Novel biomarkers of renal transplant failure/dysfunction via spectroscopic phenotyping

Successful renal transplantation not only improves patients’ quality and duration of life, but also confers a substantial economic healthcare cost saving. With the growing burden of end-stage renal disease and the requirement for renal replacement therapy, strategies to augment transplant success and subsequent graft survival become more vital than ever. Herein, an objective means of characterising renal function across the transplant journey, and appropriately stratifying in accordance to individual contingencies/factors (including the early detection of renal dysfunction), based on metabolism is explored. Patient pairs, recipients and donors, were metabolically phenotyped prior to (24 h) and post (days 1–5) transplantation using a multi-platform analytical approach (i.e., Nuclear Magnetic Resonance Spectroscopy (NMR) and Mass Spectrometry (MS)) of urine and plasma (n = 50). Using advanced statistics, the resulting metabolic profiles were subsequently modelled, and related to multiple clinical phenotypes (and outcomes), to increase the understanding of molecular changes/signatures across transplantation, capturing valuable information pertinent to transplant type, cause, co-morbidity, modality, immunology and complication (p-value < 0.05) – over donors as well as recipients. An attempt to then develop predictive algorithms for the early detection of renal dysfunction was preliminary defined within the confines of the study design, where integrated NMR and MS metabolic data improved patient stratification for complications over clinical measures (receiver operator characteristic area under curve over 0.900) and potentially replace current measures. While prospective/multicentre studies are imperative for subsequent real-world adoption (qualification/validation), the work conducted herein encompassed much of the first stage of marker development – discovery – where metabolic phenotyping renal transplantation has provided a deeper characterisation of patient journeys with new insights into multiple contingencies/factors (including complication). Such findings infer the value of metabolic phenotyping to augment and potentially replace current measures and methods to better inform decision making in the clinic on the individual/precision level.Open Acces

Nanogenomics and Nanoproteomics Enabling Personalized, Predictive and Preventive Medicine

Since the discovery of the nucleic acid, molecular biology has made tremendous progresses, achieving a lot of results. Despite this, there is still a gap between the classical and traditional medical approach and the molecular world. Inspired by the incredible wealth of data generated by the "omics"-driven techniques and the “high-trouhgput technologies” (HTTs), I have tried to develop a protocol that could reduce the actually extant barrier between the phenomenological medicine and the molecular medicine, facilitating a translational shift from the lab to the patient bedside. I also felt the urgent need to integrate the most important omics sciences, that is to say genomics and proteomics. Nucleic Acid Programmable Protein Arrays (NAPPA) can do this, by utilizing a complex mammalian cell free expression system to produce proteins in situ. In alternative to fluorescent-labeled approaches a new label free method, emerging from the combined utilization of three independent and complementary nanobiotechnological approaches, appears capable to analyze gene and protein function, gene-protein, gene-drug, protein-protein and protein-drug interactions in studies promising for personalized medicine. Quartz Micro Circuit nanogravimetry (QCM), based on frequency and dissipation factor, mass spectrometry (MS) and anodic porous alumina (APA) overcomes indeed the limits of correlated fluorescence detection plagued by the background still present after extensive washes. Work is in progress to further optimize this approach a homogeneous and well defined bacterial cell free expression system able to realize the ambitious objective to quantify the regulatory gene and protein networks in humans. Implications for personalized medicine of the above label free protein array using different test genes and proteins are reported in this PhD thesis

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Application of 1H HR-MAS-NMR spectroscopy in spatial tissue metabolic profiling

HR-MAS-NMR of intact tissue biopsies is a well-established method resulting in one NMR spectrum per whole biopsy showing all detectable metabolites at once. The aim of this project was to explore the possibility and usefulness of monitoring specific locations within the biopsy using HR-MAS-NMR. Firstly, the method was applied to a classic toxicology situation. Many drug development compounds fail because of preclinical animal liver toxicity conventionally detected using histology. Usually, only one of the murine liver lobes is used for this and is assumed to be representative of the whole organ. In this work, a metabolic variation across murine liver lobes has been investigated via a set of biopsies across all lobes. Using HR-MAS-NMR spectra analysed by various types of multivariate analysis, no lobe-specific metabolic variation could be found, confirming the general validity of the representative lobe approach. To increase location specificity, a spatially-resolved NMR pulse sequence (slice local- ized spectroscopy (SLS)) was modified and its respective effectiveness was explored. The pulse sequence was first validated using artificially created samples (phantoms), and practical examples were layered fruit separated by paraffin film and milled phantoms produced from materials which were magnetic-susceptibility-matched to the HR-MAS rotor. The HR-MAS SLS sequence was then applied to a mixed mouse renal tissue biopsy, and renal cortex and medulla successfully assigned to individual slices from spatially-resolved spectra using pure cortex and medulla reference HR-MAS-NMR spectra and orthogonal projection to latent structures discriminant analysis (OPLS-DA) to establish metabolic markers differentiating the two. Together, this work shows the potential of HR-MAS-NMR as applied to tissue biopsies. Particularly, spatially-resolved methods hold potential for improved biochemical and mechanistic understanding and the methodology could be expanded to applications in many areas of biomedical relevance.Open Acces

Using single-cell technologies to map the human immune system - implications for nephrology.

Advances in single-cell technologies are transforming our understanding of cellular identity. For instance, the application of single-cell RNA sequencing and mass cytometry technologies to the study of immune cell populations in blood, secondary lymphoid organs and the renal tract is helping researchers to map the complex immune landscape within the kidney, define cell ontogeny and understand the relationship of kidney-resident immune cells with their circulating counterparts. These studies also provide insights into the interactions of immune cell populations with neighbouring epithelial and endothelial cells in health, and across a range of kidney diseases and cancer. These data have translational potential and will aid the identification of drug targets and enable better prediction of off-target effects. The application of single-cell technologies to clinical renal biopsy samples, or even cells within urine, will improve diagnostic accuracy and assist with personalized prognostication for patients with various kidney diseases. A comparison of immune cell types in peripheral blood and secondary lymphoid organs in healthy individuals and in patients with systemic autoimmune diseases that affect the kidney will also help to unravel the mechanisms that underpin the breakdown in self-tolerance and propagation of autoimmune responses. Together, these immune cell atlases have the potential to transform nephrology

Biomarkers of glomerular inflammation and fibrosis

Progression of glomerular diseases to chronic kidney disease or renal failure is a major diagnostic and therapeutic problem. In addition to finding new treatment, more reliable biomarkers, using near patient technology, are needed to improve early detection of patients at risk of progressive renal injury. To achieve this aim, two approaches were applied to study novel biomarkers of glomerular inflammation and fibrosis. Firstly, expression of specific inflammatory cytokines and growth factors, including two members of the CCN protein family which are connective tissue growth factor (CTGF)/CCN2 and nephroblastoma overexpressed gene (NOV)/CCN3, were studied in a rat model of crescentic glomerulonephritis (GN). CCN2/CTGF is now regarded as a major profibrotic growth factor of chronic renal inflammation and fibrosis. On the other hand, recently, emerging evidence suggests that CCN3/NOV acts as an endogenous negative regulator of extracellular matrix accumulation and is capable of counteracting the fibrogenic effect of CCN2/CTGF. This targeted approach may be helpful in providing a novel insight into the pathophysiological activities of these two CCN proteins involved in progressive kidney disease and development of novel therapy. With regard to Fourier transform infrared (FTIR) spectroscopy, it has been increasingly used in biomedical research but so far it has not been investigated for diagnosing and/or screening progressive kidney disease. Thus, the second approach was aimed to investigate whether FTIR technique could be employed as an unbiased method of detecting sensitive renal biomarkers. Using FTIR spectroscopy, several characteristic urinary and plasma spectral markers related to renal inflammation and chronic fibrosis were identified from the rat model of crescentic GN. In particular the urinary 1545 cm-1 spectral marker was also reliable in assessing therapeutic responses in corticosteroid-treated rats with GN and severe lupus nephritis in mice. In addition, this urinary spectral marker was translatable in assessment of crescentic GN in patients. In conclusion, analysis of specific cytokines/growth factors and FTIR spectroscopic technology are likely to improve or assist diagnosis and evaluate progression of glomerulonephritis.Open Acces

Development of data processing methods for high resolution mass spectrometry-based metabolomics with an application to human liver transplantation

Direct Infusion (DI) Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) is becoming a popular measurement platform in metabolomics. This thesis aims to advance the data processing and analysis pipeline of the DI FT-ICR based metabolomics, and broaden its applicability to a clinical research. To meet the first objective, the issue of missing data that occur in a final data matrix containing metabolite relative abundances measured for each sample analysed, is addressed. The nature of these data and their effect on the subsequent data analyses are investigated. Eight common and/or easily accessible missing data estimation algorithms are examined and a three stage approach is proposed to aid the identification of the optimal one. Finally, a novel survival analysis approach is introduced and assessed as an alternative way of missing data treatment prior univariate analysis. To address the second objective, DI FT-ICR MS based metabolomics is assessed in terms of its applicability to research investigating metabolomic changes occurring in liver grafts throughout the human orthotopic liver transplantation (OLT). The feasibility of this approach to a clinical setting is validated and its potential to provide a wealth of novel metabolic information associated with OLT is demonstrated

Imagerie IRTF tridimensionnelle pour l'étude de l'insuffisance rénale chronique

CKD (Chronic Kidney Disease) is one of the worst public diseases in developing countries. The stages of CKD are mainly based on measured or estimated GFR (Glomerular Filtration Rate). However, this method is not sensitive enough on early stages of the pathology and thus do not offer accurate diagnostic value. Early detection and treatment can often limit or avoid the chronicity effects of the disease. This thesis focuses on the development of FTIR microscopy as a diagnostic tool for the identification by histopathology at glomerulus level of the kidney in CKD model. We developed a technique of 3D reconstruction for the FTIR imaging of biochemical components changes in glomeruli for identifying the pathological marker of CKD. The curve-fitting and spectral clustering are applied on the FTIR microscopy analysis to distinguish between healthy and pathological glomeruli of a kidney. Then, the glomerular microvasculatureis highlighted to reveal the morphological abnormalities by perfusing contrast agents into blood vessels. With advanced 3D statistical methods and 3D image visualization by microscopy, FTIR spectro-imaging can be used as a functional technique to determine the morphological and molecular changes occurring along CKD development.L’insuffisance rénale chronique (IRC) et l’une des pires maladies chroniques dans les pays développés. Les grades de l’IRC sont principalement basés sur la mesure ou l’estimation du taux de filtration rénale (GFR). Cependant, cette méthode est peu sensible sur les premiers stades de la pathologie et n’apporte donc pas de valeur diagnostique. La détection de la pathologie à des stades précoces et son traitement peuvent éviter ou limiter les effets délétères de la chronicité. Cette thèse se penche sur le développement de la microscopie IRTF en tant qu’outil diagnostic pour l’identification par histopathologie à l’échelle du glomérule dans un modèle d’IRC. Nous avons développé la technique de reconstruction 3D pour l’imagerie IRTF des modifications biochimiques à l’échelle du glomérule pour déterminer des marqueurs de l’IRC. La déconvolution spectrale et le clustering sont appliqués après analyses IRTF pour distinguer les modèles sains et pathologiques. Ensuite, la microvasculature glomérulaire est révélée par agent de contraste pour en déterminer les anomalies morphologiques. Grâce aux résultats obtenus en 3D et l’utilisation de méthodes statistiques avancées, la microscopie IRTF est utilisée comme une technique fonctionnelle pour déterminer les modifications morphologiques et moléculaires apparaissant au cours du développement de l’IRC

Diffusion-weighted magnetic resonance imaging in diagnosing graft dysfunction : a non-invasive alternative to renal biopsy.

The thesis is divided into three parts. The first part focuses on background information including how the kidney functions, diseases, and available kidney disease treatment strategies. In addition, the thesis provides information on imaging instruments and how they can be used to diagnose renal graft dysfunction. The second part focuses on elucidating the parameters linked with highly accurate diagnosis of rejection. Four parameters categories were tested: clinical biomarkers alone, individual mean apparent diffusion coefficient (ADC) at 11-different b- values, mean ADCs of certain groups of b-value, and fusion of clinical biomarkers and all b-values. The most accurate model was found to be when the b-value of b=100 s/mm2 and b=700 s/mm2 were fused. The third part of this thesis focuses on a study that uses Diffusion-Weighted MRI to diagnose and differentiate two types of renal rejection. The system was found to correctly differentiate the two types of rejection with a 98% accuracy. The last part of this thesis concludes the work that has been done and states the possible trends and future avenues

A CAD system for early diagnosis of autism using different imaging modalities.

The term “autism spectrum disorder” (ASD) refers to a collection of neuro-developmental disorders that affect linguistic, behavioral, and social skills. Autism has many symptoms, most prominently, social impairment and repetitive behaviors. It is crucial to diagnose autism at an early stage for better assessment and investigation of this complex syndrome. There have been a lot of efforts to diagnose ASD using different techniques, such as imaging modalities, genetic techniques, and behavior reports. Imaging modalities have been extensively exploited for ASD diagnosis, and one of the most successful ones is Magnetic resonance imaging(MRI),where it has shown promise for the early diagnosis of the ASD related abnormalities in particular. Magnetic resonance imaging (MRI) modalities have emerged as powerful means that facilitate non-invasive clinical diagnostics of various diseases and abnormalities since their inception in the 1980s. After the advent in the nineteen eighties, MRI soon became one of the most promising non- invasive modalities for visualization and diagnostics of ASD-related abnormalities. Along with its main advantage of no exposure to radiation, high contrast, and spatial resolution, the recent advances to MRI modalities have notably increased diagnostic certainty. Multiple MRI modalities, such as different types of structural MRI (sMRI) that examines anatomical changes, and functional MRI (fMRI) that examines brain activity by monitoring blood flow changes,have been employed to investigate facets of ASD in order to better understand this complex syndrome. This work aims at developing a new computer-aided diagnostic (CAD) system for autism diagnosis using different imaging modalities. It mainly relies on making use of structural magnetic resonance images for extracting notable shape features from parts of the brainthat proved to correlate with ASD from previous neuropathological studies. Shape features from both the cerebral cortex (Cx) and cerebral white matter(CWM)are extracted. Fusion of features from these two structures is conducted based on the recent findings suggesting that Cx changes in autism are related to CWM abnormalities. Also, when fusing features from more than one structure, this would increase the robustness of the CAD system. Moreover, fMRI experiments are done and analyzed to find areas of activation in the brains of autistic and typically developing individuals that are related to a specific task. All sMRI findings are fused with those of fMRI to better understand ASD in terms of both anatomy and functionality,and thus better classify the two groups. This is one aspect of the novelty of this CAD system, where sMRI and fMRI studies are both applied on subjects from different ages to diagnose ASD. In order to build such a CAD system, three main blocks are required. First, 3D brain segmentation is applied using a novel hybrid model that combines shape, intensity, and spatial information. Second, shape features from both Cx and CWM are extracted and anf MRI reward experiment is conducted from which areas of activation that are related to the task of this experiment are identified. Those features were extracted from local areas of the brain to provide an accurate analysis of ASD and correlate it with certain anatomical areas. Third and last, fusion of all the extracted features is done using a deep-fusion classification network to perform classification and obtain the diagnosis report. Fusing features from all modalities achieved a classification accuracy of 94.7%, which emphasizes the significance of combining structures/modalities for ASD diagnosis. To conclude, this work could pave the pathway for better understanding of the autism spectrum by finding local areas that correlate to the disease. The idea of personalized medicine is emphasized in this work, where the proposed CAD system holds the promise to resolve autism endophenotypes and help clinicians deliver personalized treatment to individuals affected with this complex syndrome