Driving the Future of Drug Development by Coupling Single Cell Sequencing and AI
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Life Sciences Review | Monday, July 13, 2020
Coupling the single-cell sequencing with statistical machine learning and AI tools paves the way for a new era to develop efficacious therapeutics.
FREMONT, CA: Single-cell sequencing has significantly transformed the study of biological tissues and systems at the cellular and molecular level. The latest advances in the technology have enabled the interrogation of distinct subsets of cell populations within tissues and connected molecular markers that may function as imperative disease drivers. Combining single-cell omics with other omics technologies and machine learning tools like artificial intelligence (AI) can offer key insights about cellular and molecular targets that drive diseases. Characterization of disease pathways and systems can help lead to more effective disease treatment strategies like cell-based and immunotherapies. Read on to know more.
Significant strides have been made in the innovation of single-cell technology and its use across different biological research areas. Single-cell RNA sequencing has contributed the most to understanding a single cell's functional biology in broader populations of cells, assisting in highlighting the presence of different cell populations within tissues and their distinct roles in disease.
Evaluating tissues at a single cell level makes it possible to capture different cell types and cell states within a sample, allowing for the interrogation of heterogeneity within a bulk cell population and identifying rare cell types.
AI-based tools give rise to personalized treatments. Going from static snapshots to full dynamics enables researchers to move from descriptive towards predictive models. This might help to understand disease progression or to unravel cell signaling in response to treatment. In the end, it is all about the samples, and the added sample improves signature scores, enabling for more accurate cell type classification, which is looped into the database and restarts the cycle. This will also allow building an in-house human cell atlas of disease, which allows comparison of healthy cells and diseased with the identification of unique cell types and the potential drivers for them.
In conclusion, combining these detailed approaches can widen the understanding of human disease biology at the cellular and molecular level to find functional targets that can navigate therapeutic drug development.
