Cited by 1,295Single-cell and spatial transcriptomicsRNA Research and SplicingMolecular Biology Techniques and Applications

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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2

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Stickels, Robert R., Murray, Evan, Kumar, Pawan, Li, Jilong, Marshall, Jamie L., Di Bella, Daniela J., Arlotta, Paola, Macosko, Evan Z., Chen, Fei·2021·Nature Biotechnology

DOI 10.1038/s41587-020-0739-1PMC PMC8606189

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Abstract

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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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: Stickels, Robert R.
Murray, Evan
Kumar, Pawan
Li, Jilong
Marshall, Jamie L.
Di Bella, Daniela J.
Arlotta, Paola
Macosko, Evan Z.
Chen, Fei
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2020-12-07
Volume: 39
Issue: 3
Pages: 313-319
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-020-0739-1
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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