Spatial transcriptomics – quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections

RNA-seq and similar methods can record gene expression within and among cells. Current methods typically lose positional information and many require arduous single-cell isolation and sequencing. A collaboration between professors Jonas Frisén (Karolinska Institutet) and Joakim Lundeberg (Royal Institute of Technology) at SciLifeLab has resulted in a novel method that allows analysis of the quantity of all RNA molecules and provides spatial information from the microscope. The researchers have developed a way of measuring the spatial distribution of transcripts by annealing fixed brain or cancer tissue samples directly to bar-coded reverse transcriptase primers, performing reverse transcription followed by sequencing and computational reconstruction, and they can do so for multiple genes.

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.

Spatially resolved gene expression


(A) Each array feature contains unique DNA-barcoded probes containing a cleavage site, a T7 amplification and sequencing handle, a spatial barcode, a unique molecular identifier (UMI), and an oligo(dT) VN-capture region, where V is anything but T and where N is any nucleotide. cDNA (red) is generated from captured mRNA by reverse transcription. (B) Visualization of the expression of three genes by spatial transcriptomics (top) and in situ hybridization (bottom). Penk and Kctd12 in situ images are from the Allen Institute. Cutoff normalized counts, Penk, 8; Doc2g, 13; and Kctd12, 19. (C) Distribution of unique genes per feature under the tissue. (D) Number of genes detected for different layers and entire tissue over sequencing depth. (E) Lateral diffusion of transcripts from genes enriched in MCL. The genes are expressed in MCL features but are not separable from the background in features adjacent to the MCL. (F) Spatial expression and in situ hybridization of four genes in (E). The leftmost feature overlaps the MCL, and the three rightmost features are situated in the GCL. The colored bar depicts the distances from feature center in (E).

“By placing tissue sections on a glass slide on which we have placed DNA strands with built in address labels we have been able to label the RNA molecules formed by active genes,” says Professor Frisén. “When we analyse the presence of RNA molecules in the sample, the address labels show where in the section the molecules were and we can get high-resolution information on where different genes are active.”

The results are also valuable for more precise diagnostics. Current practice is to take a tissue sample, grind it down and analyse the mix of cells, but the risk is that a few cancer cells become so diluted by the signals from all the other cells in the sample and are therefore overlooked.

“With our method, we can pick up the tumour signal as it is not diluted,” he continues. “Because different parts of the tissue sample have their specific address labels we can identify a small number of tumour cells.”

The method can be used on all types of tissue and diseases. It can also provide information about disease heterogeneity in cancer diagnostics, as is demonstrated in the study for breast cancer.

What do you hope your method will lead to?

“It makes it possible to study which genes are active in tissues with greater resolution and precision than ever before, which is valuable to both basic research and diagnostics,” says Professor Frisén.

Source – Karolinska Institutet

Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, Giacomello S, Asp M, Westholm JO, Huss M, Mollbrink A, Linnarsson S, Codeluppi S, Borg Å, Pontén F, Costea PI, Sahlén P, Mulder J, Bergmann O, Lundeberg J, Frisén J. (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353(6294):78-82. [abstract]

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