Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions

By Cole Trapnell, D. Cacchiarelli, J. Grimsby, Prapti Pokharel, Shuqiang Li, Michael Morse, Niall J. Lennon, K. Livak, Tarjei S. Mikkelsen, Johannes Rinn. Nature Biotechnology, 2014

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Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a34fac43d8af822019eb922d4e4169f99e04823c

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Citations in the corpus

Defining cell types and states with single-cell genomics (2015, background): “typically have a few dozen samples atmost, typical single-cell studies capture hundreds (Shalek et al. 2013; Trapnell et al. 2014; Treutlein et al. 2014) or even thousands (Shalek et al.”
The technology and biology of single-cell RNA sequencing. (2015, methodology): “From the cost perspective, each IFC chip is a substantial expense, but it does allow savings to bemade on reagents costs, as reactions are performed in nanoliter volumes (Shalek et al., 2014; Trapnell et al., 2014; Treutlein et al., 2014).”
Exploring genetic interaction manifolds constructed from rich single-cell phenotypes (2019, methodology): “To order cells in an unbiased way by “phenotype,” we computed a principal curve measuring the path of maximum variation in the data set (( 27 ); Fig.”
Deep Learning for Imaging Flow Cytometry: (2016, methodology): “For example, in the 231 analysis of single-cell transcriptomic and proteomic data, much research effort has been 232 applied to solve precisely this task [19,21,22].”
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “For example, [3] examined the differentiation pattern of skeletal myoblasts, showing that their development into myocytes and mature myotubes follows a continuous lineage, rather than discrete steps.”
Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, methodology): “We next tested SEDR’s low-dimensional representation features in trajectory inference [39].”
Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction (2018, positive): “We used Monocle v.2.8.0 24, 52 to investigate inferred developmental trajectories between macrophage/monocyte subsets.”
Lineage tracing on transcriptional landscapes links state to fate during differentiation (2018, background): “A common goal of scSeq is to order gene expression along dynamic trajectories by defining a “pseudotime” coordinate that orders transcriptomes (24).”