Cited by 2,424Single-cell and spatial transcriptomicsGene expression and cancer classificationMolecular Biology Techniques and Applications

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Open access
Rodriques, Samuel G., Stickels, Robert R., Goeva, Aleksandrina, Martin, Carly A., Murray, Evan, Vanderburg, Charles R., Welch, Joshua, Chen, Linlin M. +2 more·2019·Science

Published version of a preprint

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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Crossref · 10.1126/science.aaw1219

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  • Spatial profiling provides information on tissue organization and three-dimensional architecture, which is essential for model validation.

    “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]

    The paper’s main text (page unavailable in our copy) shows that Slide-seq resolves tissue organization and that co-registered hippocampal sections allow three-dimensional visualization. It does not say that spatial profiling is essential for model validation. (LLM, conf=0.85)

    Experimental — accuracy not yet measured
  • 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.

    “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].”
  • The lack of single-cell resolution with ST and the low sensitivity of ISS requires further technological improvements.

    “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

  • PET imaging of collagen and molecular imaging of fibrosis allow fast, non-invasive, whole-organ quantitative and longitudinal readouts of drug efficacy in antifibrotic clinical trials.

    “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]

  • Combining molecular imaging of fibrosis with single-cell genomics could improve patient diagnostics, staging, prognostication, stratification, and cohort enrichment, optimizing clinical trial design.

    “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]

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Research works printed but not linked: 2

  1. [30]Lowe DG, in Proceedings of the Seventh IEEE International Conference on Computer Vision (IEEE, 1999; http://ieeexplore.ieee.org/document/790410/), pp. 1150– 1157 vol. 2.
  2. [35]Xiao J, Systematic regional variations in Purkinje cell spiking patterns. PLoS One. 9, e105633 (2014). PMC4140808 25144311

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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.
Venue
Science
Published
2019-03-29
Volume
363
Issue
6434
Pages
1463-1467
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2915848182
Cited by
2,424
References
51
Citations by year
2026: 385 · 2025: 391 · 2024: 415 · 2023: 319 · 2022: 300 · 2021: 318 · 2020: 212 · 2019: 80 · 2018: 1 · 2017: 1
FWCI
75.91
Type
journal article
Language
en
Open access
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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