Chromatin unwinding state for in situ identification and lineage tracing of circulating tumor cells.

The detection of circulating tumor cells (CTCs) through liquid biopsy offers a non-invasive approach for accurately monitoring cancer dissemination and evaluating therapeutic efficiency. However, their rarity and heterogeneity limit conventional tumor antigen labelling-based methods in identifying and tracing CTCs. Here, we developed a novel metric, termed chromatin unwinding state (CUS), which leverages activated transcriptional regions related to cell-identity processes from single-cell transcriptomic data while overcoming technical variances. Using CUS features, we trained attention-based neural network models, panCTC, to in situ identify and lineage trace rare single CTCs directly from 5 mL of peripheral blood mononuclear cells scRNA-seq without enrichment. We benchmarked panCTC on various in silico-simulated, public, and in-house sequenced data, demonstrating its robustness across sample types and platforms. PanCTC could provide real-time scRNA-seq profiles of fresh CTCs, supporting early cancer detection and targeted anti-metastatic therapy.