{
  "id": 5345364,
  "title": "Compression Sequencing enables ultra-sensitive and scalable scRNA-seq",
  "url": "https://urgent.news/2026/09/03/compression-sequencing-enables-ultra-sensitive-and-scalable-scrna-seq",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-03T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.01.748706v1?rss=1"
  },
  "original_language": "en",
  "account": "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.\n\nThis 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.\n\nWhen 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.\n\nIn 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.",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}