SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors
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Abstract
The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type–specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model–based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.
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- Authors
- Liu, X., Tang, G., Chen, Y., Li, Y., Li, H., & Wang, X.
- Affiliations
- Washington University in St. Louis (US)
- University of Illinois Chicago (US)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Wang, Xiaowei
- Venue
- Cancer Research
- Published
- 2024-10-10
- Volume
- 85
- Issue
- 1
- Pages
- 171-182
- ISSN
- 0008-5472 (ISSN-L)
- 1538-7445
- PMC
- PMC11695180
- OpenAlex
- W4403300146
- Cited by
- 8
- References
- 37
- Citations by year
- 2026: 6 · 2025: 2
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (R01DE026471, R01CA233873, R56DE033344, R01CA287778 and R35GM141535)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- cell type deconvolution, spatial transcriptomics, solid tumors, tumor microenvironment, tumor progression
- MeSH terms
- Animals, Humans, Neoplasms, Gene Expression Regulation, Neoplastic, Cluster Analysis, Gene Expression Profiling, Mice, Single-Cell Analysis, Tumor Microenvironment, Transcriptome
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