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. 2014 Apr;32(4):381-386.
doi: 10.1038/nbt.2859. Epub 2014 Mar 23.

The Dynamics and Regulators of Cell Fate Decisions Are Revealed by Pseudotemporal Ordering of Single Cells

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Free PMC article

The Dynamics and Regulators of Cell Fate Decisions Are Revealed by Pseudotemporal Ordering of Single Cells

Cole Trapnell et al. Nat Biotechnol. .
Free PMC article

Abstract

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.

Figures

Fig 1
Fig 1
Single-cell RNA-Seq of differentiating myoblasts. A) Primary human myoblasts were cultured in high-serum media. Following a switch to low-serum media, cells were dissociated and individually captured at 24-hour intervals. An RNA-Seq library was prepared and sequenced for each cell. B) Gene expression levels averaged across individual cells harvested at time zero compared against bulk RNA-Seq (n=3, biological replicates). C) Expression levels of late-stage markers of myoblast differentiation (Enolase 3, ENO3; myosin heavy chain 3, MYH3) in individual cells. D) Representative immunofluorescence staining at the moment of cell sampling of the indicated markers (myocyte enhancer factor 2C, MEF2C in green; myosin heavy chain, MYH2/MHC in red; Hoechst staining in blue).
Fig 2
Fig 2
Monocle orders individual cells by progress through differentiation. A) An overview of the Monocle algorithm. B) Cell expression profiles (points) in a two-dimensional independent component space. Lines connecting points represent edges of the MST constructed by Monocle. Solid black line indicates the main diameter path of the MST and provides the backbone of Monocle's “pseudo-time” ordering of the cells. C) Expression levels for differentially expressed genes identified by Monocle (rows), with cells (columns) shown in pseudo-time order. Fibroblasts are excluded. D) Bar plot showing the proportion of MEF2C and MYH2 expressing cells measured by immunofluorescence at the time of collection (upper panel), RNA-Seq at the time of collection (middle panel) or RNA-Seq at pseudo-time (lower panel). MEF2C was considered detectably expressed at or above 100 FPKM, and MYH2 at 1 FPKM. MEF2C exhibits a bimodal pattern of expression across the cells (not shown), and a threshold of 100 FPKM separates the modes. E) Expression levels of key regulators of muscle differentiation, ordered by time collected. (Cyclin-dependent kinase 1, CDK1; Inhibitor of DNA binding 1, ID1; Myogenin, MYOG) F) Regulators from panel D, ordered by Monocle in pseudo-time.
Fig 3
Fig 3
Pseudo-time ordering of cells reveals genes activated or repressed early in differentiation, along with potential upstream regulators. (left) Relative gene expression levels were K-means clustered. The mean expression for each cluster is shown in red, and an example gene with a known role in myogenesis from each cluster is highlighted in blue. (middle) Selected Gene Ontology terms that are associated with genes in each cluster. (right) Number of transcription factors with conserved binding site motifs in regulatory elements for genes in each cluster. Transcription factors are segregated according to the function of regulatory elements to which they bind. Examples are shown on the right, with known myogenic factors in black and factors without a known role in muscle differentiation in red.
Fig 4
Fig 4
Loss-of-function screen on selected transcription factors. A) Fraction of nuclei within cells expressing MYH2 (upper panel), whole-well area of MYH2 (middle panel) and nuclei count (lower panel) after 4 days of culture in differentiation medium following shRNA viral infection for the indicated genes, normalized to mock shRNA controls. For each mRNA, four independent shRNA were tested and the results of the two with greatest impact on fraction of nuclei in MYH2+ cells are reported. Values reported are the average of 4 technical replicates of each infection, with significance of changes w.r.t control assessed by two-tailed Student's t-tests and corrected by Benjamini Hochberg. Error bars indicate 2 standard deviations from the mean. An asterisk represents a significant difference with respect to mock control at an FDR < 5%. B) Co-occupancy scores of conserved transcription factor binding site motifs in enhancers (green) and promoters (purple) identified by ENCODE. Scores were calculated as the log10-transformed p-values from hypergeometric tests following Bonferroni correction for multiple testing (See Methods). C) Inhibitors might prevent premature myoblast differentiation by one of two mechanisms.

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