What is transcriptomic analysis?
Transcriptomic analysis is reading a language.
Transcriptomic analysis is the investigation of how genes are expressed in an organism. Whenever a plant is growing, developing, adapting, experiencing stresses, reproducing, really…anything – these mechanisms and pathways are largely driven and affected by how the genes are expressed.
What does “expressed” mean?
Gene expression is essentially a transfer of information. DNA is a double helix source code in the cell's nucleus. DNA sequences of four bases (“A,T,G,C”) form codes for amino acid sequences, which form proteins. Proteins perform functions in the cell or throughout the organism. So, let’s say that a protein is required to carry out a particular function. To make the protein, the gene that codes for it must be copied from the DNA housed in the nucleus, and that “recipe” transferred to somewhere else in the cell where it can be read or “translated” and made into the protein. This mobile version of the gene code is a single-stranded messenger RNA or mRNA, sometimes called a transcript.
By measuring the amount of mRNA present for a certain gene, we can make some conclusions (depending on the gene) about how the abundance or lack thereof affects the downstream protein formation and function of that protein. For most genes (there are exceptions, of course), the more mRNA present, the more of that protein, and thus increased function of whatever it is that protein does.
This transfer of information of DNA —> RNA —> Protein is called the “Central Dogma” of biology and is the basis of our understanding of genomics. Many more layers can be explored, such as small interfering RNA, silencing RNA, epigenetic modification, gene redundancy, and more which affect this flow of information.
Plants have tens of thousands of genes, which means there are potentially tens of thousands of individual mRNA sequences present at any given time. Sequencing technology allows us to identify all pieces of mRNA in a tissue, count how many copies are present, and compare these counts statistically amongst replicated treatment and control groups. This is a basic transcriptomic analysis. Genes with higher counts of mRNA than a control group are considered “up-regulated” or more highly expressed. Similarly, genes with significantly lower mRNA counts are considered “down-regulated” or less expressed. Treatments, environmental conditions, stresses, and much more can influence what genes are expressed in a plant, where they are expressed within the plant, when they are expressed, and to what degree.
How is this type of data used?
When we perform transcriptomic analysis, we capture a birds-eye-view of how the organism’s genes respond to a treatment or condition. We can then group these significantly affected genes by their function and dig deeper into how they might influence the plant’s physiological response.
These genes function within systems that work together to accomplish the physical response we might see in the field. If we can pair gene expression analysis with physiological field data correctly, this presents an extremely strong argument that a product is functioning to influence a plant’s physical characteristics by the gene expression patterns it is inducing.
Transcriptomic analysis is often used as a starting point when investigating complex plant pathways. However, it requires an in-depth understanding of a plant species’ genome to make accurate conclusions. For example, if we see a down-regulation of a gene that codes for chlorophyll production, it is tempting to think that the plant produces less chlorophyll. However, what is missed is that there are six other genes that code for the same gene that are not impacted. This is called “redundancy” and exists in plant genomes in many forms and at multiple layers of signal transduction. In this case, an inaccurate conclusion might be made due to a lack of awareness of the genomic structure and context.
Biological product mechanisms can be particularly difficult to research. Typically, there are multiple modes of action, and their effects exist on a spectrum of efficacy in the field and in gene expression changes. By looking at the entire plant genome from the beginning, we are able to start with what might be a blurry picture of how a product is functioning in the plant, and then move towards greater levels of detail and clarity with follow-up research and tactful field data.
What does this type of data look like?
Transcriptomic data is often presented as a heat map or a “cluster” analysis map. This visually shows through colors and columns if groups of genes are up or down regulated, and how they compare across treatment and control groups.
Example Cluster analysis or “heat map” of differentially expressed genes. Red indicates genes more highly expressed, blue indicates genes significantly less expressed. Each column represents a replicated group of samples, and each tiny line is a gene.
While heat maps can be useful tools for summarizing large effects, or show treatments that seem to have more dramatic effects on gene expression, this does not give us much information about the functions that these genes have or what their effects on the plant physiology will be. Again, here is where an understanding of the genomic structure, plant physiology, and plant signal mechanisms are essential to interpreting this data. In the case of agricultural studies, an understanding of physiological mechanisms which drive crop performance and quality are also important.
It is very easy to get lost in the woods of transcriptomic data, but experience and an understanding of the plant biological context can make it an invaluable tool for impactful agronomic research.
