The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2
Open accessAbstract
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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STAR: ultrafast universal RNA-seq aligner
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Self-citationA Direct Approach to False Discovery Rates
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The single-cell transcriptional landscape of mammalian organogenesis
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Xu, Y., & McCord, R. P. (2021) · Figshare · doi:10.6084/m9.figshare.15139721
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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