2026/02/23 by Keerthana M. Arun, Yuval Scher, Yuhan D Zhang +4 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Cell Image Analysis Techniques #Gene Regulatory Network Analysis #Single-cell and spatial transcriptomics
paper · doi:10.64898/2026.02.22.707230
openalex publication_date 2026/02/23 · openalex created_date 2026/02/25 · openalex updated_date 2026/07/14
Gene regulatory networks (GRNs) underlie maintenance of cellular phenotypes and responses to stimuli. Modern single-cell profiling methods offer high-throughput datasets to infer GRNs en masse but do not capture dynamic information. Cell-state heterogeneity further confounds correlation-based inference approaches. We addressed these challenges with TwINFER, a conceptual framework leveraging information from recently divided sister cells, or “twins,” identifiable via recently developed barcoding techniques. We show that twin information discriminates regulatory from non-regulatory correlations and resolves interaction direction and type (activation/repression). We performed a diverse set of simulations, covering common network motifs and large-scale networks, where TwINFER outperforms state-of-the-art inference capabilities. Crucially, TwINFER resolved the commonly observed false positives in fan-out and feed-forward loop motifs where most methods perform poorly. Lastly, we applied TwINFER to a lineage-barcoded hematopoiesis dataset which refined the network inference, flagged multi-state genes, and determined causal relations. Our work exploits cellular twins as untapped information, readily complementing existing inference approaches.