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

By Robert R. Stickels, Evan Murray, Pawan Kumar, Jilong Li, Jamie L. Marshall, D. D. Di Bella, P. Arlotta, Evan Z. Macosko, Fei Chen. Nature Biotechnology, 2020

1,098 citations recorded by the source corpus.

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. An improved method for spatial transcriptomics with detection efficiency approaching that of droplet-based single-cell RNA-seq techniques.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/89d53f6d5896b40287bbd4d9c2675debc0fe0b4d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, background): “Such spatially resolved transcriptomes of histological tissues enable the reconstruction 49 of tissue architecture and cell-cell interactions.(1,2,3,4,5,6,7,8,9) This approach has proven 50 valuable in many applications including studies on brain disorders,(2,10) tumour 51 microenvironments,(3,11) a”
Cell2location maps fine-grained cell types in spatial transcriptomics (2022, methodology): “25 and 26).”
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder (2021, methodology): “We first applied STAGATE onto a Slide-seqV2 dataset with 10μm spatial resolution from mouse hippocampus 5 .”
Spatial components of molecular tissue biology (2022, positive): “For example, spot-based protocols such as HDST 12 , Slide-seq 8,9 and spatial transcriptomics/Visium 4 capture RNA or proteins from areas spanning more (or less) than one cell, without consideration of cell boundaries.”
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, methodology): “58 ) and the other on a coronal brain section containing hippocampus measured using 10x Visium 12 .”
An introduction to spatial transcriptomics for biomedical research (2022, background): “A version with improved barcoding and enzymatic library preparation, Slide-seqV2, recovers ~ 30–50% as much transcriptomic information per capture bead as dropletbased single-cell transcriptomics from 10X Genomics, meaning that hundreds or thousands of genes can be detected per 10 μm-pixel [57].”
DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, background): “Recent advancements in spatial transcriptome (ST), such as the 10 × Visium (https://www.10xgenomics.com/), Slideseq (1,2) and Stereoseq (3), make it possible to understand the tissue functions and cellular architectures on transcriptomic level via sequencing in situ .”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, methodology): “We also applied COMMOT to a large-scale spatial transcriptomics dataset, that is, Slide-seqV2 data of mouse hippocampus, containing expression of 23,264 genes in 53,173 beads (spatial spots), which are similar in size to individual cells 52 (Fig.”