Recently, the Bioinformatics & Intelligent Medicine team has published “Pairpot: a database with real-time lasso-based analysis tailored for paired single-cell and spatial transcriptomics” in the journal Nucleic Acids Research.

The rise of single-cell and spatial transcriptomics sequencing technologies has provided a brand-new perspective for exploring more cancer treatment targets. Single-cell technology can reveal the gene expression profiles of individual cells, while spatial transcriptomics technology captures the spatial distribution of gene expression. Paired single-cell and spatially resolved transcriptomics (SRT) data supplement each other, providing in-depth insights into biological processes and disease mechanisms. Previous SRT databases have limitations in curating sufficient single-cell and SRT pairs (SC–SP pairs) and providing real-time heuristic analysis, which hinder the effort to uncover potential biological insights.

Here, we developed Pairpot (http://pairpot.bioxai.cn), a database tailored for paired single-cell and SRT data with real-time heuristic analysis. Pairpot curates 99 high-quality pairs including 1,425,656 spots from 299 datasets, and creates the association networks. It constructs the curated pairs by integrating multiple slices and establishing potential associations between single-cell and SRT data. On this basis, Pairpot adopts semi-supervised learning that enables real-time heuristic analysis for SC–SP pairs where Lasso-View refines the user-selected SRT domains within milliseconds, Pair-View infers cell proportions of spots based on user-selected cell types in real-time and Layer-View displays SRT slices using a 3D hierarchical layout. Experiments demonstrated Pairpot’s efficiency in identifying heterogeneous domains and cell proportions.

Furthermore, the research team also developed a new spatial multi-modal data format (SMD) with extensible interfaces, which are compatible with multiple SRT platforms, greatly enhancing its versatility and practicality. To visually present the cell type-specific domains, the research team also developed the 3D Landscape, enabling researchers to gain a more intuitive understanding of the distribution and interactions of cell type-specific domains in space.

Pairpot is valuable to biologists not only because it offers easy-to-access paired datasets but also because its lasso-based modules provide a new perspective for developing efficient online computational platforms based on pre-analyzed information and user behavior. Pairpot is also useful for researchers to seek potential biological insights because it provides both overall observations for multiple slices in a 3D layout and precise real-time lass-based analysis for cells or domains of user’s interest.

Pairpot’s intuitive interface and heuristic analysis modules empower users to tailor their workflows according to their specific needs. For researchers who prefer to download the data and conduct analyses offline, Pairpot generates code for online heuristic analysis and alternative bioinformatics tools that allow users to replicate the analysis results offline. If researchers would like to analyze their own paired single-cell and SRT data, they can integrate the interfaces provided by Pairpot into their pipelines, including preprocessing, data integration, neighbor graph construction, and UCell scores evaluation. For researchers without programming capabilities, Pairpot provides valuable resources and powerful tools online to streamline the SRT integrative analysis and enhance the understanding of biological processes and disease mechanisms.

doi：https://doi.org/10.1093/nar/gkae986

github：https://github.com/lyotvincent/Pairpot