SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes
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Abstract
Abstract Spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections and further fine-mapped normal and neoplastic cell states. Trained on an external single-cell pancreatic tumor references, we further charted the localization of clinical-relevant and tumor-specific immune cell states, an illustrative example of its flexible application spectrum and future potential in digital pathology.
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Deconvolution methods can infer the proportion of different cell types within each spatial spot when scRNA-seq data from the same tissue are available.
“When scRNA-seq data from the same tissue are available, computational methods such as SPOTlight, robust cell type decomposition (RCTD), Tangram and conditional autoregressive deconvolution (CARD)84–87 deconvolute the spatial transcriptomic data and infer the proportion of different cell types within each spatial spot.”
From Spatial mapping of cellular senescence: emerging challenges and opportunities (2023) · cites this paper as [84]
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- Authors
- Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I., & Heyn, H.
- Affiliations
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST) , Barcelona,
- Barcelona Institute of Science and Technology (ES)
- Universitat Pompeu Fabra (ES)
- Corresponding author
- Heyn, Holger
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-02-05
- Volume
- 49
- Issue
- 9
- Pages
- e50-e50
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC8136778
- OpenAlex
- W3127422037
- Cited by
- 732
- References
- 37
- Citations by year
- 2026: 146 · 2025: 151 · 2024: 156 · 2023: 123 · 2022: 105 · 2021: 41 · 2020: 10
- FWCI
- 34.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc)
- Funders
- Ministerio de Ciencia, Innovación y Universidades (SAF2017-89109-P, AEI/FEDER)
- European Research Council (810287)
- Spanish Ministry of Science and Innovation
- European Regional Development Fund
- 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 › Gene Regulatory Network Analysis
- Keywords
- spatial transcriptomics, single-cell transcriptomics, data integration, regression analysis, cell type deconvolution, cell type markers, non-negative least squares, mouse brain, hippocampus, cortical layers, pancreatic cancer, tumor immune cells, digital pathology
- MeSH terms
- RNA-Seq, Animals, Brain, Humans, Pancreatic Neoplasms, Software, Carcinoma, Pancreatic Ductal, Mice, Single-Cell Analysis
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