Compression Sequencing enables ultra-sensitive and scalable scRNA-seq
Current sequencing methods are inefficient and bottlenecked by repeated sampling of highly abundant molecules, which dominate sequencing reads, limit assay throughput and sensitivity for rare targets. For example, single-cell RNA sequencing (scRNA-seq) can profile up to millions of cells, but remains severely constrained by sequencing cost, resulting in shallow gene coverage and high dropout…
Sequencing techniques for analyzing individual cells, such as single-cell RNA sequencing (scRNA-seq), often face limitations due to inefficient sampling of abundant molecules. These limitations include high sequencing costs, shallow gene coverage, and a high rate of missing data (dropout). Compression Sequencing is an innovative method designed to overcome these challenges.
This information-science inspired technique tackles the inefficiency in sequencing power by applying a logarithmic transform to the molecular abundances across a wide dynamic range (5 logs). This logarithmic transform serves to suppress high-abundance targets while simultaneously enriching rare ones. Importantly, Compression Sequencing maintains quantitative accuracy throughout the process.
When applied to scRNA-seq libraries, the method enables ultra-sensitive detection of low-abundance transcripts at levels of 2-5 times more unique molecular identifiers (UMIs). This results in an estimated 200-fold reduction in sequencing costs. Furthermore, Compression Sequencing preserves the accurate identification of cell types and facilitates differential expression analysis across a panel of 500-2,000 genes.
In clinical samples of Acute Myeloid Leukemia (AML), Compression Sequencing has successfully reproduced clinical diagnoses. Additionally, it provides the capability for transcriptomic profiling at an affordable cost, estimated at $10 per sample. This capability allows for ultra-sensitive and scalable single-cell analysis, making it ideal for large-scale functional genomics studies, drug discovery screens, AI cell model training, and even affordable single-cell disease diagnostics.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
