Transition state dynamics during a stochastic fate choice

Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.

Single Cell Transcriptome Analysis During Development in Dictyostelium

Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in "complexity," single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.

Collective signalling drives rapid jumping between cell states

Development can proceed in 'fits and starts', with rapid transitions between cell states involving concerted transcriptome-wide changes in gene expression. However, it is not clear how these transitions are regulated in complex cell populations, in which cells receive multiple inputs. We address this issue using Dictyostelium cells undergoing development in their physiological niche. A continuous single cell transcriptomics time series identifies a sharp 'jump' in global gene expression marking functionally different cell states. By simultaneously imaging the physiological dynamics of transcription and signalling, we show the jump coincides with the onset of collective oscillations of cAMP. Optogenetic control of cAMP pulses shows that different jump genes respond to distinct dynamic features of signalling. Late jump gene expression changes are almost completely dependent on cAMP, whereas transcript changes at the onset of the jump require additional input. The coupling of collective signalling with gene expression is a potentially powerful strategy to drive robust cell state transitions in heterogeneous signalling environments. Based on the context of the jump, we also conclude that sharp gene expression transitions may not be sufficient for commitment.

Clearing the slate: RNA turnover to enable cell state switching?

The distribution of mRNA in tissue is determined by the balance between transcription and decay. Understanding the control of RNA decay during development has been somewhat neglected compared with transcriptional control. Here, we explore the potential for mRNA decay to trigger rapid cell state transitions during development, comparing a bistable switch model of cell state conversion with experimental evidence from different developmental systems. We also consider another potential role for large-scale RNA decay that has emerged from studies of stress-induced cell state transitions, in which removal of mRNA unblocks the translation machinery to prioritise the synthesis of proteins that establish the new cell state.

Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon

Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.

Single-cell phenotypic plasticity modulates social behavior in Dictyostelium discoideum

In Dictyostelium chimeras, "cheaters" are strains that positively bias their contribution to the pool of spores, i.e., the reproductive cells resulting from development. On evolutionary time scales, the selective advantage; thus, gained by cheaters is predicted to undermine collective functions whenever social behaviors are genetically determined. Genotypes; however, are not the sole determinant of spore bias, but the relative role of genetic and plastic differences in evolutionary success is unclear. Here, we study chimeras composed of cells harvested in different phases of population growth. We show that such heterogeneity induces frequency-dependent, plastic variation in spore bias. In genetic chimeras, the magnitude of such variation is not negligible and can even reverse the classification of a strain's social behavior. Our results suggest that differential cell mechanical properties can underpin, through biases emerging during aggregation, a "lottery" in strains' reproductive success that may counter the evolution of cheating.

Zebrafish neuromesodermal progenitors undergo a critical state transition in vivo

The transition state model of cell differentiation proposes that a transient window of gene expression stochasticity precedes entry into a differentiated state. Here, we assess this theoretical model in zebrafish neuromesodermal progenitors (NMps) in vivo during late somitogenesis stages. We observed an increase in gene expression variability at the 24 somite stage (24ss) before their differentiation into spinal cord and paraxial mesoderm. Analysis of a published 18ss scRNA-seq dataset showed that the NMp population is noisier than its derivatives. By building in silico composite gene expression maps from image data, we assigned an 'NM index' to in silico NMps based on the expression of neural and mesodermal markers and demonstrated that cell population heterogeneity peaked at 24ss. Further examination revealed cells with gene expression profiles incongruent with their prospective fate. Taken together, our work supports the transition state model within an endogenous cell fate decision making event.

Single-cell Senseless protein analysis reveals metastable states during the transition to a sensory organ fate

Cell fate decisions can be envisioned as bifurcating dynamical systems, and the decision that Drosophila cells make during sensory organ differentiation has been described as such. We extended these studies by focusing on the Senseless protein which orchestrates sensory cell fate transitions. Wing cells contain intermediate Senseless numbers before their fate transition, after which they express much greater numbers of Senseless molecules as they differentiate. However, the dynamics are inconsistent with it being a simple bistable system. Cells with intermediate Senseless are best modeled as residing in four discrete states, each with a distinct protein number and occupying a specific region of the tissue. Although the states are stable over time, the number of molecules in each state vary with time. The fold change in molecule number between adjacent states is invariant and robust to absolute protein number variation. Thus, cells transitioning to sensory fates exhibit metastability with relativistic properties.

Characterization of hemocytes and hematopoietic cells of a freshwater crayfish based on single-cell transcriptome analysis

Crustaceans constitute a species-rich and ecologically important animal group, and their circulating blood cells (hemocytes) are of critical importance in immunity as key players in pathogen recognition, phagocytosis, melanization, and antimicrobial defense. To gain a better understanding of the immune responses to different pathogens, it is crucial that we identify different hemocyte subpopulations with different functions and gain a better understanding of how these cells are formed. Here, we performed single-cell RNA sequencing of isolated hematopoietic tissue (HPT) cells and hemocytes from the crayfish Pacifastacus leniusculus to identify hitherto undescribed hemocyte types in the circulation and show that the circulating cells are more diversified than previously recognized. In addition, we discovered cell populations in the HPT with clear precursor characteristics as well as cells involved in iron homeostasis, representing a previously undiscovered cell type. These findings may improve our understanding of hematopoietic stem cell regulation in crustaceans and other animals.

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Single-cell RNA sequencing of hematopoietic cell types reveals new cell types

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One cell type contains iron homeostasis-associated transcripts

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Hemocytes and hematopoietic cells differ in their transcript profiles

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Prophenoloxidase is only expressed in hemocytes

Developmental constraints enforce altruism and avert the tragedy of the commons in a social microbe

Organisms often cooperate through the production of freely available public goods. This can greatly benefit the group but is vulnerable to the "tragedy of the commons" if individuals lack the motivation to make the necessary investment into public goods production. Relatedness to groupmates can motivate individual investment because group success ultimately benefits their genes' own self-interests. However, systems often lack mechanisms that can reliably ensure that relatedness is high enough to promote cooperation. Consequently, groups face a persistent threat from the tragedy unless they have a mechanism to enforce investment when relatedness fails to provide adequate motivation. To understand the real threat posed by the tragedy and whether groups can avert its impact, we determine how the social amoeba Dictyostelium discoideum responds as relatedness decreases to levels that should induce the tragedy. We find that, while investment in public goods declines as overall within-group relatedness declines, groups avert the expected catastrophic collapse of the commons by continuing to invest, even when relatedness should be too low to incentivize any contribution. We show that this is due to a developmental buffering system that generates enforcement because insufficient cooperation perturbs the balance of a negative feedback system controlling multicellular development. This developmental constraint enforces investment under the conditions expected to be most tragic, allowing groups to avert a collapse in cooperation. These results help explain how mechanisms that suppress selfishness and enforce cooperation can arise inadvertently as a by-product of constraints imposed by selection on different traits.

Reconstructing an epigenetic landscape using a genetic pulling approach

Cells use genetic switches to shift between alternate stable gene expression states, e.g., to adapt to new environments or to follow a developmental pathway. Conceptually, these stable phenotypes can be considered as attractive states on an epigenetic landscape with phenotypic changes being transitions between states. Measuring these transitions is challenging because they are both very rare in the absence of appropriate signals and very fast. As such, it has proved difficult to experimentally map the epigenetic landscapes that are widely believed to underly developmental networks. Here, we introduce a nonequilibrium perturbation method to help reconstruct a regulatory network's epigenetic landscape. We derive the mathematical theory needed and then use the method on simulated data to reconstruct the landscapes. Our results show that with a relatively small number of perturbation experiments it is possible to recover an accurate representation of the true epigenetic landscape. We propose that our theory provides a general method by which epigenetic landscapes can be studied. Finally, our theory suggests that the total perturbation impulse required to induce a switch between metastable states is a fundamental quantity in developmental dynamics.

Abundantly expressed class of noncoding RNAs conserved through the multicellular evolution of dictyostelid social amoebas

Aggregative multicellularity has evolved multiple times in diverse groups of eukaryotes, exemplified by the well-studied development of dictyostelid social amoebas, for example, Dictyostelium discoideum However, it is still poorly understood why multicellularity emerged in these amoebas while the majority of other members of Amoebozoa are unicellular. Previously, a novel type of noncoding RNA, Class I RNAs, was identified in D. discoideum and shown to be important for normal multicellular development. Here, we investigated Class I RNA evolution and its connection to multicellular development. We identified a large number of new Class I RNA genes by constructing a covariance model combined with a scoring system based on conserved upstream sequences. Multiple genes were predicted in representatives of each major group of Dictyostelia and expression analysis confirmed that our search approach identifies expressed Class I RNA genes with high accuracy and sensitivity and that the RNAs are developmentally regulated. Further studies showed that Class I RNAs are ubiquitous in Dictyostelia and share highly conserved structure and sequence motifs. In addition, Class I RNA genes appear to be unique to dictyostelid social amoebas because they could not be identified in outgroup genomes, including their closest known relatives. Our results show that Class I RNA is an ancient class of ncRNAs, likely to have been present in the last common ancestor of Dictyostelia dating back at least 600 million years. Based on previous functional analyses and the presented evolutionary investigation, we hypothesize that Class I RNAs were involved in evolution of multicellularity in Dictyostelia.

Theoretical study of the impact of adaptation on cell-fate heterogeneity and fractional killing

Fractional killing illustrates the cell propensity to display a heterogeneous fate response over a wide range of stimuli. The interplay between the nonlinear and stochastic dynamics of biochemical networks plays a fundamental role in shaping this probabilistic response and in reconciling requirements for heterogeneity and controllability of cell-fate decisions. The stress-induced fate choice between life and death depends on an early adaptation response which may contribute to fractional killing by amplifying small differences between cells. To test this hypothesis, we consider a stochastic modeling framework suited for comprehensive sensitivity analysis of dose response curve through the computation of a fractionality index. Combining bifurcation analysis and Langevin simulation, we show that adaptation dynamics enhances noise-induced cell-fate heterogeneity by shifting from a saddle-node to a saddle-collision transition scenario. The generality of this result is further assessed by a computational analysis of a detailed regulatory network model of apoptosis initiation and by a theoretical analysis of stochastic bifurcation mechanisms. Overall, the present study identifies a cooperative interplay between stochastic, adaptation and decision intracellular processes that could promote cell-fate heterogeneity in many contexts.

Cell and molecular transitions during efficient dedifferentiation

Dedifferentiation is a critical response to tissue damage, yet is not well understood, even at a basic phenomenological level. Developing Dictyostelium cells undergo highly efficient dedifferentiation, completed by most cells within 24 hr. We use this rapid response to investigate the control features of dedifferentiation, combining single cell imaging with high temporal resolution transcriptomics. Gene expression during dedifferentiation was predominantly a simple reversal of developmental changes, with expression changes not following this pattern primarily associated with ribosome biogenesis. Mutation of genes induced early in dedifferentiation did not strongly perturb the reversal of development. This apparent robustness may arise from adaptability of cells: the relative temporal ordering of cell and molecular events was not absolute, suggesting cell programmes reach the same end using different mechanisms. In addition, although cells start from different fates, they rapidly converged on a single expression trajectory. These regulatory features may contribute to dedifferentiation responses during regeneration.

Attractor Concepts to Evaluate the Transcriptome-wide Dynamics Guiding Anaerobic to Aerobic State Transition in Escherichia coli

For any dynamical system, like living organisms, an attractor state is a set of variables or mechanisms that converge towards a stable system behavior despite a wide variety of initial conditions. Here, using multi-dimensional statistics, we investigate the global gene expression attractor mechanisms shaping anaerobic to aerobic state transition (AAT) of Escherichia coli in a bioreactor at early times. Out of 3,389 RNA-Seq expression changes over time, we identified 100 sharply changing genes that are key for guiding 1700 genes into the AAT attractor basin. Collectively, these genes were named as attractor genes constituting of 6 dynamic clusters. Apart from the expected anaerobic (glycolysis), aerobic (TCA cycle) and fermentation (succinate pathways) processes, sulphur metabolism, ribosome assembly and amino acid transport mechanisms together with 332 uncharacterised genes are also key for AAT. Overall, our work highlights the importance of multi-dimensional statistical analyses for revealing novel processes shaping AAT.

Heterogeneous response of endothelial cells to insulin-like growth factor 1 treatment is explained by spatially clustered sub-populations

A common strategy to measure the efficacy of drug treatment is the in vitro comparison of ensemble readouts with and without treatment, such as proliferation and cell death. A fundamental assumption underlying this approach is that there exists minimal cell-to-cell variability in the response to a drug. Here, we demonstrate that ensemble and non-spatial single-cell readouts applied to primary cells may lead to incomplete conclusions due to cell-to-cell variability. We exposed primary fetal pulmonary artery endothelial cells (PAEC) isolated from healthy newborn sheep and persistent pulmonary hypertension of the newborn (PPHN) sheep to the growth hormone, insulin-like growth factor 1 (IGF-1). We found that IGF-1 increased proliferation and branch points in tube formation assays but not angiogenic signaling proteins at the population level for both cell types. We hypothesized that this molecular ambiguity was due to the presence of cellular sub-populations with variable responses to IGF-1. Using high throughput single-cell imaging, we discovered a spatially localized response to IGF-1. This suggests localized signaling or heritable cell response to external stimuli may ultimately be responsible for our observations. Discovering and further exploring these rare cells is critical to finding new molecular targets to restore cellular function.

The fate of cells undergoing spontaneous DNA damage during development

Embryonic development involves extensive and often rapid cell proliferation. An unavoidable side effect of cell proliferation is DNA damage. The consequences of spontaneous DNA damage during development are not clear. Here, we define an approach to determine the effects of DNA damage on cell fate choice. Using single cell transcriptomics, we identified a subpopulation of Dictyostelium cells experiencing spontaneous DNA damage. Damaged cells displayed high expression of rad51, with the gene induced by multiple types of genotoxic stress. Using live imaging, we tracked high Rad51 cells from differentiation onset until cell fate assignment. High Rad51 cells were shed from multicellular structures, excluding damaged cells from the spore population. Cell shedding resulted from impaired cell motility and defective cell-cell adhesion, with damaged cells additionally defective in activation of spore gene expression. These data indicate DNA damage is not insulated from other aspects of cell physiology during development and multiple features of damaged cells prevent propagation of genetic error. Our approach is generally applicable for monitoring rare subpopulations during development, and permits analysis of developmental perturbations occurring within a physiological dynamic range.