Journal
NUCLEIC ACIDS RESEARCH
Volume 49, Issue 9, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkab043
Keywords
-
Categories
Funding
- Ministerio de Ciencia, Innovacion y Universidades [AEI/FEDER] [SAF2017-89109-P]
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [810287]
- Chan Zuckerberg Initiative
- Spanish Ministry of Science and Innovation
- Centro de Excelencia Severo Ochoa
- CERCA Programme/Generalitat de Catalunya
- Spanish Ministry of Science and Innovation through the Instituto de Salud Carlos III
- Generalitat de Catalunya through Departament de Salut and Departament d'Empresa i Coneixement
- European Regional Development Fund (ERDF)
Ask authors/readers for more resources
SPOTlight is a computational tool that integrates spatial transcriptomics with single-cell RNA sequencing data to infer the location of cell types and states within complex tissues. Its high prediction accuracy and flexible application spectrum were demonstrated through applications in mouse brain and human pancreatic cancer.
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. [GRAPHICS] .
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available