Di Terlizzi, I. | Search bioRxiv

bioRxiv2025

Genetic barcoding of individual cells links cancer evolutionary trajectories and prognostic outcomes

Intratumoural heterogeneity (ITH) reflects cancer progression, by integrating clonal dynamics, cell plasticity, and metastatic potential of malignant cells. Defining the origins and evolutionary trajectories of tumour cells is central to preventing tumour growth and dissemination, yet significant gaps in knowledge persist. Here, we used an original single cell resolution lineage tracing system to investigate how ITH underlies changes in tumour cell states, such as the acquisition of an epithelial-to-mesenchymal transition (EMT) phenotype and invasive capacity. By combining in vivo genetic barcoding with single-cell transcriptomics, we uncover two parallel evolutionary trajectories toward EMT: one associated with alveolar differentiation, and another characterized by gene regulatory programs activated during stress and tissue regeneration. Of interest, only the gene modules defining the regenerative trajectory were found to correlate with poor survival in breast cancer patients. Our results also provide a quantitative framework to measure cellular plasticity that enables the identification of highly plastic cells and their gene signatures, correlating with rapid changes in transcriptional state, EMT features and aggressive cancer. These findings deliver a comprehensive view of in vivo clonal evolution in breast cancer, uncovering the interplay between lineage plasticity and tumour progression with quantitative and robust statistical approaches.

cancer biology↗

bioRxiv2025

Collective Gene Expression Fluctuations Encode the Regulatory State of Cells

Gene expression is inherently stochastic, leading to substantial cell-to-cell variability in mRNA and protein abundances. Variability in the expression of individual genes has been associated both with impaired signal processing and with facilitation of stress responses and differentiation. Here, combining machine learning, theory, and analysis of scRNA-seq data across various organisms and tissues, we show that variability in gene expression can be coordinated cell-wide. We define a statistical score that quantifies this coordination in single-cell data and demonstrate that distinct coordination patterns reflect the regulatory state of cells. We further develop a physics-informed machine-learning framework that identifies and predicts such variability patterns. Coordinated gene-expression variability emerges as a hallmark of stem and progenitor cells and distinguishes intrinsic stochasticity from cell-population heterogeneity. Together, our results establish the structure of gene expression variability as a cellular-scale signature of cell identity and regulatory organization.

biophysics↗