Uniquely among mammalian organs, skin is capable of marked size change in adults, yet the mechanisms underlying this notable capacity are unclear. Here, we use a system of controlled tissue expansion in mice to uncover cellular and molecular determinants of skin growth. Through machine learning–guided three-dimensional tissue reconstruction, we capture morphometric changes in growing skin. We find that most growth is driven by the proliferation of the epidermis in response to mechanical tension, with more limited changes in dermal and subdermal compartments. Epidermal growth is achieved through preferential activation and differentiation of Lgr6 ⁺ stem cells of the epidermis, driven in part by the Hippo pathway. By single-cell RNA sequencing, we uncover further changes in mechanosensitive and metabolic pathways underlying growth control in the skin. These studies point to therapeutic strategies to enhance skin growth and establish a platform for understanding organ size dynamics in adult mammals.

Methods for spatially resolved cellular profiling of tissue sections enable in-depth study of inter- and intra-sample heterogeneity but often profile small regions, requiring evaluation of many samples to compensate for limited assessment. Recent advances in three-dimensional (3D) tissue mapping offer deeper insights; however, attempts to quantify the information gained in transitioning to 3D remains limited. Here, to compare inter- and intra-sample tissue heterogeneity, we analyze >100 pancreas samples as cores, whole-slide images (WSIs), and cm³-sized 3D samples. We show that tens of WSIs and hundreds of tissue microarrays are needed to approximate the compositional tissue heterogeneity of tumors. Additionally, spatial correlations of pancreatic structures decay significantly within microns, demonstrating that isolated two-dimensional (2D) sections poorly represent their surroundings. Through 3D simulations, we determined the number of slides necessary to accurately measure tumor burden. These results quantify the power of 3D mapping and establish sampling methods for biological studies prioritizing composition or incidence.

The fallopian tubes play key roles in processes from pregnancy to ovarian cancer where three-dimensional (3D) cellular and extracellular interactions are important to their pathophysiology. Here, we develop a 3D multicompartment assembloid model of the fallopian tube that molecularly, functionally, and architecturally resembles the organ. Global label-free proteomics, innovative assays capturing physiological functions of the fallopian tube (i.e., oocyte transport), and whole-organ single-cell resolution mapping are used to validate these assembloids through a multifaceted platform with direct comparisons to fallopian tube tissue. These techniques converge at a unique combination of assembloid parameters with the highest similarity to the reference fallopian tube. This work establishes (i) an optimized model of the human fallopian tubes for in vitro studies of their pathophysiology and (ii) an iterative platform for customized 3D in vitro models of human organs that are molecularly, functionally, and microanatomically accurate by combining tunable assembloid and tissue mapping methods.

Recent advances in imaging and computation have enabled analysis of large three-dimensional (3D) biological datasets, revealing spatial composition, morphology, cellular interactions and rare events. However, the accuracy of these analyses is limited by image quality, which can be compromised by missing data, tissue damage or low resolution due to mechanical, temporal or financial constraints. Here, we introduce InterpolAI, a method for interpolation of synthetic images between pairs of authentic images in a stack of images, by leveraging frame interpolation for large image motion, an optical flow-based artificial intelligence (AI) model. InterpolAI outperforms both linear interpolation and state-of-the-art optical flow-based method XVFI, preserving microanatomical features and cell counts, and image contrast, variance and luminance. InterpolAI repairs tissue damages and reduces stitching artifacts. We validated InterpolAI across multiple imaging modalities, species, staining techniques and pixel resolutions. This work demonstrates the potential of AI in improving the resolution, throughput and quality of image datasets to enable improved 3D imaging.