Transcriptome-wide noise controls lineage choice in mammalian progenitor cells - PubMed (original) (raw)

Transcriptome-wide noise controls lineage choice in mammalian progenitor cells

Hannah H Chang et al. Nature. 2008.

Abstract

Phenotypic cell-to-cell variability within clonal populations may be a manifestation of 'gene expression noise', or it may reflect stable phenotypic variants. Such 'non-genetic cell individuality' can arise from the slow fluctuations of protein levels in mammalian cells. These fluctuations produce persistent cell individuality, thereby rendering a clonal population heterogeneous. However, it remains unknown whether this heterogeneity may account for the stochasticity of cell fate decisions in stem cells. Here we show that in clonal populations of mouse haematopoietic progenitor cells, spontaneous 'outlier' cells with either extremely high or low expression levels of the stem cell marker Sca-1 (also known as Ly6a; ref. 9) reconstitute the parental distribution of Sca-1 but do so only after more than one week. This slow relaxation is described by a gaussian mixture model that incorporates noise-driven transitions between discrete subpopulations, suggesting hidden multi-stability within one cell type. Despite clonality, the Sca-1 outliers had distinct transcriptomes. Although their unique gene expression profiles eventually reverted to that of the median cells, revealing an attractor state, they lasted long enough to confer a greatly different proclivity for choosing either the erythroid or the myeloid lineage. Preference in lineage choice was associated with increased expression of lineage-specific transcription factors, such as a >200-fold increase in Gata1 (ref. 10) among the erythroid-prone cells, or a >15-fold increased PU.1 (Sfpi1) (ref. 11) expression among myeloid-prone cells. Thus, clonal heterogeneity of gene expression level is not due to independent noise in the expression of individual genes, but reflects metastable states of a slowly fluctuating transcriptome that is distinct in individual cells and may govern the reversible, stochastic priming of multipotent progenitor cells in cell fate decision.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1

Figure 1. Robust clonal heterogeneity

a, b, Heterogeneity in Sca-1 expression among clonal cells (a) was significantly larger than the resolution limit of flow cytometry approximated by measurement of reference MESF beads (b). c, Stability of clonal heterogeneity in Sca-1 over 3 weeks.

Figure 2

Figure 2. Restoration of heterogeneity from sorted cell fractions

a, Clonal cells with the highest (Sca-1High), middle (Sca-1Mid) and lowest (Sca-1Low) 15% Sca-1 expression independently re-established the parental extent of clonal heterogeneity after 216 h in separate culture. As an example, each cell in the Sca-1High experiment was theoretically partitioned into one of two GMM-subpopulations (blue and red). b, c, The temporal evolution of the means μ1,2 (b) and weights w1,2 (c) for the Sca-1High GMM subpopulations 1 and 2. The evolution of the weights was fitted to a sigmoidal function (c, dotted curves). Black dotted dash lines, equilibrium values for μı and wi.

Figure 3

Figure 3. Clonal heterogeneity governs differentiation potential

a–f, Sca-1Low (Low, black), Sca-1Mid (Mid, grey), and Sca-1High (High, white) fractions (a) stimulated by Epo (b) and GM-CSF (f) immediately after isolation showed variable differentiation rates into the erythroid and myeloid lineages, respectively. Upon 7, 14, and 21 days (d) of post-sort culture, Epo- treated cells showed convergence in both pre-stimulation, baseline Sca-1 expression (Fig. 2a) and relative differentiation rates (b–e). Asterisk, p < 0.001 (two-tailed normal-theory test). g, h, qRT-PCR analysis of GATA1 (g) and PU.1 (h) mRNA levels in Sca-1 sorted fractions. Means ± s.e.m. of triplicates shown; triple asterisk p < 10−5, double asterisk p < 0.0002, asterisk p < 0.003 (one-tail Student’s t-test). i, j, Western blot analysis of GATA1 (i) and PU.1 (j) protein levels in Sca-1 fractions (lanes 3–5) and mock-sorted cells (lane 6). MEL cell line (lane 1), positive control; G1E and 503 (lane 2) cell lines, negative controls for GATA1 and PU.1, respectively. GAPDH, loading control.

Figure 4

Figure 4. Clonal heterogeneity of Sca-1 expression reflects transcriptome-wide noise

Self-organizing maps of global gene expression for a subset of 2997 genes visualized with the GEDI program for Sca-1Low (L), Sca-1Mid (M), Sca-1High (H) fractions at 0 and 6 days (d) after FACS isolation and for a differentiated erythroid culture (7d Epo) and an untreated (Untreated) control sample. Pixels in the same location within each GEDI map contain the same minicluster of genes. Color of pixels indicates centroid value of gene expression level for each minicluster in log10 units of signal. Dissimilarity between transcriptomes indicated above formula image. GATA1-containing pixel boxed in white.

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