STP: single-cell partition for subcellular spatially-resolved transcriptomics

Haoyang Li, Qinan Hu, Zhaowen Qiu, Hui Xiong, Yuhui Hu () and Xin Gao ()
Additional contact information
Haoyang Li: King Abdullah University of Science and Technology (KAUST)
Qinan Hu: Southern University of Science and Technology
Zhaowen Qiu: Northeast Forestry University
Hui Xiong: The Hong Kong University of Science and Technology (Guangzhou)
Yuhui Hu: Southern University of Science and Technology
Xin Gao: King Abdullah University of Science and Technology (KAUST)

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Spatially-resolved transcriptomics (SRT) technologies now allow exploration of gene expression with spatial context. Recent advances achieving subcellular resolution provide richer data but also introduce challenges, such as aggregating subcellular spots into individual cells, which is a task distinct from traditional deconvolution. Existing methods often grid SRT data into predefined squares, which is unrealistic for accurately capturing cellular boundaries. We propose a method, STP, that integrates subcellular SRT data with nuclei-stained images to partition individual cells. STP first segments nuclei and maps their masks onto the SRT data, then uses a simulated-annealing-inspired approach to expand nuclear boundaries to the full cellular level. Evaluated on subcellular SRT datasets from Drosophila embryos at multiple developmental stages and from mouse embryos with a large field-of-view, STP demonstrated accurate single-cell partitioning, unveiling significant spatial tissue patterns and identifying undetected cell types beyond previous methods.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-59782-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59782-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-59782-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().