The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.
With recent advances in resolution and field-of-view, spatially resolved sequencing has emerged as a cutting-edge technology that provides a technical foundation for interpreting large tissues at the spatial single-cell level. To handle the high-resolution spatial omics dataset with associated images and generate spatial single-cell level gene expression, a powerful one-stop toolbox is required. Here, we propose StereoCell, an image-facilitated cell segmentation framework for high-resolution and large field-of-view spatial omics. StereoCell offers a comprehensive and systematic solution to generating high-confidence spatial single-cell data, including image stitching, registration, nuclei segmentation, and molecule labeling. In image stitching and molecule labeling, StereoCell delivers the best-performing algorithms to reduce stitching error and improve the signal-to-noise ratio of single-cell gene expression compared to existing methods. Meanwhile, as demonstrated using mouse brain, StereoCell has been shown to obtain high-accuracy spatial single-cell data, which facilitates clustering and annotation.
Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effects removal, particularly when the sections are measured by various technologies or collected at different times. Here we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings, but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.
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