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SpaGE: Spatial Gene Enhancement using scRNA-seq
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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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“It was shown to outperform other imputation methods such as gimVI225 and SpaGE226 with respect to various accuracy metrics and scalability215.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [226]
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Cites
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · cited 1×
“This integration can be performed in two different ways (11): (i) dissociated single-cells measured with scRNA-seq can be mapped to their physical locations in the tissue (12–14), or (ii) missing gene expression measurements in the spatial data can be predicted from scRNA-seq.”
BackgroundThe single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424
Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH
Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754
Shared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746
Three-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region
Moffitt, J. R., Bambah-Mukku, D., Eichhorn, S. W., Vaughn, E., Shekhar, K., Perez, J. D., Rubinstein, N. D., Hao, J., Regev, A., Dulac, C., et al. (2018) · Science · doi:10.1126/science.aau5324 · Cited by 1,365
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113
FUNDAMENTALS OF PLANARIAN REGENERATION
Reddien, P. W., & Alvarado, A. S. (2004) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.20.010403.095114 · Cited by 1,111
Xia, C., Fan, J., Emanuel, G., Hao, J., & Zhuang, X. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912459116 · Cited by 755
Spatial organization of the somatosensory cortex revealed by osmFISH
Codeluppi, S., Borm, L. E., Zeisel, A., Manno, G. L., van Lunteren, J. A., Svensson, C. I., & Linnarsson, S. (2018) · Nature Methods · doi:10.1038/s41592-018-0175-z · Cited by 635
Beyond Moran'sI: Testing for Spatial Dependence Based on the Spatial Autoregressive Model
Li, H., Calder, C. A., & Cressie, N. (2007) · Geographical Analysis · doi:10.1111/j.1538-4632.2007.00708.x · Cited by 528
High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin
Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395
Nitzan, M., Karaiskos, N., Friedman, N., & Rajewsky, N. (2019) · Nature · doi:10.1038/s41586-019-1773-3 · Cited by 275
Nontoxic, double-deletion-mutant rabies viral vectors for retrograde targeting of projection neurons
Chatterjee, S., Sullivan, H. A., MacLennan, B. J., Xu, R., Hou, Y., Lavin, T. K., Lea, N. E., Michalski, J. E., Babcock, K. R., Dietrich, S., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0091-7 · Cited by 177
Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105
Mourragui, S. M. C., Loog, M., van de Wiel, M. A., Reinders, M. J. T., & Wessels, L. F. A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz372 · Cited by 79
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Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
“GCNG 237 , Misty 238 and NCEM 236 formulate this task in terms of spatial graphs of cells and graph neural networks, SpaOTsc 239 uses optimal transport, and SVCA 240 quantifies the effect of cell–cell communication events on gene expression profiles with spatial variance component analysis.”
MethodHu, J., Li, X., Coleman, K., Schroeder, A., Ma, N., Irwin, D. J., Lee, E. B., Shinohara, R. T., & Li, M. (2021) · Nature Methods · doi:10.1038/s41592-021-01255-8 · Cited by 1,113
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059
An introduction to spatial transcriptomics for biomedical research
Williams, C. G., Lee, H. J., Asatsuma, T., Vento‐Tormo, R., & Haque, A. (2022) · Genome Medicine · doi:10.1186/s13073-022-01075-1 · Cited by 896
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459
Metadata
- 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
- 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
- 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
- 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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