Cited by 208Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Chromatin Dynamics

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SpaGE: Spatial Gene Enhancement using scRNA-seq

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Abdelaal, Tamim, Mourragui, Soufiane, Mahfouz, Ahmed, Reinders, Marcel J T·2020·Nucleic Acids Research

DOI 10.1093/nar/gkaa740PMC PMC7544237

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Abstract

Single-cell technologies are emerging fast due to their ability to unravel the heterogeneity of biological systems. While scRNA-seq is a powerful tool that measures whole-transcriptome expression of single cells, it lacks their spatial localization. Novel spatial transcriptomics methods do retain cells spatial information but some methods can only measure tens to hundreds of transcripts. To resolve this discrepancy, we developed SpaGE, a method that integrates spatial and scRNA-seq datasets to predict whole-transcriptome expressions in their spatial configuration. Using five dataset-pairs, SpaGE outperformed previously published methods and showed scalability to large datasets. Moreover, SpaGE predicted new spatial gene patterns that are confirmed independently using in situ hybridization data from the Allen Mouse Brain Atlas.

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Authors: Abdelaal, T., Mourragui, S., Mahfouz, A., & Reinders, M. J. T.
Affiliations: Leiden University Medical Center (NL)
The Netherlands Cancer Institute (NL)
Corresponding author: Reinders, Marcel J T
ORCID: Abdelaal, Tamim
Mourragui, Soufiane
Mahfouz, Ahmed
Reinders, Marcel J T
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2020-09-21
Volume: 48
Issue: 18
Pages: e107-e107
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gkaa740
PMC: PMC7544237
OpenAlex: W3087138957
Cited by: 207
References: 29
Citations by year: 2026: 27 · 2025: 44 · 2024: 55 · 2023: 33 · 2022: 30 · 2021: 17 · 2020: 1
FWCI: 6.56
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
Locations: Nucleic Acids Research (publishedVersion, cc-by-nc)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc)
Leiden Repository (Leiden University) (submittedVersion, cc-by-nc)
Research Repository (Delft University of Technology) (submittedVersion, cc-by-nc)
Funders: European Commission (675743)
H2020 (861190)
NWO TTW (17126)
ZonMw (40-00812-98-16012)
PPP allowance
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 Chromatin Dynamics
Keywords: spatial transcriptomics, single-cell RNA sequencing, spatial gene expression prediction, Allen Mouse Brain Atlas
MeSH terms: RNA-Seq, Animals, Software, Databases, Genetic, Mice, Single-Cell Analysis, Transcriptome, Datasets as Topic

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