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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
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Measurement of the location of molecules in tissues is essential for understanding tissue formation and function. Previously, we developed Slide-seq, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 μm. Here we report Slide-seqV2, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq), approaching the detection efficiency of droplet-based single-cell RNA-seq techniques. First, we leverage the detection efficiency of Slide-seqV2 to identify dendritically localized mRNAs in neurons of the mouse hippocampus. Second, we integrate the spatial information of Slide-seqV2 data with single-cell trajectory analysis tools to characterize the spatiotemporal development of the mouse neocortex, identifying underlying genetic programs that were poorly sampled with Slide-seq. The combination of near-cellular resolution and high transcript detection efficiency makes Slide-seqV2 useful across many experimental contexts.
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“Slide-seq, and later Slide-seqV2, barcode mRNA in tissues using a method conceptually similar to spatial transcriptomics, but a different strategy: instead of using arrayed barcoded reagents printed on slides, the barcoded reagents are placed directly onto the glass slide in solution, thereby creating a monolayer-packed array on a glass coverslip118,119 (Fig. 4a).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [119]
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Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
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
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationA Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603
The 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
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
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
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Yang, J., Wei, N., Qu, Y., Hu, C., Zhang, W., Liu, L., Wu, H., & Zheng, X. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf562 · Cited by 6
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Einhaus, J. F., Han, X., Feyaerts, D., Sunwoo, J. B., Gaudilliere, B. L., Ahmad, S. H., Aghaeepour, N., Bruckman, K. C., Ojcius, D. M., Schürch, C. M., et al. (2023) · Seminars in Immunopathology · doi:10.1007/s00281-022-00982-0 · Cited by 8
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Zhang, F., Shen, Z., Huang, S., Zhu, Y., & Yi, M. (2024) · Methods · doi:10.1016/j.ymeth.2024.11.006 · Cited by 5
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Su, J., Reynier, J.-B., Fu, X., Zhong, G., Jiang, J., Escalante, R. S., Wang, Y., Aparicio, L., Izar, B., Knowles, D. A., et al. (2023) · Genome biology · doi:10.1186/s13059-023-03138-x · Cited by 5
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Cheng, H.-C., Chang, C., Wu, C., Su, P., Hwang, D., Wu, C.-L., Hsu, S. N., Lim, S. Y., Liu, T., McGrath, J. A., et al. (2026) · Dermatologica Sinica · doi:10.4103/ds.ds-d-25-00298 · Cited by 4
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Hamraoui, A., Onfroy, A., Sénamaud‐Beaufort, C., Coulpier, F., Lemoine, S., Jourdren, L., & Thomas‐Chollier, M. (2026) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqag070 · Cited by 4
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Strope, B., Pendleton, K. E., Bowie, W., Echeverria, G. V., & Zhu, Q. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae608 · Cited by 4
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Ding, J., Venegas, J., Lu, Q., Wang, Y., Wu, L., Jin, W., Wen, H., Liu, R., Tang, W., Li, Z., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.11.536333 · Cited by 4
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Cui, Y., Cui, Y., Wang, R., Zhu, Z., Zeng, X., Nakai, K., Cui, F., Zhang, Z., Shi, H. W., Chen, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.12.659243 · Cited by 4
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Metadata
- Authors
- Stickels, R. R., Murray, E., Kumar, P., Li, J., Marshall, J. L., Di Bella, D. J., Arlotta, P., Macosko, E. Z., & Chen, F.
- Affiliations
- Broad Institute (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Macosko, Evan Z.
- Chen, Fei
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2020-12-07
- Volume
- 39
- Issue
- 3
- Pages
- 313-319
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC8606189
- OpenAlex
- W3112563877
- Cited by
- 1,295
- References
- 47
- Citations by year
- 2026: 247 · 2025: 277 · 2024: 304 · 2023: 192 · 2022: 186 · 2021: 88
- FWCI
- 38.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health (DP2 AG058488-01, 1DP5OD024583)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG010647)
- Burroughs Wellcome 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 › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
- Keywords
- spatial transcriptomics, dendritic mRNA localization, hippocampal neurons, neocortex development, spatiotemporal gene expression, single-cell trajectory analysis, array indexing, library construction
- MeSH terms
- Animals, Humans, RNA, Messenger, Sequence Analysis, RNA, Mice, Single-Cell Analysis, Transcriptome
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