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

Abstract

Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

SpatialDE - Identification of spatially variable genes

“…These include the computational integration of single cell RNAseq data with a spatial reference dataset 3,4 , careful collection and recording of spatial location of samples 5 , parallel profiling of mRNA using barcodes on a grid of known spatial locations [5][6][7] , and methods based on multiplexed in situ hybridization 8,9 or sequencing [10][11][12] .…”

“…However, existing approaches for identifying highly variable genes 13,14 , as in single-cell RNA-sequencing (scRNA-seq) studies, ignore the spatial location and hence do not measure spatial variability ( Figure 1A). Alternatively, researchers have applied ANOVA to test for differential expression between groups of cells, either derived using a priori defined (discrete) cell annotations, or based on cell clustering 3,4,7,8,10 , with some methods incorporating spatial information 15 . Importantly, such strategies fall short of detecting variation that is not well captured by discrete groups, including linear and nonlinear trends, periodic expression patterns and other complex patterns of expression variation.…”

Human organomics: a fresh approach to understanding human development using single-cell transcriptomics

“…Another approach uses sequential rounds of smFISH with sophisticated combinatorial fluorescent barcoding to quantify the expression of tens to hundreds of genes in situ (Shah et al, 2016). An additional strategy is to position histological sections on arrayed reverse transcription primers with unique positional barcodes, thus generating RNA-sequencing data with two-dimensional positional information maintained (Stahl et al, 2016). These approaches reveal the location of distinct cell types, and offer powerful advantages over dissociation-based methods.…”

Avian transcriptomics: opportunities and challenges

“…Maintaining spatial information on gene expression of single cells or subpopulations of cells in tissues could help us to better understand how different cells function and are regulated, where they are localised and how they interact in complex tissues (Crosetto et al 2015). Some techniques can be used in combination with RNA-seq such as laser-capture microdissection, where single cells or subpopulations of cells can be harvested from tissue samples and used for downstream analysis (Espina et al 2006); microtomy sequencing, where RNA is extracted from thin cryosections (Junker et al 2014); or spatial transcriptomics where tissues are positioned on an array with spatially barcoded primers, which allow for two-dimensional positional information to be taken into account in the analysis (Ståhl et al 2016). These technologies offer new possibilities to learn more about avian biology, in particular within areas such as neurobiology and immunology.…”