Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

By Samuel G. Rodriques, Robert R. Stickels, A. Goeva, Carly A Martin, Evan Murray, C. Vanderburg, Joshua D. Welch, Linlin M Chen, Fei Chen, Evan Z. Macosko. Science, 2019

2,093 citations recorded by the source corpus.

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 An RNA sequencing technique is developed that can measure genome-wide gene expression in complex tissues at 10-micrometer resolution. Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type–specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

Subfields: Molecular Biology, Neurology

View Paper: https://www.semanticscholar.org/paper/7045f05312b80bd7e995b5dad637938d626c3be2

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Citations in the corpus

Cell segmentation in imaging-based spatial transcriptomics (2021, background): “9a and 10).”
Museum of spatial transcriptomics (2020, methodology): “They can also be Drop-seq-like beads [94] with split pool barcodes, UMIs, and poly-T oligos spread on slides in a single layer (e.g., Slide-Seq [100]) or conﬁned in wells etched on the slides (e.g., HDST [101]), with bead barcodes subsequently located using in situ SBL. Alternatively, in DBiT-seq…”
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020, methodology): “Importantly, these technologies use an on-slide cDNA synthesis approach that captures gene expression in the architecture of intact tissue, meaning that information from cytosol and neuronal processes is retained (Rodriques et al., 2019; Ståhl et al., 2016).”
High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (2020, positive): “We demonstrated not only high spatial resolution but also excellent data quality with high genome coverage and large numbers of detectable genes per 10μm pixel as compared to Slide-seq or HDST.”
Predicting cellular responses to complex perturbations in high‐throughput screens (2023, background): “In particular, we expect spatial transcriptomics (Rodriques et al , 2019; van den Brink et al , 2020) to be a valuable source for extensions to CPA due to its high sample number and the dominance of DL models in computer vision.”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, result): “17), a result consistent with the known major functions of OXT signaling 44 .”
Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (2020, methodology): “We compared counts of CA1 marker genes (Atp2b1, Ocaid2, Slc17a7) in an equal number of cells, measured by: (1) Slide-seqV2; (2) Drop-seq, a high-throughput scRNA-seq method11,12; and (3) smFISH5,13,14 (Methods).”
Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, neutral): “They include Slide-seq [4, 7], DBiT-seq [8], Stereo-seq [5], PIXEL-seq [6], and Seq-Scope [9].”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Other developments included Slide-seq, which used arrays composed of 10 μm-diameter barcoded beads, with barcodes at each location determined prior to tissue mounting [56].”
Inferring spatial and signaling relationships between cells from single cell transcriptomic data (2020, methodology): “For mouse olfactory bulb, the spatial data by Slide-seq was downloaded from the Broad Institute Single Cell Portal (https://singlecell.broadinstitute.org/ single_cell/study/SCP354/slide-seq-study) 51 with ﬁ le ID: 180430_3.”
The technological landscape and applications of single-cell multi-omics (2023, methodology): “…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 cover-slip 118,119 (Fig.”
A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2020, background): “…and relative proportion of various cell types should ultimately be established through future spatial transcriptomic studies using approaches such as multiplexed FISH or in situ sequencing (Eng et al., 2019; Lein et al., 2017; Moffitt et al., 2018; Rodriques et al., 2019; Wang et al., 2018).”
Spatial components of molecular tissue biology (2022, neutral): “Methods also vary in the number of molecular features acquired: from tens in fluorescence in situ hybridization (FISH), cyclic immunofluorescence (CyCIF) 5 and imaging mass cytometry (IMC) 6 to hundreds or thousands in specialized probe-based spatial transcriptomics methods (MERFISH 2 or SeqFISH 3 )”
Methods and applications for single-cell and spatial multi-omics (2023, background): “These approaches allow for transcriptome-wide profiling through capturing of polyadenylated (poly(A)) RNA transcripts released from fixed and permeabilized tissue sections, making use of thousands of arrayed and spatially barcoded oligo-dT spots (for example, Spatial Transcriptomics 170,176 and 10x ”