Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

By Patrik L. Ståhl, Fredrik Salmén, S. Vickovic, Anna Lundmark, J. F. Navarro, J. Magnusson, Stefania Giacomello, Michaela Asp, J. Westholm, M. Huss and 11 more. Science, 2016

3,194 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2724bfeabb90eff9f7bb1efd2cb9497afe4cbf08

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In Molecular Biology

Citations in the corpus

Identification of spatial expression trends in single-cell gene expression data (2018, neutral): “We also identified 45 genes with significant expression primarily in granular regions of the bulb (Figures 2C-D, Supplementary Figures 3B, 4B, 5B, 6C-D and Supplementary Tables 1, 4, 5) including known genes such as Nrgn, Camk4 and Pcp4 but also novel genes such as Gpsm1 (Figure 2C).”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Commercialized techniques such as Spatial Transcriptomics [20], released as Visium by 10X Genomics, as well as GeoMx [21] and CosMx [22] by Nanostring, have made spatial transcriptomics more accessible.”
Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, positive): “Spatial location of single cells To map spatial distributions of the scRNA-seq data, we undertook ST (Ståhl et al., 2016) on tissue from across intestinal development (8 sections from 5 samples; 12 PCW, n = 5; 19 PCW, n = 1; adult, n = 2) (Figure 1A; STAR methods).”
In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus (2016, neutral): “In comparison, single cell RNA-seq measurements are 5–20% efficient based on spike-in controls and in situ sequencing is less than 1% efficient (Darmanis et al., 2015; Klein et al., 2015; Lee et al., 2014; Macosko et al., 2015; Tasic et al., 2016; Zeisel et al., 2015; Ståhl et al., 2016).”
Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, background): “Such spatially resolved transcriptomes of histological tissues enable the reconstruction of tissue architecture and cell–cell interactions [1–9].”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, neutral): “13–16).”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “Among others, further applications include spatial transcriptomics, where cellular positional information is maintained in the preparation of cDNA fragments [38], host-pathogen interactions via “dual RNA-seq”, where the transcriptomes of both host and pathogen are simultaneously assayed [39], th”
stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, methodology): “In this work, we focus on one of the ﬁrst and most established ST technologies 16 , which is now widely available as a Visium platform by 10x Genomics.”