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 rate. Here we report an information science-inspired method, “Compression Sequencing”, that tackles this fundamental inefficiency and enables highly improved (>100x) sequencing power. Our method works by performing an accurate and unbiased logarithmic transform on molecular abundances over a wide (5 logs) dynamic range, thus suppressing high-abundance targets and enriching rare ones, while maintaining quantitative accuracy. Applied to scRNA-seq libraries, our method allows ultra-sensitive detection of low-abundance transcripts (2-5x more UMIs), ultra-low sequencing cost (200x reduction), preserves accurate cell types and differential expression analysis over a 500-2,000 gene panel. In AML clinical samples, Compression Sequencing reproduces clinical diagnosis and additionally allows transcriptomic profiling at affordable cost (est. $10 per sample). Our approach thus enables ultra-sensitive and scalable single-cell analysis for large-scale functional genomics studies, drug discovery screens, AI cell model training, as well as affordable single-cell disease diagnostics.