Summary
A bioRxiv preprint describes BEACON, a method that learns background count patterns to assign single cells to their samples in multiplexed sequencing. The authors report improved classification across human datasets and applications in cancer-cell experiments and protein–transcriptome profiling.
Researchers from the University of California San Diego and Icahn School of Medicine at Mount Sinai describe BEACON, a method for assigning cells to their source samples in multiplexed single-cell RNA sequencing. In a bioRxiv preprint posted September 30, 2026, the authors say BEACON learns the background count distribution from sequencing data and removes that background from individual cells to improve classification.
Multiplexing lets researchers pool samples for sequencing, with sample-specific barcodes used to identify where each cell came from. Demultiplexing is the computational step that makes those assignments. If assignments are inaccurate or cells cannot be confidently assigned, usable cell counts can fall, reducing the information available for analysis.
How BEACON approaches cell assignment
Single-cell sequencing measures RNA molecules associated with individual cells, but droplet-based methods can also capture background RNA. BEACON’s stated approach is to estimate the background pattern from the data and account for it when classifying cells. The authors report that it outperformed other state-of-the-art methods across multiple human datasets. The abstract does not give numerical benchmark results, so the size of that performance difference cannot be assessed from the reported summary.
The team also applied BEACON to time-course experiments involving cancer cell lines grown in vitro. They report identifying genes associated with aggressive tumours in vivo. This links a laboratory cell-line experiment to tumour aggressiveness, but the abstract does not specify the genes or detail the analysis behind that association.
Extension to protein and RNA measurements
The researchers adapted BEACON for multimodal profiling, which measures proteins alongside gene expression. They report improved recovery of protein signal and identification of a CD161-positive effector memory CD4 T-cell population with a Th17-like phenotype. The abstract says the population was prospectively validated, but does not describe the validation procedure or its results in detail.
Together, these applications suggest BEACON could be useful beyond one RNA-sequencing setup: the authors say it can be applied to other droplet-based single-cell sequencing methods. The work is presented as a bioRxiv preprint, and the abstract provides high-level findings rather than detailed methods, sample counts or quantitative comparisons.