Evaluation of Kidney Histological Images Using Unsupervised Deep Learning

[Introduction] Evaluating histopathology via machine learning has gained research and clinical interest, and the performance of supervised learning tasks has been described in various areas of medicine. Unsupervised learning of histological images has the advantage of reproducibility for labeling; however, the relationship between unsupervised evaluation and clinical information remains unclear in nephrology. [Methods] We propose an unsupervised approach combining convolutional neural networks (CNNs) and a visualization algorithm to cluster the histological images and calculate the score for patients. We applied the approach to the entire images or patched images of the glomerulus of kidney biopsy samples stained with hematoxylin and eosin obtained from 68 patients with immunoglobulin A nephropathy. We assessed the relationship between the obtained scores and clinical variables of urinary occult blood, urinary protein, serum creatinine (SCr), systolic blood pressure, and age. [Results] The glomeruli of the patients were classified into 12 distinct classes and 10 patches. The output of the fine-tuned CNN, which we defined as the histological scores, had significant relationships with assessed clinical variables. In addition, the clustering and visualization results suggested that the defined clusters captured important findings when evaluating renal histopathology. For the score of the patch-based cluster containing crescentic glomeruli, SCr (coefficient = 0.09, P = 0.019) had a significant relationship. [Conclusion] The proposed approach could successfully extract features that were related to the clinical variables from the kidney biopsy images along with the visualization for interpretability. The approach could aid in the quantified evaluation of renal histopathology

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

Background and objectives: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting. Design, setting, participants, & measurements: High-magnification ( 7400) immunofluorescence images of kidney biopsies performed from the year 2001 to 2018 were collected. The report, adopted at the Division of Nephrology of the AOU Policlinico di Modena, describes the specimen in terms of \u201cappearance,\u201d \u201cdistribution,\u201d \u201clocation,\u201d and \u201cintensity\u201d of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and \u3ba- and \u3bb-light chains. The report was used as ground truth for the training of the convolutional neural networks. Results: In total, 12,259 immunofluorescence images of 2542 subjects undergoing kidney biopsy were collected. The test set analysis showed accuracy values between 0.79 (\u201cirregular capillary wall\u201d feature) and 0.94 (\u201cfine granular\u201d feature). The agreement test of the results obtained by the convolutional neural networks with respect to the ground truth showed similar values to three pathologists of our center. Convolutional neural networks were 117 times faster than human evaluators in analyzing 180 test images. A web platform, where it is possible to upload digitized images of immunofluorescence specimens, is available to evaluate the potential of our approach. Conclusions: The data showed that the accuracy of convolutional neural networks is comparable with that of pathologists experienced in the field

Deep-Learning based segmentation and quantification in experimental kidney histopathology

BACKGROUND: Nephropathologic analyses provide important outcomes-related data in experiments with the animal models that are essential for understanding kidney disease pathophysiology. Precision medicine increases the demand for quantitative, unbiased, reproducible, and efficient histopathologic analyses, which will require novel high-throughput tools. A deep learning technique, the convolutional neural network, is increasingly applied in pathology because of its high performance in tasks like histology segmentation. METHODS: We investigated use of a convolutional neural network architecture for accurate segmentation of periodic acid-Schiff-stained kidney tissue from healthy mice and five murine disease models and from other species used in preclinical research. We trained the convolutional neural network to segment six major renal structures: glomerular tuft, glomerulus including Bowman\u27s capsule, tubules, arteries, arterial lumina, and veins. To achieve high accuracy, we performed a large number of expert-based annotations, 72,722 in total. RESULTS: Multiclass segmentation performance was very high in all disease models. The convolutional neural network allowed high-throughput and large-scale, quantitative and comparative analyses of various models. In disease models, computational feature extraction revealed interstitial expansion, tubular dilation and atrophy, and glomerular size variability. Validation showed a high correlation of findings with current standard morphometric analysis. The convolutional neural network also showed high performance in other species used in research-including rats, pigs, bears, and marmosets-as well as in humans, providing a translational bridge between preclinical and clinical studies. CONCLUSIONS: We developed a deep learning algorithm for accurate multiclass segmentation of digital whole-slide images of periodic acid-Schiff-stained kidneys from various species and renal disease models. This enables reproducible quantitative histopathologic analyses in preclinical models that also might be applicable to clinical studies

Optimization of Optical Tissue Clearing Protocols

The goal of this PhD project was to optimize and develop new approaches for visualizing and 3D-imaging tissues and whole organs. Essentially, these approaches were based on optimizing OTC methodologies in combination to the use of CM and LSM. The core of this research was about the Ethyl Cinnamate-based OTC: starting from a recently published protocol52, we worked in order to speed up and automate all the procedures, making it suitable for further studies. Initially we started with the clearing only of mouse kidneys and, then, the procedure was extended to other tissues (normal and pathological tissues) not previously analysed yet. Additionally, we enhanced this methodology, with its application for different scopes, as “revitalization” of old paraffin samples and 3D immunohistochemistry for visualizing finer and inner structures. Our clearing protocol was also tested, for some preliminary studies, on human tissue, with promising results. In parallel, we also tried to figure out the best conditions for the use of both CM and LSM, in order to acquire 2D and 3D images for very large samples

Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications

Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

Application of Laser Microdissection to Uncover Regional Transcriptomics in Human Kidney Tissue

Gene expression analysis of human kidney tissue is an important tool to understand homeostasis and disease pathophysiology. Increasing the resolution and depth of this technology and extending it to the level of cells within the tissue is needed. Although the use of single nuclear and single cell RNA sequencing has become widespread, the expression signatures of cells obtained from tissue dissociation do not maintain spatial context. Laser microdissection (LMD) based on specific fluorescent markers would allow the isolation of specific structures and cell groups of interest with known localization, thereby enabling the acquisition of spatially-anchored transcriptomic signatures in kidney tissue. We have optimized an LMD methodology, guided by a rapid fluorescence-based stain, to isolate five distinct compartments within the human kidney and conduct subsequent RNA sequencing from valuable human kidney tissue specimens. We also present quality control parameters to enable the assessment of adequacy of the collected specimens. The workflow outlined in this manuscript shows the feasibility of this approach to isolate sub-segmental transcriptomic signatures with high confidence. The methodological approach presented here may also be applied to other tissue types with substitution of relevant antibody markers

Renal tubular Sirt1 attenuates diabetic albuminuria by epigenetically suppressing Claudin-1 overexpression in podocytes

Sirtuin 1 (Sirt1), a NAD[superscript +]-regulated deacetylase with numerous known positive effects on cellular and whole-body metabolism, is expressed in the renal cortex and medulla. It is known to have protective effects against age-related disease, including diabetes. Here we investigated the protective role of Sirt1 in diabetic renal damage. We found that Sirt1 in proximal tubules (PTs) was downregulated before albuminuria occurred in streptozotocin-induced or obese (db/db) diabetic mice. PT-specific SIRT1 transgenic and Sirt1 knockout mice showed prevention and aggravation of the glomerular changes that occur in diabetes, respectively, and nondiabetic knockout mice exhibited albuminuria, suggesting that Sirt1 in PTs affects glomerular function. Downregulation of Sirt1 and upregulation of the tight junction protein Claudin-1 by SIRT1-mediated epigenetic regulation in podocytes contributed to albuminuria. We did not observe these phenomena in 5/6 nephrectomized mice. We also demonstrated retrograde interplay from PTs to glomeruli using nicotinamide mononucleotide (NMN) from conditioned medium, measurement of the autofluorescence of photoactivatable NMN and injection of fluorescence-labeled NMN. In human subjects with diabetes, the levels of SIRT1 and Claudin-1 were correlated with proteinuria levels. These results suggest that Sirt1 in PTs protects against albuminuria in diabetes by maintaining NMN concentrations around glomeruli, thus influencing podocyte function.Japan. Ministry of Education, Culture, Sports, Science and Technology (Grant 22790800

Multiplexed High-Resolution Imaging Approach to Decipher the Cellular Heterogeneity of the Kidney and its Alteration in Kidney Disease and Nephrolithiasis

Indiana University-Purdue University Indianapolis (IUPUI)Kidney disease and nephrolithiasis both present a major burden on the health care system in the US and worldwide. The cellular and molecular events governing the pathogenesis of these diseases are not fully understood. We propose that defining the cellular heterogeneity and niches in human and mouse kidney tissue specimens from controls and various models of renal disease could provide unique insights into the molecular pathogenesis. For that purpose, a multiplexed fluorescence imaging approach using co-detection by Indexing (CODEX) was used, using a panel of 33 and 38 markers for mouse and human kidney tissues, respectively. A customized computational analytical pipeline was developed and applied to the imaging data using unsupervised and/or semi-supervised machine learning and statistical approaches. The goal was to identify various cell populations present within the tissues, as well as identify unique cellular niches that may be altered with disease and/or injury. In mice, we examined disease models of acute kidney injury (AKI) and in human tissues we analyzed specimens from patients with AKI, IgA nephropathy, chronic kidney disease, systemic lupus erythematosus, and nephrolithiasis. In both mice and humans, the disease and reference samples show similar broad cell populations for the main segments of the nephron, endothelium, as well as similar groups of immune cells, such as resident macrophages and neutrophils. When comparing between health and disease, however, a change in the distribution of few sub-populations occurred. For example, in human kidney tissues, the abundance and distribution of a subpopulation of proximal tubules positive for THY1 (a marker of differentiation and repair), was markedly reduced with disease. Changes observed in mouse tissues included shifts in the immune cell population types and niches with disease. We propose that our analytical workflow and the observed changes in situ will play an important role in deciphering the pathogenesis of kidney disease