AI for Science: examples of Spatial Transcriptomic data analysis

G. Wang etal. Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks. Nature Machine Intelligence, 2023.

Spatial transcriptomics (ST) technologies are revolutionizing the way to explore the spatial architecture of tissues. Currently, ST data analysis is often restricted to a single two-dimensional (2D) tissue slice, limiting our capacity to understand biological processes that take place in 3D space. Here we present STitch3D, a unified framework that integrates multiple ST slices to reconstruct 3D cellular structures. By jointly modelling multiple slices and integrating them with single-cell RNA-sequencing data, STitch3D simultaneously identifies 3D spatial regions with coherent gene-expression levels and reveals 3D cell-type distributions. STitch3D distinguishes biological variation among slices from batch effects, and effectively borrows information across slices to assemble powerful 3D models. Through comprehensive experiments, we demonstrate STitch3D’s performance in building comprehensive 3D architectures, which allow 3D analysis in the entire tissue region or even the whole organism. The outputs of STitch3D can be used for multiple downstream tasks, enabling a comprehensive understanding of biological systems.

X. Wan etal. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nature Communications, 2023.

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope’s utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes.