While all cells may contain the same gene (excluding somatic, or non-inherited, mutations for a moment), they don’t express all the genes present — otherwise we would all be large blobs of a single type of cell, like massive amoeba walking around. Cells express certain genes based on the function they’re trying to carry out — a skin cell will express genes for melanin production for instance, while a nerve cell will instead express genes for neurotransmitter production. The study of this gene expression is known as transcriptomics. Understanding the transcriptomics of a cell is key to understanding its inner workings and reactions at a cellular level.
In our post on Tumour Heterogeneity one of the issues that was presented was the development of resistance mutations when a tumour is treated with a specific targeted therapy. As cells (which are all present in the same tissue) develop different mutations, they tend to have different transcriptomic signatures leading to cell-to-cell variation within the cell population. This variation means that if a single tissue is treated with a drug, there will be different responses based on the transcriptomic signature, affecting the tumour microenvironment.
One way to find these transcriptomic signatures is by using single cell transcriptomics. In single cell transcriptomics, cells from a heterogeneous cell population are isolated using one of many possible methods, such as fluorescence-activated cell sorting or laser-capture microdissection. Once isolated, the transcriptome (comprising the mRNA) of the cell is sequenced to understand which genes are actually being expressed, implying, in turn, the identity of the cell.
In cancer, knowing cell populations and identities tells researchers, for instance, if the right type of cells — — cancer cells! — — is being killed off by a treatment regimen, as well as, say, the role of immune cell populations in the progression of a given subtype of cancer. Single-cell transcriptomics helps address some of the latest and hardest questions in precision medicine, and in the process gives us snapshots of disease states at, amazingly, at the level of the individual cell.
References/ Further Reading
- Hodzic E. Single-cell analysis: Advances and future perspectives. Bosn J Basic Med Sci. 2016 Nov 10;16(4):313–314. doi: 10.17305/bjbms.2016.1371. PMID: 27320288; PMCID: PMC5136769.
- Slyper M, Porter CBM, Ashenberg O, et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat Med. 2020 May;26(5):792–802. doi: 10.1038/s41591–020–0844–1. Epub 2020 May 11. Erratum in: Nat Med. 2020 Jun 25;: PMID: 32405060; PMCID: PMC7220853.