Analysis of the pattern of proteins or messenger RNAs (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.

Heart failure is a major health problem linked to poor quality of life and high mortality rates. Hence, novel biomarkers, such as fetal marker genes with low expression levels, could potentially differentiate disease states in order to improve therapy. In many studies on heart failure, cardiac biopsies have been analyzed as uniform pieces of tissue with bulk techniques, but this homogenization approach can mask medically relevant phenotypes occurring only in isolated parts of the tissue. This study examines such spatial variations within and between regions of cardiac biopsies. In contrast to standard RNA sequencing, this approach provides a spatially resolved transcriptome- and tissue-wide perspective of the adult human heart, and enables detection of fetal marker genes expressed by minor subpopulations of cells within the tissue. Analysis of patients with heart failure, with preserved ejection fraction, demonstrated spatially divergent expression of fetal genes in cardiac biopsies.

The human developing heart holds a greater proportion of stem-cell-like cells than the adult heart. However, it is not completely understood how these stem cells differentiate into various cardiac cell types. We have performed an organ-wide transcriptional landscape analysis of the developing heart to advance our understanding of cardiac morphogenesis in humans. Comprehensive spatial gene expression analyses identified distinct profiles that correspond not only to individual chamber compartments, but also distinctive regions within the outflow tract. Furthermore, the generated spatial expression reference maps facilitated the assignment of 3,787 human embryonic cardiac cells obtained from single-cell RNA-sequencing to an in situlocation. Through this approach we reveal that the outflow tract contains a wider range of cell types than the chambers, and that the epicardium expression profile can be traced to several cell types that are activated at different stages of development. We also provide a 3D spatial model of human embryonic cardiac cells to enable further studies of the developing human heart. 

Spatial Transcriptomics has been shown to be a persuasive RNA sequencing

technology for analyzing cellular heterogeneity within tissue sections. The

technology efficiently captures and barcodes 3’ tags of all polyadenylated

transcripts from a tissue section, and thus provides a powerful platform when

performing quantitative spatial gene expression studies. However, the current

protocol does not recover the full-length information of transcripts, and

consequently lack information regarding alternative splice variants. Here, we

introduce a novel protocol for spatial isoform profiling, using Spatial

Transcriptomics barcoded arrays.