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.
Cell types present in a biopsy provide information on disease processes and organ health, and are useful in a research setting. Multiplex imaging technologies like CODEX can provide spatial context for protein expression and detect cell types on a whole slide basis. The CODEX workflow also allows for hematoxylin and eosin (H&E) staining on the same sections used in molecular imaging. Deep learning can automate the process of histological analysis, reducing time and effort required. We seek to automatically segment and classify cells from histologically stained renal tissue sections using deep learning, with CODEX generated cell labels as a ground truth. Image data consisted of brightfield H&E whole slide images (WSIs) from a single institution, collected from human reference kidneys. Nuclei were segmented using deep learning, and CODEX markers were measured for each nucleus. Cells and their markers were clustered in an unsupervised manner, and assigned labels according to upregulated markers and spatial biological priors. Classified cell types included: proximal tubules, distal tubules, vessels, interstitial cells, and general glomerular cells. Cell maps were used to train a Deeplab V3+ semantic segmentation network. Cell maps were successfully created in all sections, with ~65% used for training and ~35% used for testing. The trained network achieved a balanced accuracy of 0.75 across all cell types. We were able to automatically segment and classify nuclei from various cell types directly from H&E stained WSIs. In future work, we intend to expand the dataset to include more CODEX markers (and therefore more granular cell types), and more samples with more variability, to test the robustness of the model to new data.
Automated cellular nuclei segmentation is often an important step for digital pathology and other analyses such as computer aided diagnosis. Most existing machine learning methods for microscopy image analysis require postprocessing such as watershed transform or connected component analysis to obtain instance segmentation from semantic segmentation results. This becomes prohibitively expensive computationally especially when used with 3D microscopy volumes. UNet Transformers for Instance Segmentation (UNETRIS) is proposed to eliminate the postprocessing steps necessary for nuclei instance segmentation in 3D microscopy images. UNETRIS, an extension of UNETR which utilizes a transformer as the encoder in the successful “U-shaped” network design for the encoder-decoder structure of U-Net, uses additional transformers to separate individual instances of cell nuclei directly during the inference step without the need for expensive postprocessing steps. UNETRIS does not require but can use manual ground truth annotations for training. UNETRIS was tested on a variety of microscopy volumes collected from multiple regions of organ tissues.
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