HistoLens is an open-source graphical user interface developed using MATLAB AppDesigner for visual and quantitative analysis of histological datasets. HistoLens enables users to interrogate sets of digitally annotated whole slide images to efficiently characterize histological differences between disease and experimental groups. Users can dynamically visualize the distribution of 448 hand-engineered features quantifying color, texture, morphology, and distribution across microanatomic sub-compartments. Additionally, users can map differentially detected image features within the images by highlighting affected regions. We demonstrate the utility of HistoLens to identify hand-engineered features that correlate with pathognomonic renal glomerular characteristics distinguishing diabetic nephropathy and amyloid nephropathy from the histologically unremarkable glomeruli in minimal change disease. Additionally, we examine the use of HistoLens for glomerular feature discovery in the Tg26 mouse model of HIV-associated nephropathy. We identify numerous quantitative glomerular features distinguishing Tg26 transgenic mice from wild-type mice, corresponding to a progressive renal disease phenotype. Thus, we demonstrate an off-the-shelf and ready-to-use toolkit for quantitative renal pathology applications.

Emerging spatially resolved molecular imaging techniques, such as co-detection by indexing (CODEX), have enabled researchers to uncover distinct cellular structures in histological kidney sections. Spatial proteomics can provide users with the intensity level of proteins synthesized in the tissue in the same histology tissue section. However, the mapping of cell type proportions and molecular signatures can be challenging which might have contributed to the limited use of these technologies in clinical practice. Developing a computational model that handles such high-dimensional whole-slide imaging (WSI) data from CODEX requires applying advanced machine learning techniques to address common challenges such as interpretability, efficiency, and usability. In this study, we propose a computational pipeline for CODEX mapping on biopsy images that features an automated registration module that utilizes nuclei segmentation in both modalities. Our pipeline provides an explainable prediction and mapping of cell type clusters on histology and analyzes the heterogeneity of molecular features in the predicted clusters. For mapping, we used an unsupervised clustering analysis of uniform manifold approximation and projection (UMAP)-reduced features to enable visualizing the predicted clusters onto the histological tissue image. To test our proposed pipeline, we used a high-dimensional CODEX panel that comprises 44 markers and visualized the intensities and the predicted clusters on whole slide images (WSI) in a set of renal histology samples collected at Indiana University. Our results delineated 14 distinct cell clusters which demonstrated high fidelity between labeled objects and specific markers. Notably, 88% of cells in the "podocytes" dominant UMAP cluster were found to have a high level of podocalyxin, although it is adjacent to two other clusters dominated by renal vasculature cells. Out of 626 features examined, 44 were central to the "podocyte" cluster, accounting for approximately 50% of its variance (p < 0.05). This study can improve the understanding of the cell type proportions and kidney functions of tissue structures, which can contribute to the human biomolecular kidney atlas; a step towards substantial advancements in the field of kidney cell biology research.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics

Collagen and elastin are prominent components in both normal and abnormal tissues, and their presence and distribution have great significance for fibrosis- and cancer-related processes. Collagen and elastin quantification in the context of fibrosis, often associated with irreparable organ injury, can predict the disease severity and patient prognosis. In the context of cancer, specific spatial collagen signatures are known to influence tumor microenvironments while identification of elastin is important in the context of treatment of metastatic cancers. Traditional methods to quantify collagen and elastin vary in accuracy, cost, and ease of use. Using DUET microscopy on H&E slides, high-resolution collagen and elastin mapping is possible without added staining steps or expensive optical instrumentation. We demonstrate this approach in chronic kidney disease (CKD), coronary artery disease (CAD), and for identifying vascular elastin in colon cancers.

Missions into Deep Space are planned this decade. Yet the health consequences of exposure to microgravity and galactic cosmic radiation (GCR) over years-long missions on indispensable visceral organs such as the kidney are largely unexplored. We performed biomolecular (epigenomic, transcriptomic, proteomic, epiproteomic, metabolomic, metagenomic), clinical chemistry (electrolytes, endocrinology, biochemistry) and morphometry (histology, 3D imaging, miRNA-ISH, tissue weights) analyses using samples and datasets available from 11 spaceflight-exposed mouse and 5 human, 1 simulated microgravity rat and 4 simulated GCR-exposed mouse missions. We found that spaceflight induces: 1) renal transporter dephosphorylation which may indicate astronauts’ increased risk of nephrolithiasis is in part a primary renal phenomenon rather than solely a secondary consequence of bone loss; 2) remodelling of the nephron that results in expansion of distal convoluted tubule size but loss of overall tubule density; 3) renal damage and dysfunction when exposed to a Mars roundtrip dose-equivalent of simulated GCR.

Renal protection afforded by sodium glucose transporter, type 2 inhibitors (SGLT2i), e.g., empagliflozin (EMPA) involves complex intertwined mechanisms. Using a novel mouse model of obesity with insulin resistance, the TallyHo/Jng (TH) mouse on a high-milk-fat diet (HMFD), we found subtle changes in metabolism including altered regulation of sodium transporters that line the renal tubule. New potential epigenetic determinants of metabolic changes relating to FOXO and cancer signaling pathways were elucidated from an altered urine exosomal microRNA signature.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Accurate quantification of renal fibrosis has profound importance in the assessment of chronic kidney disease (CKD). Visual analysis of a biopsy stained with trichrome under the microscope by a pathologist is the gold standard for evaluation of fibrosis. Trichrome helps to highlight collagen and ultimately interstitial fibrosis. However, trichrome stains are not always reproducible, can underestimate collagen content and are not sensitive to subtle fibrotic patterns. Using the Dual-mode emission and transmission (DUET) microscopy approach, it is possible to capture both brightfield and fluorescence images from the same area of a tissue stained with hematoxylin and eosin (H&E) enabling reproducible extraction of collagen with high sensitivity and specificity. Manual extraction of spectrally overlapping collagen signals from tubular epithelial cells and red blood cells is still an intensive task. We employed a UNet++ architecture for pixel-level segmentation and quantification of collagen using 760 whole slide image (WSI) patches from six cases of varying stages of fibrosis. Our trained model (Deep-DUET) used the supervised extracted collagen mask as ground truth and was able to predict the extent of collagen signal with a MSE of 0.05 in a holdout testing set while achieving an average AUC of 0.94 for predicting regions of collagen deposits. Expanding this work to the level of the WSI can greatly improve the ability of pathologists and machine learning (ML) tools to quantify the extent of renal fibrosis reproducibly and reliably.

[This corrects the article DOI: 10.1017/cts.2023.172.].

Missions into Deep Space are planned this decade. Yet the health consequences of exposure to microgravity and galactic cosmic radiation (GCR) over years-long missions on indispensable visceral organs such as the kidney are largely unexplored. We performed biomolecular (epigenomic, transcriptomic, proteomic, epiproteomic, metabolomic, metagenomic), clinical chemistry (electrolytes, endocrinology, biochemistry) and morphometry (histology, 3D imaging, miRNA-ISH, tissue weights) analyses using samples and datasets available from 11 spaceflight-exposed mouse and 5 human, 1 simulated microgravity rat and 4 simulated GCR-exposed mouse missions. We found that spaceflight induces: 1) renal transporter dephosphorylation which may indicate astronauts’ increased risk of nephrolithiasis is in part a primary renal phenomenon rather than solely a secondary consequence of bone loss; 2) remodelling of the nephron that results in expansion of distal convoluted tubule size but loss of overall tubule density; 3) renal damage and dysfunction when exposed to a Mars roundtrip dose-equivalent of simulated GCR.

Introduction New advancements in spatial tissue imaging allow for the generation of large datasets that anchor transcriptomic and proteomic expression on histology with high granularity. These highly multiplexed cellular and molecular data provide researchers with an entirely new way to interpret tissue morphology and generate visual clues that may augment existing gold-standard histopathologic interpretation. To best interpret these multimodal data, artificial intelligence methods are indispensable to fuse bright-field histology with diverse spatial -omics methods, including spatial transcriptomics; multiplex fluorescence imaging, including codetection by indexing (CODEX); imaging mass spectrometry; miFISH; and imaging mass cytometry.1–4 Use Case A 48-year-old White man with hypertension and well-controlled diabetes mellitus but no known kidney disease presented to the emergency department in respiratory distress. He later developed hypotension and pneumonia-related respiratory failure requiring intubation. He cycled through antibiotics before improvement and developed AKI on hospital day 17, requiring KRT. He was discharged on day 31, yet remained dialysis-dependent. A kidney biopsy revealed mild arteriolar hyalinosis and patchy acute tubular necrosis (ATN) with occasional foci of monocytic and lymphocytic infiltrates and occasional mitotic tubular cells. Ten percent of the glomeruli were sclerosed, and <20% of the tubule-interstitium was affected by fibrosis or atrophy. Traces of linear IgG deposits were seen on immunofluorescence. Electron microscopy revealed mild thickening of the glomerular basement membrane without immune complex deposition, consistent with early diabetic changes. After biopsy, the differential diagnosis was (1) pending recovery of ATN, (2) nonrecovery with early signs of CKD, or (3) interstitial nephritis. The biopsy results did not give insight into recovery or interstitial nephritis and AKI. An approach that integrates molecular analysis may help provide more information for clinicians and pathologists. Specifically, a fused histology and spatial -omics data would identify mitotic tubular cells and their distance to fibrosis, necrosis, and inflammation. CODEX protein immunofluorescence can characterize inflammation in specific immune cells and injury biomarkers in adjacent tubules. Spatial transcriptomics can identify injured tubules and their likelihood of leading to fibrosis. Transcriptomic evidence of receptor ligand interactions can indicate whether damage to epithelial cells arises from nearby immune cells. These technologies provide a comprehensive understanding of biologic processes compared with standard biopsy interpretation. Select Spatial -Omics Technologies We focus on spatial transcriptomics (VISIUM) and CODEX as examples of state-of-the-art transcriptomic and multiplex protein imaging modalities because of their ability to register molecular data with bright-field microscopy. Spatial Transcriptomics Current spatial transcriptomics technologies localize mRNA expression at the tissue microenvironment, cellular, or subcellular level. Recent advances in multiplexed hybridization technologies, such as merFISH, CosMX, or Xenium, allow single-cell–based transcriptomic signatures of approximately 700–1000 supervised transcripts.5 By contrast, in situ capturing methods, such as VISIUM Spatial Transcriptomics, offer nearly whole transcriptome signatures. This technology uses unique barcodes to localize mRNA expression to a spot in a known location that overlies hematoxylin and eosin histology.1,3 Spots are uniformly distributed across a tissue, allowing for a deep transcriptomic signature. With a robust single-nucleus RNA sequencing atlas, spots may be deconvolved to determine proportions of specific cell types, states, and neighborhoods.6,7 CODEX CODEX is a fluorescence-based molecular imaging method that facilitates the capture of highly multiplexed images for a high number of protein markers (approximately 40). Researchers have demonstrated the ability to capture cellular diversity in healthy and pathologic kidneys.3,4,8 The key advantage of CODEX is the ability to render equivalent spatial resolution in -omics as with bright-field histology, allowing for one-to-one mapping of cell identity to underlying morphometry. However, incorporation of new targets requires considerable optimization efforts. Note on Tissue Preparation Spatial -omics technologies are typically optimized for frozen sections of 7–10 µm thickness. However, formalin-fixed, paraffin-embedded sections with a thickness between 2 and 5 µm are the gold standard for diagnosis. Generating spatial -omics data for thin, formalin-fixed, paraffin-embedded sections is a topic of growing interest. Publicly Available Databases The Human BioMolecular Atlas Program hosts a database of diverse spatial -omics data for normal reference tissue and organs. The Kidney Precision Medicine Project hosts similar data for patients with CKD and AKI. These data are available through the consortium web portals. Additional clinical metadata are often available on request. Multi-Omics Data Fusion The question remains how to best use spatial -omics data to drive digital health. Fusion of multi-omics data with bright-field histology is a growing topic of interest for pathologists and computational researchers alike (Figure 1). The fused dataspace will allow biologists to quickly reference structural and functional relationships of cells in the context of a whole biopsy.Figure 1: Deep learning for fusion of two popular spatial technologies. A deep learning model using diverse spatial -omics data as the input and spatial mapping of -omics data on bright-field histology as the output. (A) Glomerulus image from varying modalities including brightfield histology, overlaid spot locations for Visium Spatial Transcriptomics, and Co-Detection by Indexing (CODEX) image. (B) The alignment and quality control step ensures accurate registration of spatial-omics data as well as read quality of transcriptomic or fluorescence data. (C) Representative deep learning model architecture consisting of two sets of convolutional filter banks to first compress input data into a low-dimensional vector and then decode that low-dimensional input and render predictions. (D) Cellular characterization as the output of an ML model, providing insights into both cell-type composition for a given region of interest and an estimate of cellular health (cell state). (E) Comparison of spatial technologies. ML, machine learning; PAS, periodic acid–Schiff; QC, quality control.A variety of machine learning (ML) approaches are being developed to directly translate bright-field histology images into spatially mapped -omics data. Before input into a ML model, spatial -omics datasets must first be registered to align molecular data with histology. Because these digitally scanned images are exceedingly large (gigapixel area), a patch-based approach is often applied to train models on small portions of slides at a time.9 To ensure that these models are robust to high-dimensional -omics data, it is often necessary to distill the incoming information so that only the most important features are considered. Classical ML methods focused heavily on this dimensionality reduction step to overcome limitations in computational hardware. Currently, vast ML infrastructures (Amazon Web Services Google Collab, Kubeflow, etc.) allow extremely large models to be constructed that are specially equipped to concurrently digest thousands of input values. However, dimensionality reduction of spatial -omics data in general is useful in the context of studying known biological processes. For example, VISIUM data may be better leveraged by translating gene expression into proportions of select cell types within tissue microcompartments (e.g., glomeruli, tubules, vessels).6 Similarly, one can refine CODEX images by isolating particular markers contained within these microcompartments as a better basis of comparison across many individuals. By refining the data dimensionality before injecting to large models, we can reduce the learning gap that these models must overcome and better understand and apply these models to answer important questions. Emerging research in molecular imaging, combined with novel ML approaches, has the potential to provide the medical community with ways to analyze histological data at a depth never before possible. Whether it is with CODEX or spatial transcriptomics, these high-dimensional datasets require the development of complex models to achieve robust, explainable performance for biological and medical applications.8,10 Although challenges abound, there are exciting opportunities for potential developers of data fusion techniques for innovation, developing tools for clinicians to improve patient care. Meeting these challenges requires collaborative efforts of data and image scientists, clinicians, and biologists to formulate the best algorithmic solution in a team science approach.

OBJECTIVES/GOALS: Computational pathology is an emerging discipline that resides at the intersection of engineering, computer science, and pathology. There is a growing need to develop innovative pedagogical approaches to train future computational pathologists who have diverse educational backgrounds. METHODS/STUDY POPULATION: Our work proposes an iterative approach toward teaching master’s and Ph.D. students from various backgrounds, such as electrical engineering, biomedical engineering, and cell biology the basics of cell-type identification. This approach is grounded in the active learning framework to allow for observation, reflection, and independent application. The learners are trained by a team of an electrical engineer and pathologist and provided with eight images containing a glomerulus. They must then classify nuclei in each of the glomeruli as either a podocyte (blue), endothelial cell (green), or mesangial cell (red). RESULTS/ANTICIPATED RESULTS: A simple web application was built to calculate agreement, measured using Cohen’s kappa, between annotators for both individual glomeruli and across all eight images. Automating the process of providing feedback from an expert renal pathologist to the learner allows for learners to quickly determine where they can improve. After initial training, agreement scores for cells scored by both the learner and the expert were high (0.75), however, when including cells not scored by both the agreement was relatively low (0.45). This indicates that learners needed more instruction on identifying unique cells within each image. This low-stakes approach encourages exploratory and generative learning. DISCUSSION/SIGNIFICANCE: Computation medical sciences require interdisciplinary training methods. We report on a robust approach for team-based mentoring and skill development. Future implementations will include undergraduate learners and provide opportunities for graduate students to engage in near-peer mentoring.

Renal cell carcinoma (RCC) is a common malignant tumor of the adult kidney, with the papillary subtype (pRCC) as the second most frequent. There is a need to improve evaluative criteria for pRCC due to overlapping diagnostic characteristics in RCC subtypes. To create a better prognostic model for pRCC, we proposed an integration of morphologic and genomic features. Matched images and genomic data from The Cancer Genome Atlas were used. Image features were extracted using CellProfiler, and prognostic image features were selected using least absolute shrinkage and selection operator and support vector machine algorithms. Eigengene modules were identified using weighted gene co-expression network analysis. Risk groups based on prognostic features were significantly distinct (p < 0.05) according to Kaplan-Meier analysis and log-rank test results. We used two image features and nine eigengene modules to construct a model with the Random Survival Forest method, measuring 11-, 16-, and 20-month areas under the curve (AUC) of a time-dependent receiver operating curve. The integrative model (AUCs: 0.877, 0.769, and 0.811) outperformed models trained with eigengenes alone (AUCs: 0.75, 0.733, and 0.785) and morphological features alone (AUCs: 0.593, 0.523, 0.603). This suggests that an integrative prognostic model based on histopathological images and genomic features could significantly improve survival prediction for pRCC patients and assist in clinical decision-making.

The incorporation of automated computational tools has a great amount of potential to positively influence the field of pathology. However, pathologists and regulatory agencies are reluctant to trust the output of complex models such as Convolutional Neural Networks (CNNs) due to their usual implementation as black-box tools. Increasing the interpretability of quantitative analyses is a critical line of research in order to increase the adoption of modern Machine Learning (ML) pipelines in clinical environments. Towards that goal, we present HistoLens, a Graphical User Interface (GUI) designed to facilitate quantitative assessments of datasets of annotated histological compartments. Additionally, we introduce the use of hand-engineered feature visualizations to highlight regions within each structure that contribute to particular feature values. These feature visualizations can then be paired with feature hierarchy determinations in order to view which regions within an image are significant to a particular sub-group within the dataset. As a use case, we analyzed a dataset of old and young mouse kidney sections with glomeruli annotated. We highlight some of the functional components within HistoLens that allow non-computational experts to efficiently navigate a new dataset as well as allowing for easier transition to downstream computational analyses.