Forjaz, A.; Kramer, D.; Shen, Y.; Bea, H.; Tsapatsis, M.; Ping, J.; Queiroga, V.; Han, K. S.; Joshi, S.; Grubel, C.; Beery, M. L.; Kusmartseva, I.; Atkinson, M. A.; Kiemen, A. L.; Wirtz, D.

Nuclear morphology is an indicator of cellular function and disease states, as changes in nuclear size, shape, and texture often reflect underlying disease-related genetic, epigenetic, and microenvironmental alterations. For disease diagnosis, nuclear segmentation performed in 2D hematoxylin and eosin (H&E)-stained tissue sections has long represented the gold standard. However, recent advances in three-dimensional (3D) histology, which provide a more biologically accurate representation of the spatial heterogeneity of human microanatomy, has led to improved understandings of disease pathology. Yet challenges remain in the development of scalable and computationally efficient pipelines for extracting and interpreting nuclear features in 3D space. 2D histology neglects crucial spatial information, such as 3D connectivity, morphology, and rare events missed by sparser sampling. Here, through extension of the CODA platform, we integrate 3D imaging with nuclear segmentation to analyze nuclear morphological features in human tissue. Analysis of 3D tissue microenvironments uncovered critical changes in 3D morphometric heterogeneity. Additionally, it enables the spatial characterization of immune cell distribution in relation to tissue structures, such as variations in leukocyte density near pancreatic ducts and blood vessels of different sizes. This approach provides a more comprehensive understanding of tissue and nuclear structures, revealing spatial patterns and interactions that are critical for disease progression.