Three-dimensional assessments are necessary to determine the true spatial tissue composition of diseased tissues.

Methods for partially resolved cellular profiling has enabled in-depth quantitative tissue mapping via thinly cut sections to study inter-patient and intra-patient differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited spatial regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the 3D microanatomy of surgically resected human pancreas biospecimens obtained from patients diagnosed with pancreatic cancer. To compare differences in the inter- and intra-tumoral heterogeneity, we assessed the bulk and spatially resolved tissue composition of a cohort of two-dimensional (2D) whole slide images (WSIs), and a cohort of 3D serially sectioned and reconstructed tissues of pancreata. Here, we show the strength of using 3D as the gold standard, by measuring the information loss and sampling problems when using WSIs and TMAs. We demonstrate that spatial correlation in microanatomical tissue content decays significantly within a span of just a few microns within tumors. As a corollary, hundreds of TMAs and tens of WSIs are required to estimate spatial bulk tumor composition with <10% error in any given pancreatic tumor. In sum, we demonstrate that 3D assessments are necessary to accurately assess tumor burden and tissue composition. These preliminary results show the importance of rate of sampling necessary to more reliably assess spatially resolved tissue composition.