Automated cell cycle and cell size measurements for single-cell gene expression studies

Abstract

Recent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

“…Thus, single-cell algorithms that rely on the number of expressed genes per cell to predict distinguishable properties might also report biased results. Although contradictory results exist in the literature regarding the transcriptomic effect of cell cycle-associated changes in cell size (McDavid et al, 2016;Guillemin et al, 2018), it is arguable that this is due to subtle transcriptional changes that can only be detected with greater The development and implementation of single-cell technologies for multiomics measurements of genomes, epigenomes, transcriptomes, translatomes, proteomes, and metabolomes are opening up tremendous opportunities for enhancing our mechanistic understanding of cellular phenotypes and higher-order biological systems.…”