Cells interact with their local environment to enact global tissue function. By harnessing gene–gene covariation in cellular neighborhoods from spatial transcriptomics data, the covariance environment (COVET) niche representation and the environmental variational inference (ENVI) data integration method model phenotype–microenvironment interplay and reconstruct the spatial context of dissociated single-cell RNA sequencing datasets.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022). A review article that covers recent developments in multiplexed spatial measurement technologies and analysis methods.
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018). This paper analyzes breast tumor immune data, showing that gene–gene covariance is a robust measure that overcomes artifacts such as technical batch effects.
Burdziak, C. et al. Epigenetic plasticity cooperates with cell–cell interactions to direct pancreatic tumorigenesis. Science 380, eadd5327 (2023). This paper profiles genetic mouse models of pancreatic cancer to show that oncogenic mutation and tissue injury reshape communication between receptor and ligand gene modules in the tumor microenvironment.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Haviv, D. et al. The covariance environment defines cellular niches for spatial inference. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02193-4 (2024)
Rights and permissions
About this article
Cite this article
Capturing and modeling cellular niches from dissociated single-cell and spatial data. Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02207-1
Published:
DOI: https://doi.org/10.1038/s41587-024-02207-1
