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Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
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Abstract
Gene expression at fine scale: Mapping gene expression at the single-cell level within tissues remains a technical challenge. Rodriques et al. developed a method called Slide-seq, whereby RNA was spatially resolved from tissue sections by transfer onto a surface covered with DNA-barcoded beads. Applying Slide-seq to regions of a mouse brain revealed spatial gene expression patterns in the Purkinje layer of the cerebellum and axes of variation across Purkinje cell compartments. The authors used this method to dissect the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. Science , this issue p. 1463
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“Spatial profiling provides further information on tissue organization and three-dimensional architecture, thereby being essential for model validation186,187”
From Human disease models in drug development (2023) · cites this paper as [187]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Slide-seq captures transcripts on a slide equipped with random 10-µm DNA-barcoded beads and gains the positions of barcodes via in situ indexing, enabling spatial transcriptomic analysis at a near-cellular resolution [79].”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [79]
In the citing paper
“While the technologies used here do not require harsh dissociation procedures like single-cell or single-nuclei approaches and maintain spatial information, the lack of single-cell resolution with ST and the low sensitivity of ISS requires further technological improvements (Eng et al. , 2019; Rodriques et al. , 2019; Vickovic et al. , 2019; Stickels et al. , 2020).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Rodriques et al., 2019
In the citing paper
“PET imaging of collagen and molecular imaging of fibrosis185,186, which allows fast, non-invasive, and whole-organ-quantitative and longitudinal readouts of drug efficacy in antifibrotic clinical trials”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [185]
In the citing paper
“combining molecular imaging of fibrosis185 with cutting edge omic approaches such as single cell genomics174–176 could markedly improve patient diagnostics, staging, prognostication, stratification and cohort enrichment, which would in turn optimize clinical trial design”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [185]
In the citing paper
“Spatially resolved sequencing uses slides pre-patterned with beads carrying unique DNA barcodes to tag mRNA molecules based on their location in a brain slice, enabling whole-transcriptome sequencing with ~10 μm spatial resolution (Rodriques et al., 2019; Ståhl et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Rodriques et al., 2019
In the citing paper
“For Slide-seq (Rodriques et al., 2019), this is limited by spatial resolution (~10 μm)”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Rodriques et al., 2019
In the citing paper
“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 [118]
In the citing paper
“Nonetheless, we did observe the previously reported aging-related decline of OPC 49 , NRP 35,36 and ImmN 36,50 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [50]
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Metadata
- Authors
- 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.
- Affiliations
- Broad Institute (US)
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
- Corresponding author
- Chen, Fei
- Macosko, Evan Z.
- ORCID
- Venue
- Science
- Published
- 2019-03-29
- Volume
- 363
- Issue
- 6434
- Pages
- 1463-1467
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6927209
- OpenAlex
- W2915848182
- Cited by
- 2,424
- References
- 49
- Citations by year
- 2026: 385 · 2025: 391 · 2024: 415 · 2023: 319 · 2022: 300 · 2021: 318 · 2020: 212 · 2019: 80
- FWCI
- 75.91
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH Office of the Director (DP2 AG058488-01, 1DP5OD024583)
- 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 › Molecular Biology Techniques and Applications
- Keywords
- Slide-seq, spatial transcriptomics, cerebellum, hippocampus, traumatic brain injury, single-cell RNA sequencing, mouse model
- MeSH terms
- Brain Injuries, Traumatic, Animals, Cerebellum, Disease Models, Animal, Frozen Sections, Gene Expression Regulation, Hippocampus, Purkinje Cells, RNA, Messenger, Transcription, Genetic, Sequence Analysis, RNA, Cell Size, Mice, Genome-Wide Association Study, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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