Recent Research in Transcriptomics

Recent Research on Transcriptomics 

Michelle To

Transcriptomics, as Nature explains, is the “study of the transcriptome -- the complete set of RNA transcripts that are produced by the genome, under specific circumstances or in a specific cell -- using high-throughput methods, such as microarray analysis” (“Transcriptomics,” 2020). In other words, transcriptomics is the study of what RNA is transcribed by DNA, both coding RNA, messenger RNA, and non-coding RNA (a.k.a. ncRNA). By studying the RNA that is transcribed from DNA, scientists are able to analyze the differences between what type of genes are expressed in a unique community or cell. This is important because it is a “good representative of the cellular state” (Srivastava, A., George, J., & Karuturi, R. K. M., 2018.) which allows scientists to analyze the transcriptomes of stem cells and cancer cells (Chang-Seung-Yue et. al, 2020). Analyzing the cellular state of a population also allows scientists to analyze fertilization (Wang et al., 2009), assessing chemical risks in medicine (Svabo, 2014), and inferring phylogenetic relationships among different individuals and populations (Suryamohan et al., 2020). 

In the past, it had been difficult for scientists to discover new RNA because the only way transcriptomics was able to study RNA was simply using microarray analysis. Microarray analysis, by the way, is using a sample of RNA produced by a cell and using already known complementary RNA probes that will search for the RNA present. From this method, scientists were only able to compare known RNA to others of different populations. The problem with microarray analysis was that it did not allow for the discovery of new RNA transcripts. However, with the result of next-generation sequencing (NGS) techniques, scientists are able to discover novel RNA sequences (Srivastava, A., et al., 2018.). How do they do it? Scientists are now able to sequence entire genomes faster than ever. They can upload the nitrogenous base pair sequences to a file, called a Sequence-Alignment File (SAM file), that is aligned so that there isn’t any overlap from the NGS. Using the file, that can be also compressed to a BAM file, scientists can read known sequences and sort out the ones they don’t know and discover new RNA sequences (Srivastava et al., 2018). 

Figure 1. Schematic of NGS RNA-seq technique (Credit to Srivastava et al.) (SEE ABOVE)

But NGS technology is just the beginning. Tying into the recent trend of using CRISPR, a revolutionary technology using a protein called Cas-9 to essentially “edit” genes, scientists are trying to measure gene expression using CRISPR intervention. By using CRISPR and machine learning, scientists led by Marco Jost from the University of California, San Francisco have measured different types of “single-guide RNAs” (Jost et al., 2020). Single-guide RNAs are RNA strands that attach to the CAS-9 protein that allow it to “cut” certain strands of DNA (“Glossary: Single-guided RNA (sgRNA),” 2020). By the advances in recent technology, scientists are learning more and more about how we can use transcriptomics to assess different gene expression in cells. This can be extended to cancer research, where gene expression becomes abnormal throughout cell stages. Observing the different periods of the cell stage (e.g. during cell division), cancer researchers can find out how we can prevent and treat cancer more efficiently. Empowered by this knowledge, citizens and scientists alike are able to predict different outcomes in cells just by the RNA present.  

Citations 

Chan-Seng-Yue, M., Kim, J.C., Wilson, G.W. et al. Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution. Nat Genet (2020). https://doi.org/10.1038/s41588-019-0566-9

Innovative Genomics Institute (2020). Glossary: Single-guided RNA (sgRNA) https://innovativegenomics.org/glossary/single-guide-rna/

Jost, M., Santos, D.A., Saunders, R.A. et al. Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-019-0387-5

Srivastava, A., George, J., & Karuturi, R. K. M. (2018). Transcriptome Analysis. Reference Module in Life Sciences.doi:10.1016/b978-0-12-809633-8.20161-1 

Suryamohan, K., Krishnankutty, S.P., Guillory, J. et al. The Indian cobra reference genome and transcriptome enables comprehensive identification of venom toxins. Nat Genet 52, 106–117 (2020). https://doi.org/10.1038/s41588-019-0559-8

Szabo, David (2014). "Transcriptomic biomarkers in safety and risk assessment of chemicals". Transcriptomic biomarkers in safety and risk assessment of chemicals. In Ramesh Gupta, editors:Gupta - Biomarkers in Toxicology, Oxford:Academic Press. pp. 1033–1038

Thompson, S. D., Prahalad, S., & Colbert, R. A. (2016). Integrative Genomics. Textbook of Pediatric Rheumatology, 43–53.e3.doi:10.1016/b978-0-323-24145-8.00005-3 

Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63. doi:10.1038/nrg2484