June 22, 2021
Assuming that cells from a given tissue have the same transcriptome is now a relic of the past. Single cell sequencing technologies provide a far more accurate picture of the variability between individual cells in a population — knowledge with limitless applications in drug and biomarker development.
To date, the use of single cell sequencing in a clinical setting has focused primarily on identifying cell types involved in diseased versus healthy tissues, and how those cells respond to different therapies.
For example, a distinct, novel population of macrophages was discovered exclusively in patients with lung fibrosis using single cell seq. The discovery of these cells and their exclusive appearance in fibrosis patients creates an obvious drug target for further study.
In cancer research, single cell seq has been used to identify which clonal cell populations in tumors are eliminated by chemotherapy and which are resistant. Resistant cell populations can now be identified earlier and targeted with different therapies.
But these clinical applications of single cell seq are just the beginning.
Frontiers for exploration remain in using single cell sequencing in precision medicine, creating exact cocktails of therapies to target each distinct, disease-causing cell population in a cancer patient or another complex disease. Single cell seq works like an extremely high-resolution microscope, allowing researchers to see which genes are turned on or off in individual cells and compare their features.
Analysis of single cell seq data can lead to the discovery of new drug targets, more accurate biomarkers and identification of the ideal drug for a given patient, and this has only scratched the surface of what this powerful approach can do. Advancing bioinformatics workflows for single cell seq data are sure to keep this technique relevant for many years to come.
Dan Ryder, CEO and Managing Director, Bridge Informatics
Dan is the founder of Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, he can be contacted at dan.ryder@bridgeinformatics.com.
