Dynamic heterogeneity as a strategy of stem cell self-renewal

Licensing and niche competition in spermatogenesis: mathematical models suggest complementary regulation of tissue maintenance

To maintain and regenerate adult tissues after injury, division and differentiation of tissue-resident stem cells must be precisely regulated. It remains elusive which regulatory strategies prevent exhaustion or overgrowth of the stem cell pool, whether there is coordination between multiple mechanisms, and how to detect them from snapshots. In Drosophila testes, somatic stem cells transition to a state that licenses them to differentiate, but remain capable of returning to the niche and resuming cell division. Here, we build stochastic mathematical models for the somatic stem cell population to investigate how licensing contributes to homeostasis. We find that licensing, in combination with differentiation occurring in pairs, is sufficient to maintain homeostasis and prevent stem cell extinction from stochastic fluctuations. Experimental data have shown that stem cells are competing for niche access, and our mathematical models demonstrate that this contributes to the reduction in the variability of stem cell numbers but does not prevent extinction. Hence, a combination of both regulation strategies, licensing with pairwise differentiation and competition for niche access, may be needed to reduce variability and prevent extinction simultaneously.

Emergent order in epithelial sheets by interplay of cell divisions and cell fate regulation

The fate choices of stem cells between self-renewal and differentiation are often tightly regulated by juxtacrine (cell-cell contact) signalling. Here, we assess how the interplay between cell division, cell fate choices, and juxtacrine signalling can affect the macroscopic ordering of cell types in self-renewing epithelial sheets, by studying a simple spatial cell fate model with cells being arranged on a 2D lattice. We show in this model that if cells commit to their fate directly upon cell division, macroscopic patches of cells of the same type emerge, if at least a small proportion of divisions are symmetric, except if signalling interactions are laterally inhibiting. In contrast, if cells are first 'licensed' to differentiate, yet retaining the possibility to return to their naive state, macroscopic order only emerges if the signalling strength exceeds a critical threshold: if then the signalling interactions are laterally inducing, macroscopic patches emerge as well. Lateral inhibition, on the other hand, can in that case generate periodic patterns of alternating cell types (checkerboard pattern), yet only if the proportion of symmetric divisions is sufficiently low. These results can be understood theoretically by an analogy to phase transitions in spin systems known from statistical physics.

Correspondence between multiple signaling and developmental cellular patterns: a computational perspective

The spatial arrangement of variant phenotypes during stem cell division plays a crucial role in the self-organization of cell tissues. The patterns observed in these cellular assemblies, where multiple phenotypes vie for space and resources, are largely influenced by a mixture of different diffusible chemical signals. This complex process is carried out within a chronological framework of interplaying intracellular and intercellular events. This includes receiving external stimulants, whether secreted by other individuals or provided by the environment, interpreting these environmental signals, and incorporating the information to designate cell fate. Here, given two distinct signaling patterns generated by Turing systems, we investigated the spatial distribution of differentiating cells that use these signals as external cues for modifying the production rates. By proposing a computational map, we show that there is a correspondence between the multiple signaling and developmental cellular patterns. In other words, the model provides an appropriate prediction for the final structure of the differentiated cells in a multi-signal, multi-cell environment. Conversely, when a final snapshot of cellular patterns is given, our algorithm can partially identify the signaling patterns that influenced the formation of the cellular structure, provided that the governing dynamic of the signaling patterns is already known.

A modelling framework for cancer ecology and evolution

Cancer incidence increases rapidly with age, typically as a polynomial. The somatic mutation theory explains this increase through the waiting time for enough mutations to build up to generate cells with the full set of traits needed to grow without control. However, lines of evidence ranging from tumour reversion and dormancy to the prevalence of presumed cancer mutations in non-cancerous tissues argue that this is not the whole story, and that cancer is also an ecological process, and that mutations only lead to cancer when the systems of control within and across cells have broken down. Aging thus has two effects: the build-up of mutations and the breakdown of control. This paper presents a mathematical modelling framework to unify these theories with novel approaches to model the mutation and diversification of cell lineages and of the breakdown of the layers of control both within and between cells. These models correctly predict the polynomial increase of cancer with age, show how germline defects in control accelerate cancer initiation, and compute how the positive feedback between cell replication, ecology and layers of control leads to a doubly exponential growth of cell populations.

Homeostatic regulation of renewing tissue cell populations via crowding control: stability, robustness and quasi-dedifferentiation

To maintain renewing epithelial tissues in a healthy, homeostatic state, cell divisions and differentiation need to be tightly regulated. Mechanisms of homeostatic regulation often rely on crowding feedback control: cells are able to sense the cell density in their environment, via various molecular and mechanosensing pathways, and respond by adjusting division, differentiation, and cell state transitions appropriately. Here, we determine, via a mathematically rigorous framework, which general conditions for the crowding feedback regulation (i) must be minimally met, and (ii) are sufficient, to allow the maintenance of homeostasis in renewing tissues. We show that those conditions naturally allow for a degree of robustness toward disruption of regulation. Furthermore, intrinsic to this feedback regulation is that stem cell identity is established collectively by the cell population, not by individual cells, which implies the possibility of 'quasi-dedifferentiation', in which cells committed to differentiation may reacquire stem cell properties upon depletion of the stem cell pool. These findings can guide future experimental campaigns to identify specific crowding feedback mechanisms.

A computational model of stem cells' internal mechanism to recapitulate spatial patterning and maintain the self-organized pattern in the homeostasis state

The complex functioning of multi-cellular tissue development relies on proper cell production rates to replace dead or differentiated specialized cells. Stem cells are critical for tissue development and maintenance, as they produce specialized cells to meet the tissues' demands. In this study, we propose a computational model to investigate the stem cell's mechanism, which generates the appropriate proportion of specialized cells, and distributes them to their correct position to form and maintain the organized structure in the population through intercellular reactions. Our computational model focuses on early development, where the populations overall behavior is determined by stem cells and signaling molecules. The model does not include complicated factors such as movement of specialized cells or outside signaling sources. The results indicate that in our model, the stem cells can organize the population into a desired spatial pattern, which demonstrates their ability to self-organize as long as the corresponding leading signal is present. We also investigate the impact of stochasticity, which provides desired non-genetic diversity; however, it can also break the proper boundaries of the desired spatial pattern. We further examine the role of the death rate in maintaining the system's steady state. Overall, our study sheds light on the strategies employed by stem cells to organize specialized cells and maintain proper functionality. Our findings provide insight into the complex mechanisms involved in tissue development and maintenance, which could lead to new approaches in regenerative medicine and tissue engineering.

Quantitative Modelling in Stem Cell Biology and Beyond: How to Make Best Use of It

Purpose of Review

This article gives a broad overview of quantitative modelling approaches in biology and provides guidance on how to employ them to boost stem cell research, by helping to answer biological questions and to predict the outcome of biological processes.

Recent Findings

The twenty-first century has seen a steady increase in the proportion of cell biology publications employing mathematical modelling to aid experimental research. However, quantitative modelling is often used as a rather decorative element to confirm experimental findings, an approach which often yields only marginal added value, and is in many cases scientifically questionable.

Summary

Quantitative modelling can boost biological research in manifold ways, but one has to take some careful considerations before embarking on a modelling campaign, in order to maximise its added value, to avoid pitfalls that may lead to wrong results, and to be aware of its fundamental limitations, imposed by the risks of over-fitting and "universality".

Markers of dental pulp stem cells in in vivo developmental context

Teeth and their associated tissues contain several populations of mesenchymal stem cells, one of which is represented by dental pulp stem cells (DPSCs). These cells have mainly been characterised in vitro and numerous positive and negati ve markers for these cells have been suggested. To investigate the presence and localization of these molecules during development, forming dental pulp was examined using the mouse first mandibular molar as a model. The stages corresponding to postnatal (P) days 0, 7, 14, and 21 were investigated. The expression was monitored using customised PCR Arrays. Additionally, in situ localization of the key trio of markers (Cd73, Cd90, Cd105 coded by genes Nt5e, Thy1, Eng) was performed at prenatal and postnatal stages using immunohistochemistry. The expression panel of 24 genes assigned as in vitro markers of DPSCs or mesenchymal stem cells (MSCs) revealed their developmental dynamics during formation of dental pulp mesenchyme. Among the positive markers, Vcam1, Fgf2, Nes were identified as increasing and Cd44, Cd59b, Mcam, Alcam as decreasing between perinatal vs. postnatal stages towards adulthood. Within the panel of negative DPSC markers, Cd14, Itgb2, Ptprc displayed increased and Cd24a decreased levels at later stages of pulp formation. Within the key trio of markers, Nt5e did not show any significant expression difference within the investigated period. Thy1 displayed a strong decrease between P0 and P7 while Eng increased between these stages. In situ localization of Cd73, Cd90 and Cd105 showed them overlap in differentiated odontoblasts and in the sub-odontoblastic layer that is speculated to host odontoblast progenitors. The highly prevalent expression of particularly Cd73 and Cd90 opens the question of potential multiple functions of these molecules. The results from this study add to the in vitro based knowledge by showing dynamics in the expression of DPSC/MSC markers during dental pulp formation in an in vivo context and thus with respect to the natural environment important for commitment of stem cells.

Configurational entropy is an intrinsic driver of tissue structural heterogeneity

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters - the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

Single-cell expression profile of Drosophila ovarian follicle stem cells illuminates spatial differentiation in the germarium

BACKGROUND

How stem cell populations are organized and regulated within adult tissues is important for understanding cancer origins and for developing cell replacement strategies. Paradigms such as mammalian gut stem cells and Drosophila ovarian follicle stem cells (FSC) are characterized by population asymmetry, in which stem cell division and differentiation are separately regulated processes. These stem cells behave stochastically regarding their contributions to derivative cells and also exhibit dynamic spatial heterogeneity. Drosophila FSCs provide an excellent model for understanding how a community of active stem cells maintained by population asymmetry is regulated. Here, we use single-cell RNA sequencing to profile the gene expression patterns of FSCs and their immediate derivatives to investigate heterogeneity within the stem cell population and changes associated with differentiation.

RESULTS

We describe single-cell RNA sequencing studies of a pre-sorted population of cells that include FSCs and the neighboring cell types, escort cells (ECs) and follicle cells (FCs), which they support. Cell-type assignment relies on anterior-posterior (AP) location within the germarium. We clarify the previously determined location of FSCs and use spatially targeted lineage studies as further confirmation. The scRNA profiles among four clusters are consistent with an AP progression from anterior ECs through posterior ECs and then FSCs, to early FCs. The relative proportion of EC and FSC clusters are in good agreement with the prevalence of those cell types in a germarium. Several genes with graded profiles from ECs to FCs are highlighted as candidate effectors of the inverse gradients of the two principal signaling pathways, Wnt and JAK-STAT, that guide FSC differentiation and division.

CONCLUSIONS

Our data establishes an important resource of scRNA-seq profiles for FSCs and their immediate derivatives that is based on precise spatial location and functionally established stem cell identity, and facilitates future genetic investigation of regulatory interactions guiding FSC behavior.

Sequential events during the quiescence to proliferation transition establish patterns of follicle cell differentiation in the Drosophila ovary

Stem cells cycle between periods of quiescence and proliferation to promote tissue health. In Drosophila ovaries, quiescence to proliferation transitions of follicle stem cells (FSCs) are exquisitely feeding-dependent. Here, we demonstrate feeding-dependent induction of follicle cell differentiation markers, eyes absent (Eya) and castor (Cas) in FSCs, a patterning process that does not depend on proliferation induction. Instead, FSCs extend micron-scale cytoplasmic projections that dictate Eya-Cas patterning. We identify still life and sickie as necessary and sufficient for FSC projection growth and Eya-Cas induction. Our results suggest that sequential, interdependent events establish long-term differentiation patterns in follicle cell precursors, independently of FSC proliferation induction.

A computational model of stem cells' decision-making mechanism to maintain tissue homeostasis and organization in the presence of stochasticity

The maintenance of multi-cellular developed tissue depends on the proper cell production rate to replace the cells destroyed by the programmed process of cell death. The stem cell is the main source of producing cells in a developed normal tissue. It makes the stem cell the lead role in the scene of a fully formed developed tissue to fulfill its proper functionality. By focusing on the impact of stochasticity, here, we propose a computational model to reveal the internal mechanism of a stem cell, which generates the right proportion of different types of specialized cells, distribute them into their right position, and in the presence of intercellular reactions, maintain the organized structure in a homeostatic state. The result demonstrates that the spatial pattern could be harassed by the population geometries. Besides, it clearly shows that our model with progenitor cells able to recover the stem cell presence could retrieve the initial pattern appropriately in the case of injury. One of the fascinating outcomes of this project is demonstrating the contradictory roles of stochasticity. It breaks the proper boundaries of the initial spatial pattern in the population. While, on the flip side of the coin, it is the exact factor that provides the demanded non-genetic diversity in the tissue. The remarkable characteristic of the introduced model as the stem cells' internal mechanism is that it could control the overall behavior of the population without need for any external factors.

Regenerative medicine meets mathematical modelling: developing symbiotic relationships

Successful progression from bench to bedside for regenerative medicine products is challenging and requires a multidisciplinary approach. What has not yet been fully recognised is the potential for quantitative data analysis and mathematical modelling approaches to support this process. In this review, we highlight the wealth of opportunities for embedding mathematical and computational approaches within all stages of the regenerative medicine pipeline. We explore how exploiting quantitative mathematical and computational approaches, alongside state-of-the-art regenerative medicine research, can lead to therapies that potentially can be more rapidly translated into the clinic.

Dynamic spatiotemporal coordination of neural stem cell fate decisions occurs through local feedback in the adult vertebrate brain

Neural stem cell (NSC) populations persist in the adult vertebrate brain over a lifetime, and their homeostasis is controlled at the population level through unknown mechanisms. Here, we combine dynamic imaging of entire NSC populations in their in vivo niche over several weeks with pharmacological manipulations, mathematical modeling, and spatial statistics and demonstrate that NSCs use spatiotemporally resolved local feedback signals to coordinate their decision to divide in adult zebrafish brains. These involve Notch-mediated short-range inhibition from transient neural progenitors and a dispersion effect from the dividing NSCs themselves exerted with a delay of 9–12 days. Simulations from a stochastic NSC lattice model capturing these interactions demonstrate that these signals are linked by lineage progression and control the spatiotemporal distribution of output neurons. These results highlight how local and temporally delayed interactions occurring between brain germinal cells generate self-propagating dynamics that maintain NSC population homeostasis and coordinate specific spatiotemporal correlations.

•

NSC activation events are spatiotemporally coordinated within adult NSC populations

•

This involves inhibition by neural progenitors (relying on Notch) and by dividing NSCs

•

A dynamic lattice model shows that these interactions are linked by lineage progression

•

NSCs dynamics generate an intrinsic niche that maintains the NSC population long-term

Mitophagy: A New Player in Stem Cell Biology

The fundamental importance of functional mitochondria in the survival of most eukaryotic cells, through regulation of bioenergetics, cell death, calcium dynamics and reactive oxygen species (ROS) generation, is undisputed. However, with new avenues of research in stem cell biology these organelles have now emerged as signaling entities, actively involved in many aspects of stem cell functions, including self-renewal, commitment and differentiation. With this recent knowledge, it becomes evident that regulatory pathways that would ensure the maintenance of mitochondria with state-specific characteristics and the selective removal of organelles with sub-optimal functions must play a pivotal role in stem cells. As such, mitophagy, as an essential mitochondrial quality control mechanism, is beginning to gain appreciation within the stem cell field. Here we review and discuss recent advances in our knowledge pertaining to the roles of mitophagy in stem cell functions and the potential contributions of this specific quality control process on to the progression of aging and diseases.

Single-Cell Infrared Microspectroscopy Quantifies Dynamic Heterogeneity of Mesenchymal Stem Cells during Adipogenic Differentiation

The central relevance of cellular heterogeneity to biological phenomena raises the rational needs for analytical techniques with single-cell resolution. Here, we developed a single-cell FTIR microspectroscopy-based method for the quantitative evaluation of cellular heterogeneity by calculating the cell-to-cell similarity distance of the infrared spectral data. Based on this method, we revealed the infrared phenotypes might reflect the dynamic heterogeneity changes in the cell population during the adipogenic differentiation of the human mesenchymal stem cells. These findings provide an alternative label-free optical approach for quantifying the cellular heterogeneity, and the combination with other single-cell analysis tools will be very helpful for understanding the genotype-to-phenotype relationship in cellular populations.

Heterogeneity and 'memory' in stem cell populations

Modern single cell experiments have revealed unexpected heterogeneity in apparently functionally 'pure' cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories-changes in the molecular status of a cell in response to a stimulus, that modify the ability of the cell to respond to future stimuli-are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation that takes account of mitotic memories. Using this model we argue that asynchronous mitosis generates heterogeneity that is central to stem cell population function. This model naturally explains why stem cell numbers increase through life, yet regenerative potency simultaneously declines.

Opposing JAK-STAT and Wnt signaling gradients define a stem cell domain by regulating differentiation at two borders

Many adult stem cell communities are maintained by population asymmetry, where stochastic behaviors of multiple individual cells collectively result in a balance between stem cell division and differentiation. We investigated how this is achieved for Drosophila Follicle Stem Cells (FSCs) by spatially-restricted niche signals. FSCs produce transit-amplifying Follicle Cells (FCs) from their posterior face and quiescent Escort Cells (ECs) to their anterior. We show that JAK-STAT pathway activity, which declines from posterior to anterior, dictates the pattern of divisions over the FSC domain, promotes more posterior FSC locations and conversion to FCs, while opposing EC production. Wnt pathway activity declines from the anterior, promotes anterior FSC locations and EC production, and opposes FC production. The pathways combine to define a stem cell domain through concerted effects on FSC differentiation to ECs and FCs at either end of opposing signaling gradients, and impose a pattern of proliferation that matches derivative production.

Cleaning the molecular machinery of cells via proteostasis, proteolysis and endocytosis selectively, effectively, and precisely: intracellular self-defense and cellular perturbations

Network coordinates of cellular processes (proteostasis, proteolysis, and endocytosis), and molecular chaperones are the key complements in the cell machinery and processes. Specifically, cellular pathways are responsible for the conformational maintenance, cellular concentration, interactions, protein synthesis, disposal of misfolded proteins, localization, folding, and degradation. The failure of cellular processes and pathways disturbs structural proteins and the nucleation of amyloids. These mishaps further initiate amyloid polymorphism, transmissibility, co-aggregation of pathogenic proteins in tissues and cells, prion strains, and mechanisms and pathways for toxicity. Consequently, these conditions favor and lead to the formation of elongated amyloid fibrils consisting of many-stranded β-sheets (N,N-terminus and C,C-terminus), and abnormal fibrous, extracellular, proteinaceous deposits. Finally, these β-sheets deposit, and cells fail to degrade them effectively. The essential torsion angles (φ, ψ, and ω) define the conformation of proteins and their architecture. Cells initiate several transformations and pathways during the regulation of protein homeostasis based on the requirements for the functioning of the cell, which are governed by ATP-dependent proteases. In this process, the kinetics of the molding/folding phenomenon is disturbed, and subsequently, it is dominated by cross-domain misfolding intermediates; however, simultaneously, it is opposed by small stretching forces, which naturally exist in the cell. The ubiquitin/proteasome system deals with damaged proteins, which are not refolded by the chaperone-type machinery. Ubiquitin-protein ligases (E3-Ub) participate in all the cellular activity initiated and governed by molecular chaperones to stabilize the cellular proteome and participate in the degradation phenomenon implemented for damaged proteins. Optical tweezers, a single-resolution based technique, disclose the folding pathway of linear chain proteins, which is how they convert themselves into a three-dimensional architecture. Further, DNA-protein conjugation analysis is performed to obtain folding energies as single-molecule kinetic and thermodynamic data.

Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots

The deluge of single-cell data obtained by sequencing, imaging and epigenetic markers has led to an increasingly detailed description of cell state. However, it remains challenging to identify how cells transition between different states, in part because data are typically limited to snapshots in time. A prerequisite for inferring cell state transitions from such snapshots is to distinguish whether transitions are coupled to cell divisions. To address this, we present two minimal branching process models of cell division and differentiation in a well-mixed population. These models describe dynamics where differentiation and division are coupled or uncoupled. For each model, we derive analytic expressions for each subpopulation's mean and variance and for the likelihood, allowing exact Bayesian parameter inference and model selection in the idealised case of fully observed trajectories of differentiation and division events. In the case of snapshots, we present a sample path algorithm and use this to predict optimal temporal spacing of measurements for experimental design. We then apply this methodology to an in vitro dataset assaying the clonal growth of epiblast stem cells in culture conditions promoting self-renewal or differentiation. Here, the larger number of cell states necessitates approximate Bayesian computation. For both culture conditions, our inference supports the model where cell state transitions are coupled to division. For culture conditions promoting differentiation, our analysis indicates a possible shift in dynamics, with these processes becoming more coupled over time.

A computational model of stem cell molecular mechanism to maintain tissue homeostasis

Stem cells, with their capacity to self-renew and to differentiate to more specialized cell types, play a key role to maintain homeostasis in adult tissues. To investigate how, in the dynamic stochastic environment of a tissue, non-genetic diversity and the precise balance between proliferation and differentiation are achieved, it is necessary to understand the molecular mechanisms of the stem cells in decision making process. By focusing on the impact of stochasticity, we proposed a computational model describing the regulatory circuitry as a tri-stable dynamical system to reveal the mechanism which orchestrate this balance. Our model explains how the distribution of noise in genes, linked to the cell regulatory networks, affects cell decision-making to maintain homeostatic state. The noise effect on tissue homeostasis is achieved by regulating the probability of differentiation and self-renewal through symmetric and/or asymmetric cell divisions. Our model reveals, when mutations due to the replication of DNA in stem cell division, are inevitable, how mutations contribute to either aging gradually or the development of cancer in a short period of time. Furthermore, our model sheds some light on the impact of more complex regulatory networks on the system robustness against perturbations.

Universality of clonal dynamics poses fundamental limits to identify stem cell self-renewal strategies

How adult stem cells maintain self-renewing tissues is commonly assessed by analysing clonal data from in vivo cell lineage-tracing assays. To identify strategies of stem cell self-renewal requires that different models of stem cell fate choice predict sufficiently different clonal statistics. Here, we show that models of cell fate choice can, in homeostatic tissues, be categorized by exactly two 'universality classes', whereby models of the same class predict, under asymptotic conditions, the same clonal statistics. Those classes relate to generalizations of the canonical asymmetric vs. symmetric stem cell self-renewal strategies and are distinguished by a conservation law. This poses both challenges and opportunities to identify stem cell self-renewal strategies: while under asymptotic conditions, self-renewal models of the same universality class cannot be distinguished by clonal data only, models of different classes can be distinguished by simple means.

A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration

Stem cell heterogeneity is essential for homeostasis in tissue development. This paper establishes a general mathematical framework to model the dynamics of stem cell regeneration with cell heterogeneity and random transitions of epigenetic states. The framework generalizes the classical G0 cell cycle model and incorporates the epigenetic states of individual cells represented by a continuous multidimensional variable. In the model, the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, are dependent on their epigenetic states, and the random transitions of epigenetic states between cell cycles are represented by an inheritance probability function that describes the conditional probability of cell state changes. Moreover, the model can be extended to include genotypic changes and describe the process of gene mutation-induced tumor development. The proposed mathematical framework provides a generalized formula that helps us to understand various dynamic processes of stem cell regeneration, including tissue development, degeneration, and abnormal growth.

Germline stem cell aging in the Drosophila ovary

The age-related decline of adult stem cells leads to loss of tissue homeostasis and contributes to organismal aging. Though the phenotypic hallmarks of aging are well-characterized at the organ or tissue level, the molecular processes that govern stem cell aging remain unclear. This review seeks to highlight recent research in stem cell aging in the Drosophila ovary and connect the discoveries in the fly to ongoing questions in stem cell aging.

Bioengineering Considerations for a Nurturing Way to Enhance Scalable Expansion of Human Pluripotent Stem Cells

Understanding how defects in mechanotransduction affect cell-to-cell variability will add to the fundamental knowledge of human pluripotent stem cell (hPSC) culture, and may suggest new approaches for achieving a robust, reproducible, and scalable process that result in consistent product quality and yields. Here, the current state of the understanding of the fundamental mechanisms that govern the growth kinetics of hPSCs between static and dynamic cultures is reviewed, the factors causing fluctuations are identified, and culture strategies that might eliminate or minimize the occurrence of cell-to-cell variability arising from these fluctuations are discussed. The existing challenges in the development of hPSC expansion methods for enabling the transition from process development to large-scale production are addressed, a mandatory step for industrial and clinical applications of hPSCs.

Stability and steady state of complex cooperative systems: a diakoptic approach

Cooperative dynamics are common in ecology and population dynamics. However, their commonly high degree of complexity with a large number of coupled degrees of freedom renders them difficult to analyse. Here, we present a graph-theoretical criterion, via a diakoptic approach (divide-and-conquer) to determine a cooperative system's stability by decomposing the system's dependence graph into its strongly connected components (SCCs). In particular, we show that a linear cooperative system is Lyapunov stable if the SCCs of the associated dependence graph all have non-positive dominant eigenvalues, and if no SCCs which have dominant eigenvalue zero are connected by a path.

Pathophysiology of Tumor Cell Release into the Circulation and Characterization of CTC

The traditional model of metastatic progression postulates that the ability to form distant metastases is driven by random mutations in cells of the primary tumor.

A stochastic model of adult neurogenesis coupling cell cycle progression and differentiation

Long-term tissue homeostasis requires a precise balance between stem cell self-renewal and the generation of differentiated progeny. Recently, it has been shown that in the adult murine brain, neural stem cells (NSCs) divide mostly symmetrically. This finding suggests that the required balance for tissue homeostasis is accomplished at the population level. However, it remains unclear how this balance is enabled. Furthermore, there is experimental evidence that proneural differentiation factors not only promote differentiation, but also cell cycle progression, suggesting a link between the two processes in NSCs. To study the effect of such a link on NSC dynamics, we developed a stochastic model in which stem cells have an intrinsic probability to progress through cell cycle and to differentiate. Our results show that increasing heterogeneity in differentiation probabilities leads to a decreased probability of long-term tissue homeostasis, and that this effect can be compensated when cell cycle progression and differentiation are positively coupled. Using single-cell RNA-Seq profiling of adult NSCs, we found a positive correlation in the expression levels of cell cycle and differentiation markers. Our findings suggest that a coupling between cell cycle progression and differentiation on the cellular level is part of the process that maintains tissue homeostasis in the adult brain.

The combined efficacy of OTS964 and temozolomide for reducing the size of power-law coded heterogeneous glioma stem cell populations

Glioblastoma resists chemotherapy then recurs as a fatal space-occupying lesion. To improve the prognosis, the issues of chemoresistance and tumor size should be addressed. Glioma stem cell (GSC) populations, a heterogeneous power-law coded population in glioblastoma, are believed to be responsible for the recurrence and progressive expansion of tumors. Thus, we propose a therapeutic strategy of reducing the initial size and controlling the regrowth of GSC populations which directly facilitates initial and long-term control of glioblastoma recurrence. In this study, we administered an anti-glioma/GSC drug temozolomide (TMZ) and OTS964, an inhibitor for T-Lak cell originated protein kinase, in combination (T&O), investigating whether together they efficiently and substantially shrink the initial size of power-law coded GSC populations and slow the long-term re-growth of drug-resistant GSC populations. We employed a detailed quantitative approach using clonal glioma sphere (GS) cultures, measuring sphere survivability and changes to growth during the self-renewal. T&O eliminated self-renewing GS clones and suppressed their growth. We also addressed whether T&O reduced the size of self-renewed GS populations. T&O quickly reduced the size of GS populations via efficient elimination of GS clones. The growth of the surviving T&O-resistant GS populations was continuously disturbed, leading to substantial long-term shrinkage of the self-renewed GS populations. Thus, T&O reduced the initial size of GS populations and suppressed their later regrowth. A combination therapy of TMZ and OTS964 would represent a novel therapeutic paradigm with the potential for long-term control of glioblastoma recurrence via immediate and sustained shrinkage of power-law coded heterogeneous GSC populations.

Statistics of noisy growth with mechanical feedback in elastic tissues

Tissue growth is a fundamental aspect of development and is intrinsically noisy. Stochasticity has important implications for morphogenesis, precise control of organ size, and regulation of tissue composition and heterogeneity. However, the basic statistical properties of growing tissues, particularly when growth induces mechanical stresses that can in turn affect growth rates, have received little attention. Here, we study the noisy growth of elastic sheets subject to mechanical feedback. Considering both isotropic and anisotropic growth, we find that the density-density correlation function shows power law scaling. We also consider the dynamics of marked, neutral clones of cells. We find that the areas (but not the shapes) of two clones are always statistically independent, even when they are adjacent. For anisotropic growth, we show that clone size variance scales like the average area squared and that the mode amplitudes characterizing clone shape show a slow [Formula: see text] decay, where n is the mode index. This is in stark contrast to the isotropic case, where relative variations in clone size and shape vanish at long times. The high variability in clone statistics observed in anisotropic growth is due to the presence of two soft modes-growth modes that generate no stress. Our results lay the groundwork for more in-depth explorations of the properties of noisy tissue growth in specific biological contexts.

Mathematical Modelling of Clonal Stem Cell Dynamics

Studying cell fate dynamics is complicated by the fact that direct in vivo observation of individual cell fate outcomes is usually not possible and only multicellular data of cell clones can be obtained. In this situation, experimental data alone is not sufficient to validate biological models because the hypotheses and the data cannot be directly compared and thus standard statistical tests cannot be leveraged. On the other hand, mathematical modelling can bridge the scales between a hypothesis and measured data via quantitative predictions from a mathematical model. Here, we describe how to implement the rules behind a hypothesis (cell fate outcomes) one-to-one as a stochastic model, how to evaluate such a rule-based model mathematically via analytical calculation or stochastic simulations of the model's Master equation, and to predict the outcomes of clonal statistics for respective hypotheses. We also illustrate two approaches to compare these predictions directly with the clonal data to assess the models.

Germline Stem Cell Heterogeneity Supports Homeostasis in Drosophila

Adult and embryonic stem cells exhibit fluctuating gene expression; however, the biological significance of stem cell heterogeneity is not well understood. We show that, in Drosophila, female germline stem cells (GSCs) exhibit heterogeneous expression of a GSC differentiation-promoting factor Regena (Rga). The Drosophila homolog of human SON, dsn, is required to maintain GSC heterogeneity by suppressing sustained high levels of Rga. Reducing the expression of Rga in dsn mutants restores GSC heterogeneity and self-renewal. Thus, GSC heterogeneity is linked to GSC homeostasis.

•

Female germline stem cells have heterogeneous Rga expression

•

dsn suppresses rga transcription and maintains heterogeneity

•

dsn maintains germline stem cells

•

Lowering rga restores heterogeneity and suppresses dsn phenotypes

Systems for localized release to mimic paracrine cell communication in vitro

Paracrine cell communication plays a pivotal role for signal exchange between proximal cells in vivo. However, this localized, gradient type release of mediators at very low concentrations (pg/ml), relevant during physiological and pathological processes, is rarely reflected within in vitro approaches. This review gives an overview on state-of-the-art approaches, which transfer the paracrine cell-to-cell communication into in vitro cell culture model setups. The traditional methods like trans-well assays and more advanced microfluidic approaches are included. The review focusses on systems for localized release, mostly based on microparticles, which tightly mimic the paracrine interaction between single cells in 3D microenvironments. Approaches based on single microparticles, with the main focus on affinity-controlled storage and release of cytokines, are reviewed and their importance for understanding paracrine communication is highlighted. Various methods to study the cytokine release and their advantages and disadvantages are discussed. Basic principles of the release characteristics, like diffusion mechanisms, are quantitatively described, including the formation of resulting gradients around the local sources. In vitro cell experiments using such localized microparticle release systems in approaches to increase understanding of stem cell behavior within their niches and regulation of wound healing are highlighted as examples of successful localized release systems for mimicking paracrine cell communication.

Targeting the T-Lak cell originated protein kinase by OTS964 shrinks the size of power-law coded heterogeneous glioma stem cell populations

Glioblastoma resists chemoradiotherapy, then, recurs to be a fatal space-occupying lesion. The recurrence is caused by re-growing cell populations such as glioma stem cells (GSCs), suggesting that GSC populations should be targeted. This study addressed whether a novel anti-cancer drug, OTS964, an inhibitor for T-LAK cell originated protein kinase (TOPK), is effective in reducing the size of the heterogeneous GSC populations, a power-law coded heterogeneous GSC populations consisting of glioma sphere (GS) clones, by detailing quantitative growth properties. We found that OTS964 killed GS clones while suppressing the growth of surviving GS clones, thus identifying clone-eliminating and growth-disturbing efficacies of OTS964. The efficacies led to a significant size reduction in GS populations in a dose-dependent manner. The surviving GS clones reconstructed GS populations in the following generations; the recovery of GS populations fits a recurrence after the chemotherapy. The recovering GS clones resisted the clone-eliminating effect of OTS964 in sequential exposure during the growth recovery. However, surprisingly, the resistant properties of the recovered-GS clones had been plastically canceled during self-renewal, and then the GS clones had become re-sensitive to OTS964. Thus, OTS964 targets GSCs to eliminate them or suppress their growth, resulting in shrinkage of the power-law coded GSC populations. We propose a therapy focusing on long-term control in recurrence of glioblastoma via reducing the size of the GSC populations by OTS964.

Linking of mPGES-1 and iNOS activates stem-like phenotype in EGFR-driven epithelial tumor cells

Inflammatory prostaglandin E-2 (PGE-2) favors cancer progression in epithelial tumors characterized by persistent oncogene input. However, its effects on tumor cell stemness are poorly understood at molecular level. Here we describe two epithelial tumor cells A431 and A459, originating from human lung and skin tumors, in which epithelial growth factor (EGF) induces sequential up-regulation of mPGES-1 and iNOS enzymes, producing an inflammatory intracellular milieu. We demonstrated that concerted action of EGF, mPGES-1 and iNOS causes sharp changes in cell phenotype demonstrated by acquisition of stem-cell features and activation of the epithelial-mesenchymal transition (EMT). When primed with EGF, epithelial tumor cells transfected with mPGES-1 or iNOS to ensure steady enzyme levels display major stem-like and EMT markers, such as reduction in E-cadherin with a concomitant rise in vimentin, ALDH-1, CD133 and ALDH activity. Tumorsphere studies with these cells show increased sphere number and size, enhanced migratory and clonogenic capacity and sharp changes in EMT markers, indicating activation of this process. The concerted action of the enzymes forms a well-orchestrated cascade where expression of iNOS depends on overexpression of mPGES-1. Indeed, we show that through its downstream effectors (PGE-2, PKA, PI3K/Akt), mPGES-1 recruits non-canonical transcription factors, thus facilitating iNOS production. In conclusion, we propose that the initial event leading to tumor stem-cell activation may be a leveraged intrinsic mechanism in which all players are either inherent constituents (EGF) or highly inducible proteins (mPGES-1, iNOS) of tumor cells. We suggest that incipient tumor aggressiveness may be moderated by reducing pivotal input of mPGES-1.

Biologically Relevant Heterogeneity: Metrics and Practical Insights

Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.

Accelerated cell divisions drive the outgrowth of the regenerating spinal cord in axolotls

Axolotls are unique in their ability to regenerate the spinal cord. However, the mechanisms that underlie this phenomenon remain poorly understood. Previously, we showed that regenerating stem cells in the axolotl spinal cord revert to a molecular state resembling embryonic neuroepithelial cells and functionally acquire rapid proliferative divisions (Rodrigo Albors et al., 2015). Here, we refine the analysis of cell proliferation in space and time and identify a high-proliferation zone in the regenerating spinal cord that shifts posteriorly over time. By tracking sparsely-labeled cells, we also quantify cell influx into the regenerate. Taking a mathematical modeling approach, we integrate these quantitative datasets of cell proliferation, neural stem cell activation and cell influx, to predict regenerative tissue outgrowth. Our model shows that while cell influx and neural stem cell activation play a minor role, the acceleration of the cell cycle is the major driver of regenerative spinal cord outgrowth in axolotls.

DOI:http://dx.doi.org/10.7554/eLife.20357.001