Deciphering precursor cell dynamics in esophageal preneoplasia via genetic barcoding and single-cell transcriptomics

Abstract

Although histologically normal, esophageal preneoplastic cells harbor early genetic alterations and likely exhibit lineage plasticity. However, their origins and trajectories remain unclear. To address this, we combined genetic barcoding with single-cell RNA sequencing to trace the lineage of esophageal preneoplastic cells. We identified a distinct progenitor-like cell population with high plasticity. Through a newly developed scoring system, these high-plasticity cells are mapped, revealing their contributions to proliferative and basal cell populations. This approach uncovers molecular markers, including Nfib and Qk, that define these precursor cells, validated by spatial transcriptomics and a Trp53 Cdkn2a Notch1 mouse model. These findings provide critical insights into early tumorigenesis, highlighting the potential of precursor cells as biomarkers for early detection and therapeutic targets of esophageal squamous cell cancer. By elucidating the cellular dynamics underlying esophageal preneoplasia, this research lays the foundation for strategies to prevent malignant progression, offering broader implications for improving cancer diagnostics and treatment approaches. Significance StatementPreneoplastic cells often appear histologically normal yet carry early genetic and transcriptional changes that predispose them to malignant transformation. In this study, we combine genetic barcoding with single-cell transcriptomics to uncover the lineage dynamics of esophageal preneoplastic cells. We identify a distinct progenitor population, preneoplastic cells of esophageal squamous cell carcinoma (pESCC), characterized by high plasticity and a unique trajectory that gives rise to proliferating and basal cell populations. By developing a new computational scoring method to integrate lineage topology with differentiation state, we provide a framework for tracing cellular origins beyond conventional inference-based models. Our findings shed light on the earliest events in tumor initiation and offer a new paradigm for identifying biomarkers and intervention targets in the precancerous stages of esophageal cancer.