of MT-associated ER formation, termed hooking, is identified based on distinct organizational alterations caused by interaction between ER and MT which are different from those of the other three mechanisms already known, validated by 100% discrimination accuracy in classifying four MT-associated ER formation mechanisms.

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.

Recent Advancements in Optical Harmonic Generation Microscopy: Applications and Perspectives

Second harmonic generation (SHG) and third harmonic generation (THG) microscopies have emerged as powerful imaging modalities to examine structural properties of a wide range of biological tissues. Although SHG and THG arise from very different contrast mechanisms, the two are complimentary and can often be collected simultaneously using a modified multiphoton microscope. In this review, we discuss the needed instrumentation for these modalities as well as the underlying theoretical principles of SHG and THG in tissue and describe how these can be leveraged to extract unique structural information. We provide an overview of recent advances showing how SHG microscopy has been used to evaluate collagen alterations in the extracellular matrix and how this has been used to advance our knowledge of cancers, fibroses, and the cornea, as well as in tissue engineering applications. Specific examples using polarization-resolved approaches and machine learning algorithms are highlighted. Similarly, we review how THG has enabled developmental biology and skin cancer studies due to its sensitivity to changes in refractive index, which are ubiquitous in all cell and tissue assemblies. Lastly, we offer perspectives and outlooks on future directions of SHG and THG microscopies and present unresolved questions, especially in terms of overall miniaturization and the development of microendoscopy instrumentation.

Quantitative Classification of 3D Collagen Fiber Organization From Volumetric Images

Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of diseases or injuries. While there are advanced techniques for imaging collagen fibers, published methods for quantifying 3D collagen fiber organization have been sparse and give limited structural information which cannot distinguish a wide range of 3D organizations. In this article, we demonstrate an algorithm for quantitative classification of 3D collagen fiber organization. The algorithm first simulates five groups, or classifications, of fiber organization: unidirectional, crimped, disordered, two-fiber family, and helical. These five groups are widespread in natural tissues and are known to affect the tissue's mechanical properties. We use quantitative metrics based on features such as preferred 3D fiber orientation and spherical variance to differentiate each classification in a repeatable manner. We validate our algorithm by applying it to second-harmonic generation images of collagen fibers in tendon and cervix tissue that has been sectioned in specified orientations, and we find strong agreement between classification from simulated data and the physical fiber organization. Our approach provides insight for interpreting 3D fiber organization directly from volumetric images. This algorithm could be applied to other fiber-like structures that are not necessarily made of collagen.

Three-dimensional computation of fibre orientation, diameter and branching in segmented image stacks of fibrous networks

Fibre topography of the extracellular matrix governs local mechanical properties and cellular behaviour including migration and gene expression. While quantifying properties of the fibrous network provides valuable data that could be used across a breadth of biomedical disciplines, most available techniques are limited to two dimensions and, therefore, do not fully capture the architecture of three-dimensional (3D) tissue. The currently available 3D techniques have limited accuracy and applicability and many are restricted to a specific imaging modality. To address this need, we developed a novel fibre analysis algorithm capable of determining fibre orientation, fibre diameter and fibre branching on a voxel-wise basis in image stacks with distinct fibre populations. The accuracy of the technique is demonstrated on computer-generated phantom image stacks spanning a range of features and complexities, as well as on two-photon microscopy image stacks of elastic fibres in bovine tendon and dermis. Additionally, we propose a measure of axial spherical variance which can be used to define the degree of fibre alignment in a distribution of 3D orientations. This method provides a useful tool to quantify orientation distributions and variance on image stacks with distinguishable fibres or fibre-like structures.

Modeling Controlled Cortical Impact Injury in 3D Brain-Like Tissue Cultures

Traumatic brain injury (TBI) survivors suffer long term from mental illness, neurodegeneration, and neuroinflammation. Studies of 3D tissue models have provided new insights into the pathobiology of many brain diseases. Here, a 3D in vitro contusion model is developed consisting of mouse cortical neurons grown on a silk scaffold embedded in collagen and used outcomes from an in vivo model for benchmarking. Molecular, cellular, and network events are characterized in response to controlled cortical impact (CCI). In this model, CCI induces degradation of neural network structure and function and release of glutamate, which are associated with the expression of programmed necrosis marker phosphorylated Mixed Lineage Kinase Domain Like Pseudokinase (pMLKL). Neurodegeneration is observed first in the directly impacted area and it subsequently spreads over time in 3D space. CCI reduces phosphorylated protein kinase B (pAKT) and Glycogen synthase kinase 3 beta (GSK3β) in neurons in vitro and in vivo, but discordant responses are observed in phosphprylated ribosomal S6 kinase (pS6) and phosphorylated Tau (pTau) expression. In summary, the 3D brain-like culture system mimicked many aspects of in vivo responses to CCI, providing evidence that the model can be used to study the molecular, cellular, and functional sequelae of TBI, opening up new possibilities for discovery of therapeutics.

Systemic Sclerosis Dermal Fibroblasts Induce Cutaneous Fibrosis Through Lysyl Oxidase-like 4: New Evidence From Three-Dimensional Skin-like Tissues

OBJECTIVE

Systemic sclerosis (SSc) is a clinically heterogeneous disease characterized by increased collagen accumulation and skin stiffness. Our previous work has demonstrated that transforming growth factor β (TGFβ) induces extracellular matrix (ECM) modifications through lysyl oxidase-like 4 (LOXL-4), a collagen crosslinking enzyme, in bioengineered human skin equivalents (HSEs) and self-assembled stromal tissues (SAS). We undertook this study to investigate cutaneous fibrosis and the role of LOXL-4 in SSc pathogenesis using HSEs and SAS.

METHODS

SSc-derived dermal fibroblasts (SScDFs; n = 8) and normal dermal fibroblasts (NDFs; n = 6) were incorporated into HSEs and SAS. These 3-dimensional skin-like microenvironments were used to study the effects of dysregulated LOXL-4 on ECM remodeling, fibroblast activation, and response to TGFβ stimulation.

RESULTS

SScDF-containing SAS showed increased stromal thickness, collagen deposition, and interleukin-6 secretion compared to NDF-containing SAS (P < 0.05). In HSE, SScDFs altered collagen as seen by a more mature and aligned fibrillar structure (P < 0.05). With SScDFs, enhanced stromal rigidity with increased collagen crosslinking (P < 0.05), up-regulation of LOXL4 expression (P < 0.01), and innate immune signaling genes were observed in both tissue models. Conversely, knockdown of LOXL4 suppressed rigidity, contraction, and α-smooth muscle actin expression in SScDFs in HSE, and TGFβ-induced ECM aggregation and collagen crosslinking in SAS.

CONCLUSION

A limitation to the development of effective therapeutics in SSc is the lack of in vitro human model systems that replicate human skin. Our findings demonstrate that SAS and HSE can serve as complementary in vitro skin-like models for investigation of the mechanisms and mediators that drive fibrosis in SSc and implicate a pivotal role for LOXL-4 in SSc pathogenesis.

Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.

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.

Depth-resolved mapping of muscular bundles in myocardium pulmonary junction using optical coherence tomography

Atrial fibrillation (AF) is the most common cardiac arrhythmia and has high patient morbidity. One of the root causes of AF is initiating triggers from atrial myocardium extending into the pulmonary veins. Visualizing the muscular bundles of myocardial extension is essential to guide the catheter radio-frequency ablation and confirm the curative tissue necrosis thereafter. We applied optical coherence tomography (OCT) for direct visualization of cardial muscle extension in myocardium pulmonary junction. Two perspectives (cross-sectional and en face images) are presented for imaging myocardial extensions. The results demonstrated that cross-sectional images can quickly locate the myocardium pulmonary junction. And en face images provide depth-resolved arrangement information of muscular bundles in the myocardium pulmonary junction. The results indicated that OCT could potentially be used to guide catheter radio-frequency ablation for treatment of AF.

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

Hormones play an important role in normal and diseased breast tissue development. However, they can also disrupt cell-matrix interactions and their role in extracellular matrix reorganization during epithelial morphogenesis remains poorly understood, partly due to a lack of sensitive approaches for matrix characterization. Here, we assess the hormonal regulation of matrix reorganization in a three-dimensional (3D) breast tissue culture model using a novel metric, i.e., 3D directional variance, to characterize the 3D organization of collagen fibers visualized via high-resolution, second harmonic generation imaging. This metric enables resolving and quantifying patterns of spatial organization throughout the matrix surrounding epithelial structures treated with 17β-estradiol (E2) alone, and E2 in combination with either promegestone, a progestogen, or prolactin. Addition of promegestone results in the most disorganized fibers, while the E2 alone treatment leads to the most organized ones. Location-dependent organization mapping indicates that only the prolactin treatment leads to significant heterogeneities in the regional organization of collagen fibers, with higher levels of alignment observed at the end of the elongated epithelial structures. The observed collagen organization patterns for all groups persist for tens of micrometers. In addition, a comparison between 3D directional variance and typical 2D analysis approaches reveals an improved sensitivity of the 3D metric to identify organizational heterogeneities and differences among treatment groups. These results demonstrate that 3D directional variance is sensitive to subtle changes in the extracellular micro-environment and has the potential to elucidate reciprocal cell-matrix interactions in the context of numerous applications involving the study of normal and diseased tissue morphogenesis.

Bioluminescence and second harmonic generation imaging reveal dynamic changes in the inflammatory and collagen landscape in early osteoarthritis

Osteoarthritis (OA) is a leading cause of chronic disability whose mechanism of pathogenesis is largely elusive. Local inflammation is thought to play a key role in OA progression, especially in injury-associated OA. While multiple inflammatory cytokines are detected, the timing and extent of overall inflammatory activities in early OA and the manner by which joint inflammation correlates with cartilage structural damage are still unclear. We induced OA via destabilization of the medial meniscus (DMM) in NFκB luciferase reporter mice, whose bioluminescent signal reflects the activity of NFκB, a central mediator of inflammation. Bioluminescence imaging data showed that DMM and sham control joints had a similar surge of inflammation at 1-week post-surgery, but the DMM joint exhibited a delay in resolution of inflammation in subsequent weeks. A similar trend was observed with synovitis, which we found to be mainly driven by synovial cell density and inflammatory infiltration rather than synovial lining thickness. Interestingly, an association between synovitis and collagen structural damage was observed in early OA. Using Second Harmonic Generation (SHG) imaging, we analyzed collagen fiber organization in articular cartilage. Zonal differences in collagen fiber thickness and organization were observed as soon as OA initiated after DMM surgery, and persisted over time. Even at 1-week post-surgery, the DMM joint showed a decrease in collagen fiber thickness in the deep zone and an increase in collagen fiber disorganization in the superficial zone. Since we were able detect and quantify collagen structural changes very early in OA development by SHG imaging, we concluded that SHG imaging is a highly sensitive tool to evaluate pathological changes in OA. In summary, this study uncovered a dynamic profile of inflammation and joint cartilage damage during OA initiation and development, providing novel insights into OA pathology.

Non-destructive two-photon excited fluorescence imaging identifies early nodules in calcific aortic-valve disease

Calcifications occur during the development of healthy bone, and at the onset of calcific aortic-valve disease (CAVD) and many other pathologies. Although the mechanisms regulating early calcium deposition are not fully understood, they may provide targets for new treatments and for early interventions. Here, we show that two-photon excited fluorescence (TPEF) can provide quantitative and sensitive readouts of calcific nodule formation, in particular in the context of CAVD. Specifically, by means of the decomposition of TPEF spectral images from excised human CAVD valves and from rat bone prior to and following demineralization, as well as from calcific nodules formed within engineered gels, we identified an endogenous fluorophore that correlates with the level of mineralization in the samples. We then developed a ratiometric imaging approach that provides a quantitative readout of the presence of mineral deposits in early calcifications. TPEF should enable non-destructive, high-resolution imaging of three-dimensional tissue specimens for the assessment of the presence of calcification.

3D second harmonic generation imaging tomography by multi-view excitation

Biological tissues have complex 3D collagen fiber architecture that cannot be fully visualized by conventional second harmonic generation (SHG) microscopy due to electric dipole considerations. We have developed a multi-view SHG imaging platform that successfully visualizes all orientations of collagen fibers. This is achieved by rotating tissues relative to the excitation laser plane of incidence, where the complete fibrillar structure is then visualized following registration and reconstruction. We evaluated high frequency and Gaussian weighted fusion reconstruction algorithms, and found the former approach performs better in terms of the resulting resolution. The new approach is a first step toward SHG tomography.

Multiphoton microscopy for label-free identification of intramural metastasis in human esophageal squamous cell carcinoma

For complete removal of cancerous tissue in esophageal squamous cell carcinoma (ESCC), intramural metastasis (IM) should be identified preoperatively or intraoperatively. Here, multiphoton microscopy (MPM) was introduced for label-free identification of IM in the esophageal wall, by a combination of two-photon excited fluorescence (TPEF), second harmonic generation (SHG) imaging, and spectral analysis. Three-dimensional (3D) imaging of the IM region was also performed. Quantitative parameters, including 3D fiber orientation, were measured by 3D-weighted orientation vector summation. Overall, MPM showed the potential to identify IM. With the development of the advanced MPM endoscope, clinical identification of IM by MPM will be possible.

Collagen Fiber Orientation and Dispersion in the Upper Cervix of Non-Pregnant and Pregnant Women

The structural integrity of the cervix in pregnancy is necessary for carrying a pregnancy until term, and the organization of human cervical tissue collagen likely plays an important role in the tissue's structural function. Collagen fibers in the cervical extracellular matrix exhibit preferential directionality, and this collagen network ultrastructure is hypothesized to reorient and remodel during cervical softening and dilation at time of parturition. Within the cervix, the upper half is substantially loaded during pregnancy and is where the premature funneling starts to happen. To characterize the cervical collagen ultrastructure for the upper half of the human cervix, we imaged whole axial tissue slices from non-pregnant and pregnant women undergoing hysterectomy or cesarean hysterectomy respectively using optical coherence tomography (OCT) and implemented a pixel-wise fiber orientation tracking method to measure the distribution of fiber orientation. The collagen fiber orientation maps show that there are two radial zones and the preferential fiber direction is circumferential in a dominant outer radial zone. The OCT data also reveal that there are two anatomic regions with distinct fiber orientation and dispersion properties. These regions are labeled: Region 1-the posterior and anterior quadrants in the outer radial zone and Region 2-the left and right quadrants in the outer radial zone and all quadrants in the inner radial zone. When comparing samples from nulliparous vs multiparous women, no differences in these fiber properties were noted. Pregnant tissue samples exhibit an overall higher fiber dispersion and more heterogeneous fiber properties within the sample than non-pregnant tissue. Collectively, these OCT data suggest that collagen fiber dispersion and directionality may play a role in cervical remodeling during pregnancy, where distinct remodeling properties exist according to anatomical quadrant.

Automated quantification of three-dimensional organization of fiber-like structures in biological tissues

Fiber-like structures are prevalent in biological tissues, yet quantitative approaches to assess their three-dimensional (3D) organization are lacking. We develop 3D directional variance, as a quantitative biomarker of truly 3D fibrillar organization by extending the directional statistics formalism developed for describing circular data distributions (i.e. when 0° and 360° are equivalent) to axial ones (i.e. when 0° and 180° are equivalent). Significant advantages of this analysis include its time efficiency, sensitivity and ability to provide quantitative readouts of organization over different size scales of a given data set. We establish a broad range of applications for this method by characterizing collagen fibers, neuronal axons and fibroblasts in the context of cancer diagnostics, traumatic brain injury and cell-matrix interactions in developing engineered tissues. This method opens possibilities for unraveling in a sensitive, and quantitative manner the organization of essential fiber-like structures in tissues and ultimately its impact on tissue function.