Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique

Reproducibility and prognostic ability of chronicity parameters in kidney biopsy - Comprehensive evaluation comparing microscopy and artificial intelligence in digital pathology

INTRODUCTION

Semi-quantitative scoring of various parameters in renal biopsy is accepted as an important tool to assess disease activity and prognostication. There are concerns on the impact of interobserver variability in its prognostic utility, generating a need for computerized quantification.

METHODS

We studied 94 patients with renal biopsies, 45 with native diseases and 49 transplant patients with index biopsies for Polyomavirus nephropathy. Chronicity scores were evaluated using two methods. A standard definition diagram was agreed after international consultation and four renal pathologists scored each parameter in a double-blinded manner. Interstitial fibrosis (IF) score was assessed with five different computerized and AI-based algorithms on trichrome and PAS stains.

RESULTS

There was strong prognostic correlation with renal function and graft outcome at a median follow-up ranging from 24 to 42 months respectively, independent of moderate concordance for pathologists scores. IF scores with two of the computerized algorithms showed significant correlation with estimated glomerular filtration rate (eGFR) at biopsy but not at the end of follow-up. There was poor concordance for AI based platforms.

CONCLUSION

Chronicity scores are robust prognostic tools despite interobserver reproducibility. AI-algorithms have absolute precision but are limited by significant variation when different hardware and software algorithms are used for quantification.

Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass

Kidney transplant (KTx) biopsies showing transplant glomerulopathy (TG) (glomerular basement membrane double contours (cg) > 0) and microvascular inflammation (MVI) in the absence of C4d staining and donor-specific antibodies (DSAs) do not fulfill the criteria for chronic active antibody-mediated rejection (CA-AMR) diagnosis and do not fit into any other Banff category. To investigate this, we initiated a multicenter intercontinental study encompassing 36 cases, comparing the immunomic and transcriptomic profiles of 14 KTx biopsies classified as cg+MVI DSA-/C4d- with 22 classified as CA-AMR DSA+/C4d+ through novel transcriptomic analysis using the NanoString Banff-Human Organ Transplant (B-HOT) panel and subsequent orthogonal subset analysis using two innovative 5-marker multiplex immunofluorescent panels. Nineteen genes were differentially expressed between the two study groups. Samples diagnosed with CA-AMR DSA+/C4d+ showed a higher glomerular abundance of natural killer cells and higher transcriptomic cell type scores for macrophages in an environment characterized by increased expression of complement-related genes (i.e., C5AR1) and higher activity of angiogenesis, interstitial fibrosis tubular atrophy, CA-AMR, and DSA-related pathways when compared to samples diagnosed with cg+MVI DSA-/C4d-. Samples diagnosed with cg+MVI DSA-/C4d- displayed a higher glomerular abundance and activity of T cells (CD3+, CD3+CD8+, and CD3+CD8-). Thus, we show that using novel multiomic techniques, KTx biopsies with cg+MVI DSA-/C4d- have a prominent T-cell presence and activity, putting forward the possibility that these represent a more T-cell dominant phenotype.

Periodontal disease in chronic kidney disease patients: salivomics by Fourier-transform infrared spectroscopy

It has been reported that 58% of individuals with chronic kidney disease (CKD) have moderate to advanced periodontitis due to alterations of pH and biochemical composition in the saliva. In fact, the composition of this important biofluid may be modulated by systemic disorders. Here we investigate the micro-reflectance Fourier-transform infrared spectroscopy (FTIR) spectra of saliva that CKD patients submitted to periodontal treatment, aiming to identify spectral biomarkers of kidney disease evolution and the effectiveness of periodontal treatment, proposing possible biomarkers of disease evolution. Saliva from 24 CKD patients-stage-5 men, 29 to 64 years old-was evaluated in (i) patients starting periodontal treatment; (ii) patients 30 days after periodontal treatment; and (iii) patients 90 days after periodontal treatment. Our findings indicated that there are statistically relevant changes among the groups after 30 and 90 days of periodontal treatment, when considering the overall spectra in the fingerprint region (800-1800cm^-1). The key bands presenting good prediction power (area under the receiver operating characteristic curve >0.70) were related to poly (ADP-ribose) polymerase (PARP) conjugated to DNA at 883, 1031, and 1060cm^-1 (carbohydrates at 1043 and 1049cm^-1) and triglycerides (1461cm^-1). Interestingly when analyzing the derivative spectra in the secondary structure region (1590-1700cm^-1), we detected over-expression of the β-sheet class of secondary structures in 90 days of periodontal treatment, possibly related to over-expression of human B-defensins. Conformational changes in ribose sugar in this region corroborate the interpretation concerning PARP detection. To our knowledge, PARP was detected for the first time in saliva samples of stage-5 CKD patients by FTIR. All observed changes were correctly interpreted in terms of intensive apoptosis and dyslipidemia due to kidney disease progression. Biomarkers due to CKD predominate in saliva, and the relative improvement in the periodontal state did not cause remarkable changes in the spectra of saliva.

Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies

In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3⁺ cell density within scarred regions and higher CD3⁺ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies.

Artificial Intelligence-Assisted Renal Pathology: Advances and Prospects

Digital imaging and advanced microscopy play a pivotal role in the diagnosis of kidney diseases. In recent years, great achievements have been made in digital imaging, providing novel approaches for precise quantitative assessments of nephropathology and relieving burdens of renal pathologists. Developing novel methods of artificial intelligence (AI)-assisted technology through multidisciplinary interaction among computer engineers, renal specialists, and nephropathologists could prove beneficial for renal pathology diagnoses. An increasing number of publications has demonstrated the rapid growth of AI-based technology in nephrology. In this review, we offer an overview of AI-assisted renal pathology, including AI concepts and the workflow of processing digital image data, focusing on the impressive advances of AI application in disease-specific backgrounds. In particular, this review describes the applied computer vision algorithms for the segmentation of kidney structures, diagnosis of specific pathological changes, and prognosis prediction based on images. Lastly, we discuss challenges and prospects to provide an objective view of this topic.

Automated Computational Detection of Disease Activity in ANCA-Associated Glomerulonephritis Using Raman Spectroscopy: A Pilot Study

Biospectroscopy offers the ability to simultaneously identify key biochemical changes in tissue associated with a given pathological state to facilitate biomarker extraction and automated detection of key lesions. Herein, we evaluated the application of machine learning in conjunction with Raman spectroscopy as an innovative low-cost technique for the automated computational detection of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA)-associated glomerulonephritis (AAGN). Consecutive patients with active AAGN and those in disease remission were recruited from a single UK centre. In those with active disease, renal biopsy samples were collected together with a paired urine sample. Urine samples were collected immediately prior to biopsy. Amongst those in remission at the time of recruitment, archived renal tissue samples representative of biopsies taken during an active disease period were obtained. In total, twenty-eight tissue samples were included in the analysis. Following supervised classification according to recorded histological data, spectral data from unstained tissue samples were able to discriminate disease activity with a high degree of accuracy on blind predictive modelling: F-score 95% for >25% interstitial fibrosis and tubular atrophy (sensitivity 100%, specificity 90%, area under ROC 0.98), 100% for necrotising glomerular lesions (sensitivity 100%, specificity 100%, area under ROC 1) and 100% for interstitial infiltrate (sensitivity 100%, specificity 100%, area under ROC 0.97). Corresponding spectrochemical changes in paired urine samples were limited. Future larger study is required, inclusive of assigned variables according to novel non-invasive biomarkers as well as the application of forward feature extraction algorithms to predict clinical outcomes based on spectral features.

Extracellular Vesicles and Their Relationship with the Heart-Kidney Axis, Uremia and Peritoneal Dialysis

Cardiorenal syndrome (CRS) is described as primary dysfunction in the heart culminating in renal injury or vice versa. CRS can be classified into five groups, and uremic toxin (UT) accumulation is observed in all types of CRS. Protein-bound uremic toxin (PBUT) accumulation is responsible for permanent damage to the renal tissue, and mainly occurs in CRS types 3 and 4, thus compromising renal function directly leading to a reduction in the glomerular filtration rate (GFR) and/or subsequent proteinuria. With this decrease in GFR, patients may need renal replacement therapy (RRT), such as peritoneal dialysis (PD). PD is a high-quality and home-based dialysis therapy for patients with end-stage renal disease (ESRD) and is based on the semi-permeable characteristics of the peritoneum. These patients are exposed to factors which may cause several modifications on the peritoneal membrane. The presence of UT may harm the peritoneum membrane, which in turn can lead to the formation of extracellular vesicles (EVs). EVs are released by almost all cell types and contain lipids, nucleic acids, metabolites, membrane proteins, and cytosolic components from their cell origin. Our research group previously demonstrated that the EVs can be related to endothelial dysfunction and are formed when UTs are in contact with the endothelial monolayer. In this scenario, this review explores the mechanisms of EV formation in CRS, uremia, the peritoneum, and as potential biomarkers in peritoneal dialysis.

Role of prostaglandin E2 in tissue repair and regeneration

Tissue regeneration following injury from disease or medical treatment still represents a challenge in regeneration medicine. Prostaglandin E2 (PGE2), which involves diverse physiological processes via E-type prostanoid (EP) receptor family, favors the regeneration of various organ systems following injury for its capabilities such as activation of endogenous stem cells, immune regulation, and angiogenesis. Understanding how PGE2 modulates tissue regeneration and then exploring how to elevate the regenerative efficiency of PGE2 will provide key insights into the tissue repair and regeneration processes by PGE2. In this review, we summarized the application of PGE2 to guide the regeneration of different tissues, including skin, heart, liver, kidney, intestine, bone, skeletal muscle, and hematopoietic stem cell regeneration. Moreover, we introduced PGE2-based therapeutic strategies to accelerate the recovery of impaired tissue or organs, including 15-hydroxyprostaglandin dehydrogenase (15-PGDH) inhibitors boosting endogenous PGE2 levels and biomaterial scaffolds to control PGE2 release.

Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification

INTRODUCTION

Digital pathology improves the standardization and reproducibility of kidney biopsy specimen assessment. We developed a pipeline allowing the analysis of many images without requiring human preprocessing and illustrate its use with a simple algorithm for quantification of interstitial fibrosis on a large dataset of kidney allograft biopsy specimens.

METHODS

Masson trichrome-stained images from kidney allograft biopsy specimens were used to train and validate a glomeruli detection algorithm using a VGG19 convolutional neural network and an automatic cortical region of interest (ROI) selection algorithm including cortical regions containing all predicted glomeruli. A positive-pixel count algorithm was used to quantify interstitial fibrosis on the ROIs and the association between automatic fibrosis and pathologist evaluation, estimated glomerular filtration rate (GFR) and allograft survival was assessed.

RESULTS

The glomeruli detection (F1 score of 0.87) and ROIs selection (F1 score 0.83 [SD 0.13]) algorithms displayed high accuracy. The correlation between the automatic fibrosis quantification on manually and automatically selected ROIs was high (r = 1.00 [0.99-1.00]). Automatic fibrosis quantification was only moderately correlated with pathologists' assessment and was not significantly associated with eGFR or allograft survival.

CONCLUSION

This pipeline can automatically and accurately detect glomeruli and select cortical ROIs that can easily be used to develop, validate, and apply image analysis algorithms.

Distinguishing active from quiescent disease in ANCA-associated vasculitis using attenuated total reflection Fourier-transform infrared spectroscopy

The current lack of a reliable biomarker of disease activity in anti-neutrophil cytoplasmic autoantibody (ANCA) associated vasculitis poses a significant clinical unmet need when determining relapsing or persisting disease. In this study, we demonstrate for the first time that attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy offers a novel and functional candidate biomarker, distinguishing active from quiescent disease with a high degree of accuracy. Paired blood and urine samples were collected within a single UK centre from patients with active disease, disease remission, disease controls and healthy controls. Three key biofluids were evaluated; plasma, serum and urine, with subsequent chemometric analysis and blind predictive model validation. Spectrochemical interrogation proved plasma to be the most conducive biofluid, with excellent separation between the two categories on PC2 direction (AUC 0.901) and 100% sensitivity (F-score 92.3%) for disease remission and 85.7% specificity (F-score 92.3%) for active disease on blind predictive modelling. This was independent of organ system involvement and current ANCA status, with similar findings observed on comparative analysis following successful remission-induction therapy (AUC > 0.9, 100% sensitivity for disease remission, F-score 75%). This promising technique is clinically translatable and warrants future larger study with longitudinal data, potentially aiding earlier intervention and individualisation of treatment.

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

Automatic Identification of Paraffin Pixels on FTIR Images Acquired on FFPE Human Samples

The transfer of mid-infrared spectral histopathology to the clinic will be possible provided that its application in clinical practice is simple. Rapid analysis of formalin-fixed paraffin-embedded (FFPE) tissue section is thus a prerequisite. The chemical dewaxing of these samples before image acquisition used by the majority of studies is in contradiction with this principle. Fortunately, the in silico analysis of the images acquired on FFPE samples is possible using extended multiplicative signal correction (EMSC). However, the removal of pure paraffin pixels is essential to perform a relevant classification of tissue spectra. So far, this task was possible only if using manual and subjective histogram analysis. In this article, we thus propose a new automatic and multivariate methodology based on the analysis of optimized combinations of EMSC regression coefficients by validity indices and KMeans clustering to separate paraffin and tissue pixels. The validation of our method is performed using simulated infrared spectral images by measuring the Jaccard index between our partitions and the image model, with values always over 0.90 for diverse baseline complexity and signal-to-noise ratio. These encouraging results were also validated on real images by comparing our method with classical ones and by computing the Jaccard index between our partitions and the KMeans partitions obtained on the infrared image acquired on the same samples but after chemical dewaxing, with values always over 0.84.

Analysis of Hepatic Fibrosis Characteristics in Cirrhotic Patients with and without Hepatocellular Carcinoma by FTIR Spectral Imaging

The evolution of cirrhosis is marked by quantitative and qualitative modifications of the fibrosis tissue and an increasing risk of complications such as hepatocellular carcinoma (HCC). Our purpose was to identify by FTIR imaging the spectral characteristics of hepatic fibrosis in cirrhotic patients with and without HCC. FTIR images were collected at projected pixel sizes of 25 and 2.7 μm from paraffinized hepatic tissues of five patients with uncomplicated cirrhosis and five cirrhotic patients with HCC and analyzed by k-means clustering. When compared to the adjacent histological section, the spectral clusters corresponding to hepatic fibrosis and regeneration nodules were easily identified. The fibrosis area estimated by FTIR imaging was correlated to that evaluated by digital image analysis of histological sections and was higher in patients with HCC compared to those without complications. Qualitative differences were also observed when fibrosis areas were specifically targeted at higher resolution. The partition in two clusters of the fibrosis tissue highlighted subtle differences in the spectral characteristics of the two groups of patients. These data show that the quantitative and qualitative changes of fibrosis tissue occurring during the course of cirrhosis are detectable by FTIR imaging, suggesting the possibility of subclassifying cirrhosis into different steps of severity.

Banff Digital Pathology Working Group: Going digital in transplant pathology

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

Profiling of mRNA of interstitial fibrosis and tubular atrophy with subclinical inflammation in recipients after kidney transplantation

Interstitial fibrosis and tubular atrophy (IFTA) with inflammation (IFTA-I) is strongly correlated with kidney allograft failure. Diagnosis of IFTA-I accurately and early is critical to prevent graft failure and improve graft survival. In the current study, through analyzing the renal allograft biopsy in patients with stable function after kidney transplantation (STA), IFTA and IFTA-I group with semi-supervised principal components methods, we found that CD2, IL7R, CCL5 based signature could not only distinguish STA and IFTA-I well, but predict IFTA-I with a high degree of accuracy with an area under the curve (AUC) of 0.91 (P = 0.00023). Additionally, IRF8 demonstrated significant differences among STA, IFTA and IFTA-I groups, suggesting that IRF8 had the capacity to discriminate the different classifications of graft biopsies well. Also, with Kaplan-Meier and log-rank methods, we found that IRF8 could serve as the prognostic marker for renal graft failure in those biopsies without rejection (AUC = 0.75) and the recipients expressing high had a higher risk for renal graft loss (P < 0.0001). This research may provide new targets for therapeutic prevention and intervention for post-transplantation IFTA with or with inflammation.

Reliability of Histopathology for the Early Recognition of Fibrosis in Traction Alopecia: Correlation with Clinical Severity

Traction alopecia (TA) is hair loss caused by prolonged pulling or repetitive tension on scalp hair; it belongs to the biphasic group of primary alopecia. It is non-scarring, typically with preservation of follicular stem cells and the potential for regrowth of early lesions especially if traction hairstyles are stopped. However, the alopecia may become permanent (scarring) and fail to respond to treatment if the traction is excessive and prolonged. Hence, the ability to detect fibrosis early in these lesions could predict patients who respond to treatment. Histopathological diagnosis based on scalp biopsies has been used as a gold standard to delineate various forms of non-scarring alopecia and to differentiate them from scarring ones. However, due to potential discrepant reporting as a result of the type of biopsy, method of sectioning, and site of biopsy, histopathology often tends to be unreliable for the early recognition of fibrosis in TA. In this study, 45 patients were assessed using the marginal TA severity scoring system, and their biopsies (both longitudinal and transverse sections) were systematically assessed by three dermatopathologists, the aim being to correlate histopathological findings with clinical staging. Intraclass correlation coefficients were used to determine the level of agreement between the assessors. We found poor agreement of the identification and grading of perifollicular and interfollicular fibrosis (0.55 [0.23–0.75] and 0.01 [2.20–0.41], respectively), and no correlation could be drawn with the clinical severity score. Better methods of diagnosis are needed for grading and for recognition of early fibrosis in TA.

The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide

Background

Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation.

Methods

Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018.

Results

Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%).

Conclusions

Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.

Validation of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for kidney fibrosis detection: a cross-sectional study

Background

Kidney cortical interstitial fibrosis (IF) is highly predictive of renal prognosis and is currently assessed by the evaluation of a biopsy. Diffusion magnetic resonance imaging (MRI) is a promising tool to evaluate kidney fibrosis via the apparent diffusion coefficient (ADC), but suffers from inter-individual variability. We recently applied a novel MRI protocol to allow calculation of the corticomedullary ADC difference (ΔADC). We here present the validation of ΔADC for fibrosis assessment in a cohort of 164 patients undergoing biopsy and compare it with estimated glomerular filtration rate (eGFR) and other plasmatic parameters for the detection of fibrosis.

Methods

This monocentric cross-sectional study included 164 patients undergoing renal biopsy at the Nephrology Department of the University Hospital of Geneva between October 2014 and May 2018. Patients underwent diffusion-weighted imaging, and T1 and T2 mappings, within 1 week after biopsy. MRI results were compared with gold standard histology for fibrosis assessment.

Results

Absolute cortical ADC or cortical T1 values correlated poorly to IF assessed by the biopsy, whereas ΔADC was highly correlated to IF (r=−0.52, P &lt; 0.001) and eGFR (r = 0.37, P &lt; 0.01), in both native and allograft patients. ΔT1 displayed a lower, but significant, correlation to IF and eGFR, whereas T2 did not correlate to IF nor to eGFR. ΔADC, ΔT1 and eGFR were independently associated with kidney fibrosis, and their combination allowed detection of extensive fibrosis with good specificity.

Conclusion

ΔADC is better correlated to IF than absolute cortical or medullary ADC values. ΔADC, ΔT1 and eGFR are independently associated to IF and allow the identification of patients with extensive IF.

Calcineurin inhibitors and nephrotoxicity in children

BACKGROUND

Calcineurin inhibitors (CNIs) are commonly given to transplant recipients of kidneys and other solid organs and to patients with immune disorders, such as steroid-resistant nephrotic syndrome, steroid-dependent nephrotic syndrome, and frequent relapse nephrotic syndrome. Although CNIs remain the most effective available immunosuppressant agent, there is clinical concern regarding possible long-term nephrotoxicity. This concern is especially significant in children who have a longer life expectancy and greater growth rate.

DATA SOURCES

In this review, we analyzed the literatures to identify original articles that examined use of CNIs in children who received organ transplantation and nephropathy to assess the available evidence of their nephrotoxicity. PubMed, Elsevier, and Tompson ISI Web of Knowledge were searched for identifying relevant papers.

RESULTS

Clinical research supports the presence of CNI-related nephrotoxicity. However, some researchers have questioned the prevalence and seriousness of chronic CNIs nephrotoxicity, especially because the pathological lesions typically associated with long-term CNI use are nonspecific. Many researchers have focused on early markers of CNI nephrotoxicity, and the methods that may help prevent and manage nephrotoxicity.

CONCLUSIONS

Future research should focus on investigating early markers of CNI nephrotoxicity and strategies for improved immunosuppressant therapy, and developing alternative treatments. CNI-mediated nephrotoxicity should always be taken seriously in clinic.

Predicting Fibrosis Progression in Renal Transplant Recipients Using Laser-Based Infrared Spectroscopic Imaging

Renal transplants have not seen a significant improvement in their 10-year graft life. Chronic damage accumulation often leads to interstitial fibrosis and tubular atrophy (IF/TA) and thus graft function loss over time. For this reason, IF/TA has been the chief suspect for a potential prognostic marker for long term outcomes. In this study, we have used infrared spectroscopic (IR) imaging to interrogate the biochemistry of regions of fibrosis from renal transplant biopsies to identify a biochemical signature that can predict rapid progression of fibrosis. IR imaging represents an approach that permits label-free biochemical imaging of human tissues towards identifying novel biomarkers for disease diagnosis or prognosis. Two cohorts were identified as progressors (n = 5, > 50% fibrosis increase between time points) and non-progressors (n = 5, < 5% increase between time points). Each patient had an early time point and late time point biopsy. Collagen associated carbohydrate moieties (ν(C–O), 1035 cm⁻¹ and ν(C–O–C),1079 cm⁻¹) spectral ratios demonstrated good separation between the two cohorts (p = 0.001). This was true for late and early time point biopsies suggesting the regions of fibrosis are biochemically altered in cases undergoing progressive fibrosis. Thus, IR imaging can potentially predict rapid progression of fibrosis using histologically normal early time point biopsies.

Infrared spectroscopic imaging: Label-free biochemical analysis of stroma and tissue fibrosis

Infrared spectroscopic tissue imaging is a potentially powerful adjunct tool to current histopathology techniques. By coupling the biochemical signature obtained through infrared spectroscopy to the spatial information offered by microscopy, this technique can selectively analyze the chemical composition of different features of unlabeled, unstained tissue sections. In the past, the tissue features that have received the most interest were parenchymal and epithelial cells, chiefly due to their involvement in dysplasia and progression to carcinoma; however, the field has recently turned its focus toward stroma and areas of fibrotic change. These components of tissue present an untapped source of biochemical information that can shed light on many diverse disease processes, and potentially hold useful predictive markers for these same pathologies. Here we review the recent applications of infrared spectroscopic imaging to stromal and fibrotic regions of diseased tissue, and explore the potential of this technique to advance current capabilities for tissue analysis.

Label-free molecular imaging of the kidney

In this review, we will highlight technologies that enable scientists to study the molecular characteristics of tissues and/or cells without the need for antibodies or other labeling techniques. Specifically, we will focus on matrix-assisted laser desorption/ionization imaging mass spectrometry, infrared spectroscopy, and Raman spectroscopy.

Label Free Detection of Sensitive Mid-Infrared Biomarkers of Glomerulonephritis in Urine Using Fourier Transform Infrared Spectroscopy

More reliable biomarkers using near-patient technologies are needed to improve early diagnosis and intervention for patients with renal disease. Infrared (IR) vibrational spectroscopy/microspectroscopy is an established analytical method that was first used in biomedical research over 20 years ago. With the advances in instrumentation, computational and mathematical techniques, this technology has now been applied to a variety of diseases; however, applications in nephrology are just beginning to emerge. In the present study, we used attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy to analyze urine samples collected from rodent models of inflammatory glomerulonephritis (GN) as well as from patients with crescentic GN, with the aim of identifying potential renal biomarkers; several characteristic mid-IR spectral markers were identified in urine samples. Specifically, a 1545 cm⁻¹ band increased in intensity with the progression and severity of GN in rats, mice and humans. Furthermore, its intensity declined significantly in response to corticosteroid treatment in nephritic rats. In conclusion, our results suggest that specific urinary FTIR biomarkers may provide a rapid, sensitive and novel non-invasive means of diagnosing inflammatory forms of GN, and for real-time monitoring of progress, and response to treatment.

Infrared vibrational spectroscopy: a rapid and novel diagnostic and monitoring tool for cystinuria

Cystinuria is the commonest inherited cause of nephrolithiasis (~1% in adults; ~6% in children) and is the result of impaired cystine reabsorption in the renal proximal tubule. Cystine is poorly soluble in urine with a solubility of ~1 mM and can readily form microcrystals that lead to cystine stone formation, especially at low urine pH. Diagnosis of cystinuria is made typically by ion-exchange chromatography (IEC) detection and quantitation, which is slow, laboursome and costly. More rapid and frequent monitoring of urinary cystine concentration would significantly improve the diagnosis and clinical management of cystinuria. We used attenuated total reflection - Fourier transform infrared spectroscopy (ATR-FTIR) to detect and quantitate insoluble cystine in 22 cystinuric and 5 healthy control urine samples. Creatinine concentration was also determined by ATR-FTIR to adjust for urinary concentration/dilution. Urine was centrifuged, the insoluble fraction re-suspended in 5 μL water and dried on the ATR prism. Cystine was quantitated using its 1296 cm⁻¹ absorption band and levels matched with parallel measurements made using IEC. ATR-FTIR afforded a rapid and inexpensive method of detecting and quantitating insoluble urinary cystine. This proof-of-concept study provides a basis for developing a high-throughput, cost-effective diagnostic method for cystinuria, and for point-of-care clinical monitoring