Open Collections

MacDonald, Haley

University of British Columbia

Controlled progression through and exit from the cell cycle is essential for normal development and healthy tissue function, while cell cycle dysregulation and sustained proliferation is a key hallmark of cancer. With the advent of large-scale single cell transcriptomics, a variety of methods were developed to detect cells in distinct phases of the cell cycle, including G1, S and G2/M, but characterization of non-cycling cellular states and their role in tumour evolution remains limited. Current tools for inferring cell cycle phase from single cell RNA sequencing (scRNA-seq) datasets have not been comprehensively benchmarked, and most are not compatible with heterogeneous samples containing multiple cell types. Here we present Ouroboros, a comprehensive package to accurately identify cell cycle phase and non-cycling cellular states in scRNA-seq datasets. Ouroboros facilitates integrated cell cycle visualization through the generation of representative cell cycle UMAPs and provides a random forest model that outperforms existing cell cycle tools while classifying two additional non-cycling states. Applied to heterogeneous developmental and tumour datasets, Ouroboros reveals complex cell state dynamics over pseudotime and between patient treatment time points. By enabling comprehensive visualization and classification of cycling and non-cycling cells, we anticipate that Ouroboros will offer a means to explore the complex interplay between cellular proliferation and quiescence in health and disease.

Text

2025-02-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/90330

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/