SHG/TPEF-based image technology improves liver fibrosis assessment of minimally sized needle biopsies

The Emerging Role of Second Harmonic Generation/Two Photon Excitation for Precision Digital Analysis of Liver Fibrosis in MASH Clinical Trials

Conventional histopathological evaluation of liver biopsy slides has been invaluable in assessing the causes of liver injury, the severity of the underlying disease processes, and the degree of resulting fibrosis. However, the use of conventional histologic assessments as endpoints in clinical trials is limited by the reliability of scoring systems, variability in interpretation of histologic features and translation of continuous variables into categorical scores. To increase the precision and reproducibility of liver biopsy assessment, several artificial intelligence/machine learning (AI/ML) approaches have been developed to analyse high resolution digital images of liver biopsy specimens. Multiple AI/ML platforms are in development with promising results in post-hoc analyses of clinical trial biopsies. One such technique employs images generated by Second Harmonic Generation/Two Photon Excitation (SHG/TPE) microscopy that uniquely uses unstained liver biopsies to provide high resolution images of collagen fibres to assess and quantify collagen morphometry, and avoid challenges related to staining variability. One SHG/TPE microscopy methodology coupled with AI/ML based analysis, qFibrosis™, has been used post-hoc as an exploratory endpoint in several clinical trials for metabolic dysfunction-associated steatohepatitis (MASH) demonstrating its ability to provide a consistent and more nuanced assessment of liver fibrosis that still correlates well with traditional staging. This review summarizes the development of qFibrosis and outlines the need for additional studies to validate it as a sensitive marker for changes in fibrosis in the context of treatment trials and correlate these changes with subsequent liver-related outcomes.

AI Digital Pathology Using qFibrosis Shows Heterogeneity of Fibrosis Regression in Patients with Chronic Hepatitis B and C with Viral Response

This study aimed to understand the dynamic changes in fibrosis and its relationship with the evaluation of post-treatment viral hepatitis using qFibrosis. A total of 158 paired pre- and post-treatment liver samples from patients with chronic hepatitis B (CHB; n = 100) and C (CHC; n = 58) were examined. qFibrosis was employed with artificial intelligence (AI) to analyze the fibrosis dynamics in the portal tract (PT), periportal (PP), midzonal, pericentral, and central vein (CV) regions. All patients with CHB achieved a virological response after 78 weeks of treatment, whereas patients with CHC achieved a sustained viral response after 24 weeks. For patients initially staged as F5/6 (Ishak system) at baseline, the post-treatment cases exhibited a significant reduction in the collagen proportionate area (CPA) (25-69%) and number of collagen strings (#string) (9-72%) across all regions. In contrast, those initially staged as F3/4 at baseline showed a similar CPA and #string trend at 24 weeks. For regression patients, 27 parameters (25 in the CV region) in patients staged as F3/4 and 15 parameters (three in the PT and 12 in the PP regions) in those staged as F5/6 showed significant differences between the CHB and CHC groups at baseline. Following successful antiviral treatment, the pre- and post-treatment liver samples provided quantitative evidence of the heterogeneity of fibrotic features. qFibrosis has the potential to provide new insights into the characteristics of fibrosis regression in both patients with CHB and CHC as early as 24 weeks after antiviral therapy.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Protein MRI Contrast Agents as an Effective Approach for Precision Molecular Imaging

ABSTRACT

Cancer and other acute and chronic diseases are results of perturbations of common molecular determinants in key biological and signaling processes. Imaging is critical for characterizing dynamic changes in tumors and metastases, the tumor microenvironment, tumor-stroma interactions, and drug targets, at multiscale levels. Magnetic resonance imaging (MRI) has emerged to be a primary imaging modality for both clinical and preclinical applications due to its advantages over other modalities, including sensitivity to soft tissues, nondepth limitations, and the use of nonionizing radiation. However, extending the application of MRI to achieve both qualitative and quantitative precise molecular imaging with the capability to quantify molecular biomarkers for early detection, staging, and monitoring therapeutic treatment requires the capacity to overcome several major challenges including the trade-off between metal-binding affinity and relaxivity, which is an issue frequently associated with small chelator contrast agents. In this review, we will introduce the criteria of ideal contrast agents for precision molecular imaging and discuss the relaxivity of current contrast agents with defined first shell coordination water molecules. We will then report our advances in creating a new class of protein-targeted MRI contrast agents (ProCAs) with contributions to relaxivity largely derived from the secondary sphere and correlation time. We will summarize our rationale, design strategy, and approaches to the development and optimization of our pioneering ProCAs with desired high relaxivity, metal stability, and molecular biomarker-targeting capability, for precision MRI. From first generation (ProCA1) to third generation (ProCA32), we have achieved dual high r1 and r2 values that are 6- to 10-fold higher than clinically approved contrast agents at magnetic fields of 1.5 T, and their relaxivity values at high field are also significantly higher, which enables high resolution during small animal imaging. Further engineering of multiple targeting moieties enables ProCA32 agents that have strong biomarker-binding affinity and specificity for an array of key molecular biomarkers associated with various chronic diseases, while maintaining relaxation and exceptional metal-binding and selectivity, serum stability, and resistance to transmetallation, which are critical in mitigating risks associated with metal toxicity. Our leading product ProCA32.collagen has enabled the first early detection of liver metastasis from multiple cancers at early stages by mapping the tumor environment and early stage of fibrosis from liver and lung in vivo, with strong translational potential to extend to precision MRI for preclinical and clinical applications for precision diagnosis and treatment.

Quantitative Assessment of Hepatic Steatosis Using Label-Free Multiphoton Imaging and Customized Image Processing Program

Nonalcoholic fatty liver disease is rapidly becoming one of the most common causes of chronic liver disease worldwide and is the leading cause of liver-related morbidity and mortality. A quantitative assessment of the degree of steatosis would be more advantageous for diagnostic evaluation and exploring the patterns of disease progression. Here, multiphoton microscopy, based on the second harmonic generation and 2-photon excited fluorescence, was used to label-free image the samples of nonalcoholic fatty liver. Imaging results confirm that multiphoton microscopy is capable of directly visualizing important pathologic features such as normal hepatocytes, hepatic steatosis, Mallory bodies, necrosis, inflammation, collagen deposition, microvessel, and so on and is a reliable auxiliary tool for the diagnosis of nonalcoholic fatty liver disease. Furthermore, we developed an image segmentation algorithm to simultaneously assess hepatic steatosis and fibrotic changes, and quantitative results reveal that there is a correlation between the degree of steatosis and collagen content. We also developed a feature extraction program to precisely display the spatial distribution of hepatocyte steatosis in tissues. These studies may be beneficial for a better clinical understanding of the process of steatosis as well as for exploring possible therapeutic targets.

What Mediates Fibrosis in the Tumor Microenvironment of Clear Renal Cell Carcinoma

Previous studies have demonstrated that direct targeting of interstitial cancer-associated fibroblasts (CAF) and tumor fibrosis alone seemed to be an unpromising treatment option for malignant tumors. Therefore, it is necessary to further explore the mechanism of the influence of collagen and tumor fibrosis on the biological behavior of malignant tumors. The current study aimed to explore the effect of intratumor fibrosis on the prognosis of renal clear cell carcinoma (ccRCC) and its mechanism. With the bioinformatic analysis of The Cancer Genome Atlas (TCGA) database (n = 537), the study showed that high Collagen type I α 1 (COL1A1) mRNA expression indicated the poor prognosis of ccRCC patients compared with low expression ones. We further used the Two-photon-excited fluorescence (TPEF)/second harmonic generation (SHG) microscopy to determine the intratumor fibrosis of 68 patients with surgical resection of ccRCC and confirmed that a high fibrosis level in the tumor was associated with a poor prognosis compared with patients with low expression (Progression-Free Survival: p = 0.030). We further measured the protein chips of 640 cytokines in ccRCC specimens and found that several cytokines, including prolactin (PRL), were associated with the degree of fibrosis in the tumor, as confirmed by the prolactin receptor (PRLR) immunohistochemical method. In addition, the study showed that PRLR expression decreased significantly in the ccRCC compared with adjacent normal tissue (p < 0.05). Our research shows that low expression of PRLR predicted the poor survival of the patient. We used the Cell Counting Kit-8 experiment, the transwell and the plate clone formation assay to evaluate the role of PRL in the 7860 and the ACHN cell lines. We found that PRL promoted ccRCC cell proliferation and migration. JAK-STAT3 activation was found in the high prolactin expression group by mass spectrum analysis. This study delineated the fibrosis-based tumor microenvironment characteristics of ccRCC. PRL/PRLR may be involved in the fibrosis process and are essential prognostic risk factors for ccRCC.

An Improved qFibrosis Algorithm for Precise Screening and Enrollment into Non-Alcoholic Steatohepatitis (NASH) Clinical Trials

Background: Many clinical trials with potential drug treatment options for non-alcoholic fatty liver disease (NAFLD) are focused on patients with non-alcoholic steatohepatitis (NASH) stages 2 and 3 fibrosis. As the histological features differentiating stage 1 (F1) from stage 2 (F2) NASH fibrosis are subtle, some patients may be wrongly staged by the in-house pathologist and miss the opportunity for enrollment into clinical trials. We hypothesized that our refined artificial intelligence (AI)-based algorithm (qFibrosis) can identify these subtle differences and serve as an assistive tool for in-house pathologists. Methods: Liver tissue from 160 adult patients with biopsy-proven NASH from Singapore General Hospital (SGH) and Peking University People’s Hospital (PKUH) were used. A consensus read by two expert hepatopathologists was organized. The refined qFibrosis algorithm incorporated the creation of a periportal region that allowed for the increased detection of periportal fibrosis. Consequently, an additional 28 periportal parameters were added, and 28 pre-existing perisinusoidal parameters had altered definitions. Results: Twenty-eight parameters (20 periportal and 8 perisinusoidal) were significantly different between the F1 and F2 cases that prompted a change of stage after a careful consensus read. The discriminatory ability of these parameters was further demonstrated in a comparison between the true F1 and true F2 cases as 26 out of the 28 parameters showed significant differences. These 26 parameters constitute a novel sub-algorithm that could accurately stratify F1 and F2 cases. Conclusion: The refined qFibrosis algorithm incorporated 26 novel parameters that showed a good discriminatory ability for NASH fibrosis stage 1 and 2 cases, representing an invaluable assistive tool for in-house pathologists when screening patients for NASH clinical trials.

Molecular imaging of extracellular matrix proteins with targeted probes using magnetic resonance imaging

The extracellular matrix (ECM) consists of proteins and carbohydrates that supports different biological structures and processes such as tissue development, elasticity, and preservation of organ structure. Diseases involving inflammation, fibrosis, tumor invasion, and injury are all attributed to the transition of the ECM from homeostasis to remodeling, which can significantly change the biochemical and biomechanical features of ECM components. While contrast agents have played an indispensable role in facilitating clinical diagnosis of diseases using magnetic resonance imaging (MRI), there is a strong need to develop novel biomarker-targeted imaging probes for in vivo visualization of biological processes and pathological alterations at a cellular and molecular level, for both early diagnosis and monitoring drug treatment. Herein, we will first review the pathological accumulation and characterization of ECM proteins recognized as important molecular features of diseases. Developments in MRI probes targeting ECM proteins such as collagen, fibronectin, and elastin via conjugation of existing contrast agents to targeting moieties and their applications to various diseases, are also reviewed. We have also reviewed our progress in the development of collagen-targeted protein MRI contrast agent with significant improvement in relaxivity and metal binding specificity, and their applications in early detection of fibrosis and metastatic cancer. This article is categorized under: Diagnostic Tools > in vivo Nanodiagnostics and Imaging Biology-Inspired Nanomaterials > Peptide-Based Structures Biology-Inspired Nanomaterials > Protein and Virus-Based Structures.