Applications of single-cell and spatial transcriptomics

Transcriptomics is the study of the all of the RNA sequences that are present within a cell.

The transcriptome is a product of our genome and the differences in RNA expression that we see across cell types is responsible for all of the functions performed by our tissues which themselves are made up of trillions of cells all with slightly different transcriptomes.

So for us to really, actually, understand anything about what our genome does, we need to look at the functional output of the genome.

One of those outputs is the transcriptome!

Spatial and single cell transcriptomics takes this idea one step further by singling out the expression of RNA in single cells or within specific tissue environments.

These techniques have use cases in a wide variety of scientific disciplines.

Developmental Biology: multicellular organisms develop from, at a minimum, a single cell, so how does a single cell turn into an entire organism? It's almost always a highly complex regulatory dance between the genome, the RNA and proteins it encodes, and the feedback loops that are established to regulate what is expressed by each cellular genome in a specific tissue micro environment. The only way we can understand this better is with cellular and spatial read outs of the functional biology.

Oncology (cancer): both solid tumors and leukemias (blood cancers) are perfect targets for single cell and spatial transcriptomics because the best way to attack a cancer is by better understanding its biology and its weak points. If we know what is expressed or not expressed, that can give us insight into how best to develop drugs to stop its spread.

Immunology: blood cell lineage differentiation is complex and the best way to understand how we go from stem cells to the 12-ish types of blood cells and all of their associated leukemias and autoimmune/inflammatory diseases is by looking at what genes are expressed within those individual cells.

Pathology: this is the realm of the dark art of tissue biopsies and microscope slide staining techniques. While we can learn a lot about disease just by looking at cellular morphology, if we add in transcriptomics and proteomics, we can start to understand how diseases go awry at the genetic level, opening up possibilities for developing therapeutics for specific cell types within a diseased tissue.

The biggest challenge though is the same challenge in everything Omics: does the cost justify the clinical benefit?

Certainly in some cases that answer will be yes, but the cost of processing a pathology slide is easily 1/100th the cost of doing single cell and/or spatial transcriptomics!