Second Harmonic Generation Imaging Research Articles

Abstract PO3-07-01: Multiscale Computational Analysis of Patient-Matched Multi-Modal Imaging of Breast Tissue Microenvironment

Abstract Introduction: Mammographic breast density is a strong quantitative cancer risk factor, but its link to underlying tissue and cellular level changes are weakly understood. Understanding the complex restructuring changes in the breast tumor microenvironment and its impact on breast density is integral to improving risk assessment and treatment strategies. A novel approach was developed to stratify mammographic dense breast tissue into areas of active dense tissue and passive dense tissue. Active dense tissue is structurally reorganizing and links to cancer dynamics, whereas passive dense mammographic tissue remains organized.1,2 A complementary wavelet-based analysis technique to measure the multiscale anisotropy of collagen fibers from Second-Harmonic Generation (SHG) imaging demonstrated clinical cancer diagnostic potential in mouse carcinoma and human pancreatic cancer.3,4 This tool is now leveraged for a multi-modal study in which patient-matched H&E tissue biopsies are imaged using both bright-field microscopy and SHG imaging to compare with the patient’s corresponding mammograms. Stratifying the mammographic density into active and passive dense areas, a correlation between biopsy tissue anisotropy and mammographic density subtypes can be calculated to help understand how tumor microenvironment changes affect a patient’s mammographic breast tissue composition. Methods: SHG and brightfield-imaged biopsy slides from 10 patients (five malignant, five benign) collected from Maine Medical Center (Maine, US) were analyzed using the 2D Wavelet Transform Modulus Maxima (WTMM) Anisotropy Method generating a large-scale and a small-scale anisotropy factor as well as a scale-combined anisotropy calculated from their large- and small-scale anisotropy separation. A multi-modal anisotropy score was calculated by adding the imaged area’s H&E combined anisotropy score with its respective SHG combined anisotropy score. This analysis was done on eight randomly selected ductal areas for each imaging modality on a slide and correlated with the measured areas of density subtypes from the whole mammographic view. Linear regression was used for determining correlation between H&E and SHG anisotropy factors as well as with active and passive mammographic density subtype measurements. Results: The malignant patients’ small-scale H&E anisotropy factor positively correlated with their small scale SHG anisotropy factor (R2=0.571). No correlation was found for the benign patients (R2=0.011). The strongest correlations were found between benign patients’ mammographic passive (positive correlation) and active (negative correlation) dense tissues vs the H&E anisotropy scale-combined factor (R2=0.858 and R2=0.961, respectively). The largest malignant patient correlation was a negative relationship between SHG anisotropy scale-combined factor and overall mammographic density (inclusive of both active and passive dense tissue) (R2=0.891). The multi-modal anisotropy score correlated with overall mammographic density for both benign (positive correlation, R2=0.698) and malignant patients (negative correlation, R2=0.773). Combining the multi-modal anisotropy factor from SHG and H&E with the active dense tissue measurement using K-Means clustering, 5/5 malignant and 4/5 benign patients were correctly classified. Conclusion: Density in mammograms correlates with underlying quantitative changes in a patient’s tissue and collagen anisotropy. Combining both H&E and SHG analysis on tissue biopsy with active and passive density metrics from computational mammography leads to the best predictive potential than any one method on their own.

A comparative study of CARE 2D and N2V 2D for tissue-specific denoising in second harmonic generation imaging.

This study explored the application of deep learning in second harmonic generation (SHG) microscopy, a rapidly growing area. This study focuses on the impact of glycerol concentration on image noise in SHG microscopy and compares two image restoration techniques: Noise-to-Void 2D (N2V 2D, no reference image restoration) and content-aware image restoration (CARE 2D, full reference image restoration). We demonstrated that N2V 2D effectively restored the images affected by high glycerol concentrations. To reduce sample exposure and damage, this study further addresses low-power SHG imaging by reducing the laser power by 70% using deep learning techniques. CARE 2D excels in preserving detailed structures, whereas N2V 2D maintains natural muscle structure. This study highlights the strengths and limitations of these models in specific SHG microscopy applications, offering valuable insights and potential advancements in the field .

On the quantitative analysis of lamellar collagen arrangement with second-harmonic generation imaging.

Second harmonic generation (SHG) allows for the examination of collagen structure in collagenous tissues. Collagen is a fibrous protein found in abundance in the human body, present in bones, cartilage, the skin, and the cornea, among other areas, providing structure, support, and strength. Its structural arrangement is deeply intertwined with its function. For instance, in the cornea, alterations in collagen organization can result in severe visual impairments. Using SHG imaging, various metrics have demonstrated the potential to study collagen organization. The discrimination between healthy, keratoconus, and crosslinked corneas, assessment of injured tendons, or the characterization of breast and ovarian tumorous tissue have been demonstrated. Nevertheless, these metrics have not yet been objectively evaluated or compared. A total of five metrics were identified and implemented from the literature, and an additional approach adapted from texture analysis was proposed. In this study, we analyzed their effectiveness on a ground-truth set of artificially generated fibrous images. Our investigation provides the first comprehensive assessment of the performance of multiple metrics, identifying both the strengths and weaknesses of each approach and providing valuable insights for future applications of SHG imaging in medical diagnostics and research.

Abstract 4262: AI-driven analysis of collagen characteristics in the tumor immune microenvironment predicts immune checkpoint inhibitors treatment responsiveness in gastric cancer

Abstract Background: Recent studies indicate a correlation between increased stromal composition in tumors and resistance to immune checkpoint inhibitors (ICIs) across various cancer types. Specifically, the extracellular matrix (ECM) within the stromal compartment significantly influence the tumor microenvironment, affecting cancer cell proliferation and immune cell recruitment. However, identifying and quantifying the ECM at the collagen fiber level in whole slide images (WSIs) is challenging due to its inherent heterogeneity and complexity. In this work, we utilized statistical analysis and a deep learning (DL)-based approach to identify and assess collagen in the ECM, examining its characteristics in relation to the ICIs treatment response in gastric cancer. Methods: We analyzed 116 WSIs of hematoxylin and eosin (H&E) stained slides before ICIs treatment from three institutions. Responders were defined as those with a complete response or partial response (N=18); those with stable disease (SD) (N=34) or progressive disease (PD) (N=64) were considered non-responders (N=98). For non-responders, post-treatment H&E slides were available for 21 patients; SD (N=5) and PD (N=16). We applied a DL-based second harmonic generation (SHG) synthesis algorithm to tumor regions identified by a tumor detection model. We then calculated various collagen properties, including volume, fiber orientation and alignment, waviness, straightness, and fiber thickness, based on collagen fiber centerlines extracted from the generated SHG images. An attention-based multiple instance learning (ABMIL) model was trained to automatically predict the treatment response solely based on collagen features. The dataset of 116 pre-treatment slides was divided into training and validation subsets using stratified 3-fold cross-validation. The area under the curve (AUC) was used to assess the performance of the trained models. Results: Collagen fiber features were significantly associated with ICIs response. Notably, ICIs responders have a decrease in collagen waviness and fiber orientation variation (p-value<0.05, by Mann-Whitney U test). Among non-responders, the PD group showed a statistical decrease in waviness after treatment (p-value<0.05, by Mann-Whitney U test). The ABMIL model achieved a mean AUC score of 0.823, with a standard deviation of 0.023. Conclusion: This study provides a comprehensive analysis of collagen in the ECM, and its properties in relation to gastric cancer ICIs response within Tumor Immune Microenvironment. We also developed a model using collagen features and demonstrated high accuracy in predicting treatment response. Citation Format: Minji Kim, Ekaterina Redekop, Yoonho Choi, Sung Hak Lee, Jae-Ho Cheong, Yu Aoki, Kohei Shitara, Tae Hyun Hwang. AI-driven analysis of collagen characteristics in the tumor immune microenvironment predicts immune checkpoint inhibitors treatment responsiveness in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4262.

Polarization-resolved super-resolution second-harmonic generation imaging based on multifocal structured illumination microscopy.

Polarization-resolved second-harmonic generation (PSHG) microscopy is widely used in investigating the structural and morphological alterations of collagen. However, the resolution of second-harmonic generation (SHG) imaging remains constrained by optical diffraction, resulting in the polarization extraction of collagen characteristics from the average properties of collagen fibers. In this study, multifocal structured illumination microscopy (MSIM) was combined with PSHG to achieve polarization-resolved super-resolution imaging of second-harmonic generation signals. For the first time to our knowledge, periodic structures with an average pitch of 277 nm were observed in mouse tail tendons using optical microscopy, and the orientation angle of fibrils within each period was found to exhibit an alternating arrangement along the axis in a regular pattern.

Global imaging for polarization resolved second harmonic generation of WS2 monolayers

Second-harmonic generation (SHG) is a nonlinear optical effect enhanced by broken crystallin symmetry and is very sensitive to electronic structures. SHG has recently been applied to two-dimensional transition metals dichalcogenides (2D-TMDs). 2D-TMDs have been the focus of much recent research due to their ultrathin scale, high quantum confinement, and energy gap toning ability that results in unique linear or nonlinear optical and electrical properties. WS2 monolayers are well-known 2D TMDs with strong SHG. They have attracted a great deal of interest due to their potential applications in fundamental material characterization and nanophotonic device development. In this study, we grew WS2 monolayers using the chemical vapor deposition (CVD) technique and characterized them with Raman and photoluminescence (PL) spectroscopy. The intense direct excitonic peak A at 630 nm was identified in the PL spectra, while the Raman spectra exhibited the two distinctive modes A1g (at 418 cm−1) and E2g (at 356 cm−1). The monolayers were pumped by an 830 nm circularly polarized and defused pulsed laser to produce the SHG image. global one-shot SH images for different growth shapes were obtained and crystalline domains were identified using polarization-resolved second-harmonic generation imaging (PRSHGI). The defect level was observed to clearly enhance the SHG signal following the increase in broken crystalline centrosymmetric and relaxing the optical selection roles at the valley degree of freedom.

Differences in Lipid Order and Dynamics in Plasma Membranes Assessed by Nonlinear Optical Microscopy.

When amphiphilic polar dyes were added to the cells, they intercalated predominantly in the outer leaf of the plasma membrane, making them active for second harmonic generation (SHG). The fluorescence of the dye enabled simultaneous 3D imaging of SHG and two-photon excited fluorescence (TPF). Because SHG intensity is sensitive to the alignment of the dyes, which reflects lipid ordering in the plasma membrane, we assessed the difference in lipid ordering by comparing the SHG intensity normalized to the TPF intensity. Together with an enzyme release assay that detects pore formation in the plasma membrane, our SHG assay revealed how polycations affect lipid ordering at low concentrations, where membrane damage has not yet been examined. By scaling the results of the assays with the charge concentration of the two polycations, polyethylenimine (PEI) and poly-l-lysine (PLL), we found that PEI reduced the lipid order more than PLL, and PLL formed more pores than PEI. A comparison of the SHG and TPF images of the wounded cells revealed that one of the lipid dynamics (flip-flop) was significantly enhanced in the bleb membrane. Moreover, the SHG assay indicated that the biocompatible polymer, poly(N-(2-hydroxypropyl)methacrylamide), did not affect the lipid order. Thus, our technique allows the assessment of the plasma membrane structure at the molecular level.

Combined flat-field and frequency filter approach to correcting artifacts of multichannel two-photon microscopy.

Multiphoton microscopy (MPM) is a useful biomedical imaging tool for its ability to probe labeled and unlabeled depth-resolved tissue biomarkers at high resolution. Automated MPM tile scanning allows for whole-slide image acquisition but can suffer from tile-stitching artifacts that prevent accurate quantitative data analysis. We have investigated postprocessing artifact correction methods using ImageJ macros and custom Python code. Quantitative and qualitative comparisons of these methods were made using whole-slide MPM autofluorescence and second-harmonic generation images of human duodenal tissue. Image quality after artifact removal is assessed by evaluating the processed image and its unprocessed counterpart using the root mean square error, structural similarity index, and image histogram measurements. Consideration of both quantitative and qualitative results suggest that a combination of a custom flat-field-based correction and frequency filtering processing step provide improved artifact correction when compared with each method used independently to correct for tiling artifacts of tile-scan MPM images. While some image artifacts remain with these methods, further optimization of these processing steps may result in computational-efficient methods for removing these artifacts that are ubiquitous in large-scale MPM imaging. Removal of these artifacts with retention of the original image information would facilitate the use of this imaging modality in both research and clinical settings, where it is highly useful in collecting detailed morphologic and optical properties of tissue.

Functional Assessment of Human Articular Cartilage Using Second Harmonic Generation (SHG) Imaging: A Feasibility Study.

Many arthroscopic tools developed for knee joint assessment are contact-based, which is challenging for in vivo application in narrow joint spaces. Second harmonic generation (SHG) laser imaging is a non-invasive and non-contact method, thus presenting an attractive alternative. However, the association between SHG-based measures and cartilage quality has not been established systematically. Here, we investigated the feasibility of using image-based measures derived from SHG microscopy for objective evaluation of cartilage quality as assessed by mechanical testing. Human tibial plateaus harvested from nine patients were used. Cartilage mechanical properties were determined using indentation stiffness (Einst) and streaming potential-based quantitative parameters (QP). The correspondence of the cartilage electromechanical properties (Einst and QP) and the image-based measures derived from SHG imaging, tissue thickness and cell viability were evaluated using correlation and logistic regression analyses. The SHG-related parameters included the newly developed volumetric fraction of organised collagenous network (Φcol) and the coefficient of variation of the SHG intensity (CVSHG). We found that Φcol correlated strongly with Einst and QP (ρ = 0.97 and - 0.89, respectively). CVSHG also correlated, albeit weakly, with QP and Einst, (|ρ| = 0.52-0.58). Einst and Φcol were the most sensitive predictors of cartilage quality whereas CVSHG only showed moderate sensitivity. Cell viability and tissue thickness, often used as measures of cartilage health, predicted the cartilage quality poorly. We present a simple, objective, yet effective image-based approach for assessment of cartilage quality. Φcol correlated strongly with electromechanical properties of cartilage and could fuel the continuous development of SHG-based arthroscopy.

Label-free assessment of pathological changes in pancreatic intraepithelial neoplasia by biomedical multiphoton microscopy.

Pancreatic intraepithelial neoplasia (PanIN) is the most common precursor lesion that has the potential to progress to invasive pancreatic cancer, and early and rapid detection may offer patients a chance for treatment before the development of invasive carcinoma. Therefore, the identification of PanIN holds significant clinical importance. In this study, we first used multiphoton microscopy (MPM) combining two-photon excitation fluorescence and second-harmonic generation imaging to label-free detect PanIN and attempted to differentiate between normal pancreatic ducts and different grades of PanIN. Then, we also developed an automatic image processing strategy to extract eight morphological features of collagen fibers from MPM images to quantify the changes in collagen fibers surrounding the ducts. Experimental results demonstrate that the combination of MPM and quantitative information can accurately identify normal pancreatic ducts and different grades of PanIN. This study may contribute to the rapid diagnosis of pancreatic diseases and may lay the foundation for further clinical application of MPM.

In vivo label-free tissue histology through a microstructured imaging window.

Tissue histopathology, based on hematoxylin and eosin (H&E) staining of thin tissue slices, is the gold standard for the evaluation of the immune reaction to the implant of a biomaterial. It is based on lengthy and costly procedures that do not allow longitudinal studies. The use of non-linear excitation microscopy in vivo, largely label-free, has the potential to overcome these limitations. With this purpose, we develop and validate an implantable microstructured device for the non-linear excitation microscopy assessment of the immune reaction to an implanted biomaterial label-free. The microstructured device, shaped as a matrix of regular 3D lattices, is obtained by two-photon laser polymerization. It is subsequently implanted in the chorioallantoic membrane (CAM) of embryonated chicken eggs for 7 days to act as an intrinsic 3D reference frame for cell counting and identification. The histological analysis based on H&E images of the tissue sections sampled around the implanted microstructures is compared to non-linear excitation and confocal images to build a cell atlas that correlates the histological observations to the label-free images. In this way, we can quantify the number of cells recruited in the tissue reconstituted in the microstructures and identify granulocytes on label-free images within and outside the microstructures. Collagen and microvessels are also identified by means of second-harmonic generation and autofluorescence imaging. The analysis indicates that the tissue reaction to implanted microstructures is like the one typical of CAM healing after injury, without a massive foreign body reaction. This opens the path to the use of similar microstructures coupled to a biomaterial, to image in vivo the regenerating interface between a tissue and a biomaterial with label-free non-linear excitation microscopy. This promises to be a transformative approach, alternative to conventional histopathology, for the bioengineering and the validation of biomaterials in in vivo longitudinal studies.

Automated quantitative assay of fibrosis characteristics in tuberculosis granulomas.

Granulomas, the pathological hallmark of Mycobacterium tuberculosis (Mtb) infection, are formed by different cell populations. Across various stages of tuberculosis conditions, most granulomas are classical caseous granulomas. They are composed of a necrotic center surrounded by multilayers of histocytes, with the outermost layer encircled by fibrosis. Although fibrosis characterizes the architecture of granulomas, little is known about the detailed parameters of fibrosis during this process. In this study, samples were collected from patients with tuberculosis (spanning 16 organ types), and Mtb-infected marmosets and fibrotic collagen were characterized by second harmonic generation (SHG)/two-photon excited fluorescence (TPEF) microscopy using a stain-free, fully automated analysis program. Histopathological examination revealed that most granulomas share common features, including necrosis, solitary and compact structure, and especially the presence of multinuclear giant cells. Masson's trichrome staining showed that different granuloma types have varying degrees of fibrosis. SHG imaging uncovered a higher proportion (4%~13%) of aggregated collagens than of disseminated type collagens (2%~5%) in granulomas from matched tissues. Furthermore, most of the aggregated collagen presented as short and thick clusters (200~620 µm), unlike the long and thick (200~300 µm) disseminated collagens within the matched tissues. Matrix metalloproteinase-9, which is involved in fibrosis and granuloma formation, was strongly expressed in the granulomas in different tissues. Our data illustrated that different tuberculosis granulomas have some degree of fibrosis in which collagen strings are short and thick. Moreover, this study revealed that the SHG imaging program could contribute to uncovering the fibrosis characteristics of tuberculosis granulomas.

Monitoring the response of a model protocell to dye and surfactant molecules through second harmonic generation and fluorescence imaging.

Probing the interaction between molecules and protocells is crucial for understanding the passive transport of functional molecules in and out of artificial and real cells. Second-harmonic generation (SHG) has been proven to be a powerful method for analyzing the adsorption and cross-membrane transport of molecules on lipid bilayers. In this study, we used SHG and two-photon fluorescence (TPF) imaging to study the interaction of charged dye molecules (D289) with a lipid vesicle. Unexpectedly, it was observed that the transport of D289 at a relatively high concentration is not as efficient as that at a lower dye concentration. Periodic shrinking of the model protocell and discharging of D289 out from the vesicle were revealed by combined analyses of SHG and TPF images. The response of the vesicle to a surfactant was also analyzed with D289 as a probe. This work demonstrates that the combined SHG and TPF imaging method is a unique approach that can provide detailed information on the interaction of molecules and lipids (both morphology and molecular kinetics). Determining these subtle interfacial kinetics in molecules is important for understanding the mechanism of many biophysical processes occurring on lipids.

SHG fiberscopy assessment of collagen morphology and its potential for breast cancer optical histology.

this study is to investigate the feasibility of our recently developed nonlinear fiberscope for label-free in situ breast tumor detection and lymph node status assessment based on second harmonic generation (SHG) imaging of fibrillar collagen matrix with histological details. The long-term goal is to improve the current biopsy-based cancer paradigm with reduced sampling errors. in this pilot study we undertook retrospective SHG imaging study of ex vivo invasive ductal carcinoma human biopsy tissue samples, and carried out quantitative image analysis to search for collagen structural signatures that are associated with the malignance of breast cancer. SHG fiberscopy image-based quantitative assessment of collagen fiber morphology reveals that: 1) cancerous tissues contain generally less extracellular collagen fibers compared with tumor-adjacent normal tissues, and 2) collagen fibers in lymph node positive biopsies are more aligned than lymph node negative counterparts. the results demonstrate the promising potential of our SHG fiberscope for in situ breast tumor detection and lymph node involvement assessment and for offering real-time guidance during ongoing tissue biopsy.

Effect of out of plane orientation on polarization second harmonic generation of single collagen fibrils.

Second harmonic generation (SHG) microscopy has emerged as a powerful technique for visualizing collagen organization within tissues. Amongst the many advantages of SHG is its sensitivity to collagen nanoscale organization, and its presumed sensitivity to the relative out of plane polarity of fibrils. Recent results have shown that circular dichroism SHG (CD-SHG), a technique that has been commonly assumed to reveal the relative out of plane polarity of collagen fibrils, is actually insensitive to changes in fibril polarity. However, results from another research group seem to contradict this conclusion. Both previous results have been based on SHG imaging of collagen fibrils within tissues, therefore, to gain a definitive understanding of the sensitivity of SHG to relative out of plane polarity, the results from individual fibrils are desirable. Here we present polarization resolved SHG microscopy (PSHG) data from individual collagen fibrils oriented out of the image plane by buckling on an elastic substrate. We show through correlation with atomic force microscopy measurements that SHG intensity can be used to estimate the out of plane angle of individual fibrils. We then compare the sensitivity of two PSHG techniques, CD-SHG and polarization-in, polarization-out SHG (PIPO-SHG), to the relative out of plane polarity of individual fibrils. We find that for single fibrils CD-SHG is insensitive to relative out of polarity and we also demonstrate the first direct experimental confirmation that PIPO-SHG reveals the relative out of plane polarity of individual collagen fibrils.

Categorization of collagen type I and II blend hydrogel using multipolarization SHG imaging with ResNet regression

Previously, the discrimination of collagen types I and II was successfully achieved using peptide pitch angle and anisotropic parameter methods. However, these methods require fitting polarization second harmonic generation (SHG) pixel-wise information into generic mathematical models, revealing inconsistencies in categorizing collagen type I and II blend hydrogels. In this study, a ResNet approach based on multipolarization SHG imaging is proposed for the categorization and regression of collagen type I and II blend hydrogels at 0%, 25%, 50%, 75%, and 100% type II, without the need for prior time-consuming model fitting. A ResNet model, pretrained on 18 progressive polarization SHG images at 10° intervals for each percentage, categorizes the five blended collagen hydrogels with a mean absolute error (MAE) of 0.021, while the model pretrained on nonpolarization images exhibited 0.083 MAE. Moreover, the pretrained models can also generally regress the blend hydrogels at 20%, 40%, 60%, and 80% type II. In conclusion, the multipolarization SHG image-based ResNet analysis demonstrates the potential for an automated approach using deep learning to extract valuable information from the collagen matrix.

Mask R-CNN provides efficient and accurate measurement of chondrocyte viability in the label-free assessment of articular cartilage

ObjectiveChondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement. MethodTPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement. ResultsUsing training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods. ConclusionsMask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement.

Non-invasive assessment of hair regeneration in androgenetic alopecia mice in vivo using two-photon and second harmonic generation imaging.

The identification of crucial targets for hair regrowth in androgenetic alopecia (AGA) involves determining important characteristics and different stages during the process of hair follicle regeneration. Traditional methods for assessing key features and different stages of hair follicle primarily involve taking skin tissue samples and determining them through various staining or other methods. However, non-invasive assessment methods have been long sought. Therefore, in this study, endogenous fluorescence signals from skin keratin and second harmonic signals from skin collagen fibers were utilized as probes, two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) imaging techniques were employed to non-invasively assess hair shafts and collagen fibers in AGA mice in vivo. The TPEF imaging technique revealed that the alternation of new and old hair shafts and the different stages of the growth period in AGA mice were delayed. In addition, SHG imaging found testosterone reduced hair follicle area and miniaturized hair follicles. The non-invasive TPEF and SHG imaging techniques provided important methodologies for determining significant characteristics and different stages of the growth cycle in AGA mice, which will facilitate future non-invasive assessments on human scalps in vivo and reduce the use of animal testing.

Multimodal imaging of metabolic activities for distinguishing subtypes of breast cancer.

Triple negative breast cancer (TNBC) is a highly aggressive form of cancer. Detecting TNBC early is crucial for improving disease prognosis and optimizing treatment. Unfortunately, conventional imaging techniques fall short in providing a comprehensive differentiation of TNBC subtypes due to their limited sensitivity and inability to capture subcellular details. In this study, we present a multimodal imaging platform that integrates heavy water (D2O)-probed stimulated Raman scattering (DO-SRS), two-photon fluorescence (TPF), and second harmonic generation (SHG) imaging. This platform allows us to directly visualize and quantify the metabolic activities of TNBC subtypes at a subcellular level. By utilizing DO-SRS imaging, we were able to identify distinct levels of de novo lipogenesis, protein synthesis, cytochrome c metabolic heterogeneity, and lipid unsaturation rates in various TNBC subtype tissues. Simultaneously, TPF imaging provided spatial distribution mapping of NAD[P]H and flavin signals in TNBC tissues, revealing a high redox ratio and significant lipid turnover rate in TNBC BL2 (HCC1806) samples. Furthermore, SHG imaging enabled us to observe diverse orientations of collagen fibers in TNBC tissues, with higher anisotropy at the tissue boundary compared to the center. Our multimodal imaging platform offers a highly sensitive and subcellular approach to characterizing not only TNBC, but also other tissue subtypes and cancers.

Regional biomechanical characterization of human ascending aortic aneurysms: Microstructure and biaxial mechanical response.

The ascending thoracic aortic aneurysm (ATAA) is a permanent dilatation of the vessel with a high risk of adverse events, and shows heterogeneous properties. To investigate regional differences in the biomechanical properties of ATAAs, tissue samples were collected from 10 patients with tricuspid aortic valve phenotype and specimens from minor, anterior, major, and posterior regions were subjected to multi-ratio planar biaxial extension tests and second-harmonic generation (SHG) imaging. Using the data, parameters of a microstructure-motivated constitutive model were obtained considering fiber dispersion. SHG imaging showed disruptions in the organization of the layers. Structural and material parameters did not differ significantly between regions. The non-symmetric fiber dispersion model proposed by Holzapfel etal. [25] was used to fit the data. The mean angle of collagen fibers was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. The experimental data collected in this study contribute to the biomechanical data available in the literature on human ATAAs. Region-specific parameters for the constitutive models are fundamental to improve the current risk stratification strategies, which are mainly based on aortic size. Such investigations can facilitate the development of more advanced finite element models capable of capturing the regional heterogeneity of pathological tissues. STATEMENT OF SIGNIFICANCE: Tissue samples of human ascending thoracic aortic aneurysms (ATAA) were collected. Samples from four regions underwent multi-ratio planar biaxial extension tests and second-harmonic generation imaging. Region-specific parameters of a microstructure-motivated model considering fiber dispersion were obtained. Structural and material parameters did not differ significantly between regions, however, the mean fiber angle was negatively correlated between minor and anterior regions, and the parameter associated with collagen fiber stiffness was positively correlated between minor and major regions. Furthermore, correlations were found between the stiffness of the ground matrix and the mean fiber angle, and between the parameter associated with the collagen fiber stiffness and the out-of-plane dispersion parameter in the posterior and minor regions, respectively. This study provides a unique set of mechanical and structural data, supporting the microstructural influence on the tissue response. It may facilitate the development of better finite element models capable of capturing the regional tissue heterogeneity.