DemuxHMM: Large-Scale Single-Cell Embryo Profiling via Recombination Barcoding

Abstract High-resolution developmental time-courses with single-cell RNA sequencing (scRNA-seq) increasingly target trajectory inference and other analyses in the study of development and disease [1–6]. These datasets are often generated by pooling individuals and inferring cell-to-individual mappings after sequencing, in a process called demultiplexing. Existing demultiplexing methods are limited in the number of timepoints they can support, due to either the need for individual-by-individual processing or reduced accuracy at large numbers of individuals. To address these limitations, we introduce a combined experimental and computational framework for creating large-scale, individual-resolved datasets. Our framework couples a simple breeding scheme that creates contiguous SNP patterns (recombination barcodes) with a recombination-aware demultiplexing method, DemuxHMM, that explicitly models this structure with a Hidden Markov Model (HMM). We demonstrate substantial performance and scalability gains from this combined approach on simulated data, highlighting its potential to enable the creation of large-scale single-cell time series.