Reconstruction of 3-dimensional tissue organization at the single-cell resolution.

ABSTRACT: Recent advances in spatial transcriptomics (ST) have allowed for the mapping of tissue heterogeneity, but this technique lacks the resolution to investigate gene expression patterns, cell-cell communications and tissue organization at the single-cell resolution. ST data contains a mixed transcriptome from multiple heterogeneous cells, and current methods predict two-dimensional (2D) coordinates for individual cells within a predetermined space, making it difficult to reconstruct and study three-dimensional (3D) tissue organization. Here we present a new computational method called scHolography that uses deep learning to map single-cell transcriptome data to 3D space. Unlike existing methods, which generate a projection between transcriptome data and 2D spatial coordinates, scHolography use

SUBMITTER: Fu Y 

PROVIDER: S-EPMC9881965 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature