SPATIAL TRANSCRIPTOMICS ON
An example experiment performed by the ST team.
All mRNAs with spatial resolution in a single experiment.
To showcase the potential of the Spatial Transcriptomics technology, a few basic analyses enabled by spatially resolved RNA sequencing data are presented below.
To generate this data set Spatial Transcriptomics was performed on a coronal mouse brain section.
The image shows the tissue section side by side with a schematic highlighting the main morphological regions.
CTX – Cortex
HPF – Hippocampus
CP – Caudate putamen
NFT – Nerve fiber tracts
TH – Thalamus
HY – Hypothalamus
CHOOSE A GENE
The data generated by a Spatial Transcriptomics experiment allows you to choose any gene of interest and display its spatially resolved expression on the original tissue section.
In this example we show genes from anatomically distinct regions of the mouse brain; STX1A in cortex, Prkcd in thalamus, HPCA in hippocampus and Prnch in hypothalamus.
The color scale goes from solid red to transparent orange, representing high and low levels of expression respectively. The area between the spots capturing the mRNAs has been colored based on the color of neighboring spots.
Since all mRNAs are captured, you are not limited to visualizing only a single gene but can choose any number of genes in any combination to view and analyze together.
Here we show the combined expression of three genes important for myelin production (Cldn11, Plp1 and Mbp).
The striking expression pattern is located, as expected, in the myelin-dense nerve fiber tracts of the brain.
SELECT A REGION
Freely select your region of interest to generate a list of genes expressed in that region.
The image shows a selected region corresponding to the hippocampus (in red).
Below we list the ten most highly expressed genes in the selected region (after the most common housekeeping genes were subtracted).
|Rank||Gene||Number Unique Molecules Detected|
By selecting any two regions you can easily discover the most differentially expressed genes.
In this example, we compare the hippocampus (in red) with thalamus (in black), two regions that are anatomically and functionally distinct.
Below we list the genes that were most differentially expressed.
In the volcano plot you can see the log2 fold change and adjusted p-values for all genes in the dataset. The right side of the plot represents higher expression in the hippocampus, left side represents thalamus. Blue dots did not pass our predefined criteria for significance or fold change, while red dots did.
Try selecting a few genes/dots by clicking and dragging on the plot. The genes you select will be listed in the table.
Only the top ten genes in your selection are included in the table.
Sometimes an unbiased view of differences in expression patterns is preferred. In this example the software was instructed to plot a representation of the RNAseq data from all spots and to generate groups (aka. clusters) of spots with similar mRNA content.
This unbiased computer-based analysis rendered seven clusters based on the differential gene expression.
As you can see, the clusters organise in a spatial pattern that overlaps very well with the anatomically defined regions of the brain. You can also choose to perform clustering of the sequencing data within a smaller region of your choice.
Unexpected clusters can lead to new discoveries.
In the table below we listed a subset of genes and their average normalized expression by cluster (1-7) rendered by the computer-based analysis.
By placing the pointer above a gene name within the table, spots in the tissue image will be colored based on the expression of that gene. Alternatively, by placing the pointer above a value within the table, you can observe the expression of a specific gene with the spots from an individual cluster highlighted.
You can rotate the image of the clusters by clicking and dragging the scatter plot to see how these clusters separate in 3D. Use the controls in the upper right corner to zoom and pan.
You can also use the sliders under the tissue image to adjust how you visualize and combine the tissue image and the gene expression data.
Change map spot opacity:
Change map cluster cloud opacity:
Ever tried navigating through an unknown city without a map? This is what traditional RNA-Seq in tissues can look like today. For the first time Spatial Transcriptomics provides a map by combining histology and full mRNA analysis in an innovative way.
No, we have found that laboratories have all the major infrastructure required for Spatial Transcriptomics in place. For a comprehensive list of instruments and reagents needed for the protocol see RESOURCES.
Our glass slides contain 1007 spots with a diameter of 100 μm and a center to center distance 200 μm. This gives a comprehensive overview of a tissue’s architecture while maintaining high sample throughput and limited sequencing costs.