Hidden network preserved in Slide-tags data allows reference-free spatial reconstruction
Dahlberg, S. K.; Bonet, D. F.; Franzen, L.; Stahl, P. L.; Hoffecker, I. T.
Show abstract
Spatial transcriptomics technologies aim to spatially map gene expression in tissues and typically use oligonucleotide array surfaces that have undergone spatial indexing. These arrays are used to capture nucleic acids diffusing from adjacently placed tissues, allowing subsequent sequencing to reveal both gene and position. Slide-tags is a recently developed method by Russell et al. that inverts this principle. Instead of capturing molecules released from the tissue, probes are detached from a pre-decoded bead array and diffused into tissues, tagging nuclei with spatial barcodes. We reanalyzed this data and discovered a latent, spatially informative cell-bead network formed incidentally from barcode diffusion and the biophysical properties of the tissue. This allows us to treat Slide-tags as a new network-based imaging-by-sequencing approach. By optimizing spatial constraints encoded in the cell-bead network structure, we could achieve unassisted tissue reconstruction, a fundamental shift from classical spatial technologies based on pre-indexed arrays.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Robust decomposition of cell type mixtures in spatial transcriptomics 96%
- Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation 96%
- BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution 95%
Similar papers in this journal
- Neighborhood nonnegative matrix factorization identifies patterns and spatially-variable genes in large-scale spatial transcriptomics data 96%
- geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq. 96%
- Smoother: A Unified and Modular Framework for Incorporating Structural Dependency in Spatial Omics Data 96%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.