Dr. Michael T. Eadon Profile

Dr. Michael T. Eadon

at Indiana Univ

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Publications (3)

Proceedings Article | 3 April 2024 Presentation + Paper

CODEX and H&E imaging: cell type mapping, analysis, and visualization pipeline

Julio Maragall, Nicholas Lucarelli, Ahmed Naglah, Samuel Border, Seth Winfree, Zoltan Laszik, Michael Eadon, Tarek El-Achkar, Sanjay Jain, Pinaki Sarder

Proceedings Volume 12933, 129330W (2024) https://doi.org/10.1117/12.3008471

KEYWORDS: Image segmentation, Tissues, Feature extraction, Kidney, Visualization, Biological imaging, Image registration, Analytical research, Proteins, Medicine

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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 highdimensional 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 atIndiana 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.

Proceedings Article | 3 April 2024 Poster + Paper

Correlation of glomerular histomorphometry changes with spatially resolved transcriptomic profiles in diabetic nephropathy

Ahmed Naglah, Sayat Mimar, Anindya Paul, Ricardo Melo Ferreira, Avi Rosenberg, Seung Seok Han, Jessica Ray, Michael Eadon, Pinaki Sarder

Proceedings Volume 12933, 129331G (2024) https://doi.org/10.1117/12.3008461

KEYWORDS: Tissues, Machine learning, Visualization, Medicine, Diseases and disorders, Visual process modeling, Kidney, Education and training, Data modeling, Statistical modeling, Artificial intelligence

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Diabetic nephropathy (DN), a common complication of diabetes mellitus, remains a leading cause of endstage renal disease. Histopathological assessment of renal biopsy remains the gold standard for diagnosis. Accurate diagnosis is crucial for timely intervention and personalized management plans. Machine learning (ML) models can analyze digital pathology slides, learn DN biomarkers, and aid in DN staging. Developing ML models can be challenging for the limited availability of annotated images, subjectivity in histopathology interpretation, and histology artifacts. Molecular profiling such as single-cell RNA sequencing (SC) and spatial transcriptomics (ST) can contribute to better understanding of cellular heterogeneity and molecular pathways. Clinical use of molecular tests is limited due to the absence of well-established protocols specific to DN diagnosis. In this study, we propose a framework for correlating glomerular histomorphometry with spatially resolved transcriptomics to better understand the histologic spectrum of DN. The framework uses manual tissue labels by experienced users, and hybrid labels by combining user input and unsupervised clustering of molecular data. Clustering is performed on the gene expression levels of disease biomarkers and on the cell type decomposition of tissue by integration with SC reference data from KPMP. We used a dataset of 6 DN and 3 normal cases, with frozen section histology, ST, and SC collected at Seoul National University Hospital, Seoul, South Korea. Our initial experiments identified a correlation between the imaging features histomorphometry and disease label. Our cloud-based prototype visualizes both gene markers and cell type decomposition as a heatmap on histology, enables molecular-informed validation of structures, enables adding manual labels, and visualizes the clusters on histology. In conclusion, our framework can analyze the correlation between histomorphometry and tissue labels generated in a molecular-informed environment. Our cloud-based prototype can aid the diagnosis process by visualizing these correlations overlaid on digital slides.

Proceedings Article | 3 April 2024 Poster + Paper

Computational integration of CODEX and brightfield histology for cell annotation using deep learning

Nicholas Lucarelli, Zoltan Laszik, Seth Winfree, Tarek El-Achkar, Michael Eadon, Sanjay Jain, Pinaki Sarder

Proceedings Volume 12933, 129331F (2024) https://doi.org/10.1117/12.3008457

KEYWORDS: Image segmentation, Tissues, Kidney, Pathology, Deep learning, Biological samples

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