Metastability and dynamics of stem cells

As such, we must first define which states exist in a population, determine what controls the stability of these states, and measure the high-dimensional dynamics by which these cells transition between states. Together, we identify distinct expression states in EC cells, gain fundamental insight into how a strong epigenetic modifier enforces the stability of these states, and develop and apply a new method to identify cellular trajectories using scalable in situ readouts of cell state.

Cellular Specialization

Furthermore, living cells in any of these states could be separated by FACS using a unique set of cell surface proteins. Crucially, these experiments showed that differentiated cells were capable of producing stem-like cells.

Transcriptional noise and dynamics of activation complicate the iden-

This was achieved by adapting the treatment schedule to the dynamics of cellular interconversion, in order to maximize the number of quiescent cells. In addition to the stochastic nature of transcription, cells can also behave probabilistically as a function of environmental cues.

Current methods for measuring dynamics

Alternatively, rates of transition can be measured by isolating subpopulations by fluorescence-activated cell sorting (FACS), followed by growth in which the cells are re-equilibrated. Consequently, destroying the original context of the sample can itself lead to transition rates that are biased.

Embryonic stem cells provide an ideal model system for studying metasta-

ESC is defined as the outgrowth of the inner cell mass (ICM) in culture from the E3.5 embryo. Further evidence of the strong interconnection of the network underlying pluripotency is Tbx3's direct repression of the de novo DNA methyltransferase Dnmt3b, which can lead to a small increase in the fraction of cells expressing 2C-like genes such as Zscan4 , as well as elongated telomeres [71] .

How can we improve the identification of transcriptional states and

How do cells implement state stability?

Together, using mouse ES cells, we attempt to ask how noise and different gene expression states together result in the heterogeneity observed in ES cells, how cells stabilize the resulting expression states, and how we can identify transition rates and trajectories between these characteristic states.

Figures

Pluripotency factors in embryonic stem cells regulate differentiation in germ layers.Cell (2011) (cit. on p. 8). Whole-genome bisulfite sequencing of two distinct interconvertible DNA methylomes from mouse embryonic stem cells.Cell Stem Cell (2013) (cit. on p. 11).

Figure 1.2: Schematic of methods to be applied

Introduction

Using single-molecule RNA-FISH (smFISH), we analyzed the structure of heterogeneity in the expression of key cell fate regulators and found that different cell states account for most of the variation in some genes, while stochastic bursts dominate in others. Finally, using perturbations, we observed that DNA methylation modulates the population proportion of cells in both states, consistent with the reciprocal expression of methyltransferase Dnmt3b and hydroxymethylase Tet1.

Results

Mouse ESCs show three distinct types of gene expression dis-
Bimodal genes vary coherently
The two primary states exhibit distinct DNA methylation profiles 30
Dynamic transitions between cellular states
DNA methylation modulates metastability

Consistent with the smFISH static distributions, the production rate distributions of the Nanog and Oct4 fluorescent reporters were bimodal and unimodal, respectively (Figure 4A). We found that the addition of 2i to the serum+LIF medium reduced the variability of the mRNA levels of most genes (Figure 5A). TKO cell lines had 35±2% fewer cells in the Rex1-low state (Figure 5E), with similar results observed for Nanog and Esrrb.

Within six days, the number of cells in the Rex1-low condition was reduced by more than half from 29% to 13% of all cells ( Fig. 5F ).

Discussion

Finally, methylation appears to be functionally required for transitions, as either genetic deletion of DNA methyltransferases or pharmacological inhibition affects both the populations of the two cell states and the underlying dynamics of state switching (Fig. 5E–G). It will be interesting to see if methylation plays similar functional roles in other stochastic state switching systems. Two groups have investigated transcriptional circuit models to explain the long time scales of state switching dynamics [ 69 , 70 ].

Our dynamic data show that both high Nanog and low Nanog states under serum+LIF conditions typically persist for ≥4 cell cycles and that state switching events are abrupt at the level of promoter activity.

Experimental Procedures

Culture Conditions and Cell Lines
smFISH Hybridization, Imaging, and Analysis
mRNA Distribution Fitting
Fluorescence time-lapse microscopy and data analysis
Methylation Analysis and Perturbation

To discriminate between unimodal and bimodal fits, two tests were used to ensure that the improvement in fit is counterbalanced by the additional degrees of freedom from the added parameters. Cells were segmented and tracked from the acquired images using our Matlab code (see the supplement for image analysis methods.). For treatment 2i we supplemented serum+LIF media with MEK inhibitor PD0325901 at 1uM and GSK3 inhibitor CH99021 at 3uM.

Cells grown in serum+LIF media were treated with 2i for 6 days before harvesting for smFISH assay and imaging for films.

Accession Information

Acknowledgements

Figures

FGF signal-dependent segregation of primitive endoderm and epiblast in the mouse blastocyst. Development (2010) (cited on p. 26). Single RNA counting reveals alternative modes of gene expression in yeast. Nat Struct Mol Biol (2008) (cited on p. 26). Stochastic focusing: fluctuation-enhanced sensitivity of intracellular regulation. Proc Natl Acad Sci USA (2000) (cited at p. 28).

Dual functions of Tet1 in transcriptional regulation in mouse embryonic stem cells. Nature (2011) (cit. on p. 30).

Figure 2.2: smFISH reveals gene expression heterogeneity and correlation

Supplemental Data and Figures

Values were normalized to the expression levels of the housekeeping gene Gapdh and are represented as 2 (−∆∆Ct) relative to the 'Rex1-high' subpopulation. Info.), and 2) the slopes of the two-part fit are significantly different between the two parts. iv). Average production rates of 4-hour windows before and after each cell division are compared.

Data points are discarded if any of the cells in a sister pair are lost or untraceable in the movie.

Figure 2.9: Construction and analysis of live cell reporters, and simulations based on observed kinetics

Supplemental Experimental Procedures

Detailed Culture Conditions
Correlation between Citrine and Nanog transcripts in Nanog
smFISH Procedure and imaging system
Monte-Carlo Bivariate Kolmogorov-Smirnov Test
Movie acquisition system
Movie data analysis: Segmentation and tracking
Movie data analysis: Production rate estimation and step de-
Movie data analysis: Hidden Markov Model and Viterbi Algo-
Bursty transcription simulation and mixing time analysis

For a perfectly linear trace with no noise, the average of Yt+1 - Yt equals the slope of the trace. Therefore, we estimated the fraction of the SSE of the one-piece fit that is not explained by trace noise as residual noise, defined as MSE1pc / var(Yt+1 - Yt), where n is the number of frames in the window. For (2), we obtained 95% confidence limits for the two slopes in a two-part fit and determined whether they overlapped.

We used the HMM tool for Matlab (Murphy, 1998), which produced a maximum likelihood estimate of the model parameters using the Baum-Welch algorithm.

Introduction

These include bulk and single-cell qPCR and RNA-seq using sophisticated computational algorithms [4], FACSed sub-population re-equilibration, in vivo and in vitro time-lapse microscopy for lineage tracking or direct gene expression measurements [5–11] , and spontaneous-mutation-based lineage reconstruction [12]. We use a combination of time-lapse movies followed by multiplexed endpoint single-cell measurements [13–15] in the spatially unperturbed sample. In general, these may include smRNA-FISH, immunostaining, morphological parameters, or any imaging-based in situ single-cell readout.

We demonstrate the utility of such a system by coupling films with multiplexed endpoint single cell RNA-FISH in order to (1) derive and additionally validate by a knock-in reporter directly the stochastic transitions of pluripotency marker Esrrb;.

Results

The Framework

Experimentally, C(u) can be read from the final state measurements associated with the line by counting the occurrences of all pairs of states in the tree at a line distance u. The decay rate of these correlation functions would have to be canceled out by the compensation of successive applications of a fixed transition rate, allowing the estimation of the transition rates between states from the correlation functions alone. To infer the dynamics from the measured correlation functions, we work backwards to recover T by computing Tinf erred(u) :=C(u)1/(2u) (Figure 2B).

If the assumptions are correct, Tinf erred(u) must match the actual T and be independent of u.

Transition rates between states of Esrrb can be inferred using

Because any pair must be in one of the N2 possible pairs of states, the elements of C must sum to one. As a first validation, we checked to see if the rate of change in total fluorescence of Esrrb in films (hereafter referred to as promoter activity) across the final cell cycle correlated with transcript abundance measured at the end point of the film. Using the correlation functions compiled from 14 trees (299 cells), we estimated transition rates that were constant over time, demonstrating self-consistency and Markovian transitions.

Taken together, these data show that the inference method yielded the same Markovian dynamics as those measured directly using the more cumbersome and invasive knock-in reporter.

Inferring trajectories among higher dimensional transcriptional

So, after classifying each cell into one of the resulting five states, we can proceed to construct correlation functions. In addition, some of the transition rates seem to drop out of the system due to the lack of observed pairs in those states. This suggests that cells move largely along a linear chain of states (Figure 4H), where cells tend to generally make a subset of the possible transitions, contrary to the starting model.

An inspection of the 3D scattering data alone would incorrectly lead one to assume that the Zscan4+ state originates from the persistent Esrrb state, demonstrating the utility of using lineage information.

Discussion

This does not strictly rule out these other transient events, but suggests that, in general, cells are much more likely to follow a linear chain path between opposite ends of the network. Notably, these data indicate that to access the Esrrb+/Tbx3+/Zscan4c+ state, a cell must first pass through the Esrrb+/Tbx3+ state.

Figures

The filmstrip is followed by a composite image of the membrane mTurquoise (white), DAPI (red) and Esrrb transcripts by smFISH (cyan dots). The pedigree resulting from one of the two starting cells in the example movie shown in A is shown in blue; leaves of the tree are colored by confidence of cells in the Esrrb. Inferring epigenetic dynamics from family correlations. Proceedings of the National Academy of Sciences of the United States of America (Apr. 2015) (cited on p. 83).

Martello, G.et al.Esrrb is a critical target of the Gsk3/Tcf3 axis that regulates the self-renewal of embryonic stem cells.