Visualizing liver anatomy, physiology and pharmacology using multiphoton microscopy

From Optical Fiber Communications to Bioimaging: Wavelength Division Multiplexing Technology for Simplified in vivo Large-depth NIR-IIb Fluorescence Confocal Microscopy

Near-infrared II (NIR-II, 900-1880 nm) fluorescence confocal microscopy offers high spatial resolution and extensive in vivo imaging capabilities. However, conventional confocal microscopy requires precise pinhole positioning, posing challenges due to the small size of the pinhole and invisible NIR-II fluorescence. To simplify this, a fiber optical wavelength division multiplexer (WDM) replaces dichroic mirrors and traditional pinholes for excitation and fluorescence beams, allowing NIR-IIb (1500-1700 nm) fluorescence and excitation light to be coupled into the same optical fiber. This streamlined system seamlessly integrates key components-excitation light, detector, and scanning microscopy-via optical fibers. Compared to traditional NIR-II confocal systems, the fiber optical WDM configuration offers simplicity and ease of adjustment. Notably, this simplified system successfully achieves optical sectioning imaging of mouse cerebral blood vessels up to 1000 µm in depth. It can discern tiny blood vessels (diameter: 4.57 µm) at 800 µm depth with a signal-to-background ratio (SBR) of 5.34. Additionally, it clearly visualizes liver vessels, which are typically challenging to image, down to a depth of 300 µm.

Novel Optical Criteria and Mechanisms of Critical Decline in Liver Regenerative Potential

The most effective method of treating tumors localized in the liver remains resection. However, in the presence of concomitant pathology, the regenerative potential of the liver is significantly reduced. To date, there is insufficient fundamental data on the mechanisms responsible for the disruption of liver regeneration, and there is no effective method for assessing its regenerative potential. The most suitable model for these purposes is acute liver injury (ALI). Modern non-contrast methods of multiphoton microscopy with second harmonic generation and fluorescence lifetime imaging microscopy (FLIM) modes enable intravital evaluation of the metabolic status of the hepatocytes; therefore, this expands the possibilities for studying the processes occurring in cells during regeneration in the context of any pathologies.

Accompanying Hemoglobin Polymerization in Red Blood Cells in Patients with Sickle Cell Disease Using Fluorescence Lifetime Imaging

In recent studies, it has been shown that fluorescence lifetime imaging (FLIM) may reveal intracellular structural details in unstained cytological preparations that are not revealed by standard staining procedures. The aim of our investigation was to examine whether FLIM images could reveal areas suggestive of polymerization in red blood cells (RBCs) of sickle cell disease (SCD) patients. We examined label-free blood films using auto-fluorescence FLIM images of 45 SCD patients and compared the results with those of 27 control persons without hematological disease. All control RBCs revealed homogeneous cytoplasm without any foci. Rounded non-sickled RBCs in SCD showed between zero and three small intensively fluorescent dots with higher lifetime values. In sickled RBCs, we found additionally larger irregularly shaped intensively fluorescent areas with increased FLIM values. These areas were interpreted as equivalent to polymerized hemoglobin. The rounded, non-sickled RBCs of SCD patients with homogeneous cytoplasm were not different from those of the erythrocytes of control patients in light microscopy. Yet, variables from the local binary pattern-transformed matrix of the FLIM values per pixel showed significant differences between non-sickled RBCs and those of control cells. In a linear discriminant analysis, using local binary pattern-transformed texture features (mean and entropy) of the erythrocyte cytoplasm of normal appearing cells, the final model could distinguish between SCD patients and control persons with an accuracy of 84.7% of the patients. When the classification was based on the examination of a single rounded erythrocyte, an accuracy of 68.5% was achieved. Employing the Linear Discriminant Analysis classifier method for machine learning, the accuracy was 68.1%. We believe that our study shows that FLIM is able to disclose the topography of the intracellular polymerization process of hemoglobin in sickle cell disease and that the images are compatible with the theory of the two-step nucleation. Furthermore, we think that the presented technique may be an interesting tool for the investigation of therapeutic inhibition of polymerization.

A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques

Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging

Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.

AutoFibroNet: A deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD

BACKGROUND

Liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment.

AIM

To investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD.

METHODS

AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts.

RESULTS

AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts.

CONCLUSION

AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.

Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning

According to the World Health Organization, the proportion of the world's population over 60 years will approximately double by 2050. This progressive increase in the elderly population will lead to a dramatic growth of age-related diseases, resulting in tremendous pressure on the sustainability of healthcare systems globally. In this context, finding more efficient ways to address cancers, a set of diseases whose incidence is correlated with age, is of utmost importance. Prevention of cancers to decrease morbidity relies on the identification of precursor lesions before the onset of the disease, or at least diagnosis at an early stage. In this article, after briefly discussing some of the most prominent endoscopic approaches for gastric cancer diagnostics, we review relevant progress in three emerging technologies that have significant potential to play pivotal roles in next-generation endoscopy systems: biomimetic vision (with special focus on compound eye cameras), non-linear optical microscopies, and Deep Learning. Such systems are urgently needed to enhance the three major steps required for the successful diagnostics of gastrointestinal cancers: detection, characterization, and confirmation of suspicious lesions. In the final part, we discuss challenges that lie en route to translating these technologies to next-generation endoscopes that could enhance gastrointestinal imaging, and depict a possible configuration of a system capable of (i) biomimetic endoscopic vision enabling easier detection of lesions, (ii) label-free in vivo tissue characterization, and (iii) intelligently automated gastrointestinal cancer diagnostic.

Optical Biomedical Imaging Reveals Criteria for Violated Liver Regenerative Potential

To reduce the risk of post-hepatectomy liver failure in patients with hepatic pathologies, it is necessary to develop an approach to express the intraoperative assessment of the liver's regenerative potential. Traditional clinical methods do not enable the prediction of the function of the liver remnant. Modern label-free bioimaging, using multiphoton microscopy in combination with second harmonic generation (SHG) and fluorescence lifetime imaging microscopy (FLIM), can both expand the possibilities for diagnosing liver pathologies and for assessing the regenerative potential of the liver. Using multiphoton and SHG microscopy, we assessed the structural state of liver tissue at different stages of induced steatosis and fibrosis before and after 70% partial hepatectomy in rats. Using FLIM, we also performed a detailed analysis of the metabolic state of the hepatocytes. We were able to determine criteria that can reveal a lack of regenerative potential in violated liver, such as the presence of zones with reduced NAD(P)H autofluorescence signals. Furthermore, for a liver with pathology, there was an absence of the jump in the fluorescence lifetime contributions of the bound form of NADH and NADPH the 3rd day after hepatectomy that is characteristic of normal liver regeneration. Such results are associated with decreased intensity of oxidative phosphorylation and of biosynthetic processes in pathological liver, which is the reason for the impaired liver recovery. This modern approach offers an effective tool that can be successfully translated into the clinic for express, intraoperative assessment of the regenerative potential of the pathological liver of a patient.

Liver-on-a-chip: Considerations, advances, and beyond

The liver is the largest internal organ in the human body with largest mass of glandular tissue. Modeling the liver has been challenging due to its variety of major functions, including processing nutrients and vitamins, detoxification, and regulating body metabolism. The intrinsic shortfalls of conventional two-dimensional (2D) cell culture methods for studying pharmacokinetics in parenchymal cells (hepatocytes) have contributed to suboptimal outcomes in clinical trials and drug development. This prompts the development of highly automated, biomimetic liver-on-a-chip (LOC) devices to simulate native liver structure and function, with the aid of recent progress in microfluidics. LOC offers a cost-effective and accurate model for pharmacokinetics, pharmacodynamics, and toxicity studies. This review provides a critical update on recent developments in designing LOCs and fabrication strategies. We highlight biomimetic design approaches for LOCs, including mimicking liver structure and function, and their diverse applications in areas such as drug screening, toxicity assessment, and real-time biosensing. We capture the newest ideas in the field to advance the field of LOCs and address current challenges.

Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field

Segmentation of the liver and tumours from computed tomography (CT) scans is an important task in hepatic surgical planning. Manual segmentation of the liver and tumours is a time-consuming and labour-intensive task; therefore, a fully automated method for performing this segmentation is particularly desired. An automatic two-step liver and tumour segmentation method is presented in this paper. A cascade framework is used during the segmentation process, and a fully connected conditional random field (CRF) method is used to refine the tumour segmentation result. First, the proposed fractal residual U-Net (FRA-UNet) is used to locate and initially segment the liver. Then, FRA-UNet is further used to predict liver tumours from the liver region of interest (ROI). Finally, a three-dimensional (3D) CRF is used to refine the tumour segmentation results. The improved fractal residual (FR) structure effectively retains more effective features for improving the segmentation performance of deeper networks, the improved deep residual block can utilise the feature information more effectively, and the 3D CRF method smooths the contours and avoids the tumour oversegmentation problem. FRA-UNet is tested on the Liver Tumour Segmentation challenge dataset (LiTS) and the 3D Image Reconstruction for Comparison of Algorithm Database dataset (3DIRCADb), achieving 97.13% and 97.18% Dice similarity coefficients (DSCs) for liver segmentation and 71.78% and 68.97% DSCs for tumour segmentation, respectively, outperforming most state-of-the-art networks.

Near-Infrared Emitting Ir(III) Complexes Bearing a Dipyrromethene Ligand for Oxygen Imaging of Deeper Tissues In Vivo

Phosphorescence lifetime imaging microscopy (PLIM) using a phosphorescent oxygen probe is an innovative technique for elucidating the behavior of oxygen in living tissues. In this study, we designed and synthesized an Ir(III) complex, PPYDM-BBMD, that exhibits long-lived phosphorescence in the near-infrared region and enables in vivo oxygen imaging in deeper tissues. PPYDM-BBMD has a π-extended ligand based on a meso-mesityl dipyrromethene structure and phenylpyridine ligands with cationic dimethylamino groups to promote intracellular uptake. This complex gave a phosphorescence spectrum with a maximum at 773 nm in the wavelength range of the so-called biological window and exhibited an exceptionally long lifetime (18.5 μs in degassed acetonitrile), allowing for excellent oxygen sensitivity even in the near-infrared window. PPYDM-BBMD showed a high intracellular uptake in cultured cells and mainly accumulated in the endoplasmic reticulum. We evaluated the oxygen sensitivity of PPYDM-BBMD phosphorescence in alpha mouse liver 12 (AML12) cells based on the Stern-Volmer analysis, which gave an O₂-induced quenching rate constant of 1.42 × 10³ mmHg^-1 s^-1. PPYDM-BBMD was administered in the tail veins of anesthetized mice, and confocal one-photon PLIM images of hepatic tissues were measured at different depths from the liver surfaces. The PLIM images visualized the oxygen gradients in hepatic lobules up to a depth of about 100 μm from the liver surfaces with a cellular-level resolution, allowing for the quantification of oxygen partial pressure based on calibration results using AML12 cells.

Toxicological Analysis of Hepatocytes Using FLIM Technique: In Vitro versus Ex Vivo Models

The search for new criteria indicating acute or chronic pathological processes resulting from exposure to toxic agents, testing of drugs for potential hepatotoxicity, and fundamental study of the mechanisms of hepatotoxicity at a molecular level still represents a challenging issue that requires the selection of adequate research models and tools. Microfluidic chips (MFCs) offer a promising in vitro model for express analysis and are easy to implement. However, to obtain comprehensive information, more complex models are needed. A fundamentally new label-free approach for studying liver pathology is fluorescence-lifetime imaging microscopy (FLIM). We obtained FLIM data on both the free and bound forms of NAD(P)H, which is associated with different metabolic pathways. In clinical cases, liver pathology resulting from overdoses is most often as a result of acetaminophen (APAP) or alcohol (ethanol). Therefore, we have studied and compared the metabolic state of hepatocytes in various experimental models of APAP and ethanol hepatotoxicity. We have determined the potential diagnostic criteria including the pathologically altered metabolism of the hepatocytes in the early stages of toxic damage, including pronounced changes in the contribution from the bound form of NAD(P)H. In contrast to the MFCs, the changes in the metabolic state of hepatocytes in the ex vivo models are, to a greater extent, associated with compensatory processes. Thus, MFCs in combination with FLIM can be applied as an effective tool set for the express modeling and diagnosis of hepatotoxicity in clinics.

Transcriptomic Analysis Reveals a Sex-Dimorphic Influence of GAT-2 on Murine Liver Function

Accumulating evidence shows that the γ-amino butyric acid (GABA)ergic system affects the functions of different organs, and liver is one of the most sex-dimorphic organs in animals. However, whether and how the GABAergic system influences liver function in a sex-specific manner at the intrinsic molecular level remains elusive. In this study, firstly, we find that the levels of GABA are significantly increased in the livers of female mice with GABA transporter (GAT)-2 deficiency (KO) whereas it only slightly increased in male GAT-2 KO mice. Apart from the amino acid profiles, the expressions of toll-like receptors (TLRs) also differ in the livers of female and male KO mice. Moreover, RNA-seq results show 2,227 differentially expressed genes (DEGs) in which 1,030 are upregulated whereas 1,197 that are downregulated in the livers of female KO mice. Notably, oxidative phosphorylation, non-alcoholic fatty liver disease, Huntington's disease, and peroxisome proliferator-activated receptor (PPAR) signaling pathways are highly enriched by GAT-2 deficiency, indicating that these pathways probably meditate the effects of GAT-2 on female liver functions, on the other hand, only 1,233 DEGs, including 474 are upregulated and 759 are downregulated in the livers of male KO mice. Interestingly, retinol metabolism, PPAR signaling pathway, and tuberculosis pathways are substantially enriched by GAT-2 deficiency, suggesting that these pathways may be responsible for the effects of GAT-2 on male liver functions. Collectively, our results reveal the sex-dimorphic effects of GAT-2 in guiding liver functions, and we propose that targeting the GABAergic system (e.g., GATs) in a sex-specific manner could provide previously unidentified therapeutic opportunities for liver diseases.

Multiphoton Microscopy and Mass Spectrometry for Revealing Metabolic Heterogeneity of Hepatocytes in vivo

The aim of the investigation was to study the possibility of revealing the heterogeneity of normal liver hepatocytes in terms of metabolic status using the modern methods of multiphoton microscopy and mass spectrometry.

Fluorescence lifetime imaging is able to recognize different hematopoietic precursors in unstained routine bone marrow films

Fluorescence lifetime imaging (FLIM) has been used in living cells to measure metabolic activity and demonstrate cell differentiation. The aim of this study was to investigate whether the FLIM technique could be able to demonstrate cell maturation during myelopoiesis and erythropoiesis in unlabeled routine bone marrow (BM) preparations. Air-dried, unstained smears of BM aspiration samples of 32 patients without BM disease and a normal morphology on May-Grünwald-Giemsa (MGG) stained smears entered the study. FLIM images were captured with a Zeiss LSM 780 NLO multiphoton microscope equipped with a Becker & Hickl SPC-830 TCSPC FLIM module and HPM-100-40 hybrid detector. The samples were irradiated by two-photon excitation at 800 nm with a titanium-sapphire laser of the LSM 780 NLO. FLIM images were compared with those obtained by autofluorescence high resolution imaging. FLIM images of unstained smears were highly contrasted. Different cell types could be easily recognized as they were similar to those seen in MGG stained preparations. Cytoplasm of cells from the erythroid lineage revealed relatively short fluorescence lifetimes due to the presence of hemoglobin, and therefore could easily be distinguished from granulocytic precursors. Nuclear fluorescence lifetimes of all cell types were higher than those of the corresponding cytoplasm. So, FLIM of unstained BM smears obtained under routine real-life conditions permits an easy identification of BM cells, by highlighting differences of their physicochemical properties.

Evolution of the liver biopsy and its future

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Integrative analysis of scRNA-seq and GWAS data pinpoints periportal hepatocytes as the relevant liver cell types for blood lipids

Liver, a heterogeneous tissue consisting of various cell types, is known to be relevant for blood lipid traits. By integrating summary statistics from genome-wide association studies (GWAS) of lipid traits and single-cell transcriptome data of the liver, we sought to identify specific cell types in the liver that were most relevant for blood lipid levels. We conducted differential expression analyses for 40 cell types from human and mouse livers in order to construct the cell-type specifically expressed gene sets, which we refer to as construction of the liver cell-type specifically expressed gene sets (CT-SEGS). Under the assumption that CT-SEGS represented specific functions of each cell type, we applied stratified linkage disequilibrium score regression to determine cell types that were most relevant for complex traits and diseases. We first confirmed the validity of this method (of delineating functionally relevant cell types) by identifying the immune cell types as relevant for autoimmune diseases. We further showed that lipid GWAS signals were enriched in the human and mouse periportal hepatocytes. Our results provide important information to facilitate future cellular studies of the metabolic mechanism affecting blood lipid levels.

Mapping metabolism of liver tissue using two-photon FLIM

Although fluorescence lifetime imaging microscopy (FLIM) has been extensively applied to study cellular metabolism in the liver, there is neither an established approach to analyze the data, nor have appropriate protocols been developed to maintain the optical metabolic characteristics in the ex vivo liver tissue sample. Here, we show that a tri-exponential decay fitting model for the fluorescence signal from nicotinamide adenine dinucleotide (NAD(P)H) and the use of ex vivo samples allows the most appropriate processing of the FLIM data. Moreover, we determine the medium that maintains the initial metabolic state of hepatocytes (liver cells), most effectively. Our results should be particularly relevant for the interrogation of liver samples, not only in laboratory research, but also in clinical settings in the future.

Using in vivo multiphoton fluorescence lifetime imaging to unravel disease-specific changes in the liver redox state

Multiphoton fluorescence lifetime microscopy has revolutionized studies of pathophysiological and xenobiotic dynamics, enabling the spatial and temporal quantification of these processes in intact organs in vivo. We have previously used multiphoton fluorescence lifetime microscopy to characterise the morphology and amplitude weighted mean fluorescence lifetime of the endogenous fluorescent metabolic cofactor nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) of mouse livers in vivo following induction of various disease states. Here, we extend the characterisation of liver disease models by using nonlinear regression to estimate the unbound, bound fluorescence lifetimes for NAD(P)H, flavin adenine dinucleotide (FAD), along with metabolic ratios and examine the impact of using multiple segmentation methods. We found that NAD(P)H amplitude ratio, and fluorescence lifetime redox ratio can be used as discriminators of diseased liver from normal liver. The redox ratio provided a sensitive measure of the changes in hepatic fibrosis and biliary fibrosis. Hepatocellular carcinoma was associated with an increase in spatial heterogeneity and redox ratio coupled with a decrease in mean fluorescence lifetime. We conclude that multiphoton fluorescence lifetime microscopy parameters and metabolic ratios provided insights into the in vivo redox state of diseased compared to normal liver that were not apparent from a global, mean fluorescence lifetime measurement alone.

Metabolic imaging and secondary ion mass spectrometry to define the structure and function of liver with acute and chronic pathology

Conventional techniques are insufficient precisely to describe the internal structure, the heterogeneous cell populations, and the dynamics of biological processes occurring in diseased liver during surgery. There is a need for a rapid and safe method for the successful diagnosis of liver disease in order to plan surgery and to help avoid postoperative liver failure. We analyze the progression of both acute (cholestasis) and chronic (fibrosis) liver pathology using multiphoton microscopy with fluorescence lifetime imaging and second-harmonic generation modes combined with time-of-flight secondary ion mass spectrometry chemical analysis to obtain new data about pathological changes to hepatocytes at the cellular and molecular levels. All of these techniques allow the study of cellular metabolism, lipid composition, and collagen structure without staining the biological materials or the incorporation of fluorescent or other markers, enabling the use of these methods in a clinical situation. The combination of multiphoton microscopy and mass spectrometry provides more complete information about the liver structure and function than could be assessed using either method individually. The data can be used both to obtain new criteria for the identification of hepatic pathology and to develop a rapid technique for liver quality analysis in order to plan surgery and to help avoid postoperative liver failure in clinic.

Recent advances in multiphoton microscopy combined with nanomaterials in the field of disease evolution and clinical applications to liver cancer

Multiphoton microscopy (MPM) is expected to become a powerful clinical tool, with its unique advantages of being label-free, high resolution, deep imaging depth, low light photobleaching and low phototoxicity. Nanomaterials, with excellent physical and chemical properties, are biocompatible and easy to prepare and functionalize. The addition of nanomaterials exactly compensates for some defects of MPM, such as the weak endogenous signal strength, limited imaging materials, insufficient imaging depth and lack of therapeutic effects. Therefore, combining MPM with nanomaterials is a promising biomedical imaging method. Here, we mainly review the principle of MPM and its application in liver cancer, especially in disease evolution and clinical applications, including monitoring tumor progression, diagnosing tumor occurrence, detecting tumor metastasis, and evaluating cancer therapy response. Then, we introduce the latest advances in the combination of MPM with nanomaterials, including the MPM imaging of gold nanoparticles (AuNPs) and carbon dots (CDs). Finally, we also propose the main challenges and future research directions of MPM technology in HCC.

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label-free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep-learning algorithm for classifying HCC differentiation to produce an innovative computer-aided diagnostic method. Convolutional neural networks based on the VGG-16 framework were trained using 217 combined two-photon excitation fluorescence and second-harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label-free, automated methods for various tissues, diseases and other related classification problems.

Tissue Intrinsic Fluorescence Spectra-Based Digital Pathology of Liver Fibrosis by Marker-Controlled Segmentation

Tissue intrinsic emission fluorescence provides useful diagnostic information for various diseases. Because of its unique feature of spectral profiles depending on tissue types, spectroscopic imaging is a promising tool for accurate evaluation of endogenous fluorophores. However, due to difficulties in discriminating those sources, quantitative analysis remains challenging. In this study, we quantitatively investigated spectral-spatial features of multi-photon excitation fluorescence in normal and diseased livers. For morphometrics of multi-photon excitation spectra, we examined a marker-controlled segmentation approach and its application to liver fibrosis assessment by employing a mouse model of carbon tetrachloride (CCl₄)-induced liver fibrosis. We formulated a procedure of internal marker selection where markers were chosen to reflect typical biochemical species in the liver, followed by image segmentation and local morphological feature extraction. Image segmentation enabled us to apply mathematical morphology analysis, and the local feature was applied to the automated classification test based on a machine learning framework, both demonstrating highly accurate classifications. Through the analyses, we showed that spectral imaging of native fluorescence from liver tissues have the capability of differentiating not only between normal and diseased, but also between progressive disease states. The proposed approach provides the basics of spectroscopy-based digital histopathology of chronic liver diseases, and can be applied to a range of diseases associated with autofluorescence alterations.

Autofluorescence-based optical biopsy: An effective diagnostic tool in hepatology

Autofluorescence emission of liver tissue depends on the presence of endogenous biomolecules able to fluoresce under suitable light excitation. Overall autofluorescence emission contains much information of diagnostic value because it is the sum of individual autofluorescence contributions from fluorophores involved in metabolism, for example, NAD(P)H, flavins, lipofuscins, retinoids, porphyrins, bilirubin and lipids, or in structural architecture, for example, fibrous proteins, in close relationship with normal, altered or diseased conditions of the liver. Since the 1950s, hepatocytes and liver have been historical models to study NAD(P)H and flavins as in situ, real-time autofluorescence biomarkers of energy metabolism and redox state. Later investigations designed to monitor organ responses to ischaemia/reperfusion were able to predict the risk of dysfunction in surgery and transplantation or support the development of procedures to ameliorate the liver outcome. Subsequently, fluorescent fatty acids, lipofuscin-like lipopigments and collagen were characterized as optical biomarkers of liver steatosis, oxidative stress damage, fibrosis and disease progression. Currently, serum AF is being investigated to improve non-invasive optical diagnosis of liver disease. Validation of endogenous fluorophores and in situ discrimination of cancerous from non-cancerous tissue belong to the few studies on liver in human subjects. These reports along with other optical techniques and the huge work performed on animal models suggest many optically based applications in hepatology. Optical diagnosis is currently offering beneficial outcomes in clinical fields ranging from the respiratory and gastrointestinal tracts, to dermatology and ophthalmology. Accordingly, this review aims to promote an effective bench to bedside transfer in hepatology.

Assessment of Fatty Liver in Models of Disease Programming

Nonalcoholic fatty liver disease (NAFLD) is currently the most common cause of chronic liver disease worldwide and is present in a third of the general population and the majority of individuals with obesity and type 2 diabetes. Importantly, NAFLD can progress to severe nonalcoholic steatohepatitis (NASH), associated with liver failure and hepatocellular carcinoma. Recent research efforts have extensively focused on identifying factors contributing to the additional "hit" required to promote NALFD disease progression. The maternal diet, and in particular a high-fat diet (HFD), may be one such hit "priming" the development of severe fatty liver disease, a notion supported by the increasing incidence of NAFLD among children and adolescents in Westernized countries. In recent years, a plethora of key studies have used murine models of maternal obesity to identify fundamental mechanisms such as lipogenesis, mitochondrial function, inflammation, and fibrosis that may underlie the developmental priming of NAFLD. In this chapter, we will address key considerations for constructing experimental models and both conventional and advanced methods of quantifying NAFLD disease status.

NAD(P)H and Flavin Autofluorescence Correlation with ATP in Rat Livers with Different Metabolic Steady-State Conditions

The monitoring of NAD(P)H and flavin autofluorescence (AF) is at the basis of numerous investigations on energy metabolism. Nevertheless, the ability of these AF biomarkers to accurately represent the energy currency, ATP, is poorly explored. Here, we focused on the AF/ATP correlation in lean and fatty livers with different steady-state metabolic conditions, achieved after organ isolation, preservation and recovery, in a likely dependence on both liver intrinsic metabolic features and externally induced perturbations. Within these eventual, various conditions, a significant correlation was detected between liver NAD(P)H and flavin AF, measured via fiber-optic probe, and biochemical ATP data, strengthening AF as biomarker of energy metabolism in steady-state conditions for wide-ranging experimental and diagnostic applications.

Two-photon dual imaging platform for in vivo monitoring cellular oxidative stress in liver injury

Oxidative stress reflects an imbalance between reactive oxygen species (ROS) and antioxidants, which has been reported as an early unifying event in the development and progression of various diseases and as a direct and mechanistic indicator of treatment response. However, highly reactive and short-lived nature of ROS and antioxidant limited conventional detection agents, which are influenced by many interfering factors. Here, we present a two-photon sensing platform for in vivo dual imaging of oxidative stress at the single cell-level resolution. This sensing platform consists of three probes, which combine the turn-on fluorescent transition-metal complex with different specific responsive groups for glutathione (GSH), hydrogen peroxide (H2O2) and hypochlorous acid (HOCl). By combining fluorescence intensity imaging and fluorescence lifetime imaging, these probes totally remove any possibility of crosstalk from in vivo environmental or instrumental factors, and enable accurate localization and measurement of the changes in ROS and GSH within the liver. This precedes changes in conventional biochemical and histological assessments in two distinct experimental murine models of liver injury. The ability to monitor real-time cellular oxidative stress with dual-modality imaging has significant implications for high-accurate, spatially configured and quantitative assessment of metabolic status and drug response.