An approach for detection of glomeruli in multisite digital pathology

peer reviewedWe present a novel bioimage informatics workflow that combines Icy and Cytomine software and their algorithms to enable large-scale analysis of digital slides from multiple sites. In particular, we apply this workflow on renal biopsies and evaluate empirically our approach for the automatic detection of glomeruli in hundreds of tissue sections

Collaborative analysis of multi-gigapixel imaging data using Cytomine

Motivation: Collaborative analysis of massive imaging datasets is essential to enable scientific discoveries. Results: We developed Cytomine to foster active and distributed collaboration of multidisciplinary teams for large-scale image-based studies. It uses web development methodologies and machine learning in order to readily organize, explore, share and analyze (semantically and quantitatively) multi-gigapixel imaging data over the internet. We illustrate how it has been used in several biomedical applications

Combined colour deconvolution and artificial intelligence approach for region-selective immunohistochemical labelling quantification: The example of alpha smooth muscle actin in mouse kidney.

Immunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi-quantitative and exhibits substantial intra- and inter-observer variability. Digital approaches based on stain quantification applied to IHC are precise but still operator-dependent and time-consuming when regions of interest (ROIs) must be defined to quantify protein expression in a specific tissue area. This study aimed at developing an IHC quantification workflow that benefits from colour deconvolution for stain quantification and artificial intelligence for automatic ROI definition. The method was tested on 10 whole slide images (WSI) of alpha-smooth muscle actin (aSMA) stained mouse kidney sections. The task was to identify aSMA-positive areas within the glomeruli automatically. Total aSMA detection was performed using two channels (DAB, haematoxylin) colour deconvolution. Glomeruli segmentation within the same IHC WSI was performed by training a convolutional neural network with annotated examples of glomeruli. For both aSMA and glomeruli, binary masks were created. Co-localisation was performed by overlaying the masks and assigning red/green colours, with yellow indicative of a co-localised signal. The workflow described and exemplified using the case of aSMA expression in glomeruli can be applied to quantify the expression of IHC markers within different structures of immunohistochemically stained slides. The technique is objective, has a fully automated threshold approach (colour deconvolution phase) and uses AI to eliminate operator-dependent steps

Biobanking for glomerular diseases: a study design and protocol for KOrea Renal biobank NEtwoRk System TOward NExt-generation analysis (KORNERSTONE)

Abstract Backgrounds Glomerular diseases, a set of debilitating and complex disease entities, are related to mortality and morbidity. To gain insight into pathophysiology and novel treatment targets of glomerular disease, various types of biospecimens linked to deep clinical phenotyping including clinical information, digital pathology, and well-defined outcomes are required. We provide the rationale and design of the KOrea Renal biobank NEtwoRk System TOward Next-generation analysis (KORNERSTONE). Methods The KORNERSTONE, which has been initiated by Korea Centres for Disease Control and Prevention, is designed as a multi-centre, prospective cohort study and biobank for glomerular diseases. Clinical data, questionnaires will be collected at the time of kidney biopsy and subsequently every 1 year after kidney biopsy. All of the clinical data will be extracted from the electrical health record and automatically uploaded to the web-based database. High-quality digital pathologies are obtained and connected in the database. Various types of biospecimens are collected at baseline and during follow-up: serum, urine, buffy coat, stool, glomerular complementary DNA (cDNA), tubulointerstitial cDNA. All data and biospecimens are processed and stored in a standardised manner. The primary outcomes are mortality and end-stage renal disease. The secondary outcomes will be deterioration renal function, remission of proteinuria, cardiovascular events and quality of life. Discussion Ethical approval has been obtained from the institutional review board of each participating centre and ethics oversight committee. The KORNERSTONE is designed to deliver pioneer insights into glomerular diseases. The study design allows comprehensive, integrated and high-quality data collection on baseline laboratory findings, clinical outcomes including administrative data and digital pathologic images. This may provide various biospecimens and information to many researchers, establish the rationale for future more individualised treatment strategies for glomerular diseases. Trial registration NCT03929887

Biobanking for glomerular diseases: a study design and protocol for KOrea Renal biobank NEtwoRk System TOward NExt-generation analysis (KORNERSTONE)

Backgrounds: Glomerular diseases, a set of debilitating and complex disease entities, are related to mortality and morbidity. To gain insight into pathophysiology and novel treatment targets of glomerular disease, various types of biospecimens linked to deep clinical phenotyping including clinical information, digital pathology, and well-defined outcomes are required. We provide the rationale and design of the KOrea Renal biobank NEtwoRk System TOward Next-generation analysis (KORNERSTONE). Methods: The KORNERSTONE, which has been initiated by Korea Centres for Disease Control and Prevention, is designed as a multi-centre, prospective cohort study and biobank for glomerular diseases. Clinical data, questionnaires will be collected at the time of kidney biopsy and subsequently every 1 year after kidney biopsy. All of the clinical data will be extracted from the electrical health record and automatically uploaded to the web-based database. High-quality digital pathologies are obtained and connected in the database. Various types of biospecimens are collected at baseline and during follow-up: serum, urine, buffy coat, stool, glomerular complementary DNA (cDNA), tubulointerstitial cDNA. All data and biospecimens are processed and stored in a standardised manner. The primary outcomes are mortality and end-stage renal disease. The secondary outcomes will be deterioration renal function, remission of proteinuria, cardiovascular events and quality of life. Discussion: Ethical approval has been obtained from the institutional review board of each participating centre and ethics oversight committee. The KORNERSTONE is designed to deliver pioneer insights into glomerular diseases. The study design allows comprehensive, integrated and high-quality data collection on baseline laboratory findings, clinical outcomes including administrative data and digital pathologic images. This may provide various biospecimens and information to many researchers, establish the rationale for future more individualised treatment strategies for glomerular diseases. Trial registration: NCT03929887 .ope

Translating AI to digital pathology workflow: Dealing with scarce data and high variation by minimising complexities in data and models

The recent conversion to digital pathology using Whole Slide Images (WSIs) from conventional pathology opened the doors for Artificial Intelligence (AI) in pathology workflow. The recent interests in machine learning and deep learning have gained a high interest in medical image processing. However, WSIs differ from generic medical images. WSIs are complex images which can reveal various information to support different diagnosis varying from cancer to unknown underlying conditions which were not discovered in other medical investigations. These investigations require expert knowledge spending a long time for investigations, applying different stains to the WSIs, and comparing the WSIs. Differences in WSI differentiate general machine learning methods that are applied for medical image processing. Co-analysing multistained WSIs, high variation of the WSIs from different sites, and lack of labelled data are the main key interest areas that directly influence in developing machine learning models that support pathologists in their investigations. However, most of the state-ofthe- art machine learning approaches cannot be applied in the general clinical workflow without using high compute power, expert knowledge, and time. Therefore, this thesis explores avenues to translate the highly computational and time intensive model to a clinical workflow. Co-analysing multi-stained WSIs require registering differently stained WSI together. In order to get a high precision in the registration exploring nonrigid and rigid transformation is required. The non-rigid transformation requires complex deep learning approaches. Using super-convergence on a small Convolutional Neural Network model it is possible to achieve high precision compared to larger auto-encoders and other state-of-the-art models. High variation of the WSIs from different sites heavily effect machine learning models in their predictions. The thesis presents an approach of using a pre-trained model by using only a small number of samples from the new site. Therefore, re-training larger deep learning models are not required which saves expert time for re-labelling and computational power. Finally, lack of labelled data is one of the main issues in training any supervised machine learning or deep learning model. Using a Generative Adversarial Networks (GAN) is an approach which can be easily implemented to avoid this issue. However, GANs are time and computationally expensive. These are not applicable in a general clinical workflow. Therefore, this thesis presents an approach using a simpler GANthat can generate accurate sample labelled data. The synthetic data are used to train classifier and the thesis demonstrates that the predictive model can generate higher accuracy in the test environment. This thesis demonstrates that machine learning and deep learning models can be applied to a clinical workflow, without exploiting expert time and high computing power

Linear domain interactome and biological function of anterior gradient 2

The Anterior Gradient 2 (AGR2) protein has been implicated in a variety of biological systems linked to cancer and metastasis, tamoxifen-induced drug resistance, pro-inflammatory diseases like IBD and asthma, and limb regeneration. The molecular mechanisms by which AGR2 mediates these various phenotypes in disease progression in both cancer and IBD are poorly understood, as is the biological function(s) of AGR2 under non-disease conditions. Here, we use a combination of biochemical techniques, organ culture, cell biology and mouse genetics to investigate the biological significance of AGR2 both in cell lines and in vivo. We present data based on phage-peptide inter-actomics screens suggesting a role for AGR2 in mediating the maturation and trafficking of a class of membrane and secretory proteins, and investigate a putative interaction between AGR2 and one member of this class of proteins. We also describe the construction of a universal vector for use in making a variety of transgenic animals, and then present data showing its use as a promoter reporter, and attempt to investigate the temporal and spatial expression of AGR2 in the developing and adult mouse. Further, we present data describing the localisation pattern of AGR2 in the developing murine kidney using a combination of organ culture and antibody staining, and suggest a role for AGR2 in the developing kidney based on this data that is in agreement with a chaperone function for membrane and secretory proteins. Together, these data suggest that AGR2 has an intrinsic consensus docking site for a subset of its client proteins, that AGR2 plays a role in protein maturation in ciliated cell types, and provides a novel biological model to dissect the role of AGR2 in ER-trafficking

The role of prohibitin-2 in podocytes – mitochondrial function and beyond

Diseases of the kidney filtration barrier are a major cause of renal failure and cardiovascular mortality. Podocytes maintain the glomerular filtration barrier and podocyte dysfunction leads to the development of glomerulosclerosis, i.e. glomerular scarring. Mutations in the SPFH domain containing protein podocin, which is localized to the specialized cell-cell contact of podocytes, the slit diaphragm, can cause one of the most frequent glomerulopathies, FSGS. Podocin is one of the most extensively studied proteins in podocytes but nothing is known about other SPFH domain containing proteins in podocytes so far. Since it has been speculated that mitochondrial dysfunction may contribute to podocyte injury in glomerular diseases this thesis work investigated the podocyte-specific function of a mitochondrially localized SPFH domain containing protein, prohibitin-2 (PHB2). PHB2 is important for maintaining normal cristae structures and proper mitochondrial function. Podocyte-specific loss of PHB2 in mice resulted in the development of progressive albuminuria, glomerulosclerosis and endstage renal failure. Unexpectedly, immunofluorescence stainings and immunogold labeling detected PHB2 not only in mitochondria but also at the slit diaphragm. PHB2 co-precipitated with podocin, thereby suggesting an extramitochondrial role of PHB2 at the slit diaphragm. Supporting these results, the ortholog of PHB2 in C. elegans was also not restricted to mitochondria but associated with a mechanosensory complex containing the podocin ortholog MEC 2. Given the high similarity of the mechanosensory complex in worms and the slit diaphragm complex in mammals, functional assays of the mechanosensor were performed. Knockdown of phb-2 as well as loss of mec-2 in the mechanosensitive neurons resulted in impaired touch sensitivity, showing a functional impact of PHB2 on this conserved protein-lipid supercomplex. Furthermore, it was shown before that loss of insulin signaling increases lifespan of Phb2/phb-2 deficient yeast and worms. Therefore, apart from the findings at the slit diaphragm, the impact of PHB2 on podocyte metabolism was investigated. Phb2 deficiency in podocytes led to increased activity of mTORC1. Treatment of these animals with rapamycin or additional knockout of the insulin and IGF-1 receptor prolonged survival despite progressive albuminuria. Collectively, these data indicate that loss of PHB2 at the slit diaphragm resulted in the development of albuminuria but loss of podocytes was dependent on metabolic dysregulation