@inproceedings{2e59471685834324b014790f8f2a409b,
title = "CODEX and H&E Imaging: Cell Type Mapping, Analysis, and Visualization Pipeline",
abstract = "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.",
keywords = "CODEX, Image Analysis, Molecular Imaging, Quantitative Analysis, Spatial Omics, Unsupervised Clustering",
author = "Maragall, {Julio A.} and Nicholas Lucarelli and Ahmed Naglah and Samuel Border and Seth Winfree and Zoltan Laszik and Michael Eadon and El-Achkar, {Tarek M.} and Sanjay Jain and Pinaki Sarder",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Medical Imaging 2024: Digital and Computational Pathology ; Conference date: 19-02-2024 Through 21-02-2024",
year = "2024",
doi = "10.1117/12.3008471",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, {John E.} and Ward, {Aaron D.}",
booktitle = "Medical Imaging 2024",
}