Quantification of liver fibrosis—a comparative study

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic

Resolving the fibrotic niche of human liver cirrhosis at single-cell level.

Liver cirrhosis is a major cause of death worldwide and is characterized by extensive fibrosis. There are currently no effective antifibrotic therapies available. To obtain a better understanding of the cellular and molecular mechanisms involved in disease pathogenesis and enable the discovery of therapeutic targets, here we profile the transcriptomes of more than 100,000 single human cells, yielding molecular definitions for non-parenchymal cell types that are found in healthy and cirrhotic human liver. We identify a scar-associated TREM2+CD9+ subpopulation of macrophages, which expands in liver fibrosis, differentiates from circulating monocytes and is pro-fibrogenic. We also define ACKR1+ and PLVAP+ endothelial cells that expand in cirrhosis, are topographically restricted to the fibrotic niche and enhance the transmigration of leucocytes. Multi-lineage modelling of ligand and receptor interactions between the scar-associated macrophages, endothelial cells and PDGFRα+ collagen-producing mesenchymal cells reveals intra-scar activity of several pro-fibrogenic pathways including TNFRSF12A, PDGFR and NOTCH signalling. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides a conceptual framework for the discovery of rational therapeutic targets in liver cirrhosis.Includes Wellcome, BHF, MRC, BBSRC and NIHR

Novel approaches to identify biomechanisms in systemic sclerosis

Systemic sclerosis is a severe connective tissue disease in which inflammation and autoimmunity are associated with progressive tissue remodeling and fibrosis of the skin and internal organs. Complex genetic backgrounds contribute to susceptibility and the disease can be triggered by environmental factors. It is proposed that based on the genetic susceptibility an immune inflammatory disease microenvironment is initiated leading to overexpression of cytokines and growth factors and the development of a fibrotic disease process. Analysis of copy number variation in candidate genes was performed using DNA from patients and controls. This identified a possible association between disease susceptibility and one candidate factor, LEPREL1, a prolyl 3- hydroxylase involved in collagen alignment in the endoplasmic reticulum. Deletion of the LEPREL1 gene led to resistance to dermal fibrosis in mice, whereas levels of the encoded enzyme were increased in disease fibroblasts, all consistent with an important role in the fibrotic process. Furthermore, profiling of tissue fluid from the dermal lesions revealed the presence of an inflammatory, pro-fibrotic microenvironment. When candidate factors present in the tissue fluid (e.g. PDGF), were applied to fibroblasts on aligned collagen matrices, fibroblast orientation and migration was enhanced, modeling the effect on spread of the disease. In contrast, the use of inhibitors (e.g. heparin, imatinib), particularly in combination, attenuated fibroblast alignment and migration. Finally, since this disease has proved resistant to current non-specific therapies, a novel anti-inflammatory peptide was evaluated using a mouse model of systemic sclerosis-like inflammation and fibrosis. Treatment with the peptide suppressed the pattern of inflammatory changes seen in this model of systemic sclerosis, and significantly reduced tissue fibrosis and the replacement of the normal tissue architecture with scar tissue. This approach using antiinflammatory peptides could be potentially relevant for the treatment of individuals with systemic sclerosis in order to attenuate the pathological inflammatory fibrotic process

Label-free breast histopathology using quantitative phase imaging

According to the latest World Health Organization (WHO) statistics, breast cancer is the most common type of cancer among women worldwide. The WHO has further emphasized that early diagnosis and treatment are key in mitigating the burden of disease. In spite of this assessment, the standard histopathology of breast cancer still relies on manual microscopic examination of stained tissue. Being qualitative and manual in nature, this standard diagnostic procedure can suffer from inter-observer variation and low-throughput. In addition, stain variation between different samples and different laboratories creates problems for supervised image analysis methods for automated diagnosis. A quantitative, label-free and automatable microscopic modality for breast cancer diagnosis is, thus, needed to address these shortcomings in the standard method. Furthermore, prognostic biomarkers are important tools used by clinicians in order assess the disease course in patients. Being correlated with outcomes, these markers allow pathologists to determine aggressiveness of disease and tailor treatment accordingly. However, the current set of biomarkers for breast cancer are ineffective in predicting outcomes in all patients and there is a need for additional markers of prognosis to better account for variation among individuals. Microscopic and imaging tools for extracting new, quantitative biomarkers during breast histopathology are, thus, also desirable. Although a number of new quantitative imaging modalities for diagnostic and prognostic evaluations have been proposed, a key challenge remains compatibility with the existing workflow for easier clinical translation. Quantitative methods that minimally affect the clinical pipeline already in place are expected to have a greater impact than those that require significant new infrastructure. During my graduate work I have approached these problems in modern breast histopathology by using quantitative phase imaging (QPI). QPI is a label-free microscopy technique where image contrast is generated by measuring the optical path-length difference (OPD) across the specimen. OPD refers to the product of the refractive index and thickness at a point in the field of view. Since this measurement relies on a physical property of tissue and is label-free, it provides an objective and potentially automatable basis for tissue assessment. We employ a QPI technique called Spatial Light Interference Microscopy (SLIM) for investigations carried out during this thesis research. The specific aims of my thesis research are: 1. Label-free quantitative evaluation of breast biopsies using SLIM: In this work, we show by imaging a tissue microarray (TMA) that our QPI based method can separate benign and malignant cases by relying on tissue OPD based features. By employing image processing and statistical learning, we demonstrate a label-free quantitative diagnosis scheme that can provide an objective basis for tissue assessment. A quantitative method like this can also, potentially, be automated, reducing case-load for pathologists by automatically flagging problematic cases that require further investigation. 2. Quantifying tumor adjacent collagen structure in breast tissue using SLIM: Recent evidence shows that the structure of tumor adjacent collagen fibers influences tumor progression. In particular, collagen fiber alignment and orientation can facilitate epithelial invasion to surrounding tissue. We demonstrate that SLIM can be used to detect this prognostic marker that in the past had been detected using Second Harmonic Generation Microscopy (SHGM). Our SLIM based method improves on the SHGM based method in terms of throughput and the fact that cellular information can be obtained, in addition to collagen fiber structure, in a single image. 3. Quantitative histopathology on stained tissue biopsies: The instruments and image analysis tools developed in Aims 1 and 2 are designed for unstained tissue biopsies. Since standard tissue histopathology inevitably requires staining, we aim to demonstrate that we can extend these tools to stained tissue biopsies. In this way, the standard diagnostic workflow will be minimally disrupted. In addition, from a single shot, both an OPD map and stained tissue bright field image will be obtainable for evaluation. We demonstrate that QPI images of stained tissue can be used to solve diagnostic and prognostic problems in breast tissue assessment, using quantitative markers

Searching for biomarkers of non-alcoholic fatty liver disease and metabolic syndrome

151 p.El desarrollo de esta tesis doctoral ha aportado luz sobre los mecanismos que subyacen a la enfermedad del hígado graso no alcohólico (NAFLD por sus siglas en inglés), ya que los resultados han revelado la existencia de al menos 2 subtipos diferentes de pacientes al comparar (mediante cromatografía de líquidos acoplada a espectrometría de masas) el perfil metabolómico sérico de estos con el perfil metabolómico de dos modelos animales de esta enfermedad: ratones MAT1A-KO y 0.1MCD.Por otra parte, experimentos in vitro, in cellulo e in vivo han revelado parte del mecanismo de acción del Aramchol®, una molécula compuesta por un ácido biliar y un ácido graso saturado. El tratamiento con esta molécula en ratones 0.1MCD ha mostrado una clara reducción en la acumulación de grasa en el hígado, así como una disminución de la fibrosis y de un aumento en la capacidad antioxidante, todo ello disminuyendo los síntomas de esta enfermedad. Así mismo, el estudio clínico en humanos muestra el efecto del Aramchol en el metabolismo hepático de la glucosa, resultados que hemos complementado y validado en esta tesis con el modelo 0.1MCD.Por último, hemos caracterizado una cohorte de personas pertenecientes a población general, para determinar mediante Resonancia Magnética Nuclear (RMN) una huella metabólica en orina que nos permita distinguir pacientes con síndrome metabólico de personas sana

“Fibrous nests” in human hepatocellular carcinoma express a Wnt-induced gene signature associated with poor clinical outcome

International audienc

An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD