Nature子刊：原致远/赵屹/冯建峰合作提出3D数字器官重构新算法

Core Viewpoint - The article discusses the limitations of current spatial transcriptomics (ST) technologies, which primarily operate in two dimensions, and introduces a new computational framework called SpatialZ that enables the reconstruction of dense 3D cell atlases from sparse 2D slices, significantly enhancing the understanding of biological functions and tissue organization [2][3][10]. Group 1: Limitations of Current Technologies - Current ST technologies are limited to 2D observations, making it difficult to capture the continuous gradients of gene expression and the intricate cellular interactions within organs [2]. - The compromise in sampling density along the Z-axis due to high experimental costs leads to significant gaps in data, resulting in a fragmented view of biological tissues [2]. Group 2: Introduction of SpatialZ - SpatialZ is a novel computational framework that integrates optimal transport theory to generate virtual slices between sparse real slices, facilitating the transition from discrete 2D data to dense 3D maps [3][5]. - The framework has successfully constructed a digital mouse brain atlas containing over 38 million cell gene expressions and 3D coordinates, marking a significant advancement in the field of life sciences [3][8]. Group 3: Methodology of SpatialZ - SpatialZ employs a four-step algorithm for high-fidelity 3D reconstruction, including spatial alignment, position generation, cell state inference, and expression profile inference [5]. - The methodology ensures that the generated cells not only have accurate spatial positioning but also reflect the biological states and microenvironment characteristics [5]. Group 4: Validation and Performance - The reliability of SpatialZ was validated using mouse visual cortex data, showing that it accurately restored missing intermediate layer information and maintained high consistency with ground truth data [6][7]. - The framework demonstrated improved correlation and statistical significance compared to unprocessed sparse sampling data, effectively addressing structural information gaps caused by sparse sampling [7]. Group 5: Broader Applications - SpatialZ's underlying logic is highly generalizable, allowing its application in spatial proteomics, spatial metabolomics, and other multi-omics data, providing new perspectives for complex disease research [9]. - The framework has been successfully applied to human breast cancer tissue imaging mass cytometry data, correcting expression anomalies caused by tissue loss or technical artifacts, thus aiding in spatial screening for tumor immunotherapy targets [9]. Group 6: Conclusion - SpatialZ represents a breakthrough in computational methods, bridging the gap from single-cell analysis to organ-level digitalization, and offers a standardized digital reference for neuroscience research [10]. - The framework opens new possibilities for constructing comprehensive 3D spatial maps across modalities, organs, and species, potentially leading to new discoveries in developmental biology, neuroscience, and oncology [10].