The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Open accessAbstract
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.
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Semi-supervised pseudotime analysis with Monocle 2 aligned microglial cells along a two-branched trajectory.
“Semi-supervised pseudotime analysis with Monocle 2 (Qiu et al., 2017a, 2017b; Trapnell et al., 2014) aligned microglial cells along a two-branched trajectory (Figure 2A).”
From The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques (2019) · cites this paper as Trapnell et al., 2014
In “Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions” (page not provided), Trapnell et al. describe Monocle as a pseudotime method that can reconstruct branched trajectories. They explicitly call their algorithm unsupervised, however, and do not identify it as Monocle 2 or describe semi-supervised analysis. The microglial trajectory is presented as the citing paper’s own result (Figure 2A), not a finding attributed to Trapnell et al. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMonocle is an unsupervised algorithm designed for pseudotime inference analysis, capitalizing on the high variability in gene expression levels.
“Monocle is an unsupervised algorithm designed for pseudotime inference analysis, capitalizing on the high variability in gene expression levels.”
From Single-cell sequencing to multi-omics: technologies and applications (2024) · cites this paper as [14]
Single cell transcriptome analysis enables the reconstruction of gene expression dynamics along differentiation trajectories.
“Single cell transcriptome analysis enables the reconstruction of gene expression dynamics along differentiation trajectories (Setty et al., 2016; Shin et al., 2015; Trapnell et al., 2014).”
From Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord (2019) · cites this paper as Trapnell et al., 2014
Single-cell RNA sequencing can generate cell-cell networks in which nodes represent the RNA expression state of individual cells and edges connect pairs of highly similar cells.
“Above the level of individual proteins and genomic loci, many recent studies use the technique of single-cell RNA sequencing to generate cell-cell networks, in which nodes represent the RNA expression state of an individual cell, and edges connect pairs of cells that are highly similar (Bendall et al., 2014; Trapnell et al., 2014).”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Trapnell et al., 2014
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- Authors
- Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L.
- Affiliations
- Broad Institute (US)
- Standard BioTools (United States) (US)
- Corresponding author
- Rinn, John L
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2014-03-23
- Volume
- 32
- Issue
- 4
- Pages
- 381-386
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4122333
- OpenAlex
- W1984883254
- Cited by
- 7,314
- References
- 43
- Citations by year
- 2026: 990 · 2025: 1109 · 2024: 999 · 2023: 933 · 2022: 849 · 2021: 716 · 2020: 621 · 2019: 389 · 2018: 298 · 2017: 176 · 2016: 139 · 2015: 73 · 2014: 15 · 2013: 1 · 2012: 1
- FWCI
- 55.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP2OD006670, 1DP2OD00667)
- National Human Genome Research Institute (P50HG006193)
- National Institute of General Medical Sciences (P01GM099117)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- monocle, single-cell RNA sequencing, cell differentiation, transcriptional dynamics, myogenesis, cell fate determination, gene regulatory networks, gene expression kinetics, loss-of-function screen
- MeSH terms
- Algorithms, Cell Differentiation, Cells, Cultured, Gene Expression Regulation, Humans, Transcription Factors, Reproducibility of Results, Gene Expression Profiling, Genomics, Muscle Development, Myoblasts, Transcriptome
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