Why three-dimensional tissue assessment matters
This Learning Wednesday paper note highlights “Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues.” The study is relevant to how 3D assessment alters tissue composition measurements, focusing on how three-dimensional tissue context can change what researchers see and measure.
Selected notes from the paper
“Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping.”
“These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling.”
“We applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens.”
“We assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections.”
“To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed.”
“We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues.”
“This analysis revealed that the range in heterogeneity observed in just three 3D samples was sufficient to replicate the range of inter-patient heterogeneity in tissue composition.”
“ECM consistently showed the lowest tissue heterogeneity, with an average of 19 TMAs necessary to reach <10% error in the estimation of 2D-WSI composition.”
“Estimation of content in nerves revealed that these tissue components were the less prevalent, which required 100 TMAs for <10% error in estimation of 2D WSI composition, >700 TMAs for <10% error in estimation of 3D-volume composition.”
“Few slides are needed to estimate the composition of cancer in samples with high cancer burden, but … many slides are necessary to estimate cancer composition in samples with low neoplastic content.”
“We demonstrate that 3D assessments are necessary to accurately assess tissue composition and tumor content and provide guidelines for the rate of sampling necessary to rigorously assess spatially resolved tissue composition and associated tissue density and intercellular distances.”
Alpenglow Perspective
From an Alpenglow perspective, this paper is useful because it connects the shift from 2D to 3D assessment of tissue composition measurements to a broader need in 3D spatial biology: measuring tissue architecture across depth while preserving context for quantitative analysis.
